TRAVEL DEMAND MODEL DOCUMENTATION - Memphis Urban

Appendix G

TRAVEL DEMAND MODEL

DOCUMENTATION

APPENDIX G - TRAVEL DEMAND

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1.0 INTRODUCTIONThe Memphis Urban Area MPO is developing the Direction 2040 - Long-Range Transportation Plan (LRTP) forthe Memphis region. The horizon year of the Plan is 2040. The previous LRTP, approved March 31, 2008, had ahorizon year of 2030. The Direction 2040 LRTP will use the current Memphis MPO Travel Demand Model(TDM) to forecast the future traffic conditions based on the future land use, demographic and economic growth.The base year for the Direction 2040 LRTP is 2010.

The current Memphis MPO TDM was completed in 2007 and developed with TransCAD software platform. Themodel development underwent an extensive review process through a local steering committee, an expert panelreview, and a Peer Review Process. The completed model has also been reviewed and approved by theappropriate State Departments of Transportation, Federal Highway Administration (FHWA) and Federal TransitAdministration (FTA). The existing TDM has base year of 2004 and a horizon year of 2030 with various interimyears of 2008, 2010, 2011, 2014, 2017, and 2020. Since 2007, the MPO has maintained and updated the TDM toassist the on-going LRTP and Transportation Improvement Program (TIP) amendment process.

To assist the development of the 2040 LRTP using the current travel demand model, the TDM must have theability to evaluate the future travel demand and transportation network deficiencies for horizon year 2040 andmultiple interim years. Although it would be ideal to update the TDM's base year to 2010, the MPO willpostpone updating the model base year from 2004 to 2010 for the following reasons:

Only limited Census 2010 data is available. As of September 2011, only Census 2010 redistricting data iscurrently available. There is no 2010 Traffic Analysis Zone (TAZ) level data currently available for theMPO’s use in updating the base year. It is thought that this information will be available in 2012.

The Census 2010 CTPP data is not currently available. While not required for updating the base modelyear, this data could be used for a more comprehensive model update using the latest data available if themodel update is postponed.

As identified in the current Memphis MPO Unified Planning Work Program (UPWP), a household travelsurvey will begin in late 2011, or early 2012. Updating the base year model without this data will result ina duplication of effort once the data is obtained.

The Minimum Travel Demand Model Calibration and Validation Guidelines for the State of Tennesseerequire that the travel model set used to prepare the air quality conformity analysis has a validation that isnot more than 10 years old. The existing model validation is 7 years old.

The potential negative impact on the LRTP project schedule. Based on the review schedule, the draft2040 LRTP is to be submitted for review before the end of October, 2011. Updating the base year willresult in missing this preliminary project deadline and the deadline for overall plan adoption.

The MPO and its consultant discussed the issue with the Tennessee Department of Transportation (TDOT)Long Range Planning Office in June, 2011. TDOT indicated that it will accept a delay in updating the base yearof the model until these critical data elements discussed above are available, but no later than 2014.


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With this decision in mind, the TDM was updated in the following aspects:

Existing plus committed (E+C) model highway network

Socio-economic and demographic data for all future years

Special generator forecasts

School/University forecasts

External station forecasts

Sections 2 through 6 describe the updates in detail. Section 7 presents the preliminary model run results for year2025 and 2040 on E+C network.

2.0 MODEL HIGHWAY NETWORK UPDATESAfter the completion of the previous TDM development process in 2007, the MPO has continued to maintainand update the TDM to assist the on-going LRTP and Transportation Improvement Program (TIP) amendmentprocess. The TDM highway network was updated to incorporate the following changes:

Incorporated all regionally significant roadway projects that are completed and opened to traffic from2004 to 2011.

Included all committed projects in the current TIP with construction funds allocated.

Chapter 3 - Existing Conditions and Needs Assessment of the LRTP outlines the committed projects in detail.

3.0 DEMOGRAPHIC AND EMPLOYMENT DATA FORECASTSIn September 2009, the MPO began a regional visioning and scenario planning process called IMAGINE 2040:Midsouth Transportation + Land Use Plan. IMAGINE 2040 was performed in tandem with the 2040 LRTPprocess and involved the general public, regional stakeholders and local agencies as well as local communityrepresentation to evaluate alternate growth strategies for the region. A vital component of IMAGINE 2040 is aland use model that allocates population and employment growth. CommunityViz, an extension of ESRI’sArcGIS software, was used to allocate growth across the region. CommunityViz enables MPO staff to allocateprojections of households and employment across the landscape of the study area. The allocation uses parcels asunits that can host households and jobs based on several factors; most notably land availability and suitability.Land suitability represents the likelihood that a parcel will experience growth by 2040. Factors that influence thesuitability of land include access to public infrastructure and proximity to jobs and services. Certain environmentalconstraints such as wetlands prevent allocation of growth to underlying parcels. The fine-grained nature of theanalysis allows only the upland portion of a parcel to receive growth. The model allocates growth in order of mostsuitable to least suitable land.

As part of the IMAGINE 2040 scenario planning process, participating planners throughout the regioncollaborated on the identification of two alternative growth visions, a Base (Business-as-Usual) scenario and aCenters and Corridors approach. The IMAGINE 2040 scenario planning effort and the resulting Measures ofEffectiveness evaluation allowed the MPO and Transportation Plan Advisory Committee (TPAC) to make aninformed decision regarding what alternative vision best reinforces the local goals and objectives of the region.The MPO and TPAC reviewed the results of public outreach activities, and established the vision, goals andobjectives. Through this effort, it was concluded that the Base growth scenario more closely aligns with desiredgrowth pattern than the Centers and Corridors scenario. The Base scenario was approved as the preferred


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alternative for use in the development of the LRTP by the Transportation Policy Board (TPB) on July 28, 2011.The selection of the Base scenario as the preferred growth strategy allows planners to move forward with thedevelopment of a transportation plan that responds to the allocation of growth described in the Base scenario.For more detailed discussion of the land use and scenario planning process, see Chapter #2 - Land Use andScenario Planning of the LRTP.

The CommunityViz model allocates growth to grids that is 1/4 mile by 1/4 mile in size within the MPO StudyArea. Using the allocation results from the CommunityViz model, an integration tool was developed inside theTDM to aggregate the allocation results to the TAZ level, apply the cross-classification distribution, and convertthe data to native TransCAD format for the TDM to use. The year 2040 growth data was presented to each localjurisdiction for review and comments. Manual adjustments were made based on the comments from localplanners to smooth the data.

3.1 Demographic Forecasts for the MPO Study AreaThe CommunityViz model allocates the growth of total number of households for horizon year 2040 and interimyears of 2010, 2017, 2020, 2025, and 2030. Table 1 summarizes the total number of households by county.

Table 1 - Total Number of Households by CountyCounty \ Year 2004 2010 2017 2020 2025 2030 2040

Shelby 346,194 353,964 360,383 363,457 368,879 374,420 385,762DeSoto* 44,918 58,685 73,913 80,493 91,519 102,570 124,803Fayette* 2,870 4,401 5,015 5,281 5,729 6,177 7,084

* Portion of the county within the Study Area only.

The trip generation models and the vehicle availability model of the TDM requires stratification of thehouseholds into bins for the following four categories:

Income stratification,

Household size (number of persons),

Number of workers in household, and

Number of persons under age 18.

For each category, distribution curves were developed for each TAZ based on the base year 2004 model data. Thedistribution was assumed to hold true for all future years. Using the number of household growth allocated by theCommunityViz model, the distribution curve from the base year was applied at the TAZ level to obtain thenumber of households in each stratification bin for each future year.

3.2 Employment Forecasts for the MPO Study AreaThe TDM requires employment data grouped by the following six categories:

Industrial/Manufacturing,

Wholesale/Transportation,

Retail,

Office,


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Service, and

Government.

The CommunityViz model allocates the growth of employment for all future years for the first five categories. Itwas assumed that there would be no change in government employment in future years. Table 2 summarizes thetotal employment by county. Table 3 through 7 summarizes the total employment by the five employmentcategories.

Table 2 - Total Employment by CountyCounty \ Year 2004 2010 2017 2020 2025 2030 2040

Shelby 484,344 508,732 539,225 552,491 576,885 602,737 659,268DeSoto* 36,091 41,000 47,974 51,533 58,239 66,109 86,238Fayette* 679 775 911 962 1,068 1,174 1,458


Table 3 - Industrial/Manufacturing Employment by CountyCounty \ Year 2004 2010 2017 2020 2025 2030 2040

Shelby 55,205 55,871 56,780 57,169 57,852 58,575 60,101DeSoto* 7,390 7,463 7,569 7,635 7,738 7,825 8,022Fayette* 256 272 287 291 298 308 340


Table 4 - Wholesale/Transportation Employment by CountyCounty \ Year 2004 2010 2017 2020 2025 2030 2040



Table 5 - Retail Employment by CountyCounty \ Year 2004 2010 2017 2020 2025 2030 2040




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Table 6 - Office Employment by CountyCounty \ Year 2004 2010 2017 2020 2025 2030 2040



Table 7 - Service Employment by CountyCounty \ Year 2004 2010 2017 2020 2025 2030 2040



3.3 Forecasts for Area Outside the MPO Study Area BoundaryThe Travel Demand Model boundary includes all of the MPO LRTP Area, and is larger than the MPO boundary.The TDM boundary includes all of Shelby and DeSoto County, the southern half of Tipton County, the westernquarter of Fayette County, and a small segment of northwest Marshall County, Mississippi. The MPO boundaryincludes all of Shelby County, about half of DeSoto County (including Hernando), and westernmost four miles ofFayette County.

The CommunityViz model was only developed for the areas inside the MPO boundary. Since the CommunityVizmodel does not include the area outside the MPO boundary in detail, the existing household and employmentgrowth forecasts for 2030 developed as part of the original TDM development in 2007 were used to estimate thepopulation and employment for TAZs outside the Study Area. The growth for interim year 2025 wasinterpolated from the existing year 2020 and 2030 projections. The growth for horizon year 2040 was extrapolatedfrom the existing and year 2030 projections.

4.0 SPECIAL GENERATOR FORECASTS

There are three unique special generators in the Memphis area: The Memphis Airport, Federal Express Hub, andGraceland.

The forecast methodology adopted by the existing TDM for the Airport demand was based on the FederalAviation Administration (FAA) forecasts for total airport operations and passenger enplanements. Assuming thesame growth rate will continue from 2030 to 2040, the airport person trips for 2040 were extrapolated from theexisting year 2030 projections. Similarly, the interim year 2025 person trips were interpolated from the existing2020 and 2030 projections.

Trips in and out of the FedEx Hub were assumed to grow proportionally to the operations forecasted at theairport. The same extrapolation and interpolation methods were used to obtain forecast for year 2025 and 2040.

For Graceland, the existing TDM forecasts were based on growth data provided by Graceland at an annual rate of0.89%. This same growth rate was applied to obtain the person trips for Graceland for year 2025 and 2040.


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Table 8 shows the future year demand at each special generator.

Table 8 - Special Generator Forecasts

Location \ Year 2004 2010 2017 2020 2025 2030 2040

Airport 32,000 39,680 48,640 52,480 58,880 65,280 78,080FedEx 460 570 699 754 846 938 1,122

Graceland 2,600 2,739 2,901 2,970 3,086 3,202 3,433

5.0 SCHOOL/UNIVERSITY DEMAND FORECASTS

School (K-12) and university enrollment forecasts are inputs that must be manually forecast for input into themodel. Since no long-term forecasts are available for school systems in the area, the existing TDM forecasts foreducational enrollment was tied closely to the population forecast, along with observed growth at the Universityof Memphis. Growth was then allocated to TAZs using the existing enrollment in each zone as a guide.

For both school and university enrollment, the growth for interim year 2025 were interpolated from the existingyear 2020 and 2030 projections. The growth for horizon year 2040 was extrapolated from the existing year 2030projections.

6.0 EXTERNAL STATIONS

The future year external trips were developed by applying a growth rate to the base year external trips. During theTDM development process in 2007, the growth rates were determined based on a number of different criteriaincluding state, functional class of roadway, historic count data, and historic population growth by census tractinside and outside the model boundary.

The same growth rates at each individual external station were used to obtain the external station demand for year2025 and 2040. Table 9 lists the forecasted future year Average Daily Traffic (ADT) at external stations.

In addition, the following input data in the existing 2030 forecasts were assumed to hold true for years 2025 and2040:

Auto and truck percentages at external stations,

Internal-external (IE) and external-external (EE) trip splits at external stations,

Time of day and inbound / outbound distributions, and

K-factors used for the EE gravity model.


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Table 9 - External Station ADT ForecastsID Station Name Functional

ClassificationAnnualGrowth

Rate

ADT without I-69 ADT with I-692025 2040 2025 2040

10007 I-40 E Freeway 2.5% 53,680 77,160 53,680 77,16010016 I-55 S Freeway 2.2% 58,780 80,990 58,780 80,99010027 I-40/I-55W Freeway 2.0% 158,740 212,600 158,740 212,60010002 HIGHWAY 51

/Future I-69Principal Arterial 2.6% 35,400 51,600 56,370 82,010

10009 HIGHWAY 64 Principal Arterial 3.0% 28,150 43,390 28,150 43,39010013 HIGHWAY 78 Principal Arterial 2.2% 40,750 56,150 40,750 56,15010019 HIGHWAY 61

/Future I-69Principal Arterial 2.2% 46,070 63,480 62,980 87,240

10024 HIGHWAY 72 Principal Arterial 2.5% 23,690 34,060 23,690 34,06010025 GOODMAN ROAD

EXTPrincipal Arterial 2.0% 0 15,850 0 15,850

10003 HIGHWAY 59S/MOUNT CARMEL

Minor Arterial 2.5% 8,290 11,920 8,290 11,920

10004 AUSTIN PEAY Minor Arterial 0.9% 2,590 2,940 2,590 2,94010005 HIGHWAY 79 Minor Arterial 1.5% 2,980 3,690 2,980 3,69010008 HIGHWAY 59 E Minor Arterial 1.3% 4,040 4,890 4,040 4,89010011 HIGHWAY 57 Minor Arterial 0.4% 7,940 8,430 7,940 8,43010014 HIGHWAY 305 S Minor Arterial 1.4% 5,080 4,950 5,080 4,95010032 ROUTE 3 Minor Arterial 1.4% 1,340 1,650 1,340 1,65010001 HIGHWAY 59 Major Collector 2.2% 2,670 3,660 2,670 3,66010006 STANTON ROAD N Major Collector 3.0% 1,190 1,830 1,190 1,83010012 HIGHWAY 178 Major Collector 1.7% 3,150 4,040 3,150 4,04010015 HIGHWAY 51 S Major Collector 1.7% 6,010 7,710 6,010 7,71010017 PRATT ROAD Major Collector 3.0% 1,410 2,170 1,410 2,17010018 HIGHWAY 304/713 Major Collector 1.7% 5,720 7,340 5,720 7,34010023 MACON ROAD Major Collector 1.2% 1,270 1,510 1,270 1,51010026 VICTORIA ROAD Major Collector 3.0% 940 1,450 940 1,45010028 STANTON RD S Major Collector 3.0% 1,350 2,090 1,350 2,09010030 BYHALIA ROAD Major Collector 1.7% 8,220 10,550 8,220 10,55010020 CHARLESTON

MASON ROADMinor Collector 3.0% 940 1,450 940 1,450

10029 HOLLY SPRINGSROAD

Minor Collector 3.0% 1,410 2,170 1,410 2,170

10031 OLD HIGHWAY 61 Minor Collector 3.0% 1,350 2,090 1,350 2,09010022 FEATHERS CHAPEL

ROADLocal 3.0% 1,170 1,800 1,170 1,800


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7.0 YEAR 2025 AND 2040 E+C MODEL RESULTS

After incorporating the changes into the TDM, two model runs were conducted to identify the deficiencies of theE+C network at year 2025 (the year for high priority LRTP projects) and 2040 (the year for low priority LRTPprojects).

For year 2025 model results, the Vehicle Miles of Travel (VMT) is summarized by roadway functionalclassification and compared with the base year 2004 results in Table 10. Assigned traffic volumes acrossscreenlines and cutlines are also compared with the base year 2004 results in Table 11. Figure 1 shows thescreenline and cutline locations. A roadway Level of Service (LOS) map for the E+C network is presented inFigure 2.

Table 10 - Year 2025 VMT by Functional Classification

Functional Classification 2004 VMT2025

ModelVMT

%Difference

Freeways 8,781,000 13,371,500 52%

Principal Arterials 8,420,700 12,318,500 46%

Minor Arterials 7,124,900 9,790,200 37%Collectors 2,653,000 4,709,000 77%

Total 26,980,700 40,189,200 49%

Table 11 - Year 2025 Screenline/Cutline Volume

Screen Line/Cut Line

2004CountTotal

2025Model

Volume

# ofCounts

%Difference

Screenline 1 276,861 448,031 27 62%

Screenline 2 764,201 974,195 54 27%Screenline 3 805,834 955,881 47 19%

Cutline 1 1,306,195 1,615,849 54 24%

Cutline 2 162,841 207,161 9 27%

Cutline 3 72,168 137,557 5 91%Cutline 4 74,900 141,318 6 89%

Cutline 5 31,680 43,009 3 36%


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Figure 1 - Screenline/Cutline Locations


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Figure 2 - Level of Service for Year 2025 on E+C Network


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For year 2040 model results, the Vehicle Miles of Travel (VMT) is summarized by roadway functionalclassification and compared with the base year 2004 results in Table 12. Assigned traffic volumes acrossscreenlines and cutlines are also compared with the base year 2004 results in Table 13. A roadway Level ofService (LOS) map for the E+C network is presented in Figure 3.

Table 12 - Year 2040 VMT by Functional Classification

Functional Classification 2004 VMT2040

ModelVMT

%Difference

Freeways 8,781,000 15,515,100 77%Principal Arterials 8,420,700 15,433,400 83%Minor Arterials 7,124,900 12,411,100 74%Collectors 2,653,000 7,108,700 168%Total 26,980,700 50,468,300 87%

Table 13 - Year 2040 Screenline/Cutline Volume

Screen Line/Cut Line

2004CountTotal

2040Model

Volume

# ofCounts

%Difference

Screenline 1 276,861 594,219 27 115%Screenline 2 764,201 1,211,812 54 59%Screenline 3 805,834 1,099,417 47 36%Cutline 1 1,306,195 1,794,868 54 37%Cutline 2 162,841 288,748 9 77%Cutline 3 72,168 195,720 5 171%Cutline 4 74,900 187,790 6 151%Cutline 5 31,680 54,493 3 72%


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Figure 3 - Level of Service for Year 2040 on E+C Network

Memphis Travel Demand Model

1

Technical Memoranda

1a — Network and TAZ Development

1b — Travel Time Studies

2 — Regional Economic and Demographic ForecastsMethodology and Results

3 — Trip Generation

4 — Destination Choice

5 — Time-of-Day Model

6 — Mode Choice

7 — Freight Model

8a — Highway Assignment, Transit Assignment, and FeedbackProcedures

8b — Link Capacity Development

9 — Highway Validation Procedures and Goals and TransitAssignment Reasonableness Checking Procedures

10 — Base and Future Year Signalized Intersection Tools andFuture Year Signal Location Forecasting Methodology

11 — Model Calibration and Assignment Validation Results

12 — Future Year Model Development and Results

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Technical Memorandum #1 (a)Network and TAZ DevelopmentThis memorandum details the network and transportation analysis zone (TAZ)development for the Memphis Travel Demand Model Update. It includes revisions thatwere completed between January and July 2005. These revisions include functionalclassification coding, transit route coding, traffic count location coding,screenline/cutline coding, walk access link coding, supplemental centroid connectorcoding, future year road identification, and network quality review. It also includesfuture year highway and transit network coding and revisions completed in 2006.

ContentsNetwork Development Methodology

- Overview- Identification of Network Roads- Identification of Transit Routes- Network Corrections- Collection of Network Attributes- Network Data Population- Centroid Connectors- Centerline-Mile Summary- Screenline and Cutline Development- Traffic Counts and Transit Boardings- Corridor Travel Time Study- Federal Functional Classification- Supplemental Traffic Count Request- Corridor Travel Time Analysis- Area Type- Capacity Equation Application

Refinement of Traffic Analysis Zone Structure- Overview- TAZ Refinement Criteria- Special Generators- Process

Appendix A — Base Year (2004) Transit Route AttributesAppendix B — Final TAZ Geographic Boundaries

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Network Development Methodology

Development of the highway network involved identifying the network roads to beincluded, developing the TransCAD line network, collecting network attributes, andpopulating network data in TransCAD.

Overview

In order to simulate travel within the Memphis area, a computer network must bedeveloped that represents the street system to be modeled. The network will berepresented for the entire study area, which has been expanded from the previousmodel for the 2004 base year update, as shown in Figure 1. The network developedfor the Memphis area includes all interstates, freeways, and arterials, as well assignificant collector and local roads in terms of high traffic volumes, necessaryconnectivity, or accommodating transit routes and walk connections for the transitmodel.

The study area for the Memphis MPO model (Figure 1) includes all of Shelby Countyand portions of Tipton and Fayette Counties in Tennessee, along with all of DeSotoCounty and a small portion of Marshall County in Mississippi. The Memphis modelstudy area encompasses an area of approximately 1,825 square miles, with 1,260square miles in Tennessee and 565 square miles in Mississippi. As part of thenetwork development, approximately 2,400 miles of roadway were identified forinclusion in the 2004 base year model.

The highway network database contains attributes for each link in the line layer inTransCAD. This layer contains all of the necessary attributes for proper modeling ofeach of the roadways in the model, including roadway speeds and capacities. Thisinformation was collected directly or derived from field visits and available data fromthe Tennessee Department of Transportation.

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Figure 1. Study Area Graphic

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Identification of Network Roads

The initial Memphis model network for the previous study area (shown in yellow inFigure 1) was provided by the MPO in TransCAD format. The initial network wasexpanded by the Kimley-Horn team to the final study area boundary outlined in red inFigure 1 using street data from TransCAD and ArcGIS. The network includes allroads of regional significance, including all interstates, freeways, and arterials withinthe MPO area. The model also includes collectors and local roads that have heavytraffic volumes, provide connectivity, or to accommodate transit routes and walkaccess/egress, or have plans for future upgrades or connections. The model networkhas been compared with the current Major Road and current Collector Street plans toverify that no roads were omitted in error. Figure 2 displays the Road Networkidentified for Memphis.

In addition to using the network to model auto and truck activities, the network alsoserves as a base to underlay the transit route system. In TransCAD, transit routes arenot maintained as separate database files. Instead, a transit route (such as a busroute) is represented by identifying the highway links used by the bus route.Consequently, many local roads in the urban area were added into the highwaynetwork to represent local roads used by buses to entire neighborhoods.

The base year transit route system includes bus routes (for both regular and expressfixed route buses) and trolley routes. Specifically with the trolley, there are trolleyright-of-ways that are not accessible by auto. Therefore, additional right-of-way lineswere also added. See Appendix A for base year transit route attributes.

The transit route system was coded with walk access/egress and walk transfers. Theroute system also allows drive access at four park-and-ride lots for base year: NorthEnd Terminal, Central Station, Cleveland Station, and American Way Transit Center.Access connections from the highway network to the park-and-ride lots were alsoadded.

Identification of Transit Routes

The route system was coded based on MATA hardcopy schedules published in June2004. Each route was coded as having two travel directions: inbound toward theNorth End Terminal, and outbound away from the North End Terminal. Routes whichdo not serve North End Terminal were generally coded as outbound: west to east, oroutbound: south to north.

Vehicle headway was coded for 4 time periods: AM peak, midday, PM peak, and night.The headway data was also taken from the printed schedules.

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The base year route system has two distinct modes: trolley and bus. Express bus wasnot modeled as distinct mode in the model because there is no sufficient data tocalibrate a separate mode choice model for express bus only. However, the modelallows express bus to utilize its distinct fares. This is done by using a separate expressbus fare matrix and specifying this fare matrix index in the route attributes table.Light rail is an additional distinct mode for future year.

Network Corrections

As a part of the network development process, corrections and quality checks weremade to the TransCAD network. Corrections made to the Memphis network includethe following:

Verified roadway alignments and termini

The network was “cleaned” to aerial photography, especially in Mississippi, wheresome roads were consistently misaligned, primarily a function of merging data fromthe two states with different projection systems. The Kimley-Horn team also verifiednecessary modifications to roadway links to provide for representative conditions.

Repaired fragmented roadway links

Many links (roadway sections between intersection nodes) consisted of multipleindividual fragments. This increases the likelihood of disconnected roadways, whichincreases file size and causes traffic assignment problems. Using TransCAD’s mapediting tools, the Kimley-Horn team combined fragmented roadway segments intocontinuous links between intersection nodes.

Modified disconnected intersection nodes

Some nodes in the centerline mapping were not properly aligned at as-builtintersections. Using TransCAD’s map editing tools, the Kimley-Horn team reviewedand properly connected intersecting roadways.

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Collection of Network Attributes

The Memphis model requires several important attributes for each highway link,which are used in various steps throughout the model. The primary attributesrequired for modeling include facility type, posted speed, and attributes needed forcapacity development. The attributes recorded during the data collection effortincluded:

Posted Speed LimitArea Type (CBD, Urban, Rural, Suburban)Driveways (None, Low Density, Medium Density, High Density)Median Treatment (No Median, Divided, Two-Way Left Turn Lanes)Roadway Functional Classification (Interstate, Other Freeway, Principal (Major)Arterial, Minor Arterial, Collector, Local)Heavy Vehicle RestrictionThrough Lanes per DirectionAverage Lane Width by DirectionAverage Shoulder Width by DirectionParkingComments

The data collection of network attributes came from two sources: 1) Tennessee DOTTennessee Roadway Information Management System (TRIMS) photography data and2) field assessments. The TRIMS photography data was collected in 2003 by MandliCommunications, Inc. and included a snapshot of the cross section and the side of theroad every 50 feet along each corridor. Software provided by the vendor allows usersto view the photographs for each corridor in succession as if they were moving downthe road. An example set of photographs are shown in Figure 3. This data wasprovided for all of the major roads in Tennessee included in the study area. Roadsthat were not included in this database were field reviewed by the consultant to collectthe necessary data in fall of 2004.

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Figure 3. Sample TRIMS Photography Data

Front View Side View

Network Data Population

A TransCAD data entry form was developed as part of the Network development tofacilitate data entry directly into the TransCAD network. Using the tool, data wasentered into the network either while traveling in the field or in the office while viewingthe photography data. Using this tool eliminated the need for paper forms andsubsequent data entry, and streamlined the process by using pull-down menus. Thetool also allowed for copying and pasting of data from link to link, which alsoincreased data entry efficiency. Figure 4 shows the toolbar for the data collection tool,while Figure 5 shows the form used to enter the data for each link.

Figure 4. Memphis Data Collection Toolbar

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Figure 5. Memphis Link Data Entry Form

Centroid Connectors

Centroid connectors were developed using the TransCAD automated connectorplacement process. This created a set of centroid connectors which can be readilyused to do network skimming.

The automated TransCAD process is able to be applied in one of two ways. It caneither draw one or more centroid connectors to the closest line layer nodes; or it canbe applied to draw a single centroid connector by breaking the closest line andinserting a node which is then used to accept the centroid connector.When TransCAD is used to draw connections to the closest node, most of theconnections are made at intersections. Attaching centroid connectors to line segments

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is more desirable because the connectors could be used to represent local road loadingpoints. Consequently, TransCAD was used to develop the centroid connectors byplacing a single connector to the closest line layer segment.

It is not desirable to have centroid connects attached to interstate, freeway, ramp, orexpressway facilities. Therefore, prior to using the TransCAD process, a selection setof roads eligible to receive a centroid connector was made. When TransCAD’sautomated process was used, the centroid point ID was made to be identical to thenode ID in the line layer network. This provides consistency in later modeling stepswhen trip tables are assigned to the line layer network.

The first generation of the line layer (highway network) was completed by placing onecentroid connector per zone. Where appropriate, additional connectors have beenadded manually to provide multiple connections per zone. In addition, automatedconnectors were moved when the connectors were placed inappropriately afterreviewing access, local road network, and development density.

Within the CBD and most of the urban zones, additional walking connectors weredeveloped. These connectors enhanced transit accessibility and serve to representcross block pedestrian movements which cannot be accommodated by the highwaycentroid connectors.

Centerline-Mile Summary

Part of the development process was the addition of the functional classification codesto the network links. These codes, developed by the Federal Highway Administration(FHWA) and implemented by the Tennessee and Mississippi Departments ofTransportation, categorize all of the roads in the FHWA system into various functionalclassifications. The classifications are based on such factors as road cross-section,traffic volume, access control, and traffic served. Functional classification codes wereadded for all links in the network. The FHWA codes will be used in addition to theMemphis Major Road Plan as a basis for developing capacities in the Memphis model.They also will be used to calibrate and validate the model, since calibration targetsand allowable volume differences vary by facility type.

A summary of centerline-miles by FHWA functional classification code for the existingnetwork is shown in Table 1.

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Table 1. Memphis Centerline-Miles Summary by Functional Class

FHWA Functional ClassDescription Code

CenterlineMiles

Rural Interstate 1 26Rural Principal (Major) Arterial 2 71Rural Freeway Ramp 3 6Rural Minor Arterial 6 98Rural Major Collector 7 268Rural Minor Collector 8 236Rural Local Access 9 372Urban Interstate 11 73Urban Freeway/ Expressways 12 31Urban Freeway Ramp 13 40Urban Principal (Major) Arterial 14 257Urban Minor Arterial 16 553Urban Collector 17 281Urban Local Access 19 88

Total – Rural Roads 1,077 Total – Urban Roads 1,322 Total – All Roads 2,399

Screenline and Cutline Development

Several screenlines and cutlines were developed for the Memphis model to helpdetermine the accuracy of traffic assignment, especially with regards to regional flow.Screenlines and cutlines were developed that bisect the study area crossing only roadlocations that have the most available traffic counts. Typically, screenlines follownatural boundaries and barriers, such as rivers, streams, railroad tracks, and accesscontrolled facilities, as deemed appropriate. Cutlines are applied with less rigorousstandards, and have been used to capture movements in particular corridors.

The location and number of screenlines and cutlines were coordinated with the MPO.Based on these screenline locations, supplemental traffic counts are being requestedto provide traffic counts at screenline/cutline crossing where no count is available.Figure 6 shows the locations of the screenlines and cutlines. In the TransCADnetwork, these screenlines and cutlines have been entered numerically into anattribute field titled “Screenline.” During the model calibration and validation process,these locations will be used to provide screenline and cutline performance.

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Traffic Counts and Transit Boardings

As specified in the study design, AM peak period, PM peak period, midday off-peak,and night off-peak periods, daily traffic counts, and commercial vehicle count data willbe needed throughout the model network for model calibration and validation. TheKimley-Horn team has processed and homogenized this information, bringing somecounts from electronic format and some counts in hard copy format into the sameformat. These count locations have been coded into the TransCAD network for finaldetermination of screenline/cutline location and supplemental traffic count requests.Figure 7 shows the traffic count locations that were coded into the TransCADnetwork. Traffic counts are currently being appended to the TransCAD network atthese locations. Fields that will be included in the traffic count data include:

o 2000_ADTo 2004_ADTo 2004_AM (AM Peak Period)o 2004_Midday (Midday Off-Peak Period)o 2004_PM (PM Peak Period)o 2004_Night (Night Off-Peak Period)o 2004_Auto (Daily Automobiles)o 2004_SU (Daily Single-Unit Trucks)o 2004_CU (Daily Combination-Unit Trucks)

In addition, transit ridership data (boardings) were obtained from MATA. The transitboardings were aggregated by three levels: MATA transit line, Route sub-group, andRoute group. These boarding counts were coded into the transit route table and thebase year transit assignment results were compared with the observed boarding in allthree different levels. In addition, two screenlines were developed and provided byMATA, and were used during the transit assignment validation.

Corridor Travel Time Study

During a previous contract with the City of Memphis, Kimley-Horn conducted peakperiod travel time runs along signalized major and minor arterials throughout ShelbyCounty. Additional travel time runs were taken by the Kimley-Horn team in 2005 inurban and rural Desoto County, in Shelby County along collectors and freeways, andalong facilities extending into Fayette County. These travel time runs were conductedusing the average floating vehicle method during the AM and PM peak periods in bothtravel directions. Available loaded speed data by peak period will be input for each linkbased on this information. Figure 8 shows the locations of the travel time studycorridors. 1999 and 2003 travel time study data is also available from the MPO, but it

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is currently not in GIS format. Travel time data from 1999 would need to be reviewedfor applicability (due to potential changes in corridor cross-sections, volumes, andsignal density) before inclusion in the model update.

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Federal Functional Classification

Federal functional classification refers to the federal designation (e.g., freeway, ramp,or principal arterial) needed for performing conformity analysis, capacity analysis, andquantifying the percentage of lane miles by functional class. These functional classeshave been coded into the TransCAD network for modeling purposes. Figure 2 showsthe coded functional classification link data. As specified in the study design, theconsultant team will work with the Steering Committee to develop a correspondingrelationship between FHWA functional classifications and functional class as itpertains to the Major Road Plan. Since the Memphis MPO and surrounding agenciesreference the Major Road Plan when identifying functional class, efforts will be madeto make sure that the Major Road Plan functional class designation is maintained inthe roadway network database.

Upcoming Steps

The model network will be enhanced with additional information as it is collected,developed, or made available. The list below details data that are planned to be addedto the Memphis network in the upcoming three months.

Supplemental Traffic Count Request

The Kimley-Horn team will “map out” the count coverage and assess the quality ofcount data (e.g., time periods and accuracy). This process will identify “holes” in thecount coverage. The Kimley-Horn team will coordinate with the Steering Committee todevelop plans to obtain additional traffic counts from state, county, and local agencies.It is projected that up to 75 additional bi-directional hourly traffic counts (conductedover a continuous 24-hour period) may be needed throughout the study area. Therequired number of traffic counts can be determined after coordinating with theSteering Committee to ascertain the accuracy of their available data and their abilityto commit resources to obtain remaining data needs.

Corridor Travel Time Analysis

As specified in the Memphis Study Design, the skim matrices will be determined basedon travel impedance (i.e., speed or travel time). During the peak periods, the travelimpedance should not necessarily be a free-flow speed, but rather a morerepresentative “loaded speed” or “congested speed.” Results from the travel time runswill be used during the model development and calibration process to assist invalidation that the travel demand model is effectively representing the effect that trafficvolumes have on travel speed, and that the proper volume-delay curves are beingused.

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Area Type

This identifies the type of area (e.g., urban or rural) and is used in customizing linkcapacities. While the field data collection effort identified a preliminary area type, theconsultant team and the Steering Committee will define an area type categorizationscheme that incorporates population and employment densities as variables tocategorize districts. The advantage of an automated method to determine area type isthat future year area types can be determined using the same methodology. Areatypes will be applied to links using GIS after the methodology is established.

Capacity Equation Application

Daily and hourly capacities will be developed for the Memphis model. This will allowcollected street data to provide the most accurate representation of actual capacity(levels of service A through E) on an individual link. These capacities are implementedusing an equation that takes into account data such as facility type, speed limit,lanes, median treatment, area type, average land width, and average shoulder width.The capacity equations are built into the model process, so modifications to networkattributes automatically update the capacity in subsequent runs.

Refinement of Traffic Analysis Zone Structure

Overview

As a part of the Memphis MPO regional travel demand model update, it was expressedearly in the process that the existing traffic analysis zone (TAZ) structure needed to beexpanded and refined. The geographic expansion included TAZ coverage for all ofDeSoto County and the northwest quadrant of Marshall County located in Mississippi,the southern portion of Tipton County, Tennessee, and the western portion of FayetteCounty, Tennessee. Refinement of the TAZ structure within Shelby County and theCity of Memphis was also identified as part of the update so that new developments,higher land use density, and other socioeconomic variables could be best representedduring the eventual traffic assignment phase of model development.

The TAZ development process was comprehensive and iterative as it involved theestablishment of guidelines or criteria, input from the Peer Review Committee, andlocal input from the MPO Steering Committee. This iterative approach allowed for theapplication of technical knowledge and experience associated with TAZ structuredevelopment as well as local knowledge for refinement and its influence on the finalstructure of the TAZs to serve the regional model. The expanded and refined TAZstructure now consists of 1,237 internal zones and covers approximately 1,825 square

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miles. This is approximately one zone per 1.47 square land miles, a relatively densezonal structure for a metropolitan area such as the Memphis MPO.

TAZ Refinement Criteria

In developing and refining the traffic analysis zone (TAZ) structure for the MemphisMPO regional travel demand model, several guidelines and criteria were established asa basis for development. For example, zones were developed that are homogenouswith respect to land use and socioeconomic data. Whenever possible, zone boundariesfollowed physical and natural geographic features. Finally, census tract, census blockgroup, and even census block geography boundaries were followed to the extentpossible to allow for easy access to census data.

Traffic analysis zone development and modification was influenced by the followingcriteria:

Geographic featuresTransportation facilitiesTAZ boundary configuration consistent with census tract boundaries, censusblock groups in rural/suburban areas, and census blocks in the CBDIn more densely populated areas (e.g., CBD), additional TAZs will match censusblock group boundaries or census block boundaries where appropriateEnsure population and employment density is consistent across the zone (avoida disproportionate “pocket” of population or employment within zones)Ensure land uses are consistent across the zoneEvaluation of existing land uses and zoningCross reference with an evaluation of the future land use planConfiguration will be consistent with the available transportationnetwork/infrastructure serving the zoneConfigure zones and zonal boundaries such that trips can be loadedappropriately (meaning that we will load the proper roadway functionalclassification) to the internal transportation network within the TAZ itself.

In the development of the Memphis MPO TAZ structure, these criteria or guidelineswere followed to the extent possible but not without some variation. Several locationsin outlying rural areas have TAZs that are split into smaller geographic areas than theprovided Census Block boundaries. There were also locations where the shape orconfiguration of the TAZ was illogical in relation to roadway network access or landdevelopment. In such cases these zones were either split or combined with adjacentzones to provide a more desirable zone structure.

Additionally, throughout the process TAZ boundary locations were evaluated relativeto infrastructure, right-of-way, geographic features, identified special generators, land

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uses and future land use planning. Socio-economic data by census tract and censusblock group (where applicable) along with existing land use and future land use maps,model network area coverage, and necessary regional aerial photography were all usedin determining the need for splitting, realigning, or adding additional TAZs.

Special Generators

As a part of the TAZ development special generators had to be accounted for withinthe regional travel demand model. Special generators were identified and singled outbecause the socio-economic data associated with these TAZs cannot truly reflect thetraffic volume activity going on at these locations. Such special generators typicallyinclude industrial parks, universities, major employers, and regional and localshopping centers. Special generators for the Memphis MPO regional model includethe following:

Memphis International AirportFedEx Operations at Memphis International AirportGraceland

Process

The process began using the existing TAZ structure from the previous regional modelfor the MPO and identifying additional zonal needs beyond Shelby County andportions of Fayette and DeSoto Counties. As defined in the study design process, thisincluded all of DeSoto County, the northwest portion of Marshall County, the southernpart of Tipton County, and the entire western portion of Fayette County borderingShelby County. The initial expansion of the TAZ coverage was based on Census Tractboundaries in these outlying areas. This was quickly identified as insufficient as theTAZs were very large when relying solely on the Census Tract boundary layer. At thetract level in these areas much of the newer development and intent of the future landuse plan would have been under represented in interim and future trafficassignments. Following this initial assessment it was decided that census blockboundaries would prove more effective in establishing TAZ boundaries. At the censusblock layer smaller areas of development and growth could be captured. Therefinement of TAZs in the suburban and rural areas of the model was carried over as itresulted in zones matching up well with network coverage in outlying areas andavoided large zones loading at only a few select points in the roadway network.

For Shelby County and the City of Memphis, the old TAZ structure provided the initialbackdrop. Much of the old TAZ structure was based on a combination of census tract,census block group, and in some cases census block boundaries. However, forrefinement of the TAZ structure it was determined necessary that census block

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boundaries be used for all of Shelby County and particularly in the Memphis CBD.This approach resulted in the extensive refinement of the TAZ structure for ShelbyCounty and the City of Memphis.

Only in certain cases was there deviation from the use of census origin boundaries.The focus was to limit disaggregation efforts that involved splitting or reallocatingcensus data from census tracts, census block groups, or census block data sourceconfigurations and to modified TAZs. Only in select instances (future plannedroadways, new planned developments, current large blocks with very inconsistentinternal land uses, or known future high activity centers) were such variations fromthe criteria practiced. Typically, such cases were the direct result of local knowledgeinput and review of the proposed TAZ structure. The TAZ structure was then reviewedby the MPO Steering Committee. Where appropriate, comments from the SteeringCommittee were applied and incorporated into the final TAZ structure.

Once consensus on the TAZ structure and TAZ density was achieved it was forwardedto other members of the Memphis MPO regional model development team for review.Other team members were then responsible for identifying and coding necessary tripgeneration variable data (population, households, auto ownership, employment, etc.)from employment and census data resources into the TAZ database. Additionally,with the completion of the TAZ structure and the regional model network, centroidconnectors for each TAZ were then coded into the regional model network.

A map showing the new TAZ boundaries with other geographic features is included asAppendix B.

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Appendix A — Base Year (2004) Transit Route Attributes

Route_Name RETIREYEAR LineName ModeFareMatrixIndex AM_Headway MD_Headway PM_Headway OP_Headway

AM_DwellTime

MD_DwellTime

PM_DwellTime

OP_DwellTime

2A 2015 2-Medical Center[2A,2C] 2 0 60 60 60 9999 0.24 0.26 0.22 0.052A_R 2015 2-Medical Center[2A,2C] 2 0 60 60 48 9999 0.21 0.23 0.19 0.052C 9999 2-Medical Center[2A,2C] 2 0 60 60 48 9999 0.18 0.18 0.15 0.052C_R 9999 2-Medical Center[2A,2C] 2 0 60 60 48 9999 0.18 0.19 0.17 0.052L 9999 2-Lauderdale [2L,2W] 2 0 60 60 60 9999 0.05 0.05 0.05 0.052L_R 9999 2-Lauderdale [2L,2W] 2 0 45 60 60 9999 0.12 0.12 0.11 0.052W 9999 2-Lauderdale [2L,2W] 2 0 60 60 48 9999 0.05 0.05 0.05 0.052W_R 9999 2-Lauderdale [2L,2W] 2 0 45 60 60 9999 0.08 0.09 0.08 0.054A 2015 4-Walker [4A,4C] 2 0 30 43 34 36 0.05 0.05 0.05 0.054A_R 2015 4-Walker [4A,4C] 2 0 23 50 34 30 0.08 0.08 0.07 0.094C 2015 4-Walker [4A,4C] 2 0 30 50 30 90 0.05 0.05 0.05 0.064C_R 2015 4-Walker [4A,4C] 2 0 20 50 30 90 0.05 0.05 0.05 0.057 2015 7-Air Park[7A, 7B] 2 0 36 9999 9999 9999 0.09 0.05 0.05 0.057_R 2015 7-Air Park[7A, 7B] 2 0 9999 9999 60 9999 0.05 0.05 0.06 0.058 9999 8-Chelsea [8] 2 0 16 23 24 30 0.13 0.12 0.08 0.138_R 9999 8-Chelsea [8] 2 0 18 21 24 30 0.05 0.05 0.05 0.0610S 2015 10-Lamar [10C,10S] 2 0 45 9999 60 90 0.07 0.05 0.05 0.1110S_R 2015 10-Lamar [10C,10S] 2 0 45 9999 80 9999 0.07 0.05 0.06 0.0510C 2015 10-Lamar [10C,10S] 2 0 60 50 40 9999 0.13 0.14 0.08 0.0510C_R 2015 10-Lamar [10C,10S] 2 0 36 60 40 9999 0.10 0.14 0.10 0.0510RG 9999 10-Watkins[10RG,10RL] 2 0 90 100 48 30 0.19 0.18 0.13 0.1910RG_R 9999 10-Watkins[10RG,10RL] 2 0 36 100 80 60 0.05 0.05 0.05 0.0510RL 9999 10-Watkins[10RG,10RL] 2 0 60 100 48 9999 0.12 0.12 0.07 0.0510RL_R 9999 10-Watkins[10RG,10RL] 2 0 45 100 60 9999 0.05 0.05 0.05 0.0511C 9999 11-Thomas[11F,11C] 2 0 90 75 60 9999 0.05 0.05 0.05 0.0511C_R 9999 11-Thomas[11F,11C] 2 0 60 75 48 9999 0.05 0.05 0.05 0.0511F 9999 11-Thomas[11F,11C] 2 0 90 75 48 9999 0.05 0.05 0.05 0.0511F_R 9999 11-Thomas[11F,11C] 2 0 60 75 60 9999 0.05 0.05 0.05 0.0511S 9999 11-Tulane/Hodge[11T,11S] 2 0 9999 9999 9999 36 0.05 0.05 0.05 0.0511S_R 9999 11-Tulane/Hodge[11T,11S] 2 0 9999 9999 9999 45 0.05 0.05 0.05 0.0511T 9999 11-Tulane/Hodge[11T,11S] 2 0 30 75 30 9999 0.05 0.05 0.05 0.0511T_R 9999 11-Tulane/Hodge[11T,11S] 2 0 45 60 34 9999 0.05 0.05 0.05 0.0515 9999 15-Presidents Island [15] 2 0 60 9999 120 9999 0.05 0.05 0.05 0.0515_R 9999 15-Presidents Island [15] 2 0 180 9999 80 9999 0.05 0.05 0.05 0.0519M 9999 19-Vollintine[19RA,19NA,19M] 2 0 45 75 60 9999 0.05 0.05 0.05 0.0519M_R 9999 19-Vollintine[19RA,19NA,19M] 2 0 45 75 60 9999 0.05 0.05 0.05 0.0519NA 9999 19-Vollintine[19RA,19NA,19M] 2 0 90 9999 120 9999 0.05 0.05 0.05 0.0519NA_R 9999 19-Vollintine[19RA,19NA,19M] 2 0 60 9999 240 9999 0.05 0.05 0.05 0.0519R 9999 19-Third [19W, 19R] 2 0 45 100 120 9999 0.07 0.06 0.05 0.05

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AM_DwellTime

MD_DwellTime

PM_DwellTime

OP_DwellTime

19R_R 9999 19-Third [19W, 19R] 2 0 36 75 60 9999 0.05 0.05 0.05 0.0519RA 9999 19-Vollintine[19RA,19NA,19M] 2 0 90 100 60 9999 0.06 0.06 0.05 0.0519RA_R 9999 19-Vollintine[19RA,19NA,19M] 2 0 90 100 60 9999 0.05 0.05 0.05 0.0519W 9999 19-Third [19W, 19R] 2 0 45 100 48 9999 0.12 0.11 0.06 0.0519W_R 9999 19-Third [19W, 19R] 2 0 36 75 48 9999 0.08 0.11 0.11 0.0520 2015 20-Bellevue/Winchester [20] 2 0 26 50 27 180 0.05 0.05 0.05 0.0520_R 2015 20-Bellevue/Winchester [20] 2 0 23 43 30 180 0.05 0.08 0.05 0.1222L 9999 22-Poplar [22] 2 0 90 9999 9999 9999 0.05 0.05 0.05 0.0522L_R 9999 22-Poplar [22] 2 0 9999 9999 80 9999 0.05 0.05 0.12 0.0530 9999 30-Perkins [30] 2 0 36 75 40 9999 0.05 0.05 0.05 0.0530_R 9999 30-Perkins [30] 2 0 36 75 40 9999 0.05 0.05 0.05 0.0531 2015 31-Crosstown [31] 2 0 15 30 16 26 0.16 0.16 0.15 0.1731_R 2015 31-Crosstown [31] 2 0 14 30 17 26 0.19 0.19 0.17 0.2032A 2015 32-E Parkway[32A,32F,32N] 2 0 90 75 48 9999 0.14 0.18 0.13 0.0532A_R 2015 32-E Parkway[32A,32F,32N] 2 0 90 100 48 9999 0.05 0.05 0.05 0.0532F 2015 32-E Parkway[32A,32F,32N] 2 0 90 100 80 9999 0.14 0.18 0.13 0.0532F_R 2015 32-E Parkway[32A,32F,32N] 2 0 180 100 80 9999 0.17 0.19 0.11 0.0532N 2015 32-E Parkway[32A,32F,32N] 2 0 90 9999 240 9999 0.09 0.05 0.09 0.0532N_R 2015 32-E Parkway[32A,32F,32N] 2 0 90 9999 240 9999 0.09 0.05 0.05 0.0533 9999 33-Highland[33] 2 0 180 9999 120 9999 0.05 0.05 0.05 0.0533_R 9999 33-Highland[33] 2 0 90 9999 120 9999 0.05 0.05 0.05 0.0534B 9999 34-Union/WalnutG[34R,34B] 2 0 45 50 80 9999 0.05 0.05 0.05 0.0534B_R 9999 34-Union/WalnutG[34R,34B] 2 0 90 60 60 9999 0.05 0.06 0.05 0.0534M 2015 17-McLemore [34M,34N] 2 0 36 50 34 9999 0.13 0.14 0.12 0.0534M_R 2015 17-McLemore [34M,34N] 2 0 26 50 34 180 0.14 0.15 0.14 0.1634N 2015 17-McLemore [34M,34N] 2 0 9999 9999 9999 90 0.05 0.05 0.05 0.0534N_R 9999 17-McLemore [34M,34N] 2 0 9999 9999 9999 180 0.05 0.05 0.05 0.0534R 9999 34-Union/WalnutG[34R,34B] 2 0 60 9999 80 9999 0.05 0.05 0.05 0.0534R_R 9999 34-Union/WalnutG[34R,34B] 2 0 60 300 80 9999 0.07 0.09 0.06 0.0535 9999 35-Southgate [35] 2 0 45 60 60 9999 0.10 0.12 0.07 0.0535_R 9999 35-Southgate [35] 2 0 45 60 60 9999 0.16 0.18 0.13 0.0540 9999 40-Raleigh[40,40B] 2 0 60 100 80 45 0.19 0.18 0.08 0.2040_R 9999 40-Raleigh[40,40B] 2 0 180 150 60 60 0.11 0.19 0.16 0.2440B 9999 40-Raleigh[40,40B] 2 1 9999 9999 240 9999 0.05 0.05 0.05 0.0540B_R 9999 40-Raleigh[40,40B] 2 1 180 9999 9999 9999 0.05 0.05 0.05 0.0541 9999 41-Collierville[41] 2 0 90 9999 120 9999 0.10 0.05 0.05 0.0541_R 9999 41-Collierville[41] 2 0 90 9999 120 9999 0.05 0.05 0.07 0.0543B 2015 43-ElvisPresley [43B,43H,43S] 2 0 36 9999 30 9999 0.14 0.05 0.10 0.0543B_R 2015 43-ElvisPresley [43B,43H,43S] 2 0 26 9999 48 9999 0.07 0.05 0.08 0.0543H 2015 43-ElvisPresley [43B,43H,43S] 2 0 36 9999 34 9999 0.14 0.05 0.10 0.0543H_R 2015 43-ElvisPresley [43B,43H,43S] 2 0 26 9999 40 9999 0.09 0.05 0.11 0.0543S 2015 43-ElvisPresley [43B,43H,43S] 2 0 180 30 9999 36 0.18 0.18 0.05 0.19

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AM_DwellTime

MD_DwellTime

PM_DwellTime

OP_DwellTime

43S_R 2015 43-ElvisPresley [43B,43H,43S] 2 0 90 38 48 36 0.10 0.13 0.11 0.1550G 9999 50-Poplar[50G,50W,50Y] 2 0 26 23 34 26 0.06 0.07 0.05 0.1150G_R 9999 50-Poplar[50G,50W,50Y] 2 0 30 30 34 26 0.05 0.05 0.05 0.1050W 9999 50-Poplar[50G,50W,50Y] 2 0 60 9999 40 9999 0.12 0.05 0.06 0.0550W_R 9999 50-Poplar[50G,50W,50Y] 2 0 45 9999 120 9999 0.10 0.05 0.09 0.0550Y 9999 50-Poplar[50G,50W,50Y] 2 0 90 9999 9999 9999 0.25 0.05 0.05 0.0550Y_R 9999 50-Poplar[50G,50W,50Y] 2 0 180 9999 120 9999 0.21 0.05 0.21 0.0552B 2015 52-Park[52Q,52B,52SF] 2 0 180 9999 9999 9999 0.07 0.05 0.05 0.0552B_R 2015 52-Park[52Q,52B,52SF] 2 0 9999 9999 80 9999 0.05 0.05 0.05 0.0552M 9999 52-Jackson[52M,52R,52SE] 2 0 45 50 40 9999 0.21 0.21 0.17 0.0552M_R 9999 52-Jackson[52M,52R,52SE] 2 0 36 60 60 9999 0.12 0.14 0.13 0.0552Q 2015 52-Park[52Q,52B,52SF] 2 0 36 50 34 9999 0.13 0.13 0.10 0.0552Q_R 2015 52-Park[52Q,52B,52SF] 2 0 30 60 60 90 0.17 0.19 0.16 0.2252R 9999 52-Jackson[52M,52R,52SE] 2 0 45 60 48 36 0.15 0.14 0.07 0.1552R_R 9999 52-Jackson[52M,52R,52SE] 2 0 45 60 48 36 0.09 0.15 0.13 0.1852SE 9999 52-Jackson[52M,52R,52SE] 2 0 60 9999 60 9999 0.17 0.05 0.09 0.0552SE_R 9999 52-Jackson[52M,52R,52SE] 2 0 36 9999 80 9999 0.05 0.05 0.06 0.0552SF 2015 52-Park[52Q,52B,52SF] 2 0 30 50 34 45 0.16 0.16 0.12 0.1952SF_R 2015 52-Park[52Q,52B,52SF] 2 0 23 60 30 30 0.13 0.15 0.13 0.1853B 9999 53-Summer[53B,53S] 2 0 45 60 48 45 0.19 0.19 0.14 0.2153B_R 9999 53-Summer[53B,53S] 2 0 36 60 40 180 0.14 0.17 0.15 0.2053I 9999 53-Florida [53I,53L,53W] 2 0 60 100 60 9999 0.13 0.13 0.10 0.0553I_R 9999 53-Florida [53I,53L,53W] 2 0 45 100 80 9999 0.10 0.12 0.11 0.0553L 9999 53-Florida [53I,53L,53W] 2 0 60 150 48 9999 0.22 0.22 0.19 0.0553L_R 9999 53-Florida [53I,53L,53W] 2 0 60 75 80 9999 0.21 0.25 0.24 0.0553S 9999 53-Summer[53B,53S] 2 0 60 60 40 9999 0.25 0.25 0.19 0.0553S_R 9999 53-Summer[53B,53S] 2 0 45 60 40 90 0.09 0.13 0.11 0.1653W 9999 53-Florida [53I,53L,53W] 2 0 60 100 48 9999 0.05 0.05 0.05 0.0553W_R 9999 53-Florida [53I,53L,53W] 2 0 60 75 80 180 0.05 0.05 0.05 0.0556 2015 56-Union [56] 2 0 26 43 24 26 0.05 0.06 0.05 0.0956_R 2015 56-Union [56] 2 0 20 33 30 36 0.16 0.17 0.15 0.2058B 9999 58-FoxMeadowsB[58B] 2 1 9999 9999 80 9999 0.05 0.05 0.14 0.0558B_R 9999 58-FoxMeadowsB[58B] 2 1 60 9999 9999 9999 0.25 0.05 0.05 0.0562G 9999 62-Frayser/EMemphis[62G,62W]2 0 9999 100 9999 9999 0.05 0.05 0.05 0.0562G_R 9999 62-Frayser/EMemphis[62G,62W]2 0 9999 150 9999 9999 0.05 0.05 0.05 0.0562W 9999 62-Frayser/EMemphis[62G,62W]2 0 60 9999 120 9999 0.05 0.05 0.05 0.0562W_R 9999 62-Frayser/EMemphis[62G,62W]2 0 90 9999 80 9999 0.05 0.05 0.05 0.0569 2015 69-Winchester[69] 2 0 45 50 48 90 0.08 0.12 0.05 0.1969_R 2015 69-Winchester[69] 2 0 45 50 48 90 0.07 0.11 0.05 0.1780 9999 80-Cordova [80] 2 0 90 9999 60 9999 0.05 0.05 0.05 0.0580_R 9999 80-Cordova [80] 2 0 90 9999 60 9999 0.05 0.05 0.06 0.0580B 9999 80-Cordova [80] 2 0 180 9999 9999 9999 0.05 0.05 0.05 0.05

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AM_DwellTime

MD_DwellTime

PM_DwellTime

OP_DwellTime

80B_R 9999 80-Cordova [80] 2 0 9999 9999 240 9999 0.05 0.05 0.05 0.0581 9999 81-ShelbyDr/HickoryHill [81] 2 0 60 9999 80 9999 0.38 0.05 0.29 0.0581_R 9999 81-ShelbyDr/HickoryHill [81] 2 0 90 9999 60 9999 0.46 0.05 0.46 0.0582 9999 82-GermantownPkwy[82] 2 0 60 9999 60 9999 0.05 0.05 0.05 0.0582_R 9999 82-GermantownPkwy[82] 2 0 60 9999 80 9999 0.05 0.05 0.05 0.0589 9999 89-WalkerHomes/Westwood 2 0 9999 9999 9999 30 0.05 0.05 0.05 0.0589_R 9999 89-WalkerHomes/Westwood 2 0 9999 9999 9999 36 0.05 0.05 0.05 0.0590 9999 90-Neely/Shelby Dr 2 0 9999 9999 9999 30 0.05 0.05 0.05 0.0590_R 9999 90-Neely/Shelby Dr 2 0 9999 9999 9999 45 0.05 0.05 0.05 0.0593 9999 93-HickoryHill/Winchester[93] 2 0 60 75 48 9999 0.20 0.20 0.11 0.0593_R 9999 93-HickoryHill/Winchester[93] 2 0 36 100 48 9999 0.22 0.31 0.27 0.05Trolley MadisonSt IB 9999 Madison St Trolley 1 2 10 10 10 25 1.36 1.37 1.37 1.38Trolley MadisonSt OB 9999 Madison St Trolley 1 2 10 10 10 25 1.26 1.25 1.23 1.26Trolley Main StNB 9999 Main St Trolley 1 2 10 10 10 10 0.93 0.93 0.93 0.93Trolley Main StSB 9999 Main St Trolley 1 2 10 10 10 10 0.80 0.80 0.80 0.80Trolley RiverfrontLoop 9999 Riverfront Trolley 1 2 10 10 10 10 1.45 1.46 1.44 1.46

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Appendix B — Final TAZ Geographic Boundaries

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1

Technical Memorandum #1bTravel Time Studies

This memorandum details the development of the Travel Time adjustment factors forthe Memphis Travel Demand Model Update.

ContentsMethodology

- Overview- Determination of Travel Time Study Corridors- Travel Time Study

Appendix A — Summary of Travel Time Runs

Methodology

Development of the travel time factors for the Memphis MPO Model includedidentifying study corridors, administering a travel time survey, analyzing the surveydata to develop speed adjustment factors by area type, roadway facility type, postedspeeds, and time-of-day. These factors were used to develop free-flow speeds andcongested speeds for use in the Memphis model.

Overview

Travel time is defined as the total time for a vehicle to complete a designated trip overa section of road or from a specified origin to a specified destination. A travel timestudy provides valuable information about the delay associated with the studycorridor, including congestion delay and intersection delay.

Traditionally, free flow speeds have been used for trip distribution procedures in traveldemand models. In the absence of observed speed measurements, posted speeds haveoften been used as a surrogate to estimate free flow speeds. However, during peakperiods, when many trips are made, the travel impedance is not necessarily free flowspeed, but rather a more representative “loaded speed” or “congested speed.”Correspondingly, often during non-peak periods or in the non-peak direction of travel,the motorists are observed traveling in excess of the posted speed limit.

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2

For the development of the Memphis Travel Demand Model (TDM), posted speeds foreach link in the network were collected as a part of the network data collectionprocess. Subsequent to this, travel time studies were conducted along selectedcorridors to estimate congested speeds on to develop system-wide congested speedsthat will be compared to model congested speeds.

Results from the travel time runs are being used during the model development for thetrip distribution, mode choice, and highway/transit assignment submodels. Theyhave been used to develop free-flow speed adjustment factors and to estimatecongested speeds for each time period.

Determination of Travel Time Study Corridors

The emphasis of the study was mainly on the freeways and arterials — not on collectoror local streets. Corridors were selected based on their functional classification,significance, geographic location, posted speeds, and length.

Travel time data was collected on approximately 20% (390 miles) of the total freewaylane miles and 10% (910 miles) of the arterial streets included in the model network.Travel time studies were conducted on a total of 177 miles of roadway network. Table1 shows the description of each travel time corridor. Figure 1 shows all the travel timestudy corridors.

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Table 1. Travel Time Study Corridors

RouteID Route From To Functional

ClassLength

(mi)

1 Perkins Rd Sam Cooper Blvd US 78/Lamar Ave Minor Arterial 8.81

Highway 300 Highway 51 I-40 Other Freeway 1.1

I-40 Highway 300 I-240 Interstate 8.942

I-240/I-40 Loop to Highway 300 Interstate 22.17

Sam CooperBlvd Holmes St I-40 Other Freeway 3.99

3I-40 Sam Cooper Blvd Paul Barrett Pkwy

Overpass Interstate 13.74

4 Highway 70 I-40 Underpass Paul Barrett PkwyOverpass Major Arterial 15.27

Jackson Ave Bellevue Blvd I-40 Major Arterial 6.425

Austin PeayPkwy I-40 Loosahatchie Pkwy Major Arterial 6.96

6 Poplar Ave Goodlett Street Kirby Pkwy Major Arterial 4.61

7 Bill MorrisParkway I-240 Highway 72 Other Freeway 14.77

8 Lamar Ave/US 78

I-240 Downtown(West Side of

Loop)Goodman Rd Major Arterial 15.35

9 Elvis PresleyBoulevard Goodman Road Brooks Rd Major Arterial 8.53

Highway 61 I-55 State Line Rd Major Arterial 7.3110

State LineRoad Highway 61 I-55 Minor Arterial 7.89

I-55 North I-55/I-240Interchange Riverside Dr Interstate 5.34

11Riverside Dr I-55 I-40 Minor Arterial 2.21

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Table 1. Travel Time Study Corridors (cont.)

RouteID Route From To Functional

ClassLength

(mi)

11 I-40Downtown

West of I-40/RiversideInterchange

I-240 Interstate 1.44

Union Ave Riverside Dr I-240 Downtown Major Arterial 1.87

Madison Ave I-240 Downtown Front Street Minor Arterial 2.42

Jefferson Ave Front St I-240 Downtown Minor Arterial 1.7912

Poplar Ave I-240 Downtown Front Street Major Arterial 1.91

2nd St G.E. Patterson St Chelsea Avenue Minor Arterial 2.47

3rd St Chelsea Ave G.E. Patterson St Minor Arterial 2.9

Manassas St Chelsea Ave Union Ave Minor Arterial 1.86

13

Walnut St Union Ave Linden Ave Local 0.23

14 Highway 51Millington Rd

(SouthIntersection)

Fite Rd Major Arterial 5.42

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6

Travel Time Study

The most common method used to conduct travel time surveys is performing floatingcar runs. This method was employed for the Memphis TDM. The driver of the vehiclewas instructed to maintain the profile of an average car in the traffic stream accordingto his or her best judgment of the traffic stream’s speed.

A GPS unit was used to continuously log travel time information directly to TransCAD.The position of the GPS receiver is automatically recorded to a geographic layer atpredefined time intervals (2 seconds for this study). TransCAD stores the data in astandard format geographic file (which is native to TransCAD) as a series of points.This is a relatively simple and cost-effective procedure in which a single person with aGPS unit hooked to a laptop with TransCAD software can collect travel time data onstudy corridors.

Travel Time Survey Data Processing

The travel time runs were conducted during February and March 2005. The field datacollected is stored in TransCAD’s native standard geographic file format (.dbd). Thedata was exported to spreadsheets to enable further analysis. The distances betweenpoints along the corridor between two major cross streets were summed to determinethe total distance between the cross streets and then converted to miles.

The travel time was then determined based on the difference in time from one majorcross street to another and the distance between them. The average travel time wasthen compared to the posted speed for each of these roadway segments andadjustment factors were developed. These factors were summarized based on roadwayfacility type, area type, posted speed, and time-of-day.

Roadway functional classes 1, 11, and 12 were grouped to form the Freeways;functional classes 2, 6, 14, and 16 were grouped to form the arterials; and classes 7,8, 9, 17, and 19 were grouped to form the collectors/locals. A description of thefunctional classifications can be found in the Network and TAZ Development Memo –Technical Memorandum #1(a).

Arterials were sub-divided into two speed categories based on their posted speeds:greater than or equal to 45 mph and less than 45 mph. No significant advantage wasfound to performing such an exercise for the Freeway and collector/local.

The Memphis TDM uses four time-of-day periods. These are the AM peak period (6AM – 9 AM), midday off-peak period (9 AM – 2 PM), PM peak period (2 PM - 6 PM), andnight off-peak period (6 PM – 6 AM). Time-of-Day Memo – Technical Memorandum #5contains a detailed discussion of the determination of these time periods.

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The travel time surveys were conducted for the AM, midday, and PM periods. Data wasnot collected for the night period. Factors have been developed for comparison andcalibration purposes for AM, midday, and PM time periods. Night time congestedspeeds will not be evaluated.

Table 2 shows the free-flow speed adjustment factors used to estimate free-flow traveltimes in the Memphis Model. These factors, which are primarily based on the Middaytime period, are applied to the posted speed to estimate free-flow speeds used for tripassignment. For example, if an arterial has posted speed of 40 mph, and is located inthe urban area, then a factor of 0.85 (from table 2) is applied to its posted speed tocalculate its free-flow speed (40 *0.85 = 34 mph). The calculated free-flow speed isthen used as an input of the volume-delay function in the highway assignmentprocedure.

Table 2. Free-Flow Speed Adjustment Factors

Area Type Freeways Arterial Collector and LocalAll >=45 mph <45 mph All

CBD 1.025 0.96 0.92 0.80Urban 1.025 0.96 0.92 0.80

Suburban 1.025 0.96 0.95 0.85Rural 1.05 0.96 0.95 0.90

The travel time factors were summarized for each time period by facility type, areatype, and speed category. Table 3 shows the congested speed estimation factors thathave been developed based on the travel time studies. These factors are used to createan estimated congested travel time, by time period, in the initial network skims for usein the initial trip distribution and mode choice submodels. For example, if an arterialhas posted speed of 40 mph, and is located in the urban area, then a factor of 0.80(from table 3) is applied to its posted speed to calculate its congested speed for AMpeak period (40 *0.80 = 32 mph). The calculated congested speed is then used tocalculate the initial highway travel times for this arterial. The calculated congestedtravel time is used as a starting cost for the highway assignment procedure, in orderto achieve faster convergence of the equilibrium assignment procedure.

As part of the model calibration process, a table similar to Table 3 will be created formodel results to confirm that the travel demand model is effectively representing the

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8

effect that traffic volumes have on travel speed, and that the proper capacities andvolume-delay curves are being used.

Table 3. Congested Speed Estimation Factors

Freeways Arterial Collector and LocalArea Type Time-of-DayAll >=45 mph <45 mph All

AM 1.04 0.92 0.75 0.78PM 1.05 0.86 0.72 0.72

Midday 1.10 0.92 0.85 0.80CBD

Night 1.05 0.92 0.85 0.80AM 1.04 0.92 0.80 0.79PM 0.97 0.86 0.83 0.70

Midday 1.09 0.92 0.84 0.69Urban

Night 1.05 0.92 0.85 0.70AM 1.04 0.93 1.02 0.95PM 1.07 0.86 0.94 0.93

Midday 1.09 0.92 1.00 0.96Suburban and Rural

Night 1.05 0.92 0.95 0.90AM 1.04 0.93 1.02 0.95PM 1.07 0.86 0.94 0.93


Night 1.05 0.92 0.95 0.90

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9

Appendix A — Summary of Travel Time Runs

Route #1: Perkins Road

Average Travel Speed (mph)Section_ID From To Distance(Mi) NB_AM NB_MD NB_PM SB_AM SB_MD SB_PM

1 Delp St. US 78 0.31 45 19 0 0 0 02 US 78 Old Lamar 0.29 57 52 47 54 56 483 Old Lamar Winchester 0.86 50 50 42 46 47 424 Winchester Knight Arnold 0.83 26 46 24 21 41 39

5 Knight Arnold AmericanWay 0.80 29 39 26 41 22 19

6 AmericanWay I240 0.48 33 36 37 17 36 14

7 I240 Dunn Ave. 1.17 37 34 31 18 36 178 Dunn Ave. Quince 0.08 27 45 3 50 36 269 Quince Park 1.15 32 33 21 39 36 28

10 Park Southern 0.62 34 17 37 23 36 2111 Southern Poplar 0.13 12 12 10 13 36 4

12 Poplar WalnutGrove 0.99 28 31 16 24 36 25

13 WalnutGrove Sam Cooper 1.10 41 20 35 27 29 33

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10

Route #2: Highway 300, I-40 and I-240 Loop

Average Travel Speed (mph)SectionID From To Distance

(Mi) NB/EB_AM NB/EB_MD NB/EB_PM SB/WB_AM SB/WB_MD SB/WB_PM1 Highway 51 I-40 E/W 1.10 52 48 52 51 52 602 I-40 E/W Watkins 0.41 60 66 68 65 63 643 Watkins Hollywood 2.09 67 71 69 66 69 62

4 Hollywood New Allen /Warford 1.15 64 69 67 70 67 61

5 New Allen /Warford Jackson 1.56 70 70 63 68 68 68

6 Jackson CovingtonPike 2.34 68 70 68 67 69 70

7 CovingtonPike Highway 70 1.16 67 69 68 67 70 45

8 Highway 70 Sam Cooper 0.22 66 54 62 48 65 56

9 Sam Cooper WalnutGrove 1.84 66 64 55 17 71 65

10 WalnutGrove Poplar 1.61 70 69 42 53 69 67

11 Poplar Bill MorrisPkwy 1.72 75 71 20 66 68 71

12 Bill MorrisPkwy Mt. Moriah 0.86 74 84 10 66 72 70

13 Mt. Moriah Perkins 0.98 76 73 20 65 70 6614 Perkins Getwell 1.29 68 74 31 68 56 6915 Getwell Lamar 1.76 71 74 66 69 67 6416 Lamar Airways 1.43 67 71 50 61 69 6617 Airways Millbranch 1.43 66 69 41 59 73 6218 Millbranch I-240 0.78 64 64 44 64 70 5519 I-240 Norris 0.82 62 62 60 60 60 52

20 Norris SouthParkway 1.87 62 62 62 63 66 65

21 SouthParkway Crump 1.44 62 66 62 63 65 62

22 Crump Union 0.51 55 66 16 66 63 62

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11

Route #2: Highway 300, I-40 and I-240 Loop (cont.)


(Mi) NB/EB_AM NB/EB_MD NB/EB_PM SB/WB_AM SB/WB_MD SB/WB_PM23 Union Madison 0.14 49 55 6 70 65 5924 Madison I-40 N/S 0.72 52 51 9 53 60 5825 I-40 N/S Jackson 0.59 50 36 34 31 49 4726 Jackson Chelsea 0.85 58 55 54 33 55 5527 Chelsea I-40 E/W 1.53 65 60 68 59 62 6328 I-40 E/W Highway 51 0.97 55 52 57 54 56 53

Route #3: Sam Cooper / I-40


(Mi) EB_AM EB_OP EB_PM WB_AM WB_OP WB_PM1 Holmes Rd. Highland 0.45 54 59 61 45 57 512 Highland Graham St. 1.03 64 61 64 53 62 633 Graham St. Perkins Rd. 1.01 67 61 69 60 63 684 Perkins Rd. I-40/I-240 1.50 70 61 67 43 65 665 I-40/I-240 Sycamore View Rd. 1.74 75 65 60 23 68 66

6 Sycamore ViewRd. Whitten Rd. 1.59 76 68 66 45 66 70

7 Whitten Rd. Appling Rd. 1.34 76 71 70 73 69 758 Appling Rd. Germantown Pkwy. 1.33 77 68 66 74 65 73

9 GermantownPkwy. Highway 64 1.57 72 67 69 74 68 70

10 Highway 64 Canada Rd. 2.32 76 67 71 71 68 74

11 Canada Rd. Paul Barrett Pkwy/TN-385 3.85 70 64 70 76 67 71

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12

Route #4: Highway 70


(Mi) EB_AM EB_OP EB_PM WB_AM WB_OP WB_PM1 I-40 Bartlett Rd. 0.91 21 33 43 29 31 412 Bartlett Rd. Sycamore View 0.82 28 31 31 18 45 -3 Sycamore View Raleigh-LaGrange 0.79 17 43 31 28 31 -

4 Raleigh-LaGrange Elmore 0.85 28 42 44 35 44 -

5 Elmore Alturia 0.21 23 52 46 53 44 106 Alturia Kirby-Whitten 0.72 17 45 25 47 46 467 Kirby-Whitten Highway 64 0.44 16 19 13 16 25 138 Highway 64 Yale 1.02 30 45 46 18 37 349 Yale Appling 1.11 29 49 35 47 52 5010 Appling Germantown Rd. 1.13 26 33 39 25 32 24

11 GermantownRd. Brunswick 1.35 48 45 51 45 39 36

12 Brunswick Canada Rd. 1.45 53 54 56 57 52 5813 Canada Rd. Chamber's Chapel 2.77 54 60 59 56 50 55

14 Chamber'sChapel

Paul Barrett Pkwy. /385 1.71 32 54 53 52 47 51

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13

Route #5: Jackson Ave / Austin Peay Pkwy


(Mi) NB/EB_AM NB/EB_MD NB/EB_PM SB/WB_AM SB/WB_MD SB/WB_PM1 Bellevue Watkins 0.41 30 35 21 28 33 242 Watkins Evergreen 0.64 28 35 37 41 41 323 Evergreen McLean 0.26 26 23 34 43 16 244 McLean University 0.30 39 35 22 47 37 26

5 University NorthParkway 0.63 41 39 28 22 42 31

6 NorthParkway Hollywood 0.24 15 18 10 25 31 45

7 Hollywood Warford 0.77 45 40 31 26 22 468 Warford Chelsea 1.69 42 36 29 36 37 359 Chelsea I-40 1.48 30 36 38 39 36 44

10 I-40 James /Stage 0.71 58 53 48 61 57 63

11 James /Stage

Frayser /Yale 1.55 41 39 23 42 38 47

12 Frayser /Yale

CovingtonPike 1.19 37 30 34 44 47 31

13 CovingtonPike

EgyptCentral 0.63 48 50 39 23 51 53

14 EgyptCentral

OldBrownsville 1.48 40 57 48 48 50 52

15 OldBrownsville Loosahatchie 1.40 61 51 46 23 46 44

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14

Route #6: Poplar Ave


(Mi) EB_AM EB_OP EB_PM WB_AM WB_OP WB_PM1 Goodlett Perkins Extd. 1.06 25 27 26 23 44 342 Perkins Extd. Perkins Rd. 0.15 28 30 11 33 7 93 Perkins Rd. Mendenhall 0.63 23 14 36 35 25 284 Mendenhall White Station Rd. 0.52 22 14 30 25 28 155 White Station Rd. Yates 0.68 27 18 27 22 22 226 Yates I-240 0.15 42 42 40 27 27 15

7 I-240 Ridgeway/ShadyGrove 0.26 29 23 19 42 48 14

8 Ridgeway/ShadyGrove Kirby 1.16 42 29 25 24 28 14

Route #7: Bill Morris Parkway


(Mi) EB_AM EB_OP EB_PM WB_AM WB_OP WB_PM1 I-240 Ridgeway 1.69 65 62 60 20 64 312 Ridgeway Kirby 1.32 66 65 72 7 70 723 Kirby Riverdale 1.11 66 69 60 9 65 674 Riverdale Winchester 0.98 61 68 59 23 67 62

5 Winchester HacksCross 1.57 64 67 67 74 70 66

6 HacksCross

ForestIrene 2.15 74 73 73 74 75 69

7 ForestIrene

HoustonLevee 2.20 69 76 79 72 76 74

8 HoustonLevee Byhalia 2.01 71 75 74 74 74 76

9 Byhalia Hwy. 72 1.76 56 36 41 65 65 67

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15

Route #8: Lamar Ave / US-78


1 Goodman Craft 1.16 74 75 71 69 61 772 Craft Stateline 1.40 76 72 75 69 65 733 Stateline Holmes 0.99 44 45 29 55 40 444 Holmes Shelby 1.18 45 30 25 45 47 235 Shelby Perkins 1.65 56 48 38 32 35 166 Perkins Winchester 1.07 21 22 31 57 50 527 Winchester Getwell 0.57 42 46 40 45 12 248 Getwell Knight Arnold 0.68 27 45 31 41 45 379 Knight Arnold Democrat 0.34 40 43 36 15 46 1710 Democrat American Way 0.67 20 39 38 34 36 26

11 American Way I-240(East/West) 0.33 47 42 43 52 30 34

12 I-240(East/West) Prescott 0.22 57 29 18 57 34 30

13 Prescott Semmes 0.36 43 39 14 48 30 31

14 Semmes Pendleton &Kimball 0.76 39 33 29 42 29 32

15 Pendleton &Kimball Barron 0.58 42 43 32 17 24 15

16 Barron Airways 0.56 17 19 18 44 40 3517 Airways Park 0.29 25 29 9 11 17 1418 Park South Parkway 0.23 19 17 13 43 38 3619 South Parkway Southern 0.55 40 40 33 24 34 2020 Southern McLean 0.16 18 48 27 43 53 3321 McLean Central 1.10 32 41 37 32 36 2622 Central Cleveland 0.07 46 49 54 56 52 3523 Cleveland Bellevue 0.18 40 41 36 43 43 27

24 Bellevue I-240(North/South) 0.25 39 19 17 38 41 12

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Route #9: Elvis Presley Blvd


1 Goodman Rd. DeSoto Rd. 1.01 33 36 36 21 20 152 DeSoto Rd. Stateline Rd. 1.12 41 38 38 32 40 233 Stateline Rd. Holmes Rd. 1.03 49 28 25 34 36 354 Holmes Rd. Shelby Dr. 1.00 23 39 43 34 16 345 Shelby Dr. Raines Rd. 1.00 37 29 35 31 32 246 Raines Rd. Winchester Rd. 1.29 30 33 24 29 27 427 Winchester Rd. Brooks Rd. 0.52 36 17 27 28 19 19

8 Brooks Rd. Elvis PresleyBlvd. 0.16 77 0 85 76 0 76

9 Elvis PresleyBlvd. I-240 1.39 69 0 66 66 0 0

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17

Route #10: Highway 61 and State Line Road


(Mi) NB/EB_AM NB/EB_MD NB/EB_PM SB/WB_AM SB/WB_MD SB/WB_PM

1 I-55Highway51 1.22 24 29 - 24 31 -

2Highway51 Tulane 1.15 33 33 - 47 49 -

3 TulaneHornLake 1.02 43 54 - 46 45 -

4HornLake Weaver 2.01 54 48 - 53 49 -

5 WeaverHighway61 1.92 58 53 - 55 54 -

6Highway61 Holmes 1.77 61 55 - 59 53 -

7 Holmes Weaver 0.63 57 40 - 52 35 -

8 WeaverShelbyDr. 0.77 44 33 - 39 55 -

9ShelbyDr. Raines 1.31 47 48 - 48 52 -

10 RainesHornLake 0.36 46 39 - 45 45 -

11HornLake Mitchell 1.22 48 41 - 39 39 -

12 Mitchell Brooks 0.54 51 22 - 48 50 -13 Brooks I-55 1.29 42 36 - 31 37 -

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18

Route #11: I-55 North, Riverside Dr, and I-40 Downtown



1 I-55 Highway61 2.46 79 79 73 70 72 79

2 Highway61 Mallory 0.10 60 52 55 63 76 76

3 Mallory SouthParkway 1.44 67 65 65 32 28 27

4 SouthParkway McLemore 0.67 67 57 69 47 53 48

5 McLemore Crump 0.66 65 47 54 55 73 496 Crump Union 1.48 41 39 32 63 70 597 Union Monroe 0.07 35 38 12 66 42 668 Monroe Jefferson 0.24 8 45 14 64 43 679 Jefferson I-40 E/W 0.42 40 22 48 78 54 57

10 I-40 E/W Crump 0.62 61 18 66 64 52 5911 Crump I-40 N/S 0.82 45 12 19 64 59 69

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19

Route #12: Union Ave, Madison Ave, Jefferson Ave, and Poplar Ave


(Mi) NB/EB_AM NB/EB_MD NB/EB_PM SB/WB_AM SB/WB_MD SB/WB_PM1 Riverside Front 0.08 28 - 6 6 - 112 Front 2nd 0.15 29 - 10 14 - 113 2nd 3rd 0.07 29 - 17 23 - 114 3rd Crump 0.34 14 - 16 16 - 205 Crump Manassas 0.56 27 - 39 34 - 246 Manassas Dunlap 0.15 19 - 45 32 - 11

7 Dunlap Pauline /Ayers 0.29 17 - 12 22 - 41

8 Pauline /Ayers I-240 0.23 42 - 41 11 - 43

9 I-240 Pauline /Ayers 0.89 11 - 41 38 - 34

10 Pauline /Ayers Dunlap 0.30 17 - 23 31 - 38

11 Dunlap Manassas 0.12 33 - 40 35 - 3812 Manassas Crump 0.52 24 - 30 37 - 3913 Crump 3rd 0.36 21 - 30 31 - 3514 3rd 2nd 0.07 24 - 30 30 - 015 2nd Front 0.15 8 - 10 10 - 016 Front 2nd 0.15 8 - 10 9 - 1017 2nd 3rd 0.07 25 - 21 35 - 918 3rd Crump 0.33 33 - 35 39 - 3119 Crump Manassas 0.51 43 - 37 35 - 4020 Manassas Dunlap 0.12 47 - 34 30 - 10

21 Dunlap Pauline /Ayers 0.32 21 - 17 39 - 34

22 Pauline /Ayers I-240 0.27 14 - 24 46 - 13

23 I-240 Pauline /Ayers 0.61 40 - 31 43 - 45

24 Pauline /Ayers Dunlap 0.15 45 - 37 38 - 28

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20

Route #12: Union Ave, Madison Ave, Jefferson Ave, and Poplar Ave (cont.)


(Mi) NB/EB_AM NB/EB_MD NB/EB_PM SB/WB_AM SB/WB_MD SB/WB_PM25 Dunlap Manassas 0.12 38 - 43 12 - 2126 Manassas Crump 0.52 27 - 38 22 - 3627 Crump 3rd 0.28 11 - 33 24 - 1428 3rd 2nd 0.08 27 - 27 12 - 2929 2nd Front 0.15 14 - 17 31 - 10

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21

Route #13: 2nd St, 3rd St, Manassas St, and Walnut St



1 Chelsea Highway70 0.57 - - - 31 23 24

2 Highway70 Jackson 0.15 - - - 34 29 12

3 Jackson Exchange 0.21 - - - 28 24 184 Exchange Poplar 0.08 - - - 38 5 55 Poplar Jefferson 0.22 - - - 12 29 136 Jefferson Madison 0.14 - - - 26 11 257 Madison Monroe 0.07 - - - 27 23 348 Monroe Union 0.07 - - - 10 22 349 Union Linden 0.44 - - - 28 22 3410 Linden Vance 0.25 - - - 23 15 11

11 Vance G.E.Patterson 0.27 - - - 8 14 23

13 G.E.Patterson Vance 0.23 28 22 21 - - -

14 Vance Linden 0.14 32 20 10 - - -15 Linden Union 0.37 31 1 17 - - -16 Union Monroe 0.07 8 1 10 - - -17 Monroe Madison 0.07 21 1 23 - - -18 Madison Jefferson 0.14 15 1 23 - - -19 Jefferson Poplar 0.22 10 16 13 - - -20 Poplar Exchange 0.07 26 17 32 - - -21 Exchange Jackson 0.22 19 37 19 - - -

22 Jackson Highway70 0.15 34 33 21 - - -

23 Highway70 Chelsea 0.58 23 19 23 - - -

24 Chelsea Manassas 0.64 20 29 24 - - -25 Chelsea Jackson 0.59 - - - 18 24 21

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22

Route #13: 2nd St, 3rd St, Manassas St, and Walnut St (cont.)



26 Jackson Highway70 0.10 - - - 5 15 4

27 Highway70 Poplar 0.67 - - - 26 21 24

28 Poplar Jefferson 0.22 - - - 8 11 1229 Jefferson Madison 0.14 - - - 10 24 3430 Madison Monroe 0.07 - - - 25 26 3231 Monroe Union 0.06 - - - 0 4 3232 Union Linden 0.23 - - - 0 26 15

Route #14: Highway 51


(Mi) NB_AM NB_MD NB_PM SB_AM SB_MD SB_PM

1 Millington Rd. (SouthIntersection)

OvertonCrossing 2.77 52 54 54 58 53 55

2 Overton Crossing Fite Rd. 2.66 53 53 55 60 55 57

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1031

1101

1503

1128

1030

1029

2276

1028

2275

2274

2273

1513

1538

2272

2271

1512

2270

1522

1118

1027

2269

1026

2268

2267

11421126

1558

2266

1103

1025

2518

1104

1519

1141

1511

1102

1139

2265

1505

1112

1514

2264

1138

2261

1517

1122

2263

2262

2260

1024

2259

1023

1021

1121

1022

2258

1019

1116

1541

1521

1539

2257

1515

1559

1540

1129

1020

1134

1518

2256

2255

1532

1018

1528

1017

2254

1016

1127

1546

2253

1130

1015

1136

1014

2251

1509

1114

1542

2252

1556

2516

1135

1525

2509

1011

2250

2249

1013

2501

1012

2248

2247

25082246

1010

1120

2245

1524

2244

1548

2241

1554

1508

1009

2243

2242

1550

1140

999

1145

2515

2240

1531

2239

1504

2238

1008

2237

1007

998

2236

2235

1006

997

996

2505

1005

1561

995

2506

991

1133

1004

1549

1501

2234

2503

1003

2233

2231

994

993

992

2502

2232

2521

1563

1108

990

989

988

1001

2230

9871002

2229

2504

2228

981

1117

2227

2226

1000

986

985

1143

1534

1106

984

2517

983

982

1107

1502

980

979

1533

1131

2522

978

971

1544

977

976

1137

975

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Fayette CountyTN

Desoto CountyMS

Shelby CountyTN

2004-2030 Transportation Analysis Zone BoundariesMemphis MPO Model

Marshall CountyMS

Tennessee

Mississippi

Arkansas

G - 63


1

Technical Memorandum #2Regional Economic and DemographicForecasts Methodology and Results

ContentsIntroductionSummary of Methodology and Results

– Definition of the Study Region– Past Regional Trends– Summary of Regional Forecast Methodology– Summary of Forecast Findings

Regional Forecast Methodology– Forecasting Strategy and Partitioning of the Regional Economy– Development of Predictive Relationships– Development of Forecasts

Regional Forecast Results– Population Forecast– Employment Forecast– Explanation of Forecast Magnitudes– Employment Adjustment

Appendix A– Forecasting Philosophy

Introduction

This is the first of a series of reports on socioeconomic forecasting in support of theMemphis-Shelby County MPO Travel Demand Model. The overall forecasting programwill describe future demographic and economic conditions in 1,237 TransportationAnalysis Zones (TAZs) used in travel modeling. The present subject is thedevelopment of forecasts for the Memphis region as a whole. The next steps include:

allocation of regional growth increments to sub county areas (SCAs) using astatistically calibrated model that emphasizes demand-side influences andreconciling these results with forecasts prepared by local experts through amodified Delphi process; andallocation of SCA changes to TAZs using supply-side relationships tailored to localconditions and based partly on professional judgment.

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2

The memorandum is organized in three broad sections:

Summary of Forecast Methodology and Results

This section summarizes the approach and methodology for regional forecasts. It alsodiscusses past regional trends and key findings of the forecast.

Regional Forecast Methodology

This section describes in considerable detail the methodology utilized to obtainregional. A reader not seeking immersion in technicalities could choose to skip thismaterial entirely and proceed to the third section. An alternative would be to perusethe graphs presented here in the ten parts of Figure 1 and skim through theaccompanying discussion. These graphs describe long-term historical trends in the“final demand” components of regional industries, meaning the components that drivethe region’s economy (and hence its population).

Forecast Results

This section discusses the full forecast results in detail. It presents individualcomponents of population change and sector breakdowns of employment forecast.The section also includes a discussion on the magnitude of the forecast and plausiblefactors behind the forecasted change.

Summary of Methodology and Results

Definition of the Study Region

The Memphis metropolitan statistical area (MSA) was defined for purposes of the 2000census to include: Crittenden County, Arkansas; DeSoto County, Mississippi; andFayette, Shelby and Tipton counties, Tennessee. This territory differs somewhat fromthe area relevant for transportation planning and also from the MSA as defined in2003, creating a need for some commentary on the choice of a study region.

The planning district addressed by the travel demand model is commonly referencedas “the study area”. The study area excludes a major part of the 2000 Memphis MSAand includes a small external zone. Specifically, the study area covers only thesouthern portion of Tipton County and the western part of Fayette County, and itstops at the Mississippi River rather than embracing any of Crittenden County,Arkansas. In the State of Mississippi, however, it includes not only DeSoto County in

G - 65


3

entirety but also the northwest corner of Marshall County. This additional zone iscensus tract 9502, one of five tracts in Marshall.

Study Area and MSA Boundary

The following table presents 2000 population data and related percentages for thevarious counties and components under discussion. The absolute numbers aredominated by Shelby County, as is true for all socioeconomic variables and alldefinitions of the Memphis area ranging from four counties (the MSA in priorcensuses) to eight counties.

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Table 1. County, MSA and MPO Population in 2000Population in Study Area**

CountyPopulatio

n

No. of % of Co. Co. Share

Population in MSA* Persons in StudyArea

of StudyArea

Crittenden, AR 50,866 50,866 0 0.0% 0.0%Fayette, TN 28,806 28,806 15,148 52.6% 1.4%Shelby, TN 897,472 897,472 897,472 100.0% 84.7%Tipton, TN 51,271 51,271 31,776 62.0% 3.0%DeSoto, MS 107,199 107,199 107,199 100.0% 10.1%Marshall, MS 34,993 0 7,875 22.5% 0.7%

Total 1,135,614

1,059,470 100.0%

* Memphis MSA is the five-county area referenced in the 2000 census.** Memphis MPO is shorthand for the planning district addressed by the MPO.

The table’s first two columns describe the total populations of all relevant counties andthose within the 2000 MSA. The remaining columns focus on population inside theStudy Area. In 2000 the Study Area had a total of 1,059,470 residents, as comparedwith 1,135,614 persons in the MSA. Along with all of Shelby and DeSoto counties, theStudy Area included somewhat over half the populations of Fayette and Tiptoncounties, but less than a quarter of the Marshall County population. The percentagedistribution offered in the table’s last column shows that Shelby County supplied overfive-sixths of the Study Area population, while the Marshall County MPO zone – tract9502 – accounted for less than 1%.

A key aspect of regional forecasting in the present approach is reliance on employmentdata from the U.S. Bureau of Labor Statistics (BLS). BLS employment forms thelinkage between the regional and national economies and serves as the “ground truth”for tabulation of detailed baseline data. The available BLS statistics for metropolitanMemphis cover the five-county 2000 MSA. This fact and the need to includeCrittenden County for forecasting reasons create a strong incentive to define the studyregion as the 2000 Memphis MSA.

The exclusion of Marshall County tract 9502 from the region thus defined is not asignificant problem given the small size of this zone. Once forecasts have beenprepared for the five-county region, the second phase of the forecasting process canallocate the regional growth increments to all SCAs including the one consisting of

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Marshall tract 9502. The resulting Marshall increments can then be added to theregional totals and the allocation repeated. Since factoring in the Marshall zone willonly add about 1% to the totals, this procedure cannot introduce any appreciable errorrelative to the ideal of covering tract 9502 in the first phase as well as the second.

Another consideration, however, is that the MSA has recently been redefined by theCensus Bureau. During 2000-03 the Bureau developed new standards for designatingmetropolitan and micropolitan areas. The Bureau announced the new standards inmid-2003 and released new MSA definitions based on them in December of that year.The result for Memphis was a major expansion of its MSA by the inclusion ofMarshall, Tate and Tunica counties in Mississippi. This has raised the question ofwhether the present study region should include the three additional Mississippicounties as well.

Along with the problem that these counties are not covered by BLS employment datafor the MSA, the issue turns upon special economic circumstances in the expansionarea. Tunica County, located on the river south of DeSoto, was a poor jurisdictionthat lost over half of its population during 1960-90 to reach a mere 8,164 inhabitants.However, the arrival of “riverboat” gaming houses caused the earnings fromemployment in Tunica to increase sevenfold between 1992 and 1996. By 2000 TunicaCounty had 1.5 jobs per capita, and over three-fourths of those jobs were held bypersons living elsewhere.

Ordinarily it is important for the territory addressed by a regional forecasting programto include all the outlying counties subject to significant urban sprawl, because failingto acknowledge spillover of growth into such areas can distort expectations for otherdistricts. This is a major motivation for covering Crittenden County in the presentcase despite its exclusion from the Study Area. But for greater Memphis theMississippi expansion area is special because its new gambling-related economic basehas created a reciprocal relationship with the metropolis. Table 2 is a worker flowmatrix showing the relative symmetry that prevails in terms of commuting. Thefigures in bold type indicate that the expansion area (minus Marshal tract 9502) nowattracts almost as many in-commuters from the five-county MSA as the number itsends to work there.

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Table 2. Worker Flow Place of Work Matrix Based on Crit., Fay., DeSoto & Tunica, All Total (=

2000 Census Data Shelby & Marshall Tate & Other ResidentTipton Tract

9502Rest M. Areas Workers)

Place of ResidenceCrit., Fayette, Shelby &Tipton

445,633 8,099 3,458 7,654 464,845

DeSoto & Marshall tract9502

30,704 20,501 5,133 862 57,201

Tunica, Tate & RestMarshall

5,580 3,771 14,066 1,251 24,668

All Other Areas 24,689 1,623 6,169Total (= Number of Jobs) 506,607 33,994 28,826

In the future the expansion area may well spin off as much growth into the five-countyMSA as it receives through the operation of centrifugal forces. Hence whether or not itshould be included in the study region largely depends on whether the regionaleconomy can be forecasted more reliably with the expansion area in or out. Given theemployment data problem and various uncertainties surrounding the future of thegaming industry, we have judged that the safest course is to leave it out.Consequently the study region addressed by the regional forecasting phase is theMemphis MSA as it existed until the end of 2003, and all further mention of the MSAwill refer to this five-county area.

Past Regional Trends

Table 3 on the next page summarizes growth trends in the region since 1980, bycounty. The population figures for 1980 through 2000 are from the decennialcensuses, while those for 2004 have been synthesized from Census Bureau estimatesfor counties through 2003 and states through 2004. The employment figures for theregion (MSA) as a whole describe BLS employment with the addition of agriculturaland uniformed military personnel. The figures for counties are the sums ofbreakdowns prepared on an industry-by-industry basis using a variety of datasources.

Population gains in the study region were somewhat sluggish during the 1980s,proceeding at a compound rate of 0.71% per year versus a national growth rate of0.94% per year. The region’s population growth accelerated after 1990 and has beentracking very close to U.S. rates since then, at 1.21% versus 1.24% per year during1990-2000 and 0.94% versus 1.01% per year during 2000-04. The regional gainshave been distributed quite unequally among the five counties. Residential

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development in DeSoto County has been almost explosive, yielding nearly two-and-a-half times as many residents in 2004 as in 1980. Growth has also been rapid since1990 in Tipton County and has recently accelerated in Fayette. At the other extreme,Crittenden County has gained practically no population at all. Meanwhile ShelbyCounty has supplied a steady decreasing share of the region’s population growth, fromnearly three-quarters during the 1980s to about half in the 1990s and less than one-third since 2000.

Table 3. Population and Employment in the Five-County MSA, 1980-2004 Number of Persons or Jobs (At-Place) Annual Percent Change 1980 1990 2000 2004 1980-

90 1990-

00 2000-

04Population*

Crittenden 49,499 49,939 50,866 51,374 0.09% 0.18% 0.23%Fayette 25,305 25,559 28,806 33,456 0.10% 1.20% 3.58%Shelby 777,113 826,330 897,472 910,968 0.62% 0.83% 0.35%Tipton 32,930 37,568 51,271 55,082 1.33% 3.16% 1.70%DeSoto 53,930 67,910 107,199 130,823 2.33% 4.67% 4.80% Total 938,777 1,007,306 1,135,614 1,181,701 0.71% 1.21% 0.94%

EmploymentCrittenden 14,116 15,491 18,794 18,957 0.93% 1.95% 0.22%Fayette 6,068 6,951 9,274 9,332 1.37% 2.93% 0.16%Shelby 373,754 447,877 524,949 519,066 1.83% 1.60% -0.28%Tipton 8,078 9,441 13,253 13,660 1.57% 3.45% 0.76%DeSoto 14,267 23,863 40,953 43,562 5.28% 5.55% 1.56% Total 416,283 503,623 607,223 604,578 1.92% 1.89% -0.11%

* Populations for 1980, 1990 and 2000 are April 1 census figures. Populations for2004 areJuly 1 figures based on census estimates (for counties through 2003 and statesthrough 2004).

Employment in the study region increased by about 1.9% per year during both the1980s and the 1990s. The relative excess of employment growth over populationgrowth would imply a progressive tightening of the job market and rising participationin the labor force. After 2000, however, the region was set back by the nationaleconomic slump and recovered slowly, with the result that annual average BLSemployment was lower in 2004 than in 2000. Among individual counties, theemployment changes since 1980 have followed a pattern resembling populationgrowth, except that Crittenden County became a significant job gainer after 1990despite its nearly static population.

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Summary of Regional Forecast Methodology

Regional forecasts are prepared by linking the regional economy to the nationaleconomy, with both expressed in terms of employment. Consequently the first task inthe forecasting sequence is the preparation of a national employment forecast. As inall subsequent forecasting steps, the baseline year or jumping-off point for this task is2004 and the forecast period extends through 2040. The BLS employment statisticsused to describe the national and regional economies incorporate a one-job-per-persondefinition of employment. That is, each worker is counted only once, at his or herprimary job.

The federal government has not published long-term forecasts of national employmentsince the mid-1990s, but there are three federal projection series that accomplishlarge portions of the job when combined to this end. These series are: 1) CensusBureau projections of the U.S. population by age and sex; 2) BLS projections ofnational labor force participation rates by age and sex; and 3) BLS projections ofemployment by detailed industry. The first two of these projection series extendthrough 2040, whereas the employment projections only go ten years out.

After the projected labor force participation rates are adjusted slightly to replicatebaseline employment and deal with a minor limitation, these rates in combination withthe population projections yield a forecast of the U.S. resident labor force through2040. Future unemployment rates (translated into employment rates) are the onlyadditional data needed to forecast the total number of U.S. resident workers. Giventhat commuting in and out of the country is insignificant, this number equals totalemployment on a one-job-per-worker basis.

Thus the chosen procedure has been to forecast total U.S. employment through 2040by assuming future unemployment rates, then use these figures as control totals inextrapolating forward the industry-specific BLS employment projections. Animportant reason why total employment can be obtained in this fashion with someconfidence is that the rate of increase in U.S. labor supply is bound to decline sharplyafter 2010 due to aging of the population. This promises tight labor market conditionsand a future in which overall job growth is demographically constrained. Thedemographic constraint has been expressed in the national forecast by assumingunemployment rates of 5% in 2010, 4% in 2020 and 2030, and 5% in 2040.

Given the resulting control totals, the industry-specific employment projections havebeen extrapolated across three intervals from 2012 using multiplicative relationshipsfor declining industries and linear relationships plus an adjustment factor for gainingindustries. An interpolation routine and some marginal revisions then yielded annualforecasts through 2040 for 49 industries defined in terms of NAICS categories.

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Table 4 summarizes the national forecast in terms of total population and totalemployment. Gains in the U.S. population are expected to be well below the 13% rateachieved during the 1990s, stepping down from 9.5% during 2000-10 to 7.8% during2030-40. An even bigger influence on employment will be the fact that over half of allpopulation growth during 2010-30 will be supplied by persons aged 65-plus. Thepopulation aged 16-64 will then be increasing by less than 3.5% per decade. Theresult is that even at full employment, jobholding will increase at ten-year rates 0.8 to2.0 percentage points lower than those for population. The rate of employment growthper decade will trend down to 6.3% before rebounding to 6.8% in the last decade of theforecast period. The right-hand side of Table 4 dramatizes the effects of aging onemployment by contrasting two measures of employment relative to population. Laborforce participation is expected to increase among persons in most age-sex categories,especially the elderly, so after 2010 employment will rise relative to the population oftraditional working age (16 to 64). Nevertheless the rapid decline in this group’s shareof total population will yield a steady erosion in overall employment per capita.

Table 4. Summary of National Forecast

Population(Midyear)

Total Employment

Number Percent Number Percent Per PerPerson

(000) Change (000) Change Capita Aged 16-64

2000 282,125 --- 136,581 --- 0.484 0.7472010 308,936 9.5% 148,434 8.7% 0.480 0.7332020 335,805 8.7% 160,215 7.9% 0.477 0.7652030 363,584 8.3% 170,312 6.3% 0.468 0.7892040 391,945 7.8% 182,113 6.8% 0.464 0.791

Regional employment and demographics have been forecasted by formingstraightforward linkages between the regional economy and the national economyunder the abovementioned assumption that long-term regional growth is economicallydriven. The linkages have been developed using 49-industry BLS employment profilesfor the national and regional economies in all years since 1969. (This year was thestart of data availability for a key source used in piecing together the county andregional profiles, which was an arduous process in the Memphis case due to SIC-to-NAICS conversion requirements and other issues.) For each year from 1969 through2004 the employment in each regional industry has been split into “basic” and “localsupport” components through the application of an input-output table. Basicemployment in each regional industry has been expressed as a ratio to totalemployment in the corresponding national industry, and time trends in these ratioshave been established using statistical methods. The time trends have then been

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extrapolated through 2040 and the resulting ratio values applied to future nationalemployment, yielding forecasts of regional basic employment. Lastly, local supportemployment has been derived from basic employment using the input-output table,and the two components have been combined for each industry and year to yieldoverall profiles of the future regional economy.

Demographic forecasts have been obtained by finding a regional population profile foreach future year that yielded a labor force consistent with the expected employmentlevel. This was accomplished through cohort-survival projection methods, whichstarted with the development of historical birth, death and net migration rates for theregion. The cohort-survival tableau used projected values of these rates to simulatethe transition of the regional population across each future decade. Future labor forceparticipation rates (estimated from national trends and current regional values) werethen applied to the results, and the net migration rates in the tableau were scaled sothat the projected number of employed persons in each year – after allowing forunemployment and net commuting – was equal to the forecast of total employmentalready established.

The use of input-output analysis to partition the regional economy rendered theregional forecasting process rather complicated in execution. (There were actuallymany different input-output tables for different years, and their use involved forwardand backward applications of matrix inverses.) But in substance the process wasmechanical and merely implemented an assumption that the past long-termrelationships between regional economic drivers and national industries wouldcontinue to hold.

Summary of Forecast Findings

Table 5 presents the population and employment totals forecasted for the Memphisregion through 2040. The table includes data for 1990 and 2000 along with the 2004baseline year to allow comparison of past and future trends. Because the inclusion of2004 creates intervals of varying length, trends are described in terms of annualcompound rates of change as well as ten-year percentage changes. (Population in2004 differs from the figure given earlier in Table 3 because all values here pertain toApril 1.)

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Table 5. Summary of Forecast for the Five-County Memphis Region

Population (April 1) Employment Percent Change Percent Change

Number PerDecade

AnnualNumber

PerDecade

Annual

1990 1,007,306 503,6232000 1,135,614 12.7% 1.21% 607,224 20.6% 1.89%2004 1,178,322 0.93% 604,578 -0.11%2010 1,278,991 12.6% 1.38% 683,303 12.5% 2.06%2020 1,464,853 14.5% 1.37% 795,923 16.5% 1.54%2030 1,641,924 12.1% 1.15% 890,658 11.9% 1.13%2040 1,828,274 11.3% 1.08% 998,691 12.1% 1.15%

By 2040 the five-county Memphis region is expected to have over 1.8 million residentsand nearly 1 million jobs. These totals are respectively 55% and 65% higher than thepopulation and employment levels prevailing in 2004. The expected rates of growthnot only contrast with conditions during the recent stagnant period but comparefavorably with the region’s rate of expansion during the 1990s, which will be exceededduring 2004-20 for population and 2004-10 for employment.

The region’s percentage gains will taper off in the later years of the forecast period butwill remain substantial. Comparisons with the steadily declining growth ratesexpected for the U.S. as a whole are particularly impressive. Annual populationgrowth will be 0.52 to 0.53 percentage points higher in the region than the nation from2004 until 2020, and will be 0.33 to 0.35 points higher during 2020-40. Foremployment, the corresponding excesses of regional over national growth will be 0.6 to0.77 percentage points per year before 2020 and 0.48 to 0.52 points per yearthereafter.

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Regional Forecast Methodology

This section describes in considerable detail the methodology utilized to obtainforecasts for the five-county Memphis region (equaling the 2000 MSA as discussedearlier). A note about the forecasting philosophy used in this analysis is included inAppendix A.

Two other introductory comments pertain to the nature of long-term forecasting andthe data resources utilized in the present enterprise. The first involves the fact thatthe forecasting component of a travel demand model must address a time frame inwhich practically none of the commonly discussed economic indicators are relevant.Observers of financial markets and real estate development and other businessactivity rarely look more than a few years ahead. Yet one can assume that the majortransportation projects likely to occur within the present decade have already beenplanned, at least to a point at which they are no longer forecast-sensitive.Transportation-related forecasting must focus on the next decade, and even moreimportantly on the two decades after that.

Most of the economic prognostications offered for public consumption are attempts topredict various magnitudes within, say, a 1% error margin in the first year out and a2% margin in the second year. Given the proximity of the target periods and thesmallness of the ranges, a great deal of analytical attention must be paid to what areessentially cyclical phenomena. In contrast, the goal for transportation planning is topredict within 10% or 15% the levels of economic activity that will prevail two-plusdecades in the future. At this scale cyclical phenomena lose importance (along withbecoming entirely unpredictable), and what matters is underlying economic structure.

Thus, information sources that strongly reflect the workings of the business cycle lackrelevance and pose hazards in the present context. Building permit statistics are acase in point. Land developers are notorious for overbuilding markets and leavingexcess inventory to be drawn down later (as illustrated by Florida in the mid-1970s,Texas in the mid-1980s, and the Northeast around 1990). In the residential market,building permits may not accurately describe net growth even when there is a supply-demand balance for new units, since a low-interest-rate environment like that ofrecent years can recruit homebuyers from the rental housing stock. Building permitand certificate-of-occupancy statistics thus add so little and have such capacity tomislead that the present forecasting methodology makes no formal use of them at all.

Most of the variables that play central roles in national economic models – such asconsumption, investment, savings, trade flows and so forth – are unavailable at thecounty and regional levels. Variables that might have special relevance to regionaldevelopment, such as capacity for adaptation and innovation, tend not to be measuredat all. Beyond income and employment and demographics, the statistics available

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below the state level are widely characterized by gaps, inconsistencies and lack ofhistorical coverage. Then comes the pivotal problem in long-term forecasting that anyvariables intended for use as predictors must themselves be predicted. One canestablish empirical relationships linking regional growth to variables like unearnedincome components and labor force characteristics, but such relationships can addaccuracy to forecasts only if the given variables are more amenable to independentforecasting than the quantities they would predict. Otherwise the relationships aremore trouble than they are worth. These are among the reasons why long-termforecasting efforts at the region level and below tend to focus largely if not exclusivelyon employment, and why beneath its procedural complexities the present approach isquite simplistic.

The other background comment is that employment is not an absolute concept, andthe process of obtaining usable employment data for metropolitan forecasting is farfrom automatic.

The standard approach in studies of the present type is to assemble employment datain three separate stages. The first stage consists of synthesizing employment profilesfor counties in the study region from published sources. The outcome – referenced forthe moment as a “county-level” database even though much of its use occurs inregional forecasting – is a gap-free record that offers considerable industrial detail andgoes back many years before the baseline year. The second stage consists ofassembling current data for individual employers in the study region, as required totabulate baseline descriptions of employment for TAZs and sub-county areas (SCAs).In the Memphis investigation these establishment-level statistics have come from aproprietary source plus employment security files and have required a great deal ofprocessing. The third stage of data assembly then consists of using County BusinessPatterns data for zip codes to take the SCA employment profiles back in time.

Ordinarily the county-level database serves as the master file, in that its employmentfigures are used as control totals for adjusting the establishment-level data. Theemployees at all establishments in a given industry in a given county are scaled by afactor that equates their sum with the total employment specified for that countyindustry by the county-level file. In the Memphis case, however, this adjustmentprocess has been forestalled by unusual numerical discrepancies. The establishment-level figures have instead been pegged to employment totals based on the populationcensus, which has created a statistical disconnect between the first and secondphases of forecasting. This situation and the resultant need to adjust the regionalforecasts are discussed at the end of the final section. Further comments here willaddress only the county-level database.

As noted in the first section, the present approach utilizes a one-person/one-jobdefinition of employment wherein each worker is counted only once, at his or her

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primary job. This is the definition used by the U.S. Bureau of Labor Statistics (BLS)and by state employment security offices when reporting current labor force,employment and unemployment levels. From a transportation planning standpoint,there are arguments both for and against counting second jobs. On one hand, secondjobs generate worktrips in the same fashion as primary jobs (unless they are home-based). On the other hand, multiple jobholding adds relatively little to peak-hourtravel demands, which determine an area’s requirements for transportation systemcapacity. In any case the choice is not pivotal for transportation modeling so long asdata are prepared consistently. A one-person/one-job definition is preferred herebecause it maximizes the correspondence between economic and demographicvariables, given that the census of population necessarily uses a similar definitionwhen reporting resident workers. Coverage of part-time employment would cause thelevels of jobholding specified by economic forecasts to substantially exceed thenumbers of workers specified by household tabulations. An all-inclusive definition ofemployment causes the BEA data series noted below to credit the five-county Memphisregion with 20% more employees than cited by BLS (although in this case even BLS iswell above the population census).

While incorporating the BLS definition of employment, the county-level database forMemphis has not been assembled primarily from data supplied by BLS. The reasonsare that BLS statistics are only available for the five-county-region as a whole, andthat even these numbers must be greatly augmented to yield adequate industrialdetail. The other data sources most heavily consulted in the present study have beenthe regional information system maintained by the U.S. Bureau of Economic Analysis(BEA), and a Census Bureau publication known as County Business Patterns. TheBEA system contains data going back to 1969 and is generally the most completesource of county-level economic information. It documents worker earnings as well asemployment, and the Memphis study has utilized both because the earnings dataprovide more industrial detail. The statistics in County Business Patterns have thedisadvantage of omitting self-employed persons, but they provide good industrial detailand include establishment-size distributions that are very helpful in filling data gapsdue to disclosure regulations.

The data assembly process involving these sources has addressed two majorcomplications over and above the need for myriad conversion factors to obtain BLS-consistent employment. The first was the longstanding problem of federal disclosureregulations, which prohibit the release of data that would disclose, or even hint at, thecharacteristics of individual establishments. Filling the resultant data gaps throughvarious modes of estimation is an activity that routinely occupies much of a regionaleconomist’s time. The other complication was the conversion of all relevant datasources from the SIC industry classification system to the NAICS system. The federalsuppliers of employment data shifted from SIC to NAICS at various times between1998 and 2002, but none besides BLS converted the historical data in their files while

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adopting NAICS for current reporting. Consequently several different SIC-to-NAICSconversion matrices have been developed on the basis of national data and applied toobtain consistent historical records.

The result was a database describing employment in 49 NAICS industries for theregion and its five constituent counties in each year from 1969 through 2004. Theregional statistics from this database, and the corresponding employment levels forU.S. industries, have comprised the inputs to the regional forecasting processdescribed below.

Forecasting Strategy and Partitioning of the Regional Economy

The forecasting of national employment by industry has already been described in thefirst section. The process involved extrapolating industry trends from a ten-year BLSforecast while enforcing control totals based on federal projections of population andlabor force participation. The acknowledgement of demographic constraints and theuse of a one-job-per-worker definition of employment yielded lower totals than oftenseen in national forecasts.

In broad outline, regional forecasts have been obtained by: 1) quantitatively linking theregional economy to the national economy; 2) projecting the regional-national linkagesinto the future; 3) applying these relationships to the national forecast; and 4)translating the resulting regional magnitudes into full economic and demographicdescriptions. The regional-national linkages are limited to economic variables (exceptfor a connection between government and population) and do not cover the wholeregional economy. The approach basically consists of taking the regional economyapart, estimating future trends in the sectors considered to be its drivers, and re-assembling it to obtain aggregate descriptions. Much of the effort involves the use ofinput-output analysis to isolate the economic drivers, which are not whole industriesas conventionally defined, and to establish their relationships with the rest of theeconomy.

Input-output models are basically expanded versions of the familiar economic basemultiplier model, which says (when applied on the margin) that any independenteconomic stimulus in an area will have “multiplier” effects yielding an overall growthincrement larger than the original stimulus. Input-output analysis expressesmultiplier effects on an industry-specific basis by using a table of purchasecoefficients to trace the individual transactions required to support an industryexpansion. In static terms, input-output modeling attributes all economic activity to aset of industry components that are collectively called “final demand.” These aregenerally not whole industries but the estimated shares of industries that bring inrevenue from the outside world. The shares assigned to final demand are typically

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large for manufacturing and other goods-producing activities and small to moderatefor most population-serving functions (although such differences are fading in thepost-industrial era).

The present study has utilized an input-output table prepared specifically for the five-county Memphis region by the RIMS division of BEA. Since the customers of this dataoutlet are generally engaged in impact analysis rather than forecasting, RIMS onlysupplies input-output tables in inverse form. An I-O inverse is a coefficient matrixthat when postmultiplied by a final-demand vector will yield a vector of totalemployment (or output or earnings if the matrix is denominated in those terms).However, since the linear equations comprising an input-output model yield uniquesolutions in both directions, a matrix inverse can also be used to solve iteratively forthe final-demand vector associated with any given pattern of total activity. Thus inconcept the same matrix inverse can be used to isolate final demand for years ofrecord, then later translate forecasts of final demand back into descriptions of overalleconomic activity.

In actuality the use of input-output in the present approach is not this simple becauseinput-output coefficients are subject to change over time. The coefficients expresspatterns of demand for the products of various industries, and there are long-termtrends in these patterns due to changes in economic structure. For example, relativedemand for employment services has risen dramatically as companies substitute laborcontractors and temp workers for permanent employees, while demand for health carehas risen because of population aging and the increasing variety of medicaltreatments, et cetera. Realistically isolating final demand requires projecting thesechanges back in time thirty-five years, and realistically forecasting total employmenton the basis of final demand requires projecting them forward for thirty-six years, butno guidance is available for individual coefficients other than the values obtained forthe baseline year. The matrix adjustment process (which has been accomplished inthe Memphis case by preparing a matrix for every fifth year and handling intermediateyears by interpolation) can draw upon various types of professional experience and istightly constrained by tests of reasonableness that emerge when applying the matrix toactual employment profiles.

Furthermore there is need when adjusting the matrix to avoid building in an overallforecasting bias. Such bias can exist if the matrix implicitly specifies a varyingrelationship between final demand and other economic activity (or more precisely, ifthe relationship varies across the forecast period in a pattern that is not a directextension of an historical pattern). The precautionary step in this regard is to controlthe overall multiplier – i.e., the ratio of total employment to final-demand employment– specified by the matrix. For metropolitan Memphis the employment multiplier in thebaseline year was 2.57, given the treatment of government noted below, so protectionagainst bias was sought by holding the multiplier at 2.57 throughout both the

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historical period and the forecast period. (Sometimes the multiplier for a very fast-growing area is allowed to rise in a straight-line fashion, but constancy has beenjudged appropriate for Memphis.) Control of the multiplier is only achievable in theprocess of partitioning the economy and preparing forecasts, rather than on an apriori basis, and can only exist in a relative sense given that I-O multipliers dependupon industry mix; but experience suggests that this step is appropriate and normallyadequate.

Table 6 on the next page shows the partitioning of the Memphis regional economyachieved by input-output calculations for the baseline year and two prior years, whichspan the historical period used to calibrate predictive relationships. The first threecolumns of the table show total employment, the next three describe final-demandemployment, and the last three give percentage distributions of final demand.

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Table 6. PARTITIONING OF REGIONAL ECONOMY FOR SAMPLE YEARS AND INDUSTRIES

Category and Total Employment Final Demand Employment Share of Final DemandNAICS Code Industry Description 1969 1987 2004 1969 1987 2004 1969 1987 2004

I/M 111-115, 21 Ag., mining & resource-related 9,466 5,916 5,660 6,910 4,450 4,295 5.4% 2.5% 1.8%I/M 23 Construction 14,108 19,968 25,146 3,348 6,238 8,293 2.6% 3.5% 3.5%I/M 31,322-6 Nondurable goods mfg. 35,005 31,809 28,131 26,460 25,869 22,367 20.6% 14.3% 9.5%I/M 321,7; 33 Durable goods manufacturing 31,569 26,352 22,071 23,863 21,431 17,548 18.5% 11.9% 7.5%W/T 42 Wholesale trade 25,158 32,554 37,371 9,221 16,174 19,527 7.2% 9.0% 8.3%

R 44,45 Retail trade 32,398 58,284 68,360 2,761 8,224 9,415 2.1% 4.6% 4.0%W/T 484 Truck transportation 8,513 9,606 14,530 2,764 3,956 6,305 2.1% 2.2% 2.7%W/T 492 Courier & messenger service 934 11,974 31,103 887 11,375 29,548 0.7% 6.3% 12.6%W/T Rest 48; 493 Other transportation & utilities 7,253 10,168 16,348 2,355 4,188 7,094 1.8% 2.3% 3.0%

O 51 Information 5,730 6,743 9,430 790 1,675 2,731 0.6% 0.9% 1.2%O 521-3,5; 533 Finance 7,079 14,518 18,596 664 2,095 3,760 0.5% 1.2% 1.6%O 524,531 Insurance & real estate 8,109 9,534 11,371 1,294 2,040 3,006 1.0% 1.1% 1.3%O 5411-3 Legal, accounting, A&E serv. 5,698 6,851 12,446 850 1,699 4,085 0.7% 0.9% 1.7%O 5414-9 Other prof., sci. & tech. serv. 3,152 5,702 14,749 470 1,414 4,842 0.4% 0.8% 2.1%O 551 Mgmt. of co.s & enterprises 6,794 7,840 8,100 3,108 4,051 4,293 2.4% 2.2% 1.8%O 5613 Employment services 1,811 4,959 15,972 280 1,429 5,900 0.2% 0.8% 2.5%O Rest 561&2 Other admin. support services 7,587 11,941 22,316 1,175 3,440 8,244 0.9% 1.9% 3.5%S 61 Educational services (private) 2,751 3,914 5,218 231 580 1,109 0.2% 0.3% 0.5%S 621 Ambulatory health services 3,894 10,189 20,497 649 2,740 6,871 0.5% 1.5% 2.9%S 622 Hospitals 5,754 15,804 23,631 959 4,250 7,921 0.7% 2.4% 3.4%S 623,4 Nursing, res. care, social serv. 4,349 7,312 16,674 725 1,966 5,590 0.6% 1.1% 2.4%S 71 Arts, entertainment & recr. 2,154 2,438 4,802 380 776 1,881 0.3% 0.4% 0.8%S 721 Accommodations 4,764 13,423 14,370 763 4,729 7,185 0.6% 2.6% 3.1%R 722 Food services 10,787 25,496 39,981 828 4,568 9,954 0.6% 2.5% 4.2%S 532; 811,2 Rental, repair & personal serv. 5,149 10,347 13,357 363 1,736 2,931 0.3% 1.0% 1.2%S 813 Religious, grantmaking & civic 4,535 6,823 11,720 227 994 2,353 0.2% 0.6% 1.0%G 91-97 part Federal & state government 48,515 51,149 37,594 36,386 38,362 28,195 28.3% 21.3% 12.0%G 91-97 part Local government 27,774 42,139 55,032 0 0 0 0.0% 0.0% 0.0%

I/M INDUSTRIAL/MANUFACTURING 90,147 84,044 81,008 60,581 57,987 52,503 47.1% 32.1% 22.3%W/T WHOLESALE/TRANSPORTATION 41,858 64,302 99,353 15,228 35,693 62,474 11.8% 19.8% 26.6%

R RETAIL 43,185 83,780 108,341 3,589 12,792 19,369 2.8% 7.1% 8.2%S SERVICE 33,350 70,249 110,270 4,297 17,772 35,841 3.3% 9.8% 15.2%O OFFICE 45,961 68,088 112,980 8,632 17,843 36,861 6.7% 9.9% 15.7%G GOVERNMENT (INCL. PUBLIC ED.) 76,289 93,287 92,626 36,386 38,362 28,195 28.3% 21.3% 12.0%

TOTAL 330,790 463,751 604,578 128,712 180,448 235,244 100.0% 100.0% 100.0%

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The industries listed in the main part of Table 6 constitute a somewhat condensedclassification relative to the 49 industries used in the actual partitioning process andthe 37 for which predictive relationships have been developed. The six aggregateindustry groups at the bottom of the table are the employment categories ultimatelyused by the transportation planners. (These are denoted by letters that are repeatedearlier in the table’s first column to show the more detailed industries they contain.)The aggregate groups have been formed on the basis of trip generation characteristicsbut suffice here for general summary purposes. The Service and Office groups aredistinguished by the fact that activities in the former group occupy widely varyingtypes of facilities and tend to draw consumer visitation as well as business-to-business interaction.

A characteristic of the industry classification in Table 6 that applies throughout theforecasting process is that all public functions are included under government ratherthan distributed by type of function. In particular this means that the U.S. PostalService and all public schools and colleges are treated as government activities ratherthan classified respectively under transportation and educational services.

The final-demand components of a region’s employment – or earnings or output if itseconomy is partitioned in those terms – can be collectively referenced as the region’s“economic base.” A point of interest in Table 6 is the extent to which the economicbase of metropolitan Memphis changed over the given historical period, due partly togeneral trends in the U.S. economy and partly to special circumstances. In 1969,three-fourths of the Memphis economic base was supplied by activities in the first andlast of the groups, namely agriculture, mining, manufacturing and government. Thatis, these activities collectively supported three-fourths of the entire regional economy.By 1987, however, they were providing only a bit over half of the region’s economicsupport, and by 2004 their collective share was nearly down to one-third. (Therespective fractions were the sums of percentages equaling 75.4%, 53.4% and 34.3%.)Meanwhile the transportation sector and a wide variety of service and office functionswere emerging as economic drivers. Along with the extraordinary rise of FedEx as aneconomic pillar, metropolitan Memphis became a significant visitor attraction andinter-regional exporter of health services, financial services, and professional andadministrative services.

Table 6 provides a clear reminder of why economic partitioning is worthwhile despitethe required exercises of judgment. In terms of total employment, the “courier andmessenger service” industry that is locally dominated by FedEx accounts for aboutone-twentieth of the regional economy, so it would effectively receive a one-twentiethweighting in any forecasting process that did not discriminate among activities. (Thisdescription represents the process as one in which the future percent change inregional employment is obtained as a weighted sum of individual percent changes.)The courier and messenger service industry supplies fully one-eighth of final demand,

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however, so in a process focusing just upon economic drivers it will receive a one-eighth weighting. Notwithstanding the imprecision involved in isolating final demand,this is a much more accurate description of how the region’s future will be determined.

Input-output analysis nevertheless has an inherent limitation that is well illustratedby the FedEx case and must be mentioned at this point. An input-output table onlycovers backward linkages among industries, whereby each industry produces “rippleeffects” on others through its input purchases. (When an I-O table is closed withrespect to households as in the present case, the ripples include effects promulgatedthrough personal consumption expenditures.) Long-term regional development is alsoshaped by forward linkages, whereby input suppliers stimulate the growth of buyingindustries, and sideways linkages whereby industries benefit from joining anestablished pool of buyers or sellers. FedEx has contributed importantly to theMemphis economy through forward linkages as well as backward linkages, becausethe availability of high-speed logistical support has increased the area’s viability forhighly time-sensitive activities. Only the backward linkages are covered explicitly bythe present framework, however, because input-output analysis has this limitationand because forward linkages are difficult to address systematically in any fashion.The forecasting methodology simply assumes in effect that any growth impetusimparted in the past by forward linkages with FedEx will continue similarly in thefuture.

Development of Predictive Relationships

Regional economic forecasts have been obtained by linking final-demand employmentin each regional industry to total U.S. employment in the same industry. Theresulting relationships have been extrapolated into the future and applied to thenational forecast to yield future levels of final demand. Then the appropriate versionof the input-output table for each future year has been used to derive totalemployment.

For each industry the linkages between regional final demand and nationalemployment have been established by expressing the former numbers as ratios to thelatter for all years in the historical record. A linear trend line has then been fitted tothe ratios. The historical record in the Memphis study extends from 1969 through2004 and thus offers 36 years of observation. Consulting the longest possiblehistorical record is standard policy in applications of the present methodology, on thepremise that long-term forecasts should be based upon long-term relationships. Someof the earlier data points may be omitted when fitting the trend lines, however, if itseems clear that more recent data alone will provide a better guide to futuredevelopments. The Memphis study has yielded a mix of 36-year and 18-yearrelationships, with the latter slightly outnumbering the former.

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The data and predictive relationships that underlie the entire Memphis regionalforecasting process are presented graphically in the ten parts of Figure 1, which starton the next page. Every sheet contains two pairs of graphs, each addressing one tothree industries. The left-hand graph in each pair plots regional final-demandemployment in absolute numbers, and the right-hand graph describes final-demandemployment as a ratio to total U.S. employment in the same industry. The straightline in each right-hand graph is a trend relationship that has been fitted statisticallyto the points for 1969-2004 or 1987-2004 for purposes of extrapolation into thefuture. Some of the 49 industries in the original database have been combined toyield more reliable-looking relationships, with the result that the ten parts of Figure 1address only 37 separate industry groups.

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Figure 1. FINAL-DEMAND TRENDS AND PREDICTIVE RELATIONSHIPS -- Part 1

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MINING, FORESTRY, FISHERIES & AG. SUPPORT

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The following three points should be noted about the scales of the graphs in the tenparts of Figure 1. First, the scales differ across pages and across graphs on a givenpage. The scales of the left-hand graphs have been chosen to convenientlyaccommodate the magnitudes of regional final demand for the given industries, whilethose of the right-hand graphs reflect how large the ratios of final demand to U.S.employment happen to turn out. Second, the ratios plotted in the right-hand graphshave been computed with U.S. employment measured in thousands, so they expressregional jobs per thousand national jobs. And third, one must remember that theregional numbers refer to final demand rather than total employment. Themagnitudes plotted in the left-hand graphs thus reflect the importance of industries asdrivers of the regional economy, not as overall suppliers of employment.

Part 1 of Figure 1 addresses farming, other resource-related activities andconstruction. (Hereafter “Figure 1” will no longer be mentioned, so the text will justrefer to Part 1, Part 2, et cetera.) Following a pattern widely observed for urbanizingareas, metropolitan Memphis has lost farm employment in absolute terms and alsorelative to the U.S. The other resource-related category includes agricultural supportfunctions, forestry, fisheries and mining. Agricultural support dominates thenumbers, since metro Memphis has little employment in the other categories, andincludes various activities that have been expanding in urban centers. Hence theregion has gained final-demand employment in the second resource-related industrygroup in both absolute and relative terms. Lastly, construction activity in theMemphis region has followed an up-and-down pattern involving reduced activityduring the recessions of the mid-1970s and the early years of each decade thereafter.Expressing regional employment as a ratio to U.S. employment dampens this patternconsiderably but does not eliminate it.

The solid lines drawn through the points in the right-hand graphs are the trendrelationships established for predictive purposes. These and the relationships in thegraphs to follow are all statistically fitted regression lines (with two exceptions), butthe process of establishing them has not been a hypothesis-testing exercise. Thehypothesis that regional final-demand employment is linked to U.S. employment istaken as given when forming the ratios, so the statistical objective is simply to predictthe future linkage magnitudes as reliably as possible. If one imagines the addition of athird graph to the right of each pair, with points showing future region-to-U.S. ratios,the estimation problem can be visualized as one of establishing a line in the secondgraph that would best fit the imaginary points in the third graph if extended across.

For all three industry groups addressed by Part 1, the chosen trend lines are 18-yearrelationships rather than 36-year relationships. The two cases covered by the uppergraphs feature markedly less variation among region-to-U.S. ratios in the second halfof the historical period than across the period as a whole. Fitting trend lines to thewhole 36-year period rather than the second half would yield much higher R-square

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values, but the resulting relationships would rather clearly be too pessimistic forfarming and too optimistic for the other resource-related industry group. The 18-yearrelationships both feature moderate slope (indicating a tendency to follow nationalpatterns) and appear reasonably safe for projection into the future. Regarding theconstruction industry, the 18-year record similarly features less variation than the 36-year record, but the trend lines obtained in the two cases are very similar. The 18-year relationship is chosen because its slightly lower slope and the absence of anylarge deviations suggest greater reliability.

Parts 2 and 3 of the graphical sequence occupy the next two pages and addressmanufacturing activity. Four nondurable goods manufacturing industries and threedurable goods industries are targeted for attention based on their relative prominencein the study region. With the addition of “other nondurable goods” and “other durablegoods” categories, these selections respectively yield the five industry groups coveredby Part 2 and the four addressed by Part 3.

Metropolitan Memphis has always had a substantial amount of food and beveragemanufacturing (a combination of NAICS industries 311 and 312), but its employmentin this sector has been trending downward since the mid-1970s in both absolute andrelative terms. Based on goodness of fit, an 18-year relationship has been chosen tocharacterize the trend in region-to-U.S. ratios for this group. A relationship fitted toall 36 points would be similar but with a bit less downslope by virtue of covering theearly growth period. (Trend lines have been obtained by linear regressions in whichthe independent variable was calendar time in years. Both 18-year and 36-yearversions have always been prepared for inspection and have differed only in thenumber of observations covered.)

Paper products manufacturing has been an opposite case in which the study regiongained employment during the first half of the historical period and held fairly steadyduring the second half, yielding an uptrend relative to the U.S. throughout the period.(Since final demand accounts for the great bulk of total employment in manufacturing,the comments here apply to both.) An 18-year relationship has been chosen for paperproducts because this trend is little affected by the jump in paper product employmentthat occurred during the mid-1980s and hence is more conservative than the 36-yeartrend. An 18-year relationship is likewise utilized for the industry group coveringproduction of chemicals plus petroleum and coal products (NAICS 324 and 325). Herethe choice makes a great deal of difference because employment rose during the firsthalf of the historical period and declined during the second half, in both absolute andrelative terms. The chosen relationship describes a significant downtrend in theregion-to-U.S. ratios, whereas the 36-year relationship would be nearly flat.

The printing industry is mildly prominent the Memphis region and until recently wasone of the few sources of net manufacturing gains. Since 1999, however, absolute and

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relative growth have gone in different directions for printing because small declines inregional employment have been accompanied by much larger declines in nationalemployment, yielding an accelerated rise in region-to-U.S. ratios. A 36-yearrelationship has been chosen for predictive purposes in this case due to concernsabout over-emphasizing the recent pattern. Lastly, the region’s employment in allother nondurable goods production declined dramatically during the early 1980s dueto loss of work in the needle trades, but since then has followed the national pattern(which recently has involved large declines). An 18-year trend line is clearlyappropriate for this group.

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FABRICATED METAL PRODUCTS MFG.ELECTRONIC & ELECTRICAL

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Employment levels in the four durable goods manufacturing sectors described by Part3 have moved erratically over the period of record, even when expressed as ratios tonational employment. The only sector for which increases have predominated duringmost of the period, in either absolute or relative terms, is the electronic and electricalequipment industry (a composite of NAICS industries 334 and 335). The 18-year and36-year trend lines are quite similar in this case, but neither inspires confidence. Thelatter has been chosen largely on general principles. Employment in fabricated metalproducts manufacturing, the other industry addressed by the upper graphs in Part 3,generally gained relative to U.S. employment during the first half of the historicalperiod and declined during the second half. As in the case of chemical manufacturing,an 18-year trend line appears appropriate for predictive use under thesecircumstances.

The two industry groups addressed by the lower graphs in Part 3 are machinery plustransportation equipment manufacturing (NAICS 333 and 336) and all other durablegoods manufacturing. Each of these sectors has exhibited a long-term pattern ofgradual employment decline, in both absolute and relative terms, and in each case a36-year predictive relationship appears more prudent than an 18-year relationship.

Wholesaling and transportation have always been prominent in greater Memphis dueto the area’s role as a regional distribution center. Part 4 on the next page deals withwholesale trade, retail trade, and three categories of transportation: trucktransportation, courier and messenger service, and all other transportation andutilities (except the U.S. Postal Service). At this point the analysis starts to encounterindustries in which much of the region’s employment is locally oriented rather thanconsisting of final demand. Nearly all courier and messenger service activity is finaldemand because the region contains the global hub of FedEx, but in the other casesthe final-demand shares have recently equaled about half for wholesale trade,somewhat less than half for trucking and other transportation, and only one-seventhfor retail trade.

Final-demand employment in wholesale and retail trade moved generally upwardthroughout the historical period, except for an early-1990s slump in wholesaling andtwo plateaus in retailing that spanned the years 1987-93 and 1998-2004. The region-to-U.S. ratios for wholesaling are described reasonably well by a gently upward-sloping36-year relationship, while those for retail final demand are described with very littleerror by an 18-year relationship that is almost perfectly flat.

In the sector that covers transportation other than trucking and courier service, theregion’s final-demand employment has increased in a pattern resembling the nationaltrend but with generally faster growth. The region-to-U.S. ratios in this case aretracked closely by a 36-year relationship having substantial upward slope. (Thegoodness of fit is hard to see from the lower right-hand graph of Part 4 due to the

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effect of courier service on its scale.) In contrast, the historical records for truckingand courier service pose significant analytical problems. These sectors are thereforeaddressed not only by Part 4 but by another set of graphs showing alternative trendrelationships. These graphs are labeled Part 5 and occupy the second following page.

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OTHER TRANSPORTATION AND UTILITIES


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Figure 1. FINAL-DEMAND TRENDS AND PREDICTIVE RELATIONSHIPS -- Part 5ALTERNATIVE RELATIONSHIPS FOR TRUCKING AND COURIER SERVICE

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Ratio = 16.9 (Log of year minus 1976) - 5.5

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Ratio of FD to U.S. Employment

Ratio of FD to U.S. Employment

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The problem encountered in the statistical record for the trucking industry is theexistence of large absolute employment declines during 1978-83 and 2000-04 despitegains in nearly all other years. The declines may have been exacerbated by themethods used to delineate final demand, but in percentage terms the losses of totalemployment were respectively five-sixths and three-fourths as great as the final-demand declines shown by the diagrams. An explanation for the earlier slump mightbe the deregulation of the trucking industry, which produced a massive turnover oftrucking companies after the late 1970s and may have worked to the Memphis area’sdisadvantage. For the later slump – which according to direct BLS testimony involveda 21% decline in total employment over four years – there is no immediately availableexplanation, and the possibility of a statistical anomaly must be considered. In anycase the result is an erratic pattern even with final-demand employment expressed inrelative terms. Part 4 shows the 36-year trend in the region-to-U.S. ratios fortrucking. This relationship has been considered unusable for predictive purposes dueto its poor fit and its substantial positive slope despite the post-2000 slump. Since an18-year trend line would have arbitrary aspects and would also feature a positiveslope, the decision for trucking has been to posit a relationship with zero slope. Whenextrapolated into the future, this simply says that the region’s final-demandemployment in trucking will increase at the same rate as national employment. Thechosen relationship is based on average conditions during 1990-2004 and is shown inthe upper portion of Part 5.

The extraordinary rise of the courier and messenger service industry – consisting verylargely but not entirely of FedEx – has been the leading economic story in Memphis forthe past quarter-century. In absolute terms, this sector’s employment has increasedin practically a straight-line fashion since the late 1970s, albeit with some slowingafter the mid-1990s. In relative terms, a breakpoint occurred for the region in 1986due to an abrupt acceleration of national growth. Prior to 1986 the region-to-U.S.ratios increased at a meteoric rate, whereas afterward they merely followed a strongupward trend. This trend for 1987-2004 is describable rather well on a straight-linebasis as shown by the lower-right graph in Part 4.

The issue for courier and messenger service is the possibility that even the 18-yearrelationship would be overly optimistic when projected 36 years into the future. Thisrelationship says that the region-to-U.S. ratio for courier service will increase by 50%between 2004 and 2040. Meanwhile courier service is expected to be one of thefastest-growing industries nationally, with employment nearly doubling between 2004and 2040 according to the BLS-based forecast. Together these factors would yield ascenario in which the region’s final-demand employment increased from about 29,500jobs in 2004 to nearly 85,000 in 2040. This gain appears excessive given thatcompanies rarely expand forever in one spot and FedEx has been developing andplanning new hub facilities elsewhere (most recently in the Piedmont Triad of NorthCarolina).

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On the other hand, there are circumstances that argue for some ongoing increase inthe Memphis area’s share of national courier-service employment, rather than a flat orasymptotic trend in its region-to-U.S. ratios. FedEx clearly intends to keep expandingits operations in Memphis (and will have new opportunities to do so given therelocation of Air National Guard facilities). Furthermore the advantages of Memphisas a logistics base – including its central location, its weather-favored airport and itsexisting logistics infrastructure – should attract more and more activity by companiesother than FedEx.

A balancing of these factors has yielded the compromise predictive relationship shownin the lower graph of Part 5. This is a curvilinear rather than straight-line trend thathas been fitted to the points from 1978 onwards by a regression analysis wherein theindependent variable was the logarithm of calendar time minus 1976. (The“predictions” for 1977 and earlier years have been arbitrarily set at zero, but even withthis feature the relationship explains over 95% of the variation in region-to-U.S. ratiosfor 1969-2004.) When extrapolated into the future, this trend line continues to risebut at a gradually decreasing rate. It yields a forecast of final-demand employment in2040 that is nearly 15% lower than the figure obtained from the 18-year straight-linerelationship.

Part 6 and Part 7 on the next two pages address information, finance, insurance andreal estate activity, plus professional, technical, management and administrativefunctions. Except in the management sector (which covers management and salesoffices of companies mainly engaged in other activities), final demand has recentlyconstituted only 20% to 37% of total employment in these industries, with lowershares tending to prevail in earlier decades. Thirty-six-year relationships have beenchosen to describe trends in region-to-U.S. ratios for all of these industries besidesinsurance and real estate. In all but one of the cases where 36-year relationships areemployed, using 18-year trend instead lines would have been more optimistic (i.e.,would have yielded higher forecasts when extrapolated and applied to nationalprojections), usually by substantial margins.

The predictive relationships for insurance, real estate and management functions areconsidered satisfactory, while those for finance, information, employment services andadministrative support services are somewhat less so. Two special problem areas arelegal services and other professional and technical services. The absolute levels offinal-demand employment in the latter cases were relatively stagnant from the mid-1970s until the mid-1980s, then grew rapidly in most years until 2001. Meanwhilethe nation as a whole followed an opposite pattern, with higher percentage growthbefore 1985 than after. This yielded the up-and-down pattern of region-to-U.S. ratiosshown in the lower-right graph of Part 6 for legal services and the upper-right graph ofPart 7 for other professional and technical services. The pronounced nature of this

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pattern suggests that it may be partly an artifact of the methods used to delineatefinal demand. Similarities with other graphs suggest that some of the ratio variationfor other industries might be partly spurious as well. This possibility goes back to theearlier commentary on input-output adjustment and its implication that long-termtrends in final demand are to some extent an artificial construct. On the other hand,the statements about the value of partitioning the economy and the control achievedby holding the overall multiplier constant still apply. The importance of the constant-multiplier constraint is that errors in final demand for one industry should be offset byerrors elsewhere that have opposite influence on predicted total employment. Thus allof the relationships in Part 6 and Part 7 are utilized in the belief that the results willbe reliable at a higher level if not for every industry.

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2,000

2,500

3,000

1 6 11 16 21 26 31 360.00

0.20

0.40

0.60

0.80

1.00

1.20

1 6 11 16 21 26 31 36


REAL ESTATEFINANCE

INSURANCE

FINANCE

1969 1974 1979 1984 1989 1994 1999 2004 1969 1974 1979 1984 1989 1994 1999 2004

1969 1974 1979 1984 1989 1994 1999 2004 1969 1974 1979 1984 1989 1994 1999 2004


INFORMATION

INFORMATION

REALESTATE

INSURANCE

LEGAL SERVICES

LEGAL SERVICES

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1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

1 6 11 16 21 26 31 360.00

0.20

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0.80

1.00

1.20

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1.80

2.00

1 6 11 16 21 26 31 36

0

1,000

2,000

3,000

4,000

5,000

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7,000

8,000

9,000

1 6 11 16 21 26 31 360.00

0.50

1.00

1.50

2.00

2.50

3.00

1 6 11 16 21 26 31 36


EMPLOYMENT SERVICESPROFESSIONAL, SCIENTIFIC

EMPLOYMENTPROFESSIONAL, SCIENTIFIC & TECH. SERVICES

1969 1974 1979 1984 1989 1994 1999 2004 1969 1974 1979 1984 1989 1994 1999 2004

1969 1974 1979 1984 1989 1994 1999 2004 1969 1974 1979 1984 1989 1994 1999 2004


MANAGEMENT OF ADMINISTRATIVE

AND TECHNICAL SERVICES,(EXCL. LEGAL SERVICES)

SERVICES

ADMINISTRATIVE SUPPORT SERVICES(EXCL. EMPLOYMENT SERVICES)

COMPANIES ANDENTERPRISES

MANAGEMENT OF COMPANIES & ENTERPRISES

SUPPORT SERVICES

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Parts 8, 9 and 10 of Figure 1 cover the remaining industries and occupy the followingthree pages. Part 8 addresses private education and three health-related sectors:hospitals, ambulatory health services, and nursing and residential care plus socialservices. The final-demand shares of total employment in these industries haverecently ranged from 21% to 34%. The predictive relationships obtained for hospitalsand ambulatory health services are very closely fitting 18-year trends with strongupward slope (which would be even stronger in the 36-year versions). A 36-yearrelationship is used for nursing, residential care and social services in the belief thatthe 18-year trend for this industry, featuring twice as much positive slope, would beexcessively optimistic. Meanwhile the region-to-U.S. ratios for private education areclosely describable by an 18-year relationship with virtually no slope, implying growthat the national rate.

Part 9 addresses arts and entertainment, accommodations, food services and drinkingplaces, and the sector containing most nonprofit organizations (namely religious,grantmaking and civic organizations). The final-demand shares of total employment inthese cases have recently equaled about half for accommodations, 35% to 40% for artsand entertainment, and less than 25% for the others. Eighteen-year relationshipshave been chosen to describe the region-to-U.S. ratios in three of these cases, yieldinga very good fit for arts and entertainment and reasonably good descriptions foraccommodations and food services (marred in both cases by late departures from thetrend lines). A 36-year relationship provides a good description of recent ratios for thenonprofit sector.

Part 10 deals with a last service category – covering rental, repair, personal andlaundry services – and two components of government employment. The servicecategory involves only a small amount of final-demand employment and is wellhandled by a 36-year trend line. A background circumstance for government is thatinput-output tables follow a convention in which all government activity is routinelytreated as final demand. (An I-O table includes a government vector only to registerthe impacts of government on everything else.) This reflects an unwillingness, perhapsphilosophically based, to treat public-service demands and the associated taxpayments in the same fashion as input purchases. But local government and somehigher-level government functions are in fact linked to the rest of a region’ssocioeconomy no less tightly than the participants in most of the input relationshipscovered by an input-output table. Hence the Memphis investigation has followedearlier studies in setting aside all of local government and part of nonlocal civiliangovernment for endogenous determination on the basis of population. (The populationlinkages used for government mean that the forecasting procedures described belowmust be iterated to reconcile government and total employment with population.)Final demand is limited to all military employment and a share of state plus federalcivilian employment that ranges up to 70%. These are the employment figures plottedin absolute and relative terms in Part 10.

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The final-demand component of state and federal civilian employment in the Memphisregion has been static since the late 1980s. Meanwhile there has been a slightuptrend at the national level, yielding a pattern of region-to-U.S. ratios describable bya slightly downward-sloping 18-year trend line.

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3,000

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1.00

1.20

1.40

1.60

1 6 11 16 21 26 31 36

0

1,000

2,000

3,000

4,000

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7,000

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1 6 11 16 21 26 31 360.00

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1.00

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2.00

1 6 11 16 21 26 31 36


AMBULATORY HEALTH SERVICES

AMBULATORY HEALTH SERVICES

EDUCATION SERVICES (PRIVATE)

EDUCATION SERVICES (PRIVATE)

1969 1974 1979 1984 1989 1994 1999 2004 1969 1974 1979 1984 1989 1994 1999 2004

1969 1974 1979 1984 1989 1994 1999 2004 1969 1974 1979 1984 1989 1994 1999 2004


RESIDENTIAL

HOSPITALSHOSPITALS

NURSING,

CARE & SOCIALSERVICES NURSING, RESIDENTIAL CARE & SOCIAL SERVICES

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3,000

4,500

6,000

7,500

1 6 11 16 21 26 31 360.00

0.50

1.00

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2.00

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3.00

3.50

4.00

4.50

1 6 11 16 21 26 31 36

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

1 6 11 16 21 26 31 360.00

0.20

0.40

0.60

0.80

1.00

1.20

1 6 11 16 21 26 31 36


ACCOMMODATIONSACCOMMODATIONS

ARTS, ENTERTAINMENT AND ARTS, ENTERTAINMENT & RECREATION SERVICES

1969 1974 1979 1984 1989 1994 1999 2004 1969 1974 1979 1984 1989 1994 1999 2004

1969 1974 1979 1984 1989 1994 1999 2004 1969 1974 1979 1984 1989 1994 1999 2004


FOOD SERVICES & DRINKING PLACES

RELIGIOUS, GRANTMAKING

RECREATION SERVICES

FOOD SERVICES & DRINKING PLACES

RELIGIOUS, GRANTMAKING ANDCIVIC ORGANIZATIONS

AND CIVIC ORGANIZATIONS

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15,000

20,000

25,000

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1.00

1.50

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2.50

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3.50

1 6 11 16 21 26 31 36

0

5,000

10,000

15,000

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25,000

30,000

1 6 11 16 21 26 31 360.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

1 6 11 16 21 26 31 36


STATE AND FEDERAL CIVILIAN GOVERNMENTSTATE AND FEDERAL CIVILIAN GOVERNMENT

RENTAL, REPAIR, PERSONAL & LAUNDRY SERVICES

RENTAL, REPAIR, PERSONAL & LAUNDRY SERVICES

1969 1974 1979 1984 1989 1994 1999 2004 1969 1974 1979 1984 1989 1994 1999 2004

1969 1974 1979 1984 1989 1994 1999 2004 1969 1974 1979 1984 1989 1994 1999 2004


FEDERAL MILITARY

FEDERAL MILITARY

1997-2004 Average

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Military employment in greater Memphis – referring just to uniformed personnel, notcivilian employees of the military – exceeded 25,000 persons in 1969 and stayed at orabove 15,000 persons through 1992. Nearly all of these servicemen and women wereassociated with the naval installation at Millington (NSA Mid-South). Then came areduction in force that lowered the military presence by about 4,000 persons during1992-96 and another 5,000 persons in 1996-97. Since 1997 the region’s militaryemployment has remained between 5,950 and 6,650 persons, with no clear trend ineither absolute numbers or region-to-U.S. ratios. Under the circumstances the onlyreasonable predictive relationship is a horizontal line plotted at the average of the1997-2004 ratio values, which is the trend line shown in the lower right-hand graph ofPart 10.

Development of Forecasts

Forecasts of regional final demand have been obtained in a straightforward fashion byapplying ratio values from the predictive relationships to forecasts of nationalemployment in the same industry groups. These computations were carried out forseven years spaced at six-year intervals from 2004 to 2040. The results were theninterpolated to 2020 and 2030 in order to describe final-demand employment at ten-year intervals from 2010 to 2040.

The other economic forecasting task consisted of using conventional forwardapplications of input-output to translate the forecasted values of final demand intodescriptions of total employment. Though simple in concept, this step wascomplicated by: 1) the need to convert employment between different levels ofaggregation because the input-output table did not cover all the final-demandcategories; 2) the required adjustment of I-O coefficients to reflect the trendsestablished in the initial partitioning process; 3) the need to control (i.e., holdconstant) the overall employment multiplier specified by the matrix, which could onlybe observed after-the-fact; and 4) the need to iterate the adjustment, control andcohort-survival forecasting process for each year to enforce consistency betweengovernment employment and forecasted population. These details were handled asstraightforwardly as possible and do not require elaboration here.

Given the premise that regional population growth will be driven by economic growth,the demographic forecasting process consisted of finding the future population levelsthat would yield just enough resident workers to staff the economy, given assumptionsabout the region’s future commuting balance.

Economic and demographic magnitudes have been linked using standard cohort-survival methods of population projection. Cohort-survival calculations are used tosimulate the transition of an area’s population – tabulated in five-year age groups by

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sex – across one or more time intervals such as decades. At the end of any given timeinterval, the number of persons in each age-sex group (which becomes a “cohort”when defined to include the same people as they age) equals the number of groupmembers at the beginning of the interval, plus the volume of net migration for thegroup during the interval, minus the deaths of group members, plus the number ofbirths (if the group is one that includes age zero at the beginning of the interval). Thenecessary inputs for this computation are birth, death and net migration rates foreach age-sex group, which are estimated on the basis of historical data and nationaltrends.

In the present study and similar investigations, economic and demographicmagnitudes have been linked in the cohort-survival tableau by applying employmentparticipation rates to the population numbers computed for age-sex groups. Thisallowed a determination of total resident employment, which became a statement ofsupportable at-place employment when converted by a commuting adjustment. Thetableau could be made to yield a population consistent with a predeterminedemployment level (i.e., with the total employment dictated by the input-output tablegiven the final-demand forecasts and a provisional assumption about governmentemployment) by scaling all the net migration rates up or down by some uniformpercentage. The cohort-survival tableau could not be solved analytically to make itsnet migration rates into explicit functions of employment, so the process of obtaining aconsistent population profile for each year had to be conducted iteratively.

The employment participation rates that formed the bridge between population andemployment were critical to the forecasts and thus were prepared carefully to avoidinadvertent creation of bias. The first step consisted of estimating detailedparticipation rates for the Memphis region in 2000 and 2003, based on Memphisdemographics and employment combined with the U.S. pattern of rates for age-sexgroups. (The U.S. rates for current and future years pertained to labor forceparticipation rather than employment, but conversions back and forth only requiredmaking allowance for unemployment. The detailed rates obtained for the U.S. anddeveloped for Memphis pertained to fourteen age groups, 16-19 through 80-84, foreach sex.) The second step was to project the Memphis rates across the forecastperiod by assuming that the rate for each age-sex group would maintain the sameratio to the corresponding U.S. rate as the relationship prevailing in 2003. Whilegenerally supported by the region’s past experience, the assumption that regionalrates would move in lockstep with the U.S. rates forecasted by BLS was chosen in partfor lack of any reasonable alternative. It turned out that despite the parallel trendsassumed for individual rates, the region’s overall level of employment participation wasforecasted to diverge substantially from U.S. level because population aging wasexpected to proceed more slowly in the region.

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Regional Forecast Results

Table 7 below provides a summary that compares the region’s expected growth withtrends in the United States as a whole. The U.S. population forecast is from theCensus Bureau, and the national employment forecast has been prepared as part ofthis study as described earlier. Table 7 starts in 1980 to provide historicalperspective and compares percentage growth over the 24-year historical period from1980 to 2004 with expected trends over the 36-year forecast period. (It should beremembered that these periods have different lengths.)

Table 7. Summary of Memphis Region Forecast and Comparison with the U.S.Memphis Region United States (in Thousands)

Employ- Popula- Empl. Per Employ- Popula- Empl. Per ment tion Capita ment tion Capita

Actual1980 416,283 938,777 0.443 95,868 226,542 0.4231990 503,623 1,007,306 0.500 114,708 248,710 0.4612000 607,223 1,135,614 0.535 136,581 281,422 0.4852004 604,578 1,181,701 0.512 136,027 292,939 0.464

‘80-04 Chg. 45.2% 25.9% 41.9% 29.3%Forecasted

2010 683,303 1,278,991 0.534 148,434 308,299 0.4812020 795,923 1,464,853 0.543 160,215 335,133 0.4782030 890,658 1,641,924 0.542 170,312 362,890 0.4692040 998,691 1,828,274 0.546 182,113 391,236 0.465

‘04-40 Chg. 65.2% 54.7% 33.9% 33.6%

By 2040 the Memphis region is expected to have over 1.8 million inhabitants and justunder a million jobs (defined on the one-job-per-worker basis used throughout thisstudy). These figures imply a 55% population gain and a 65% employment increaserelative to 2004 levels. Comparisons between regional and national trends before andafter 2004 are particularly impressive. During 1980-2004, the Memphis region led theU.S. in employment growth and trailed in population growth by similar margins of 3-plus percentage points. But over the next 36 years the region is expected to outgainthe nation by margins of about 31 percentage points for employment and 21 points forpopulation.

The argument for the reasonableness of this seemingly optimistic forecast will bepresented near the end of the present section. Discussion of the employment-per-capita ratios in Table 7 will likewise be postponed until more material is in hand.

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Population Forecast

Table 8 on the next page presents the full forecast of regional population by age andsex. Summaries are provided at the bottom of this table and in Table 9 to follow.

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1990 2000 2010 2020 2030 2040 2000 2020 2040Male

0-4 41,674 44,145 47,164 53,450 59,877 63,774 3.9% 3.6% 3.5%5-9 40,436 47,672 47,918 54,693 61,080 65,386 4.2% 3.7% 3.6%

10-14 38,913 46,118 49,597 55,603 60,495 68,799 4.1% 3.8% 3.8%15-19 40,993 42,874 52,444 55,376 60,881 68,934 3.8% 3.8% 3.8%20-24 37,791 37,223 45,597 49,192 55,129 60,089 3.3% 3.4% 3.3%25-29 42,851 41,331 42,139 51,683 54,591 60,138 3.6% 3.5% 3.3%30-34 42,629 40,906 41,420 52,323 54,664 62,233 3.6% 3.6% 3.4%35-39 39,938 44,084 44,668 47,881 56,163 60,572 3.9% 3.3% 3.3%40-44 34,797 43,068 42,446 44,605 54,534 57,905 3.8% 3.0% 3.2%45-49 25,791 39,088 43,757 45,416 48,225 56,947 3.4% 3.1% 3.1%50-54 20,488 33,654 42,145 42,693 44,508 54,731 3.0% 2.9% 3.0%55-59 17,975 24,038 36,707 41,811 43,417 46,651 2.1% 2.9% 2.6%60-64 17,077 17,608 29,327 37,183 38,102 40,129 1.6% 2.5% 2.2%65-69 14,902 14,041 19,107 29,694 34,550 36,293 1.2% 2.0% 2.0%70-74 11,075 11,905 12,428 21,225 27,777 28,854 1.0% 1.4% 1.6%75-79 7,159 9,138 8,813 12,299 19,765 23,529 0.8% 0.8% 1.3%80-84 4,298 5,051 5,454 5,557 10,814 14,650 0.4% 0.4% 0.8%85+ 2,607 3,400 4,495 4,492 6,081 10,496 0.3% 0.3% 0.6%

Total male 481,396 545,344 615,627 705,176 790,651 880,111 48.0% 48.1% 48.1%Female

0-4 39,848 42,417 45,479 51,676 57,701 61,539 3.7% 3.5% 3.4%5-9 38,803 45,252 45,197 51,326 57,559 61,449 4.0% 3.5% 3.4%

10-14 36,718 44,450 47,488 53,294 58,203 65,953 3.9% 3.6% 3.6%15-19 37,724 40,492 49,448 51,672 56,720 64,375 3.6% 3.5% 3.5%20-24 39,237 38,642 49,001 54,543 59,154 65,635 3.4% 3.7% 3.6%25-29 46,100 43,467 47,240 59,813 60,312 67,657 3.8% 4.1% 3.7%30-34 47,567 43,387 44,287 57,697 61,748 68,317 3.8% 3.9% 3.7%35-39 43,667 48,036 47,182 53,160 64,594 66,526 4.2% 3.6% 3.6%40-44 37,336 47,885 44,374 46,210 59,054 63,678 4.2% 3.2% 3.5%45-49 28,365 43,301 47,997 47,830 53,494 65,162 3.8% 3.3% 3.6%50-54 23,370 36,035 46,369 43,159 45,021 57,665 3.2% 2.9% 3.2%55-59 21,474 26,265 40,296 44,525 44,703 50,026 2.3% 3.0% 2.7%60-64 21,133 21,182 32,850 42,423 39,706 41,534 1.9% 2.9% 2.3%65-69 19,990 18,305 22,456 34,686 38,800 38,999 1.6% 2.4% 2.1%70-74 15,303 17,287 17,505 27,386 35,629 33,639 1.5% 1.9% 1.8%75-79 12,878 14,681 13,520 16,717 26,468 29,906 1.3% 1.1% 1.6%80-84 9,044 9,874 11,444 11,940 18,781 24,866 0.9% 0.8% 1.4%85+ 7,355 9,312 11,235 11,619 13,623 21,236 0.8% 0.8% 1.2%

Tot. female 525,910 590,270 663,364 759,677 851,272 948,163 52.0% 51.9% 51.9% Total pop. 1,007,306 1,135,614 1,278,991 1,464,853 1,641,924 1,828,274 100.0% 100.0% 100.0%

Both Sexes0-17 283,622 320,074 343,978 384,271 425,476 466,884 28.2% 26.2% 25.5%18-24 108,515 109,211 135,355 146,555 161,323 179,048 9.6% 10.0% 9.8%25-44 334,885 352,164 353,755 413,372 465,659 507,026 31.0% 28.2% 27.7%45-64 175,673 241,171 319,448 345,040 357,177 412,846 21.2% 23.6% 22.6%65+ 104,611 112,994 126,455 175,617 232,289 262,469 10.0% 12.0% 14.4%

Total pop. 1,007,306 1,135,614 1,278,991 1,464,853 1,641,924 1,828,274 100.0% 100.0% 100.0%

Table 8. Actual and Forecasted Population in the Memphis Region by Age and Sex

Forecasted PopulationActual Population Share of Population

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Table 8 describes the region’s population at ten-year intervals from 1990 to 2040 andgives the percent share of total population in each age-sex group for three selectedyears. The percent shares reveal a general pattern of population aging, with sharestending to decline for younger age groups and rise for older groups. There are manysmall countertrends at the level of five-year age groups, however, so the aging patternis more readily visible in the figures for aggregate age groups at the bottom of Table 8.The last two rows indicate that the share of the region’s population aged 65 and overwill rise from 10.0% in 2000 to 14.4% in 2040, while the combined population sharefor persons aged 45-plus promises to increase from 31.2% to 37.0%. Table 9 belowreproduces the summary data from the bottom of Table 8 and adds more descriptivestatistics. These include ten-year percent changes in population and percentdistributions across age groups for the U.S. as well as the region.

Table 9. Population Forecast for the Memphis Region by Major Age Group Actual Population Forecasted Population 1990 2000 2010 2020 2030 2040

Population0-17 283,622 320,074 343,978 384,271 425,476 466,88418-24 108,515 109,211 135,355 146,555 161,323 179,04825-44 334,885 352,164 353,755 413,372 465,659 507,02645-64 175,673 241,171 319,448 345,040 357,177 412,84665+ 104,611 112,994 126,455 175,617 232,289 262,469Total 1,007,306 1,135,614 1,278,991 1,464,853 1,641,924 1,828,274

% Change0-17 12.9% 7.5% 11.7% 10.7% 9.7%18-24 0.6% 23.9% 8.3% 10.1% 11.0%25-44 5.2% 0.5% 16.9% 12.6% 8.9%45-64 37.3% 32.5% 8.0% 3.5% 15.6%65+ 8.0% 11.9% 38.9% 32.3% 13.0%Total 12.7% 12.6% 14.5% 12.1% 11.3%

Share of Total0-17 28.2% 28.2% 26.9% 26.2% 25.9% 25.5%18-24 10.8% 9.6% 10.6% 10.0% 9.8% 9.8%25-44 33.2% 31.0% 27.7% 28.2% 28.4% 27.7%45-64 17.4% 21.2% 25.0% 23.6% 21.8% 22.6%65+ 10.4% 10.0% 9.9% 12.0% 14.1% 14.4%Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

U.S. Share0-17 25.6% 25.7% 24.2% 23.9% 23.6% 23.4%18-24 10.8% 9.7% 9.8% 8.7% 9.0% 8.9%25-44 32.5% 30.1% 26.8% 26.2% 25.2% 24.7%45-64 18.6% 22.1% 26.2% 24.9% 22.6% 22.6%65+ 12.6% 12.4% 13.0% 16.3% 19.7% 20.4%Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

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The percent changes occupying the second part of Table 9 are somewhat hard tointerpret because they are dominated by movement of the “baby-boom” generation –i.e., persons born from 1946 through 1964 – through the various age brackets. Theonly ten-year increases above 30% are observed when the boomers enter the 45-64bracket, a process that started in 1990-2000 and continues in the present decade,and when they move into the top age group during 2010-20 and 2020-30. Adeceptively small gain then occurs in the 65-plus group during 2030-40 because thenumbers will then be depressed by mortality among the boomers (who will be aroundage 80 on average) and because the “baby bust” generation will be the source of newentrants to that group.

The most striking aspect of Table 9 is the contrast between the population shares forthe region and the U.S. that appear in the table’s last two sections. Although theMemphis region’s population will be growing older, it will be aging much less rapidlythan the national population. The region has always had a relatively large admixtureof children, with a population share two to three percentage points higher than theU.S. share for the under-18 age group, and similar margins are expected to prevailthroughout the forecast period. In the recent past the region has resembled the U.S.very closely in terms of persons aged 18-24 and has had a population share withinone percentage point of the U.S. level for persons aged 25-44. During the forecastperiod, however, regional excesses of about one percentage point and three percentagepoints are expected to emerge for these groups, respectively. The differences will becompensated by slower growth in the 65-plus population group, where the region’spopulation share will rise by 4.4 percentage points (relative to 2000) while the nation’sshare increases by eight points. The consequence is that in 2040 the region andnation will differ by six full percentage points in elderly population share: 14.4%versus 20.4%. For such statistics this is a profound difference, and it will have majorimplications for the population-employment balance as demonstrated later.

Table 10 below looks at the components of population change in each decade asestablished by cohort-survival analysis. These components are: the number of births;the number of deaths (which of course subtract from population and hence areentered negatively); and net migration of persons into the region. The figures for1990-2000 represent observed data, with net migration obtained as a residual. Thosefor later decades have been yielded by the process described in the second sectionwherein net migration was adjusted to yield a labor force consistent with the economicforecast.

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Table 10. Actual and Forecasted Components of Population Change Components of Population Change

Starting Deaths Net Total Pop. EndingInterval Population Births (Negative) Migration Change Population

1990-00 1,007,306 184,297 -95,134 39,145 128,308 1,135,6142000-10 1,135,614 191,176 -104,331 56,532 143,377 1,278,9912010-20 1,278,991 218,076 -118,026 85,812 185,862 1,464,8532020-30 1,464,853 242,254 -137,066 71,882 177,070 1,641,9242030-40 1,641,924 258,986 -162,928 90,292 186,350 1,828,274

The Memphis region is characterized by relatively high birth rates. The number ofannual births per capita in the region averaged 0.0172 during the 1990-2000 intervaland is projected to equal 0.0158 during 2000-10. In contrast, annual births percapita in the U.S. during 2000-04 averaged only 0.0141. The ratio of births to deathsworks out to 1.94 and 1.83 in the region versus 1.67 in the U.S. for these periods.The region’s history of high birth rates – perhaps 20% higher than the nation untilrecently – accounts for its relatively youthful population at present. This initialcondition along with an ongoing birth-rate margin and the rejuvenating effects ofpositive migration will account for the expected future divergence between the regionand nation in terms of age profiles.

Even with gains from natural population increase averaging 9,000 to 10,000 personsper year, the Memphis region will experience substantial in-migration of personsunder the impetus of job growth. Net migration is forecasted to average 7,613 personsper year over the four decades from 2000 and will equal about 8,000 persons per yearfor the 36-year forecast period from 2004. On a per capita basis, net migration to theregion from all locations will proceed at a rate somewhat above the recent rates ofinternational migration to the U.S. Economically motivated in-migration has arejuvenating effect on an area’s age profile because relocating workers and job-seekerstend to be relatively young and are often accompanied by children. This holdsespecially in the Memphis case, where seven-eighths of all net migration is supplied bypersons under age thirty (in part because there is significant out-migration of olderpersons).

Employment Forecast

Table 11 on the next page presents the regional employment forecast in 39-industrydetail. Like other tables it offers data for forecast years spaced at 10-year intervalsafter 2010, and its last column shows percentage changes over the 36-year forecastperiod. (These and all other forecasts will interpolated to five-year intervals in a laterdocument.) Along with being identified by NAICS codes and verbal descriptions, the

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39 industries are arranged in the six groupings used here for aggregate employmentdescription and transportation planning purposes.

The forecasted annual percent changes in total employment are presented in thetable’s last row. As already shown in Table 5, the expected pattern involves rapid butgenerally declining employment growth across the forecast period, at annualcompound rates of change equaling 2.08% in the rest of the present decade, 1.54%during 2010-20, 1.13% during 2020-30, and 1,15% during 2030-40.

The forecasted rates of change for individual industries are extremely variable. Thehighest percentage gain – 190% – is expected for employment services, reflecting acontinuation of the dramatic increase in corporate reliance upon labor contractors,temp services and so forth. (The employees in this category are actually engaged in awide but unknowable variety of other industries.) Next comes courier and messengerservice, which will expand by about 146% and contribute the region’s largest absoluteincrease in jobs. Employment in two health-related sectors will rise by about 130%,while gains exceeding 100% are also expected for some professional and nonprofitfunctions.

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2004-402004 2010 2020 2030 2040 % Chg.

Industrial/Manufacturing111,112 Farming 2,808 2,486 2,199 1,936 1,746 -38%113-115,21 Ag. support, mining, forestry 2,852 3,091 3,381 3,590 3,849 35%23 Construction 25,146 27,697 31,260 33,968 37,057 47%311,312 Food & beverage mfg. 9,141 8,409 7,506 6,376 5,255 -43%322 Paper products mfg. 7,701 7,844 8,093 8,095 8,037 4%323 Printing 3,958 4,031 4,663 5,166 5,648 43%324,325 Chemical, petro.& coal prod. 4,065 3,689 2,946 2,196 1,513 -63%313-316,326 Other nondurable goods mfg. 3,266 3,234 3,109 3,021 2,989 -8%332 Fabricated metal prod. mfg. 5,062 5,196 5,115 4,778 4,387 -13%333,336 Machinery & trans. eq. mfg. 4,753 4,713 4,434 3,982 3,517 -26%334,335 Electronic & electrical equip. 5,388 5,684 6,155 6,465 6,749 25%321,7;331,7,9 Other durable goods mfg. 6,868 7,196 6,735 5,997 5,227 -24%Wholesale/Transportation42 Wholesale trade 37,371 39,388 43,601 46,658 50,136 34%484 Truck transportation 14,530 16,905 19,589 21,112 22,671 56%492 Courier & messenger service 31,103 42,127 54,497 64,933 76,500 146%Rest 48; 493 Other transportation & utilities 16,348 19,888 24,059 27,760 31,962 96%Retail44,45 Retail trade 68,360 76,795 87,326 95,200 104,164 52%722 Food services 39,981 45,134 53,414 60,185 67,846 70%Office51 Information 9,430 10,669 12,527 14,068 15,841 68%521-3,5; 533 Finance 18,596 20,215 23,472 26,131 29,153 57%524 Insurance 5,830 6,594 7,307 7,762 8,249 41%531 Real estate 5,541 5,885 6,405 6,715 7,061 27%5411 Legal services 3,702 4,028 4,648 5,120 5,630 52%5412-9 Other prof., sci. & tech. serv. 23,493 27,978 35,112 41,787 49,623 111%551 Mgmt. of co.s & enterprises 8,100 8,925 9,838 10,486 11,227 39%5613 Employment services 15,972 21,506 29,099 36,830 46,268 190%Rest 561&2 Other admin. support services 22,316 25,341 30,959 35,817 41,188 85%Service61 Educational services (private) 5,218 5,948 6,857 7,558 8,357 60%621 Ambulatory health services 20,497 24,974 32,023 38,892 47,074 130%622 Hospitals 23,631 26,886 31,587 35,353 39,359 67%623,4 Nursing, res. care, social serv. 16,674 20,477 26,260 31,905 38,646 132%71 Arts, entertainment & recr. 4,802 5,676 6,975 8,164 9,546 99%721 Accommodations 14,370 15,778 18,274 20,546 23,111 61%532; 811,2 Rental, repair & personal serv. 13,357 14,793 16,759 18,188 19,768 48%813 Religious, grantmaking & civic 11,720 14,400 17,503 20,300 23,542 101%Government91-97 pt. Federal government, civilian 16,271 16,778 17,591 18,246 18,966 17%91-97 pt. Federal military 6,053 6,551 7,157 7,564 8,026 33%91-97 pt. State govt. (incl. public educ.) 15,271 16,424 18,642 20,823 23,445 54%91-97 pt. Local govt. (incl. public educ.) 55,032 59,969 68,844 76,986 85,358 55%Total Employment

Number of Employees 604,578 683,303 795,923 890,658 998,691 65%Annual Percent Change 2.06% 1.54% 1.13% 1.15%

Table 11. Employment Forecast for the Memphis Region

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At the opposite extreme, employment declines are expected for six of the ninemanufacturing industries covered by the forecast. Employment in the manufacturingsector as a whole is likely to trend downward from 50,202 workers in 2004 to about43,300 in 2040, yielding a 36-year loss of 14%. Farming is the only other sectorforecasted to decline, with a loss of 38% occurring from a relatively small base.

Table 12 below shows the forecasted magnitudes for the six aggregate industrygroups, with data added for years prior to the baseline and with a section givingpercent distributions of total employment across the groups. The percentdistributions reveal a rapid transformation in the region’s economy. At the end of thehalf-century covered by the table, the share of employment supplied by theIndustrial/Manufacturing category will have declined by half, while the sharessupplied by the Office, Service and Wholesale/ Transportation groups will each haveincreased by 3.8 to 6.7 percentage points.

Table 12. Employment Forecast for the Memphis Region by Aggregate Industry Group Actual Employment Forecasted Employment

1990 2000 2004 2010 2020 2030 2040No. ofEmployeesIndustrial/Mfg. 86,932 90,241 81,008 83,270 85,595 85,570 85,975Wholesale/Trans.

72,396 102,769 99,353 118,308 141,746 160,462 181,268

Retail 90,196 107,968 108,341 121,929 140,740 155,385 172,010Office 74,288 111,765 112,980 131,141 159,367 184,716 214,240Service 82,343 105,785 110,270 128,932 156,239 180,906 209,404Government 97,468 88,695 92,626 99,722 112,235 123,618 135,795 Total 503,62

3607,224 604,578 683,303 795,923 890,658 998,691

Share of TotalIndustrial/Mfg. 17.3% 14.9% 13.4% 12.2% 10.8% 9.6% 8.6%Wholesale/Trans.

14.4% 16.9% 16.4% 17.3% 17.8% 18.0% 18.2%

Retail 17.9% 17.8% 17.9% 17.8% 17.7% 17.4% 17.2%Office 14.8% 18.4% 18.7% 19.2% 20.0% 20.7% 21.5%Service 16.4% 17.4% 18.2% 18.9% 19.6% 20.3% 21.0%Government 19.4% 14.6% 15.3% 14.6% 14.1% 13.9% 13.6% Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

The upper panels of Figure 2 on the next page show the aggregate employment trendsgraphically, using annual data to describe historical conditions and going back to1980 rather than 1990. The plots serve to dramatize the growth expected for mostsectors and the transformation that can be described without much exaggeration as

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an economic turnover. Up through 1992, the region’s three leading aggregate groupsin terms of employment were Industrial/Manufacturing, Retail and Government. After2017 these will be the region’s three trailing groups, and the leading groups will beWholesale/Transportation, Office and Service. Large-scale economic restructuring hasbeen and will continue to be common in U.S. metropolitan areas, but the Memphisregion stands out for achieving a full reversal in a mere quarter-century.

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Figure 2. Memphis Region Employment and Population/Employment Relationships

0

25,000

50,000

75,000

100,000

125,000

150,000

175,000

200,000

225,000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 610

25,000

50,000

75,000

100,000

125,000

150,000

175,000

200,000

225,000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 611980 1990 2000 2010 2020 2030 2040

Actual Forecast

1980 1990 2000 2010 2020 2030 2040

Actual Forecast

OFFICE

WHOLESALE /TRANSPORTATION

INDUSTRIAL / MANUFACTURING

RETAIL

GOVERNMENT

SERVICE

Employment Employment

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2,000,000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 611980 1990 2000 2010 2020 2030 20400.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 611980 1990 2000 2010 2020 2030 2040

Population and Employment Employment Per Capita

POPULATION

TOTALEMPLOYMENT

Actual Forecast Actual Forecast

MEMPHIS REGION

UNITED STATES

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The lower panels of Figure 2 describe total population, total employment and per-capita employment. (The historical data points here pertain only to 1980, 1990, 2000and 2004 rather than any intervening years.) Interest is focused on the trends inemployment per capita described by the right-hand graph. The points plotted here arethe figures presented earlier in the third and last columns of Table 7.

Employment increased relative to population in both the Memphis region and the U.S.during the 1980s and 1990s, with the Memphis levels of per-capita employmentexceeding and gradually pulling away from the national levels. (The gap and itsincrease were both attributable partly but not entirely to net commuting into theMemphis region.) The post-2000 economic slump then reduced employment percapita in the region and the nation by essentially equal amounts. In the future,however, the regional and national trends will start moving in different directions.Due to population aging, the nation’s employment per capita will start edging downafter 2010 and will decline by a significant amount during 2020-30. Even thoughmost rates of labor force and employment participation for individual age-sex groupsare expected to increase, especially for older persons, the shifting of population intogroups where participation is relatively low will dominate the national trend and turnit downward. This will not happen in Memphis due to the region’s slower rate ofpopulation aging. Instead the region’s employment per capita will keep risingsignificantly until 2020 and gain a bit more during 2030-40, with the result that in2040 it will exceed the national employment per capita by one-sixth of the latter.(After a correction for commuting, the gap will be about one-eighth.) This divergenceis remarkable given that the forecasting methodology has assumed exactly parallelmovements in employment participation, as explained at the end of the secondsection.

Explanation of Forecast Magnitudes

The demographic pattern just noted serves to minimize the regional populationforecast given the economic forecast, so all else being equal, the population forecast isconservative. This leaves the aforementioned question of whether the economicforecast itself is optimistic. The projected improvement upon the region’s pastperformance relative to the U.S. would suggest that it is.

Over and above the details of the forecasting methodology and its concern withobjectivity, there are two intuitively satisfying reasons why the economic forecastturned out the way it did. The less important explanation is that the Memphis regionhas a quite favorable industry mix. The region is about one-quarter less dependentthan the U.S. on the job-losing farm and manufacturing sectors, and its role as abusiness center for a large hinterland has given it strength in the higher-level servicefunctions that have become the nation’s leading engines of growth. Its specializationin a particularly favorable area – courier service and related forms of logistical support

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– is an extra advantage. The resulting industry profile would deliver substantiallyhigher growth in total employment than achieved by the U.S. even if every regionalindustry grew at the national rate.

The more important explanation is that the forecast does not in fact promise anythingin the way of economic performance that the Memphis region has not alreadydelivered. The region’s growth in the 1990s was depressed by a non-recurring event.Despite this event, the region kept up with the U.S. economically during an almostunprecedented national boom. With the given event past, there is now a presumptionthat the Memphis region should move decisively ahead of the nation, and the forecastis merely confirming this presumption.

The depressing event was the drastic downsizing of the U.S. Navy presence atMillington, which cost the region thousands of federal civilian employees as well asuniformed military personnel. Table 13 below revisits the region’s employmentsituation and estimates how the 1990s trend would have looked without the Millingtonbase realignment. The region had 10,032 fewer persons in uniform and 5,451 fewerfederal civilian employees in 2000 than in 1990, and the loss of these workers hadrepercussions throughout the economy. Assuming an employment multiplier of 2(since military installations are relatively low in this regard) yields a follow-on effect ofroughly another 15,000 workers. The conclusion is that without the Millingtonrealignment the region’s 2000 employment would have been higher by 30,000-pluspersons. Table 13 places the total number of jobs in this hypothetical scenario atabout 637,700.

Table 13. Examination of Actual and Forecasted Employment ChangesMemphis Annual % Change in Employ-Region ment Over Previous Interval

Employment Memphis U.S. DifferenceActual 1990 employment 503,623Actual 2000 employment 607,223 1.89% 1.76% 0.13% 1990-2000 Memphis losses: Federal military -10,032 Federal civilian govt. -5,451 Estimated multiplier effect -15,000 Total estimated losses -30,483Hypothetical 2000 empl.* 637,706 2.39% 1.76% 0.63%Actual 2004 employment** 604,578 -0.11% -0.10% -0.01%Forecasted 2010 employment 683,303 2.06% 1.47% 0.60%Forecasted 2020 employment 795,923 1.54% 0.77% 0.77%Forecasted 2030 employment 890,658 1.13% 0.61% 0.52%Forecasted 2040 employment 998,691 1.15% 0.67% 0.48%

* Equals actual 2000 employment minus 1990-2000 losses (creating an addition).

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** Percent change for Memphis region computed using actual 2000 employment.

The right-hand side of Table 13 looks at annual percent changes in total employment.The actual pattern during the 1990s was that employment expanded by 1.89% peryear in the Memphis region versus 1.76% per year in the U.S., yielding a Memphisedge of only 0.13%. Without the Millington realignment, however, the region’s annualrate of employment growth would have been a full half-percentage-point higher, atabout 2.39%. This would have exceeded the national rate by 0.63%. For the fourintervals of the forecast period (2004-10 and the decades thereafter), the expectedrates of regional employment growth exceed the forecasted national rates by 0.48% to0.77%. The overall excess is 0.59% (involving 36-year growth rates of 1.404% and0.814%). Thus the employment forecast for the Memphis region works out to astraightforward extrapolation of what would have happened in the 1990s without themilitary withdrawal.Employment Adjustment

According to the workplan for the Memphis-Shelby travel demand model, various localexperts will prepare independent allocations of regional forecasts to sub-county areas,which will subsequently be reconciled with the sub county area (SCA) profilesgenerated by the second phase of consultant forecasting. If this activity is to involveallocations of employment as well as population, the numbers utilized should be theemployment magnitudes that will ultimately provide input to the transportationmodeling process. These magnitudes will be different from the figures alreadydiscussed, and hence it is necessary to comment on the differences and why theyexist.

The socioeconomic database for the travel demand model needs to include threedifferent types of employment numbers, as follows.

1) County and regional employment profiles developed from published datasources. These are the numbers discussed here, which the presentinvestigators customarily prepare using the BLS definition of employment.

2) Employment data for individual establishments, usually obtained at least inpart from a proprietary source and usually in need of substantial processing.Such numbers provide the only means of describing employment at the TAZlevel.

3) Statistics from the census of population that pertain to employment. The mostimportant such statistics for transportation planning are usually the numbersof households tabulated by workers per household.

It is important to enforce consistency between these sets of statistics in order to obtaina seamless employment database. Normally consistency is achieved by treating thenumbers from published data sources as “truth” and using them as control totals for

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adjustment of the establishment-level data. That is, the employment levels for allestablishments in a given county and industry are scaled by a factor that equates theirsum with the employment specified by published sources for that county industry.Using the BLS definition of employment usually assures reasonable consistencybetween the data obtained in this fashion and the employment descriptions providedby the population census (with allowance for the fact that the census describesemployment by place of residence rather than place of work).

For the Memphis region, however, BLS employment has turned out to be much higherthan census employment, and the employment total based on establishment-level datais still higher. The unusual level of disagreement in this case has made it necessary tobypass the usual reconciliation process and peg the establishment-level data directlyto census employment.

The bottom line is that the BLS employment statistics used in the regional forecastingprocess and discussed above do not play their usual ground-truth role and havethemselves been adjusted. Table 14 summarizes the adjustment process by showingvarious employment descriptors for 2004 that form a bridge between unadjusted andadjusted employment.

Table 14. Description of Adjusted EmploymentEmpl. In Estab.-Level Census-Adjusted

BLS Employment File Prior toAdjustm.

Employment

Region* MPO (Est.) Counties** MPO MPO Region*

2000 607,2242004 604,578 572,077 617,807 599,854 531,276 560,6872010 683,303 633,6972020 795,923 738,1412030 890,658 825,9982040 998,691 926,188

* Five-county Memphis MSA addressed by regional forecasting program.** Five counties containing portions of MPO (including Marshall, notCrittenden).

What matters for present purposes is that the numbers shown in bold type on theright-hand side of Table 14 are the employment magnitudes that should be utilized inpreparing any independent allocations of employment to SCAs. Note that thesenumbers pertain to the region – i.e., the five-county Memphis MSA, includingCrittenden County but not Marshall – rather than the MPO. No partitioning of theemployment forecast below the region level can be offered until the allocation model isavailable to make geographic assignments in the second phase of forecasting.

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Appendix A

Forecasting Philosophy

The chosen forecasting approach has been applied by the present investigators in nineprior studies over the past five years. Its basic features are shaped by the followingcircumstances. First, long-term demographic trends at a metropolitan scale tend to beeconomically driven. That is, population and households ultimately followemployment. This description does not apply to retirement areas or to many foreigncountries, but in most of America, job availability is the ruling factor. Second, thefunctional integration of U.S. metropolitan economies and the penchant of Americansfor long-distance worktrips create high levels of interaction between the componentdistricts of an urban region. Together these circumstances mean that no part of ametropolis can be forecasted in isolation and that demographic changes must besystematically linked to economic changes. In theory it is possible to forecast allregional magnitudes and their spatial distributions simultaneously, but in practicethis requires either an infeasible level of effort or an undue reliance on subjectivejudgment. So the most workable solution is to partition the process and address theregion first, as a unit. The regional forecasts are then held fixed in all subsequentforecasting steps.

Other circumstances come into play below the region level. Along with the need toaddress demographic and economic changes on a mutually determinate basis, there isthe fact that different causal factors tend to dominate at different spatial scales.Trends in major districts of a metropolis mostly reflect what can be called demand-side influences. These include the existing activities in each district (operating asgrowth attractants) plus each district’s location relative to everything else in theregion, where “everything else” refers to the levels and growth of all other activities inall other districts. But at smaller geographic scales, the dominant role passes tosupply-side influences. These include infrastructure support, land use controls,environmental constraints and other factors determining the supply of land suitablefor various types of development.

In concept all demand-side and supply-side influences on growth at all spatial scalescan be addressed simultaneously, but again, practicalities impinge. The processmust rely to a substantial extent on professional judgment, whether exercised incontext or embedded in a planning model developed for general use. Thus anintegrated approach exposes all forecasting results to influence by parameters thathave never been rigorously tested and may be untestable. (Examples are formulationsexpressing the ability of present comprehensive plans and foreseeable infrastructureprojects to shape growth decades in the future.) Integrated modeling is a fullylegitimate enterprise, and indeed is the path most often taken. But because it leavesuncertainty about the impacts of judgment calls and even their existence, the present

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approach diverges from it by partitioning the forecasting process in a way that extendsthe use of strictly objective methods as far as possible. Demand-side influences canbe captured by statistically calibrated relationships, so the present approach usessuch relationships to allocate growth from the region level to the smallest scale atwhich demand-side influences usually dominate.

This scale is represented by districts called Sub-County Areas (SCAs). Past studieshave yielded a rule for SCA designation specifying that every such area must have acurrent population of at least 25,000 persons or a land area exceeding 50 squaremiles. A close application of this rule has led to the selection of 50 SCAs for use asforecasting units in metropolitan Memphis.

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Technical Memorandum #3 – Trip GenerationThis memorandum covers the development of the following specific submodels relatedto trip generation:

• Internal person trip productions• Internal person trip attractions• Journey to work stops• Vehicle availability (auto ownership)• Externalinternal vehicle trips• Special generators

This memorandum was prepared by Cambridge Systematics, Inc. Staff who workedon the development of these submodels include Edward Bromage, Thomas Rossi,Ashish Agarwal, Maya Abou Zeid, Yasasvi Popuri, and Kevin Tierney.

ContentsMethodology — Internal Person Trip Productions

Data AnalysisMethodology — Internal Person Trip Attractions

Data AnalysisMethodology — Journey to Work Stops

Data AnalysisMethodology — Vehicle Availability (Auto Ownership)

Data AnalysisMethodology – ExternalExternal and ExternalInternal TripsMethodology – Special Generators

Memphis International Airport Federal Express Graceland Implementation

Appendix A — Base Year (2004) External Station Attributes

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Methodology – Internal Person Trip Productions

Trip production models were developed for the following nine trip purposes:

• Journey to work• Home based school• Home based university• Home based shopping• Home based socialrecreational• Home based pickup/dropoff• Home based other• Nonhome based work• Nonhome based nonwork

These are the trip purposes defined in the project’s scope of work, with the added trippurpose of home based pickup/dropoff. Journey to work trips are defined as tripswith or without stops between home and work. This differs from most conventionalfourstep models in that the intermediate stops are not treated as separate trips.

(Note that among other trip purposes defined in the project scope of work, commercialvehicle trips are modeled separately; this process is described in TechnicalMemorandum #7. Externalinternal trips are discussed later in this memorandum.Externalexternal trips are defined from the statewide models for Tennessee andMississippi.)

The trip production models are twodimensional crossclassification models based onvarious demographic variables. Households for each zone are crossclassified byincome level, number of persons, number of workers, and numbers of persons age017, 1864, and 65 or more (see Technical Memorandum #2). Necessary crossclassifications of households by pairs of these variables were prepared by iterativeproportional fitting. The vehicle availability model (see below) was used to furtherclassify households by the number of vehicles.

The crossclassification trip production models were estimated from the 1997Memphis household travel survey. Tables 1 through 9 show the trip productionmodels. It should be noted that none of the models uses income as a variable sincestatistical tests (ANOVA) showed that vehicle availability is a much stronger indicatorof trip generation for all trip purposes except nonhome based work.

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Table 1. Trip Production Model for Journey to Work Trips

VehiclesWorkers 0 1 2 3+ Avg.0 0 0 0 0 01 1.495 1.511 1.544 1.544 1.5232 2.982 2.982 3.017 3.045 3.0153+ 4.691 4.691 4.914 4.914 4.880Avg. 0.956 1.267 2.248 2.784 1.809

Table 2. Trip Production Model for Home Based School Trips

Total Persons in HouseholdPersonsage 017 1 2 3 4+ Avg.0 0 0 0 0 01 n/a 1.051 1.051 1.258 1.0822 n/a n/a 1.968 2.294 2.2203+ n/a n/a n/a 4.114 4.104Avg. 0 0.119 0.856 2.749 0.898

Table 3. Trip Production Model for Home Based University Trips

WorkersTotalPersons 0 1 2 3+ Total1 0.084 0.017 n/a n/a 0.0482 0.149 0.149 0.119 n/a 0.1373 0.169 0.169 0.197 0.197 0.1854+ 0.169 0.169 0.197 0.197 0.185Total 0.114 0.114 0.147 0.361 0.135

Table 4. Trip Production Model for Home Based Shop Trips

VehiclesPersons 0 1 2 3+ Avg.1 0.294 0.333 0.333 0.333 0.3252 0.377 0.465 0.619 0.619 0.5613 0.425 0.515 0.619 0.619 0.5804+ 0.433 0.601 0.740 0.767 0.696Avg. 0.355 0.424 0.596 0.764 0.536

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Table 5. Trip Production Model for Home Based Pickup/DropOff Trips

Persons# 017 1 2 3 4+ Avg.0 0.105 0.188 0.249 0.547 0.1661 n/a 0.641 0.843 1.514 0.9082 n/a n/a 0.757 1.332 1.2163+ n/a n/a n/a 1.694 1.690Avg. 0.105 0.232 0.653 1.440 0.586

Table 6. Trip Production Model for Home Based SocialRecreational Trips


Table 7. Trip Production Model for Home Based Other Trips


Table 8. Trip Production Model for NonHome Based Work Trips


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Table 9. Trip Production Model for NonHome Based NonWork Trips


Data analysis

The total number of trips per household (for all purposes) on average is 7.5. Tocompare this total with those from other urban areas, it is necessary to convert thejourney to work trip chains to the individual stops. From the survey data, this total is2.551 trips, compared to 1.809 journey to work trip chains. This raises the total tripsper household to 8.2. The average number of trips per household is about 9 to 10 inmost urban areas although some urban areas have as low as 6 to 7 trips perhousehold. An examination of the trips from the Memphis household survey indicatesthat the average numbers of trips by income level are similar to those from otherareas; it is the distribution of households by income level that is different.

The percentages of trips by aggregated purpose are shown below, compared to rangesfrom other areas:

Percent Expected RangeJourney to work 25 1827Home based nonwork 54 4556Nonhome based 21 2030

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Methodology – Internal Person Trip Attractions

Trip attraction models were developed from the household survey data for the nine trippurposes. All models are ordinary least squares regressions with no intercept and areof the following form:

Total attractions purpose i = A1 * employment for category 1+ A2 * employment for category 2+ B * total households+ C * school/university enrollment

The number of observations is 14, which corresponds to the number of districts forwhich the household survey data were aggregated within the survey sampling area.The models are summarized in Table 10. The tstatistics are measures of thestatistical significance of the parameter estimates. In general, a tstatistic of 1.96 orgreater shows significance at the 95% level; a statistic of 1.64 or higher indicatessignificance at the 90% level.

Table 10. Trip Attraction Model Summary

Journey to work

Variable Parameter Estimate t ValueTotal Employment 1.33 27.16

Home based school

Variable Parameter Estimate t ValueSchool Enrollment 1.66 52.43

Home based university

Variable Parameter Estimate t ValueUniversity Enrollment 1.05 8.81

Home based shop

Variable Parameter Estimate t ValueRetail Employment 2.44 15.21

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Table 10. Trip Attraction Model Summary (continued)

Home based pickup dropoff

Variable Parameter Estimate t ValueService Employment 0.49 3.15Retail Employment 1.04 3.06Enrollment 2003 Schools 0.43 4.15

Home based social recreational

Variable Parameter Estimate t ValueService Employment 0.33 2.77Total Households 0.39 11.23

Home based other

Variable Parameter Estimate t ValueService Employment 1.32 3.47Retail Employment 1.46 1.92Total Households 0.76 5.69

Nonhome based work

Variable Parameter Estimate t ValueOffice Employment 0.32 2.48Service Employment 0.34 3.40Total Households 0.17 7.05

Nonhome based nonwork

Variable Parameter Estimate t ValueRetail Employment 2.49 2.84Service Employment 0.88 2.00Total Households 0.56 3.66

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Data analysis

The production and attraction models were applied using the 2004 socioeconomic data(see Technical Memorandum #2). Table 11 shows the resulting total number ofproductions and attractions for each trip purpose. As the table shows, the balancebetween attractions and productions is good. (Productions and attractions will beexactly balanced as part of the application of the trip generation models.)

Table 11. Trip Attraction and Production Totals, 2004

Trip Purpose Productions Attractions%

DifferenceJourney to work 783,436 706,159 9.9%Home based school 343,361 372,993 8.6%Home based university 56,147 46,202 17.7%Home based shopping 223,496 232,395 4.0%Home based socialrecreational 238,801 254,492 6.6%Home based pickup/dropoff 207,017 201,188 2.8%Home based other 612,326 608,710 0.6%Nonhome based work 138,182 142,692 3.3%Nonhome based nonwork 512,547 573,675 11.9%

Methodology – Journey to work stops

The journey to work stops model is a multinomial logit model that estimates thenumber of stops (0, 1, or 2+) for journey to work trips. This model was also estimatedfrom the household survey data. Table 12 shows the utility functions for the threealternatives (tstatistics are shown in parentheses).

Data analysis

The journey to work stops model was disaggregately validated by applying the model tothe household survey data set from which it was estimated. Tables 13, 14, and 15show the number chosen and predicted for each of the three alternatives (0, 1, and 2+stops) for three different market segmentation schemes corresponding to householdincome, household size, and number of workers in household, respectively. A (*) in agiven cell indicates that the difference between the predicted and chosen numbersexceeds one standard deviation of the difference. Overall, the fit is acceptable for eachof the three market segments.

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Table 12. Journey to Work Stops Model Utility Functions

Number of Stops Variable 0 1 2+Constant

1.55(6.51)

2.73(6.21)

Hometowork chain0.29(3.67)

0.75(6.13)

1vehicle household0.58(2.52)

1.24(2.86)

2vehicle household0.58(2.54)

1.21(2.77)

3+ vehicle household0.56(2.31)

0.92(2.00)

Presence of kids in household0.76(9.38)

0.98(7.80)

2+ adults in household0.97(5.65)

0.45(2.41)

Model Statistics

Number of observations 4100Initial Likelihood 4504.31Final Value of Likelihood 3111.97"RhoSquared" w.r.t. Zero 0.3091"RhoSquared" w.r.t. Constants 0.0347

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Table 13. Validation table by household income

Household Income

Number of StopsLess than$15,569

$15,570to

$51,899

$51,900to

$77,849$77,850or more

ZeroNumber Chosen 263 1411 672 523Number Predicted 247 1405 672 545Predicted Chosen 16 (*) 6 0 22 (*)(Predicted Chosen)/Chosen*100 6.0 0.4 0.0 4.2OneNumber Chosen 70 448 201 179Number Predicted 78 466 197 158Predicted Chosen 8 18 4 22 (*)(Predicted Chosen)/Chosen*100 10.9 4.0 2.1 12.0Two +Number Chosen 23 195 63 52Number Predicted 31 183 67 52Predicted Chosen 8 (*) 12 4 1(Predicted Chosen)/Chosen*100 34.8 6.1 7.0 1.0

Table 14. Validation table by household size

Household SizeNumber of Stops 1 2 3 4 +ZeroNumber Chosen 342 888 695 944Number Predicted 324 919 709 917Predicted Chosen 18 (*) 31 (**) 14 27 (*)(Predicted Chosen)/Chosen*100 5.3 3.5 2.0 2.9OneNumber Chosen 89 217 261 331Number Predicted 104 196 240 358Predicted Chosen 15 (*) 21 (*) 21 (*) 27 (*)(Predicted chosen)/Chosen*100 17.1 9.8 8.0 8.1Two +Number Chosen 47 75 81 130Number Predicted 50 65 88 130Predicted Chosen 3 10 (*) 7 0(Predicted Chosen)/Chosen*100 6.2 13.5 8.8 0.1

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Table 15. Validation table by number of workers

Number of workersNumber of Stops 1 2 3 +ZeroNumber Chosen 986 1551 332Number Predicted 957 1600 312Predicted Chosen 30 (*) 49 (**) 20 (**)(Predicted Chosen)/Chosen*100 3.0 3.2 5.9OneNumber Chosen 310 513 75Number Predicted 325 485 88Predicted Chosen 15 28 (*) 13 (*)(Predicted Chosen)/Chosen*100 4.7 5.4 17.5Two +Number Chosen 125 188 20Number Predicted 140 167 27Predicted Chosen 15 (*) 21 (*) 7 (*)(Predicted Chosen)/Chosen*100 11.8 11.3 33.0

Methodology – Vehicle Availability

Two model specifications were tested for the vehicle availability model. The first is amultinomial logit model where the alternatives are 0, 1, 2, and 3+ vehicles owned. Thesecond is an ordered response logit model with the same alternatives. Bothspecifications were estimated from the household survey data set. The utilityfunctions for the multinomial logit and ordered response logit specifications are shownin Tables 16 and 17 respectively. (In Table 17, in each submodel the utility of the firstalternative equals zero, and the utility of the second is as defined in the column.)

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Table 16. Multinomial Logit Model Estimation Results

Vehicle Availability LevelVariable 0 1 2 3+

Coeff t Coeff t Coeff tAlternativeSpecific Constant 0.64 2.66 0.45 1.58 2.29 4.401 Worker in Household 0.83 5.63 1.10 6.11 1.66 4.812+ Workers in Household 0.54 2.54 2.47 11.08 3.32 9.23Income Between $15,570$51,899 1.16 8.08 2.18 11.19 2.26 5.32Income Between $51,900$77,849 0.87 3.12 3.04 10.26 3.64 7.59Income Above $77,850 1.78 3.31 4.31 8.01 5.28 8.06Percent Employment within 15min. B

ase

(Zer

o U

tilit

y)0.03 3.30 0.08 7.36 0.12 9.21

Number of Observations 2508Log Likelihood with Zero Coeff. 3476.83Log Likelihood with Constants Only 3088.48Final Value of Likelihood 2445.71"RhoSquared" w.r.t Zero 0.2966"RhoSquared" w.r.t Constants 0.2081

Table 17. Ordered Response Logit Model Estimation Results

Vehicle Availability DecisionVariable 0/1+ 1/2+ 2/3+

Coeff t Coeff t Coeff tAlternativeSpecific Constant 1.12 4.82 2.41 9.58 1.62 7.182 Persons in Household 2.20 12.693+ Persons in Household 2.25 12.86 0.59 3.501 Worker in Household 0.90 6.212+ Workers in Household 1.48 7.36 1.09 8.463+ Workers in Household 1.76 6.59Income between $15,570$51,899 1.42 10.20 1.19 6.98Income between $51,900$77,849 1.86 7.05 2.40 11.09 0.58 3.10Income above $77,850 3.10 5.98 2.88 11.40 0.98 5.28Percent Employment within 15 min 0.05 5.33 0.05 6.50 0.04 4.69Number of Observations 2508 2166 1207Log Likelihood with Zero Coeff. 1738.41 1501.36 836.63Log Likelihood with Constants Only 997.11 1487.13 603.43Final Value of Likelihood 786.09 974.04 537.09"RhoSquared" w.r.t Zero 0.5478 0.3512 0.3580"RhoSquared" w.r.t Constants 0.2116 0.3450 0.1099

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Data analysis

Both versions of the auto ownership model were validated using the 2000 PUMS data.Table 18 shows these validation results; as this table shows, the ordered responselogit model produced better validation results and is recommended for use in theMemphis model. The model was calibrated by adjusting the alternative specificconstants, and the final model specification is shown in Table 19. The final validationresults by PUMA, after subsequent revisions to the networks during the modelvalidation process, are shown in Table 20.

Methodology – ExternalExternal and ExternalInternal Trips

The number of base year vehicle trips at each external station is set equal to the trafficcount at the station. The Tennessee Statewide Travel Demand Model was used todetermine the percent splits between ExternalExternal (EE) and ExternalInternal (EI)for each station. A through trip matrix was developed with the statewide model thatidentified the total number of trips and the through trips for each station. The matrixalso identified the origin and destination of each through trip for each station. Thiswas then used to calculate the percent EE for each external station by dividing thethrough trips by the total trips in the statewide model. Since autos and trucks (SUand CU) are modeled separately in the statewide model, the through trips were alsodetermined separately for automobiles and trucks. If any issues with assignment inthe statewide model were observed, such as illogical routes or overassignment on aparticular facility, the percentages of through trips were adjusted as deemedappropriate. Appendix A shows the average daily traffic (ADT), the percentage ofautomobile trips that are EE and EI, the percentage of trucks, and the timeofday anddirectional distributions for each external station. Truck trip information can be foundin Technical Memorandum #7 – Freight Model.

EI trips are assumed to be produced at external stations and attracted to internalzones. While it is a simplifying assumption that EI trips are produced externally andattracted internally, the majority of trips are that way as shown in data from otherurban areas. External trip productions are held at the external station locations sincewe have a higher level of certainty with the volumes at these locations than theattractions being derived at the TAZ level. Furthermore, when trips are converted fromproductionattraction to origindestination, half of the (daily) trips are "produced"internally and "attracted" externally. During the timeofday procedure, these trips arealso split further into inbound/outbound trips at each station by time period.

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Table 18. Model Validation ResultsObserved SharesPUMA 0 1 2 3+ Total100 5.2 31.7 44.9 18.2 100200 6.8 32.6 41.1 19.5 1003000 8.6 29.3 41.0 21.1 1003101 27.0 45.9 20.8 6.3 1003102 10.8 44.3 33.2 11.7 1003103 7.8 43.4 37.4 11.4 1003104 6.0 45.0 36.7 12.3 1003105 12.8 44.2 29.0 14.0 1003201 2.5 24.8 50.3 22.4 1003202 1.6 19.7 53.7 25.0 100Overall 8.5 35.3 39.6 16.6 100

Predicted Shares Multinomial LogitPUMA 0 1 2 3+ Total100 7.6 29.0 46.8 16.6 100200 6.6 27.8 46.3 19.3 1003000 6.4 25.5 46.5 21.6 1003101 20.7 44.1 29.9 5.4 1003102 15.0 40.1 37.5 7.4 1003103 10.5 35.2 43.0 11.3 1003104 11.2 37.6 42.6 8.6 1003105 12.3 37.2 40.5 10.0 1003201 4.8 22.7 51.8 20.7 1003202 3.4 18.5 54.5 23.6 100Overall 9.4 31.3 44.2 15.1 100

Predicted Shares Ordered Response LogitPUMA 0 1 2 3+ Total100 7.6 30.5 44.7 17.3 100200 6.5 31.1 43.5 18.9 1003000 6.3 27.7 43.5 22.4 1003101 21.0 45.4 27.8 5.8 1003102 15.1 40.9 35.7 8.2 1003103 10.5 36.5 41.0 12.0 1003104 11.3 39.3 39.8 9.6 1003105 12.4 37.2 38.4 12.1 1003201 5.0 24.4 48.9 21.8 1003202 3.6 19.4 51.6 25.4 100Overall 9.5 33.0 41.8 15.7 100

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Table 19. Final Calibrated Ordered Response Logit Vehicle Availability Model

Variable Vehicle Availability Decision0/1+ 1/2+ 2/3+Coeff Coeff Coeff

AlternativeSpecific Constant 1.12 2.41 1.622 persons in household 2.203+ persons in household 2.25 0.591 worker in household 0.902+ workers in household 1.48 1.093+ workers in household 1.76Income between $15,570$51,899 1.42 1.19Income between $51,900$77,849 1.86 2.40 0.58Income above $77,850 3.10 2.88 0.98Percent Employment within 15 minutes 0.05 0.05 0.04

Note: In this table, in each submodel the utility of the first alternative equals zero, andthe utility of the second is as defined in the column.

Table 20. Final Model Validation Results

PUMA 0 1 2 3+ Total100 5.7 31.9 43.3 19.2 100200 6.8 34.2 40.6 18.3 1003000 5.1 28.7 42.9 23.3 1003101 22.3 50.6 22.6 4.5 1003102 12.6 46.3 32.4 8.7 1003103 9.6 41.2 37.0 12.3 1003104 8.9 43.2 36.5 11.4 1003105 11.8 42.0 34.6 11.6 1003201 4.9 29.7 43.5 21.9 1003202 3.6 23.1 45.9 27.4 100Overall 10.1 38.4 36.8 14.7 100

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Linear regression models based on employment and number of households in eachinternal zones are developed for EI trip attractions. The trip attraction rates are brokendown into Auto, Single Unit, and Combination Unit. The linear regression coefficientsare using the coefficients in the RaleighDurham and Charlotte models as the startingpoints, which are of similar population and geographic size as Memphis. Thecoefficients are then calibrated to match the local condition based on traffic counts.The number of externalinternal (EI) trips attracted to internal zones () is given by theformula:

univgovofficewsretailemphhE Autoj 05.005.01.01.017.016.025.0 ++++++= (Eq. 1)

govofficeserwsindretailhhE SUj 05.005.005.028.028.022.008.0 ++++++= (Eq. 2)

govofficeserwsindretailhhE CUj 01.001.001.019.019.01.004.0 ++++++= (Eq. 3)

where:AutojE = Number of EI Auto trips attracted into internal zone jSUjE = Number of EI SU truck trips attracted into internal zone jCUjE = Number of EI CU truck trips attracted into internal zone j

hh = total household size in internal zone jemp = total employment in internal zone jretail = retail type employment in internal zone jind = industrial type employment in internal zone jws = wholesale type employment in internal zone jser = service type employment in internal zone joffice = office type employment in internal zone jgov = government related employment in internal zone juniv = university related employment in internal zone j

The total average daily traffic (ADT) for each station is factored using the percentage ofvehicles that are autos (from vehicle classification counts) and the percentage of autotrips that are EI, as opposed to externalexternal (through) trips. The productions andAttractions are balanced by holding the production side at each external station.

Methodology – Special Generators

In discussion with local officials in Memphis, it was determined that there should bethree attractions treated as special generators:

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• Memphis International Airport• Federal Express facility at the airport• Graceland

Airport

In the year 2000, according to the airport master plan, emplanements averaged about17,000 per day. According to the MemphisShelby County Airport Authority (MSCAA)passenger survey, about 46% of those trips, or 7,700, were made by passengersoriginating from Memphis (as opposed to having a connecting flight). According to thesame survey, each passenger was accompanied by, on average, 0.42 well wishers.This results in approximately 11,000 persons going to the airport. The number ofother airport visitors unrelated to persons flying to and from the airport (such aspersons purchasing tickets for later travel, meeting attendees, or others doingbusiness there) was estimated at 2,000 per day.

The growth rate in emplanements between 2000 and 2005 was 13%. Applying thisgrowth rate to the estimated 13,000 daily airport trips in 2000 results in a total of15,000 person trips to the airport in 2005. According to the MSCAA survey, 42.7% oftrips to the airport by air passengers were from the passenger's residence. Therefore,42.7% of trips was assumed to be home based other and 57.3% assumed to be nonhome based.

The Federal Aviation Administration (FAA) prepares forecasts of airport emplanementsfor large airports in the U.S. The FAA forecast indicates 4% growth per year (notcompounded) from 2008 to 2020. This rate was assumed for forecast year analysis.So, for example, the forecast for 2030 would be 30,000 trips (15,000 * (1 + (0.04 *25))).

Work trips made by airport employees are assumed to already be considered in thejourney to work attraction model.

Federal Express

Based on information provided by Federal Express, a total of 230 trucks travel intoand 230 travel out of the facilities adjacent to the airport. These are assumed to bestandard FedEx size trucks, which would be medium trucks by the definition used inthe truck model (see Technical Memorandum # 7).

Graceland

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According to Graceland staff, the site has 1,300 visitors on the average weekday. Thisestimate will be used for the base year. Graceland is estimating a future annualattendance of 700,000. The weekday average attendance with this annual attendanceis roughly 1,500 people per day. This number will be used for forecast year scenarios.It is estimated that approximately 95% of visitors are from outside Memphis, and so itwill be assumed that 5% of trips are home based socialrecreational and 95% nonhome based.

Implementation

The special generator trips for the airport and Graceland will be added to the persontrip attractions for the appropriate trip purposes, as described above. For FederalExpress, the trips will be added to the truck trips generated by the truck tripgeneration process (see Technical Memorandum # 7).

The zones for the special generators are:

• Airport – zone 993• Federal Express – zone 662• Graceland – zone 300

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Appendix A — Base Year (2004) External Station Attributes

ID STATION NAME2004ADT

%AUTO

%HOV

%SU

%CU

% AUTO(EE)

%SU(EE)

%CU(EE)

%AM

%MD

%PM

%OP

% AM(IB)

%MD(IB)

%PM(IB)

%OP(IB)

10001 HIGHWAY 59 1700 94.6 20 3.7 1.7 0 0 0 17.8 21.8 33.7 26.7 63 49 49 4010002 HIGHWAY 51 23480 89.2 20 2.8 8 1.87 5.67 6.03 18.9 30.7 29.5 20.9 62 51 48 42

10003HIGHWAY 59 S/MOUNT

CARMEL 4900 93.5 20 1.8 4.7 4.845 2.25 2.88 19.9 28.1 29.4 22.6 63 49 49 4010004 AUSTIN PEAY 2170 82.5 20 3.5 14 7.14 21.33 11.88 19.6 29 27.1 24.3 63 49 49 4010005 HIGHWAY 79 2200 83 20 4.4 12.6 1.7 3.69 3.42 17.5 30.6 29.5 22.4 63 49 49 4010006 STANTON ROAD N 630 94.6 20 3.7 1.7 42.5 19.53 24.57 17.3 27.8 29.1 25.8 63 49 49 4010007 I40 E 31720 60.6 20 5.3 34.1 50.915 51.66 34.92 12.9 28.5 23.7 34.9 70 51 39 4310008 HIGHWAY 59 E 3070 92.8 20 2.9 4.3 1.785 10.08 10.89 16 23.4 31.8 28.8 63 49 49 4010009 HIGHWAY 64 14970 92.5 20 2.8 4.7 12.155 9 12.69 20 27.9 30.3 21.8 62 51 48 4210011 HIGHWAY 57 7300 95.1 20 1.4 3.5 7.99 8.19 6.57 21.4 22.8 30 25.8 63 49 49 4010012 HIGHWAY 178 22001 94.6 20 3.7 1.7 3.825 3.33 9.72 15.4 26.9 30.6 27.1 63 49 49 4010013 HIGHWAY 78 25650 78 20 5.2 16.8 10.965 33.57 34.2 16.1 31.6 31.1 21.2 62 51 48 4210014 HIGHWAY 305 S 3000 91.3 20 7.2 1.5 0 0 0 22.1 27 26.5 24.4 63 49 49 4010015 HIGHWAY 51 S 42001 92.6 20 5.5 1.9 0 0 0 17 39.5 18.4 25.1 63 49 49 4010016 I55 S 31000 81.7 20 3.4 14.9 6.545 22.41 19.08 16.3 28.6 27.3 27.8 70 51 39 4310017 PRATT ROAD 750 94.6 20 3.7 1.7 0 0 0 17.3 27.8 29.1 25.8 63 49 49 4010018 HIGHWAY 304/713 2001 92.1 20 5.5 2.4 0 0.45 3.78 15.4 33.1 28.3 23.2 63 49 49 4010019 HIGHWAY 61 290001 96.1 20 3.3 0.6 13.26 29.52 41.4 12.8 38.7 23.7 24.8 62 51 48 4210020 CHARLESTON MASON ROAD 500 97 20 2 1 0 0 0 17.3 27.8 29.1 25.8 63 49 49 4010022 FEATHERS CHAPEL ROAD 620 98.5 20 1 0.5 0 0 0 15.2 22.8 31.5 30.5 63 49 49 4010023 MACON ROAD 1000 97 20 1.4 1.6 0 0 0 20.2 23 32.2 24.6 63 49 49 4010024 HIGHWAY 72 14000 92.74 20 3 4.3 22.78 32.76 31.5 16.8 24.4 29 29.8 62 51 48 4210025 GOODMAN ROAD EXT 0 89 20 3.5 7.5 0 0 0 16.5 27.9 28 27.6 62 51 48 4210026 VICTORIA ROAD 5001 94.6 20 3.7 1.7 17 9.45 9.99 17.3 27.8 29.1 25.8 63 49 49 4010027 I40/I55W 104220 72 20 6 22 18.955 27.18 27.36 15.8 27.7 28.6 27.8 70 51 39 4310028 STANTON RD S 720 94.6 20 3.7 1.7 39.1 43.29 52.47 17.3 27.8 29.1 25.8 63 49 49 4010029 HOLLY SPRINGS ROAD 7501 97 20 2 1 0 0 0 24.2 22.4 29.7 23.7 63 49 49 4010030 BYHALIA ROAD 5750 94.2 20 4.8 1 7.395 3.87 6.21 16.8 30 29.3 23.9 63 49 49 4010031 OLD HIGHWAY 61 7201 97 20 2 1 0 0 0 17.3 27.8 29.1 25.8 63 49 49 4010032 ROUTE 3 10001 89 20 3.5 7.5 7.65 10.26 7.2 18.4 26.2 31.6 23.8 63 49 49 40

Notes:1. Count is from 2003.

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Technical Memorandum #4 – DestinationChoiceThis memorandum covers the development of the destination choice models. It wasprepared by Cambridge Systematics, Inc. Staff who worked on the development ofthese submodels include Edward Bromage, Thomas Rossi, Yasasvi Popuri, AshishAgarwal, Maya Abou Zeid, and Kevin Tierney.

ContentsMethodology — Intrazonal Travel TimesMethodology — Terminal TimesMethodology — Primary Destination Choice

- Logit Model Formulation- Model Estimation and Testing Procedure- Data Analysis- Model Validation

Methodology — Intermediate Stop Destination Choice

Methodology – Intrazonal Travel Times

The intrazonal travel time is the average travel time associated with a trip that beginsand ends in the same zone. This travel time reflects two basic characteristics aboutthe zone. The first is the average speed associated with the roads in the zone; thesecond is the size of the zone. A common method for determining the intrazonal traveltime is to consider that the travel time is approximately half the travel time to thenearest neighboring zone. By using this method, both the average road speeds andsize of the zone are considered. However, zones are not typically perfect circles withboth the geographic center and activity center at the same point. To address the non-symmetric zonal characteristics, the intrazonal travel time is often computed byexamining the travel time to two or more of the closest neighboring zones.

Based on a random sampling of the existing traffic zones, it was determined that themost frequently occurring value for the number of abutting zones was four.Consequently, the intrazonal travel times are computed by taking half the averagetravel time to the four closest neighboring zones.

A component of the intrazonal travel time is the centroid connector travel time. Thezone centroids in Memphis are placed at the geographical center of the zone. Thegeographic center was used because no database exists which would allow for the

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computation of the zonal center of activity. The travel time from the centroid to themodeled highway network is based on the length of the connector and the averagetravel speed. Centroid connector travel speeds were set based on the area type of thezone. There are four area types in the model: CBD, Urban, Suburban, and Rural.For each area type, travel speed similar to the congested travel speed of local streetswas used for the centroid connectors. The area type speeds are as follows: CBD = 20mph; Urban/Suburban = 25 mph; and Rural = 30 mph. 30mph speed was alsoapplied to all external centriod connectors.

For transit or walk trips, intrazonal travel times are different from auto trips. For walktrips, the travel route from the point of origin to a bus stop might be more direct thanan auto trip. Although it would be more direct, the walk trip would take longer due tothe travel speed differences. For walk trips, additional centroid connectors were addedto the highway network. These more direct connectors were set to be exclusivelyavailable to the walk mode. The travel speed used for the walk mode is 3 mph.

Methodology – Terminal Times

The terminal time is the time associated with a person leaving their point of trip originand accessing the modeled transportation network. If a person leaves home for a tripto work, then the terminal time at the point of origin might be the time needed toaccess the family car, and drive the car onto the local road system. At the destinationend, the car might be parked in a lot and that lot might not be near the final point ofdestination. Typically, terminal times in residential areas are small to reflect that thecar is parked near the home. While in attraction zones, the terminal times are longersince the vehicle might not be parked near the final destination building.

For the Memphis model, the terminal times used were taken directly from NCHRPReport 365: Travel Estimation Techniques for Urban Planning. These terminal timesare as follows:

CBD = 5 minutesUrban = 3 minutesSuburban = 2 minutesRural = 1 minute

Methodology – Primary Destination Choice

Destination choice models were developed for the following nine trip purposes:

Journey to work;Home based school;Home based university;

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Home based shopping;Home based social-recreational;Home based pickup/dropoffHome based other;Non-home based work; andNon-home based non-work.

These are the same trip purposes used in trip generation (See Technical Memorandum#3).

Logit Model Formulation

The destination choice models are multinomial logit models. Logit models are discretechoice models, which attempt to explain the behavior of individuals making a choicebetween a finite number of separate alternatives, in this case destination zones. In thelogit model, the probability of choosing a particular alternative i is given by thefollowing formula:

P(i) = exp (Ui)/ j exp(Uj)

where:P(i) = probability of choosing alternative i

Ui = utility of alternative iexp = exponential function

The utility function Ui represents the worth of alternative i compared to otheralternatives and is expressed as a linear function:

Ui = B0i + B1iX1i + B2iX2i + …+ BniXni

where the Xki variables represent attributes of alternative i, the decision maker, or theenvironment in which the choice is made and Bki represents the coefficient reflectingthe effect of variable Xki on the utility of alternative i. The coefficients are estimatedusing statistical “maximum likelihood” methods using logit model estimation softwaresuch as ALOGIT. In the case of logit destination choice models, the alternatives arethe destination zones while the attributes may include attributes of the zones (e.g.,travel time from the origin zone), the decision maker (e.g., auto ownership), and theenvironment (e.g., production or attraction zone area type).

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Model Estimation and Testing Procedure

In summary, the destination choice model estimation and application processincluded the following steps:

1. Assembly of model estimation data sets from the household survey trip recordsas previously assembled, zonal data developed earlier in the project (seeTechnical Memorandum #2), the distance skims from the model highwaynetwork, and the computed logsums from the estimated mode choice models(see Technical Memorandum #5);

2. Multinomial model estimation using all zonal alternatives for each trip purposeusing the ALOGIT choice behavior analysis package; and

3. Model validation for each trip purpose.

The logsum is a measure of the impedance, or cost, of traveling from one zone toanother. It is a combined measure of the impedance using the various availablemodes (highway, transit, and non-motorized) and is computed from the logit modechoice model utilities. The logsum is computed for each trip purpose as follows:

Logsumij = ln k exp(Uijk)

where:Uijk = utility of modal alternative k from zone I to zone j) from the mode choice model

exp = exponential function

Both the mode choice logsum and polynomial functions of the highway distance weretested for use as the impedance measure for each trip purpose. For the final models,for some trip purposes the logsum is used as the impedance measure while for othersa function of highway distance is used. For some trip purposes, both measures wereused.

Size variables are used to measure the attractiveness of particular zones. For mosttrip purposes the size variable is the number of modeled attractions (see TechnicalMemorandum #3) for the trip purpose. For journey to work trips several size variablesare used, representing various employment types. Size variables are entered into theutilities as the natural logarithms of the particular variables (for example,ln(attractions)). For the journey to work model, one employment type (serviceemployment) was chosen as the base with coefficient 1.0, and the coefficients for theother employment types were estimated during the model estimation process.

The destination choice models were estimated from the 1997 Memphis householdtravel survey data set. Tables 1 through 9 show the destination choice models for thenine trip purposes.

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Table 1. Destination Choice Model for Journey to Work Trips

VariableParameterEstimate t-statistic

Mode Choice LogsumLogsum 0.0629 74.49 *

Production-Attraction DummiesCBD to CBD 0.287 3.3Urban to CBD 0.287 3.3Suburban to CBD 0.287 3.3Rural to CBD 0.287 3.3CBD to Urban 0.118 1.7Urban to Urban 0.118 1.7Suburban to Urban 0.118 1.7Rural to Urban 0.118 1.7CBD to Suburban 0.150 2.2Urban to Suburban 0.150 2.2Suburban to Suburban 0.150 2.2Rural to Suburban 0.150 2.2CBD to Rural 0.000 BaseUrban to Rural 0.000 BaseSuburban to Rural 0.000 BaseRural to Rural 0.000 Base

Production-Attraction Highway Distance Power SeriesDistance -0.345 -19.2Square of Distance 0.0158 9.7Cube of Distance -0.00033 -7.8

Attraction Zone Area in Square Miles 0.0498 7.7

Natural log (JTW Modeled Attractions) 0.708 68.5

Model StatisticsNumber of Observations 5157

Initial Likelihood -36,511.70

Final Value of Likelihood -31,124.6

Rho-Squared w.r.t Zero 0.148

(*): t-statistic with respect to 1.

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Table 2. Destination Choice Model for Home Based School Trips


Mode Choice LogsumLogsum 0.20 19.64 *

Production-Attraction DummiesCBD to CBD -0.61 -3.16Urban to CBD -0.61 -3.16Suburban to CBD -0.61 -3.16Rural to CBD -0.61 -3.16CBD to Urban -0.61 -3.16Urban to Urban -0.61 -3.16Suburban to Urban -0.61 -3.16Rural to Urban -0.61 -3.16CBD to Suburban -0.56 -3.32Urban to Suburban -0.56 -3.32Suburban to Suburban -0.56 -3.32Rural to Suburban -0.56 -3.32CBD to Rural 0.00 BaseUrban to Rural 0.00 BaseSuburban to Rural 0.00 BaseRural to Rural 0.00 Base



Natural log (HB School Modeled Attractions) 0.68 18.37

Model StatisticsNumber of Observations 1339Initial Likelihood -7515.98Final Value of Likelihood -4219.08Rho-Squared w.r.t. Zero 0.3161

(*): t-statistic with respect to 1.

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Table 3. Destination Choice Model for Home Based University Trips


Mode Choice Log SumLogsum 0.263 15.69 *

Production-Attraction DummiesCBD to CBD -0.545 -2.9Urban to CBD -0.545 -2.9Suburban to CBD -0.545 -2.9Rural to CBD -0.545 -2.9CBD to Urban -0.545 -2.9Urban to Urban -0.545 -2.9Suburban to Urban -0.545 -2.9Rural to Urban -0.545 -2.9CBD to Suburban 0.000 BaseUrban to Suburban 0.000 BaseSuburban to Suburban 0.000 BaseRural to Suburban 0.000 BaseCBD to Rural 0.000 BaseUrban to Rural 0.000 BaseSuburban to Rural 0.000 BaseRural to Rural 0.000 Base

Production-Attraction Highway Distance Power SeriesDistance -0.094 -3.9

Natural log (HB University Modeled Attractions) 0.828 11.0


Initial Likelihood -719.70

Final Value of Likelihood -640.9

Rho-Squared w.r.t. Zero 0.11(*): t-statistic with respect to 1.

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Table 4. Destination Choice Model for Home Based Shop Trips


Mode Choice Logsum

Logsum 0.18 18.20 *



Natural log (HB Shopping Modeled Attractions) 0.68 31.13

Model Statistics

Number of Observations 1131




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Table 5. Destination Choice Model for Home Based Pickup/Dropoff Trips


Production-Attraction Dummies

CBD to CBD -1.13 -3.81

Urban to CBD -1.13 -3.81

Suburban to CBD -1.13 -3.81

Rural to CBD -1.13 -3.81

CBD to Urban -0.59 -4.23

Urban to Urban -0.59 -4.23

Suburban to Urban -0.59 -4.23

Rural to Urban -0.59 -4.23

CBD to Suburban -0.64 -4.98

Urban to Suburban -0.64 -4.98

Suburban to Suburban -0.64 -4.98

Rural to Suburban -0.64 -4.98

CBD to Rural 0.00 Base

Urban to Rural 0.00 Base

Suburban to Rural 0.00 Base

Rural to Rural 0.00 Base



Natural log (HB Pick-up Drop-off Modeled Attractions) 0.41 21.51

Model StatisticsNumber of Observations 1304Initial Likelihood -9154.33Final Value of Likelihood -6735.66Rho-Squared w.r.t. Zero 0.2642

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Table 6. Destination Choice Model for Home Based Social-Recreational Trips


Mode choice logsum

Logsum 0.167 16.96 *

Production-Attraction DummiesCBD to CBD -0.231 -0.9Urban to CBD -0.231 -0.9Suburban to CBD -0.231 -0.9Rural to CBD -0.231 -0.9

CBD to Urban -0.334 -2.1

Urban to Urban -0.334 -2.1

Suburban to Urban -0.334 -2.1Rural to Urban -0.334 -2.1CBD to Suburban -0.310 -2.2Urban to Suburban -0.310 -2.2Suburban to Suburban -0.310 -2.2Rural to Suburban -0.310 -2.2CBD to Rural 0.000 BaseUrban to Rural 0.000 BaseSuburban to Rural 0.000 BaseRural to Rural 0.000 Base



Natural log (HB Soc/Rec Modeled Attractions) 0.457 12.4

Model StatisticsNumber of Observations 1090Initial Likelihood -7751.55



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Table 7. Destination Choice Model for Home Based Other Trips


Mode Choice Log SumLogsum 0.0907 56.13 *



Natural log (HB Other Modeled Attractions) 0.821 44.2





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Table 8. Destination Choice Model for Non-Home Based Work Trips


Mode choice logsumLogsum 0.58 3.37 *

Production-Attraction DummiesCBD to CBD -0.60 -1.69



Rural to CBD -0.60 -1.69CBD to Urban -1.06 -2.55Urban to Urban -0.52 -1.42Suburban to Urban -0.65 -2.37Rural to Urban -0.23 -0.50CBD to Suburban -0.12 -0.23Urban to Suburban -0.33 -0.84Suburban to Suburban -0.48 -1.85Rural to Suburban -0.44 -1.14CBD to Rural 0.00 BaseUrban to Rural 0.00 BaseSuburban to Rural 0.00 BaseRural to Rural 0.00 Base


Natural log (NHBW Modeled Attractions) 0.72 19.51

Model StatisticsNumber of Observations 790Initial Likelihood -5620.67Final Value of Likelihood -4338.66


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Table 9. Destination Choice Model for Non-Home Based Non-Work Trips


Mode choice logsumLogsum 0.78 2.40 *

Production-Attraction DummiesCBD to CBD -0.77 -4.76



Rural to CBD -0.77 -4.76

CBD to Urban -0.51 -5.16Urban to Urban -0.51 -5.16Suburban to Urban -0.51 -5.16Rural to Urban -0.51 -5.16CBD to Suburban -0.45 -4.98Urban to Suburban -0.45 -4.98Suburban to Suburban -0.45 -4.98Rural to Suburban -0.45 -4.98CBD to Rural 0.00 BaseUrban to Rural 0.00 BaseSuburban to Rural 0.00 BaseRural to Rural 0.00 Base



Natural log (NHBNW Modeled Attractions) 0.70 34.96

Model Statistics




Rho-Squared w.r.t. Zero 0.2221

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Data analysis

Models for all trip purposes were successfully estimated and appear reasonable. Theestimated coefficients of the distance variable functions are increasingly negative overthe range of distances for which the models would be applied. For example, for thejourney to work trips, the part of the utility function related to the distance variable,for a one mile trip, would be:

-0.261 (1) + 0.009 (12) – 0.00018 (13) = -0.25218

For a 20 mile trip, the part of the utility function related to the distance variable, for aone mile trip, would be:

-0.261 (20) + 0.009 (202) – 0.00018 (203) = -3.06

The estimated coefficients of the logsum variables are positive. This is expected, sincethe utilities from the mode choice models from which the logsums are computed havenegative coefficients for all time and cost variables (see Technical Memorandum #5),meaning that the parts of the utility functions related to the logsum variable areincreasingly negative.

Model Validation

Because the destination choice model uses the logsums from the mode choice model,the validation and calibration of the destination and mode choice models wereperformed together. The validation results for the mode choice model are described inTechnical Memorandum #5.

The validation of the destination choice model included the following tests comparingmodel results to the data from the household survey:

Mean travel time comparisonsPercentage intrazonal trip comparisonCorrelation between predicted and observed trip length frequenciesCoincidence ratioDistrict to district flows

The model was initially run, and the various validation checks performed. Based onthe initial results, the coefficients of the trip distance for the various trip purposeswere revised, and the model was rerun with these changes (along with revisions tomode choice coefficients, as described in Technical Memorandum #6). This processwas repeated until the results for both the destination and mode choice models were

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close to the observed information from the survey data. The revised distancecoefficients are shown in Table 10.

Table 10. Original and Recalibrated Trip Distance CoefficientsOriginal Recalibrated

TripPurpose

0 Vehicle 1+Vehicle

JTW -0.345 -0.374 -0.324HBO -0.699 -0.694 -0.679HBPUDO -1.09 -1.241 -0.972HBSchool -1.09 -0.944 -0.723HBShop -1.00 -1.522 -0.970HBSR -0.822 -0.672 -0.681HBUniv -0.094 -0.380 -0.933NHBW -0.673 -0.673 (all trips)NHBNW -0.542 -0.542 (all trips)

Table 11 shows the comparison between the results of the final destination choicemodel and the household survey data for mean travel times by trip purpose andvehicle availability level. The comparison shows a close match between model resultsand observed trip lengths.

Table 12 shows the comparison between the results of the final destination choicemodel and the household survey data for the percentage of intrazonal trips by purposeand vehicle availability level. The comparison shows a close match between modelresults and observed intrazonal percentages by trip purpose for households withvehicles and for all households (since the vast majority of households have vehicles).The model underestimates intrazonal trip percentages for zero-vehicle households.Since the sample size from the survey is low for these households, and the average triplengths match well (as shown in Table 10), it was decided not to make any furthermodel adjustments to attempt to get a better match.

A comparison of the trip length frequencies, at two-minute intervals, between modelresults and observed household survey data, was made. Three ways of examining thecomparison are presented. Table 13 presents the correlation between the observedand modeled results by trip purpose and vehicle availability level. The correlation ishigh except for zero-vehicle home based university trips, of which there were fewobservations in the survey data.

The coincidence ratio is a measure of the fit between two curves in a graph. Thecoincidence ratio was computed for the fit between the observed and modeled triplength frequency distributions for each trip purpose and vehicle availability level. The

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coincidence ratios are shown in Table 14. The coincidence ratios are high forhouseholds with vehicles and for all households. They are somewhat lower for zero-vehicle households, but this is not surprising since there are relatively few of them.

Table 11. Modeled Vs. Observed Mean Travel Times (minutes)0 Vehicle

Households1+ VehicleHouseholds

All Households

TripPurpose

Model Observed Model Observed Model Observed

JTW 15.8 15.8 19.1 19.1 19.0 18.9HBO 12.7 12.7 13.5 13.5 13.4 13.4HBPUDO 9.5 9.6 12.7 12.7 11.9 12.6HBSchool 8.9 9.4 10.7 10.9 10.3 10.7HBShop 9.2 9.1 12.4 12.4 12.3 12.2HBSR 13.2 13.2 13.9 14.0 13.9 13.9HBUniv 17.1 20.2 18.8 18.0 18.7 18.1NHBW n/a 13.1 13.0NHBNW n/a 13.3 13.4

Table 12. Modeled Vs. Observed Intrazonal Trip Percentages0 Vehicle

Households1+ VehicleHouseholds

All Households

TripPurpose

Model Observed Model Observed Model Observed

JTW 4.3% 4.0% 2.7% 2.7% 2.7% 2.7%HBO 5.8% 9.2% 5.6% 5.6% 5.6% 5.6%HBPUDO 10.8% 3.6% 6.0% 5.7% 7.1% 5.7%HBSchool 13.2% 13.9% 8.4% 10.1% 9.5% 10.1%HBShop 10.7% 21.9% 5.3% 5.6% 5.5% 5.6%HBSR 8.3% 21.4% 7.4% 7.4% 7.5% 7.4%HBUniv 1.5% 0.0% 0.8% 1.1% 0.9% 1.1%NHBW n/a 7.5% 7.5%NHBNW n/a 5.3% 5.5%

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Table 13. Correlation Between Modeled Vs. Observed Trip Length FrequencyTripPurpose

0 VehicleHouseholds

1+ VehicleHousehold

s

AllHouseholds

JTW 88.5% 99.1% 99.0%HBO 90.8% 98.3% 98.1%HBPUDO 85.6% 98.1% 98.1%HBSchool 94.2% 99.0% 99.0%HBShop 88.8% 96.8% 96.9%HBSR 82.6% 99.0% 98.8%HBUniv 44.1% 94.4% 93.8%NHBW n/a 99.0%NHBNW n/a 98.7%

Table 14. Coincidence Ratio Between Modeled Vs. Observed Trip LengthFrequencyTripPurpose

0 VehicleHouseholds

1+ VehicleHousehold

s

AllHouseholds

JTW 67.5% 91.6% 91.2%HBO 67.5% 85.1% 85.0%HBPUDO 55.2% 83.1% 84.5%HBSchool 68.4% 87.2% 86.6%HBShop 60.5% 79.0% 78.7%HBSR 50.9% 87.9% 87.6%HBUniv 34.3% 77.3% 77.1%NHBW n/a 88.4%NHBNW n/a 87.2%

As a visual check, the comparisons between the observed and modeled trip lengthfrequencies by trip purpose are shown graphically in Figures 1 through 9. Noproblems are indicated by these graphs.

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Figure 1. Modeled Vs. Observed Trip Length Frequency for JTW Trips

Observed vs. Predicted TLD - All Households

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Figure 2. Modeled Vs. Observed Trip Length Frequency for HBU Trips


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Figure 3. Modeled Vs. Observed Trip Length Frequency for HBO Trips


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Figure 4. Modeled Vs. Observed Trip Length Frequency for HBSc Trips


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Figure 5. Modeled Vs. Observed Trip Length Frequency for HBSh Trips


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Figure 6. Modeled Vs. Observed Trip Length Frequency for HBSR Trips


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Figure 7. Modeled Vs. Observed Trip Length Frequency for HBPD Trips


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Figure 8. Modeled Vs. Observed Trip Length Frequency for NHBW Trips


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Figure 9. Modeled Vs. Observed Trip Length Frequency for NHBO Trips


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A set of 25 districts was defined for the model validation process. These districts areshown in Figure 10.

An observed daily district-to-district trip table was created from the expandedhousehold survey data. The district-to-district trip table from the model wascompared to this table. The correlation between the observed and modeled trips in thetrip table was computed and is shown in Table 15.

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Figure 10. District Boundaries

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Table 15. Correlation Between Predicted and Observed Trips at District LevelDistrict Trips Produced From Trips Attracted ToCBD 98.1% 99.4%North Memphis 98.9% 98.7%Midtown and Depot 99.3% 99.1%East Memphis 99.6% 99.5%Southwest Memphis 99.4% 99.4%Hickory Hill 99.7% 99.8%East Shelby County 98.3% 96.4%Collierville 98.8% 99.1%Northeast Shelby County 97.5% 98.0%Raleigh Bartlett 99.7% 99.9%Millington 98.4% 98.0%Frayser 99.1% 99.3%Northwest Shelby County 93.9% 97.5%East Desoto County 99.2% 99.3%West Desoto County 97.7% 99.6%South Desoto County 98.4% 99.9%Mashall County 97.6% 95.9%North Fayette County 87.7% 83.9%West Tipton County 75.0% 65.3%East Tipton County 85.2% 54.4%South Fayette County 94.5% 84.4%McKellar Lake 88.7% 97.7%University 99.1% 99.4%Shelby Farms Germantown 99.9% 99.8%Airport 99.5% 99.2%All 99.8% 99.8%

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Methodology – Intermediate Stop Destination Choice

The intermediate stop destination choice model estimates the locations of theintermediate stops of journey to work chains. The number of stops is estimated by thejourney to work stops model (see Technical Memorandum #3), which modeled whetherjourney to work chains had zero, one, or two stops. In effect, the intermediate stopdestination choice splits the journey to work chains with one or two stops into thecomponent trips that comprise them.

Like the primary destination choice models, the intermediate stop destination choicemodel is a multinomial logit model, with the alternatives being the potentialdestination zones. The variables included in the model are the natural logarithms ofthe total employment and total households in the zone (a measure of theattractiveness of the zone as a stop), the additional travel time to stop at the zone asopposed to traveling directly between the home and the workplace, and dummyvariables indicating area types of the origin/destination zones and whether the trip isintrazonal. For the second stop on chains with two stops, the time variable representsthe additional time as opposed to traveling directly between the first stop and thedestination. Separate parameters are estimated for the home-work and work-homedirections.

The model estimation results are shown in Table 16.

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Table 16. Intermediate Stop Destination Choice Model Estimation Results


ln(total employment in stop zone) 0.34 18.5ln(total households in stop zone) 0.13 7.3

O-D characteristics for home-to-work chainsStop zone is origin zone dummy 0.85 4.0Stop zone is destination zone dummy 1.77 12.2Origin zone and stop zone are CBD/urban - dummy 0.96 5.6

O-D characteristics for work-to-home chainsStop zone is destination zone dummy 2.40 21.4Origin zone and stop zone are CBD/urban - dummy 0.28 2.0Destination zone and stop zone are CBD/urban - dummy 0.59 3.9Extra time to stop -0.17 -35.9

Model StatisticsNumber of observations 1,462Initial Likelihood -10,410.09Final value of Likelihood -7,669.95"Rho-Squared" w.r.t. Zero 0.263"Rho-Squared" w.r.t. Constants 0.099

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Technical Memorandum #5TimeofDay Model

This memorandum details the development of the TimeofDay Model for the MemphisTravel Demand Model Update.

ContentsMethodology

Overview Determination of Peak Hours Model Application Internal Person TimeofDay Trip Factors External TimeofDay Trip Factors

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Methodology

Development of the Memphis MPO TimeofDay Model included identifying peak traveltime periods, developing peak period factors, and percentage of trips by purposeduring each time period by direction. These factors will be used to reflect peak periodtraffic behavior. Factors also are used for external station trips to convert the dailyvehicle flows into traffic by direction by time period.

Overview

The timeofday factors are applied in several steps of the Memphis Model: after tripgeneration, after mode choice, and during network assignment. These factors areused to convert daily person trips to person trips by time period and to convert vehicletrips to directional trips. This process results in trip ends by each time period for eachof the trip purposes. The advantage of this approach is that the travel characteristicsby timeofday can be considered in trip distribution and mode choice. Also, peakperiod travel times can be considered for peak period trips in trip distribution andmode choice models.

These factors are independent of the congestion level, which means that these factorswill be applied to the trip ends assuming that the same patterns of peaking andcongestion will be captured in the base year and future year model. The time periodsconsidered should be developed so that the AM and PM peaks are fully captured, alongwith additional room for potential “peakspreading” in the future year.

Separate timeofday factors are applied to trip table output from the trip generation and modechoice models. The four periods that will be used for the Memphis Model are AM peak period(6 9 AM), Midday Offpeak period (9 AM – 2 PM), PM peak period (2 6 PM), and Night Offpeak period (6 PM – 6 AM). The peak period trip tables are assigned to the network. To get thedaily volumes on the network, the traffic volumes for each period are added together. Theremainder of this document details the determination of the peak periods and as well as theapplication factors.

Internal freight trips also will have timeofday factoring, and these factors will beaddressed during the freight model development (to be documented in a separatetechnical memorandum).

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Determination of Peak Hour Periods

Peak hours can be defined as periods of excess demand. A peak can be characterizedby its maximum trip rate. Although different trip purposes have different peakingcharacteristics, the peak hour periods are generally determined based on peakingcharacteristics of internal auto trips since they are the majority of the trips.

The peak hour periods are typically determined from the travel characteristicsexhibited in a Household Travel Behavior Survey. For the Memphis Model, internalperson trip factors were derived using the Memphis Household Travel Surveycompleted in 1998 by AMPG. The survey contains information such as trip origin anddestination, start and end time of the trip, trip length, trip time, trip purpose, and tripmode. All the home based trips were split into trips originating from home and going tohome. This survey recorded information for 2,246 households with 19,819 trips,which were weighted and expanded to reflect the trip making characteristics of theregion during the trip generation process.

During the trip generation model development, nine internal person trip purposeswere identified:

• JourneytoWork (JTW)• HomeBased School (HBSc)• HomeBased University (HBU)• HomeBased Shopping (HBSh)• HomeBased Pickup/Dropoff (HBPD)• HomeBased Social Recreational (HBSR)• HomeBased Other (HBO)• NonHomeBased Work (NHBW)• NonHomeBased Non Work (NHBO)

Based upon previous experience, the general guidelines for selecting the peak periods in theMemphis model were:

• Five Percent or more of the total daily trips should occur in the time period• JourneytoWork trips should account for the majority of the trips

Both of the criteria are typically met for a onehour time period to be included in the timeperiod. However, the peak periods selected should also allow for the capturing of peakspreading in the future, since the same timeofday factors will be applied to the base year andfuture years. Also, the Memphis model had significant peaking of HBSchool and HBUniv thatneeded to be considered in the PM peak period model. For this timeofday analysis, the tripsummaries are based on the midpoint time of each trip. Table 1 shows the distribution of theinternal auto trips by period in onehour periods, with the periods that best meet peak

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conditions shaded. Often, trips are also reviewed at the halfhour increment as well. Becausetraffic count data is only available in hourly increments consistently throughout the region, thepeak periods selected would need to be in hourly increments so that assignment results can bevalidated using traffic counts.

Table 2. Trips by Purpose by Time Period

Percent of Trips by Purpose

Time PeriodJourneyto

WorkHBSchool/

HBUniversity

Other HomeBased

PurposesNonHome

Based All Purposes0:001:00 0.8 0.0 0.4 0.1 0.401:002:00 0.2 0.0 0.1 0.1 0.152:003:00 0.3 0.0 0.2 0.1 0.153:004:00 0.4 0.0 0.1 0.0 0.174:005:00 0.7 0.0 0.2 0.0 0.305:006:00 2.9 0.2 0.5 0.2 1.166:007:00 9.3 7.8 2.5 0.8 5.467:008:00 16.7 23.6 7.0 3.8 12.528:009:00 7.8 11.7 5.8 3.4 6.799:0010:00 3.1 3.1 5.1 3.8 3.90

10:0011:00 1.3 2.6 4.4 5.4 3.2711:0012:00 1.8 3.3 4.7 13.2 4.4212:0013:00 2.2 3.7 4.8 19.1 5.1713:0014:00 2.4 2.1 4.7 12.2 4.4114:0015:00 4.0 13.8 7.0 11.4 8.5415:0016:00 7.1 12.3 8.4 9.0 9.4016:0017:00 10.1 3.6 7.3 5.2 7.3917:0018:00 12.3 4.4 8.6 3.7 8.5618:0019:00 6.4 1.9 8.9 3.1 6.2219:0020:00 3.1 1.6 7.4 2.3 4.2020:0021:00 2.0 2.3 5.1 1.4 2.9521:0022:00 1.9 0.9 3.7 1.0 2.2422:0023:00 1.7 1.0 2.1 0.4 1.3223:0024:00 1.4 0.2 1.2 0.2 0.90

Total 100.0% 100.0% 100.0% 100.0% 100.0%

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The AM peak period lasts from 6 to 9 AM. As expected, JourneytoWork and HomeBased School/University trips have a significant spike during this period. The PMpeak period lasts from 2 PM to 6 PM, which is reflected in the high frequency of tripsfor all purposes spread around a longer time period. Having a longer PM peak periodwill also allow the model to capture potential peak spreading between the base yearand the design year in the model, which is typically more pronounced in the PM thanthe AM.

Figure 1 shows a graphical display of the trip peaking by trip purpose. The figureillustrates that the AM peak has a more pronounced, shorter spike, while the PM peakis spread over a longer time period. It also illustrates that the trips involved shoppingand other purposes experience most trips in the Midday and PM periods.

Figure 1. Percent of Trips by Time and Purpose

0.0

5.0

10.0

15.0

20.0

25.0

01

12

23

34

45

56

67

78

89

910

101

111

12

121

313

14

141

515

16

161

717

18

181

919

20

202

121

22

222

323

24

Time Period

Perc

ent o

f Trip

s

JourneytoWorkHomeBased School/UniversityHomeBased OtherNon HomeBasedAll Trip Purposes

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The Midday Offpeak period lasts from 9 AM to 2 PM. The Night Offpeak periodincludes any times that are not included in the AM, PM, or Midday Offpeak periods, atotal of 12 hours from 6 PM to 6 AM. A summary of the time periods is shown in Table2.

Table 2. Time Period Summary

Time Period Time Range Period LengthAM Peak 6 AM 9 AM 3 Hours

Midday Offpeak 9 AM 2 PM 5 HoursPM Peak 2 PM 6 PM 4 Hours

Night Offpeak 6 PM 6 AM 12 Hours

Model Application

The Memphis Model will apply timeofday factors at multiple points in the process.For internal person trips, factors are applied after trip generation to divide the trips bypurpose into productions and attractions by time period. After mode choice, a secondset of directional factors are applied to convert the distributed productions andattractions into origins and destinations. Since the directionality of trips vary greatlyby time period, these factors are applied by each time period and trip purpose.

The process is similar for external trips (auto and freight) except that trips are alreadyvehicular and do not have a modechoice component. Figure 2 illustrates the timeofday modeling process to be used in the Memphis Model.

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Figure 2. TimeofDay Modeling Process

Trip Generation

PostGenerationTimeofDay Modeling

Trip Ends by Trip Purpose(for AM, Midday, PM,and Night Periods)

Trip Distribution

Mode Choice

TimeofDay Factors(by trip purpose)

Trip Tables by Purpose,Mode, and Time Period

PostMode ChoiceTimeofDay Modeling

Directional Split Factors(e.g., HometoWork vs.

WorktoHome)

Peak PeriodOriginDestination

Trip Tables

Trip Assignment(AM, Midday, PM, Night)

Source: TimeofDay Modeling Procedures Report, Figure 2.4, Cambridge Systematics, Inc.

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Internal Person TimeofDay Trip Factors

For the model update, internal person trip factors were developed for nine differenttrip purposes and four time periods. The time periods that have been identified are theAM peak period (6 AM – 9 AM), PM peak period (2 PM – 6 PM), Midday Offpeak period(9 AM – 2 PM), and Night Offpeak period (6 PM – 6 AM). The trip purposes used forthe development of the timeofday factors correspond to those used in the tripgeneration. Trip factors are applied at two points in the modeling process – first, aftertrip generation, and then again after mode choice.

PostTrip Generation Trip Factors

For the Memphis Model, timeofday factors are used after trip generation to distributethe trips, by purpose, into the four time periods. Table 3 shows the expanded internalperson trips and Table 4 shows the trip factors calculated using the expanded persontrips to apply after trip generation is complete.

Table 3. Expanded Internal Person Trips

Trip Purpose AM PeakMidday

Offpeak PM PeakNight Off

peakGrandTotal

1 JTW 321,361 102,158 320,498 205,878 949,8952 HBSc 177,821 8,530 142,511 5,545 334,4073 HBU 16,924 13,333 12,806 7,134 50,1974 HBSh 6,945 72,122 65,330 54,744 199,1415 HBPD 80,516 28,740 80,788 28,262 218,3066 HBSR 11,309 37,366 41,079 92,873 182,6277 HBO 82,255 137,226 176,656 149,875 546,0128 NHBW 4,142 89,040 25,203 3,789 122,1749 NHBO 57,051 161,143 172,609 66,354 457,157

ALL TRIPS 758,323 649,658 1,037,480 614,454 3,059,917

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Table 4. TimeofDay Internal Person Trip Factors (PostTrip Generation)

Trip Purpose AM PeakMidday


peakGrandTotal

1 JTW 33.83 10.75 33.74 21.67 100.002 HBSc 53.18 2.55 42.62 1.66 100.003 HBU 33.71 26.56 25.51 14.21 100.004 HBSh 3.49 36.22 32.81 27.49 100.005 HBPD 36.88 13.16 37.01 12.95 100.006 HBSR 6.19 20.46 22.49 50.85 100.007 HBO 15.06 25.13 32.35 27.45 100.008 NHBW 3.39 72.88 20.63 3.10 100.009 NHBO 12.48 35.25 37.76 14.51 100.00

ALL TRIPS 24.78 21.23 33.91 20.08 100.00

Table 4 shows that 34 percent of the JTW trips originate in the AM peak period and34 percent occur in the PM peak, since most people are going to and coming backfrom work in these times. HBSchool and HBUniv trips exhibit similar behavior as JTWtrips, but a higher percentage of these trips occur during the AM and PM peaks.HBShop, HBPUDO, HBSR, and HBO trips are spread more equally throughout theday, while NonHome Based trips experience pronounced peaks during the middayand PM periods.

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PostMode Choice Trip Factors

Once the Memphis model runs through mode choice, trips will need to be convertedfrom productions and attractions to origins and destinations by applying directionalfactors. These directional factors are applied by purpose and timeofday. Like theposttrip generation factors, these are also derived from the Memphis HouseholdTravel survey. Table 5 shows the trip factors calculated using the expanded persontrips to apply after mode choice is complete. NonHome Based trips do not apply adirectional factor, since they do not have the home as an origin or destination point.These trips are distributed equally in both directions.

Table 5. TimeofDay Directional Trip Factors (PostMode Choice)

Trip Purpose Direction AM PeakMidday


peak1 JTW % From Home 95.59 64.42 10.81 26.93

% To Home 4.41 35.58 89.19 73.072 HBSc % From Home 99.69 42.46 1.45 13.49

% To Home 0.31 57.54 98.55 86.513 HBU % From Home 96.60 40.13 25.78 9.19

% To Home 3.40 59.87 74.22 90.814 HBSh % From Home 63.65 53.43 38.01 37.57

% To Home 36.35 46.57 61.99 62.435 HBPD % From Home 64.42 63.00 43.36 38.75

% To Home 35.58 37.00 56.64 61.256 HBSR % From Home 85.22 58.93 58.41 38.58

% To Home 14.78 41.07 41.59 61.427 HBO % From Home 88.58 54.90 35.46 37.65

% To Home 11.42 45.10 64.54 62.358 NHBW N/A 50.0 50.0 50.0 50.09 NHBO N/A 50.0 50.0 50.0 50.0

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External TimeofDay Trip Factors

External trip factors for automobile and truck trips also have been developed forapplication in the model. The Memphis MPO area does not have an external stationsurvey that was administered in the recent past that can be used to estimate timeofday factors for auto and commercial trips that are entering or exiting the study area.Instead, these factors were developed using traffic counts (with timeofday andclassification data) in conjunction with the Tennessee Statewide Model, which iscurrently being developed by PBS&J. The beta version of this TransCAD model wasused to perform select link queries on auto and truck assignment to pull togetherExternalExternal (EE) trip making characteristics at each external station. Table 6shows the external timeofday trip factors for the Memphis model. The factors havebeen developed by type of station (interstate, minor arterial, etc.) to be appliedthroughout the region.

Table 6. TimeofDay External Trip Factors

FunctionalClassification Direction AM Peak

MiddayOffpeak PM Peak

Night Offpeak

% of Daily 16.4 30.3 24.3 29.0% Inbound 70 51 39 431 Interstate

% Outbound 30 49 61 57% of Daily 16.9 30.7 28.7 23.7% Inbound 62 51 48 422 Major

Arterial% Outbound 38 49 52 58% of Daily 19.6 26.6 29.0 25.1% Inbound 63 49 49 406 Minor

Arterial% Outbound 37 51 51 60% of Daily 18.0 27.5 29.2 25.3% Inbound 63 49 49 407/8/9 Collector/

Local% Outbound 37 51 51 60

Like the internal person trips, these factors are applied after trip generation and tripdistribution, because the trips are already vehicleonly, and do not have a mode choicecomponent to consider like internal auto trips. Since the traffic counts cannotdetermine if a trip is an internalexternal trip or a through trip, and limited data isavailable for vehicle type by timeofday, these factors are applied equally to all vehicletypes (auto, singleunit (SU) truck, combinationunit (CU) truck).

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Technical Memorandum #6 – Mode ChoiceThis memorandum covers the development of the mode choice models. It wasprepared by Cambridge Systematics, Inc. Staff who worked on the development ofthese submodels include Edward Bromage, Thomas Rossi, Yasasvi Popuri, AshishAgarwal, Maya Abou Zeid, and Kevin Tierney.

ContentsMethodology

- Logit Model Formulation- Model Estimation and Testing Procedure- Observation Exclusions- Unavailability of Modes- Model Estimation Results- Data Analysis- Use of Nested Model Structures- Model Validation

Methodology

Mode choice models were developed for the following trip purposes:

Journey to work (JTW) combined with home based university (HBU);Home based school (HBSc);Home based shopping (HBSh);Home based social-recreational (HBSR);Home based pickup/dropoff (HBPD);Home based other (HBO);Non-home based work (NHBW); andNon-home based non-work (NHBO).

These are the same trip purposes used in trip generation (see Technical Memorandum#3) and trip distribution (see Technical Memorandum #4). Because of limited dataavailable for the home based university trip purpose, it was combined with the journeyto work trip purpose for the mode choice model estimation.

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The mode choice models were estimated from three data sources: the 1997 Memphishousehold travel survey, the 2004 transit on-board survey, and the 2001 MATA trolleysurvey.

Logit Model Formulation

The mode choice models are logit models. Logit models are discrete choice models,which attempt to explain the behavior of individuals making a choice between a finitenumber of separate alternatives, in this case travel modes. In the logit model, theprobability of choosing a particular alternative i is given by the following formula:

P(i) = exp (Ui)/ j exp(Uj)

where:P(i) = probability of choosing alternative i

Ui = utility of alternative iexp = exponential function

The utility function Ui represents the worth of alternative i compared to otheralternatives and is expressed as a linear function:

Ui = B0i + B1iX1i + B2iX2i + …+ BniXni

where the Xki variables represent attributes of alternative i, the decision maker, or theenvironment in which the choice is made and Bki represents the coefficient reflectingthe effect of variable Xki on the utility of alternative i. The coefficients are estimatedusing statistical “maximum likelihood” methods using logit model estimation softwaresuch as ALOGIT. In the case of logit mode choice models, the alternatives are thetravel modes while the attributes may include attributes of the modes (e.g., travel timefrom the origin to destination by the particular mode), the decision maker (e.g., autoownership), and the environment (e.g., production or attraction zone area type).

Model Estimation and Testing Procedure

In summary, the mode choice model estimation and application process included thefollowing steps:

1. Assembly of model estimation data sets from the household survey and transiton-board survey trip records, zonal data developed earlier in the project (seeTechnical Memorandum #2), and the various highway and transit distance,time, and cost skims from the model networks;

2. Multinomial model estimation using all zonal alternatives for each trip purposeusing the ALOGIT choice behavior analysis package; and

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3. Model validation for each trip purpose.

The modes considered for inclusion in the models are:Transit with auto access (including bus with auto access, trolley with auto access, and bus/trolley with autoaccess);Bus with walk access ;Trolley with walk access (including trolley with walk access and bus/trolley with walk access);Non-motorized (including walk/wheelchair and bicycle)Shared-ride; andDrive-alone.

Table 1 shows the number of trips by mode and trip purpose from the combinedsurvey data set.

Table 1. Distribution by Chosen Mode and Purpose in the Survey Data Set

Chosen Mode JTW HBSc HBU HBSh HBPD HBSR HBO NHBW NHBO AllBus Auto Access 225 18 63 31 - 17 88 36 47 525Bus Walk Access 1,238 121 293 158 - 89 394 107 131 2,531Trolley AutoAccess 4 - - 3 - 4 19 2 17 49Trolley WalkAccess 25 - - 6 - 3 29 32 21 116Bus/Trolley AutoAccess 7 1 4 3 - 1 6 4 3 29Bus/Trolley WalkAccess 26 - 6 11 - 3 23 6 13 88Walk/Wheel Chair 124 469 4 94 38 137 154 52 83 1,155 Bicycle 11 20 - - - 5 5 2 4 47 School Bus 4 554 2 - 5 - 48 - 107 720 Shared Ride 793 1,010 45 599 889 603 2,068 225 1,792 8,024 Drive Alone 3,292 46 257 598 465 421 1,182 498 917 7,676 Taxi/Limo 8 - 1 2 - - 5 1 1 18 Other 3 - 2 2 2 4 16 - 1 30 Refused 2 - - - - - 2 - - 4All Modes 5,762 2,239 677 1,507 1,399 1,287 4,039 965 3,137 21,012

It should be noted that trips that used both trolley and bus need to be treated astrolley trips in the mode choice model, both for ease of determining transit paths andto have enough trolley trips to model the mode separately from bus. Even with thisdefinition, however, there are not enough trolley trips in the data set to model walkand auto access separately (there are not as many as 20 trolley with walk access tripsfor any trip purpose). For some trip purposes (HBSc, HBSh, and HBSR), there are notenough trolley trips with or without bus to model trolley separately from bus (there are

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not as many as 10 trolley trips for these purposes). For the HBSR trip purpose, thereare only 10 total transit trips with auto access, and so it is impossible to distinguishbetween transit access modes in the model for this trip purpose. It should also benoted that there are not as many as 20 bicycle trips for any trip purpose, and so it isimpossible to model them separately from the walk mode.

Based on the data availability and the discussion above, the final set of modes for eachtrip purpose was determined as follows:

JTW/HBU, HBO, NHBW, NHBO: Transit Auto Access, Bus Walk Access, Trolley Walk Access, Non-motorized, Shared Ride, Drive AloneHBSc: Transit, Non-motorized, School bus, Shared-ride, Drive aloneHBSh: Transit with auto access, Transit with walk access, Non-motorized, Shared-ride, Drive aloneHBPD: Non-motorized, Shared-ride, Drive-aloneHBSR: Transit, Non-motorized, Shared-ride, Drive alone

Observation Exclusions

There are a total of 21,153 trip observations in the original combined data set asshown in Table 1. It was necessary to remove from the data set observations for whichrequired data was unavailable. The following criteria were used to excludeobservations that could not be used for estimation:

Intrazonal trips (since there is no available level of service information from the network skims);Origin or destination zone missing;Invalid mode for mode choice model (school bus for non-school trips, taxi/limo, refused, other);Chosen mode not available; orHousehold auto ownership or number of persons missing (for home based trips).

Unavailability of Modes

It was assumed that the shared ride modes were available to all travelers. Householdswithout autos are assumed to not have the drive alone mode available; thisassumption was borne out by the fact that households without cars made very fewdrive alone trips in the household survey data set. It was also assumed that eachtransit submode (walk or auto access) was available to any trip where the transit levelof service variables were defined in the transit network skims. It was also assumedthat the non-motorized mode is not available if the trip highway distance is greaterthan five miles. This assumption was also borne out by the household survey dataset.

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Model Variables

The following variables were considered for the mode choice utilities:

Level of service variables

In-vehicle time (including walk access time)Out-of-vehicle time (including walk access time, walk egress time, initial wait time, transfer wait time, transferwalk time)Cost (including transit fare, auto parking cost, and auto operating cost)Trip distance (for non-motorized trips)

All time variables are in minutes, all cost variables in dollars, and all distancevariables in miles.

Daily parking costs were estimated at the zone level by Kimely-Horn and Associates,Inc. in consultation with the Memphis MPO staff. The daily cost was assumed toapply for JTW trips. For all other trip purposes, the average activity durations wereestimated from the household survey data and expressed as a fraction of a four-hourperiod (it was assumed that daily parking costs were reached when a vehicle wasparked for four hours). This fraction was applied to the daily parking cost to obtainthe parking cost for each trip purpose.

Auto operating costs were estimated at 15 cents per mile. This estimate was based onestimates from other urban area models and updated to reflect gasoline priceincreases in the years leading up to the base year of 2004. Auto operating costs weredivided by the number of persons in the vehicle. The vehicle occupancy for sharedride trips was estimated directly from the household survey data and varied by trippurposes:

JTW - 2.43HBSc - 2.40HBU - 2.90HBSh - 2.47HBPD - 2.73HBSR - 2.59HBO - 2.48NHBW -2.33NHBO - 2.45

All other level of service variables were estimated directly from the network skims. Fortransit walk acces and egress, the assumed walk speed is 3 mph.

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Demographic variables

Vehicle availability affects mode choice. The mode choice model is applied by vehicleavailability level for home based trip purposes. Dummy variables representing vehicleavailability levels were used in the utility functions for some trip purposes. Inaddition, a variable representing the number of vehicles per person in the householdwas used for some trip purposes.

Density variables

The density of development can also affect mode choice. Generally speaking, transitand non-motorized trips are more likely to be made in more densely developed areas.For some trip purposes, variables representing the density of employment orpopulation at the produciotn or attraction zone were used. For the HBPD trip purposearea type dummy variables were used instead of density variables.

Model Estimation Results

Tables 2 through 9 show the estimated mode choice models for all trip purposes.These tables show the models after incorporating constraints to produce reasonablecoefficients and to be consistent with Federal Transit Administration (FTA) guidelines.

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Table 2. Mode Choice Model for Journey to Work Trips and Home BasedUniversity Trips

Alternative

Variable

TransitAuto

Access

BusWalk

Access

TrolleyWalk

Access

Non-motorize

dSharedRide

DriveAlone

Mode Specific Constants

Constant, JTW3.506(16.8)

7.71(39.1)

6.58(17.2)

4.98(11.6)

-0.0857(-0.7)

0.00(Base)

Constant, HBU5.09(16.1)

9.10(36.0)

9.10(36.0)

7.46(6.6)

-0.614(-2.3)

0.00(Base)

Level of ServiceIn-vehicle travel time, JTW -0.025* -0.025* -0.025* -0.025* -0.025*In-vehicle travel time, HBU -0.020* -0.020* -0.020* -0.020* -0.020*Out-of-vehicle time, JTW -0.050* -0.050* -0.050*Out-of-vehicle time, HBU -0.040* -0.040* -0.040*Cost, JTW -0.250* -0.250* -0.250* -0.250* -0.250*

Cost, HBU-0.592(-3.7)

-0.592(-3.7)

-0.592(-3.7)

-0.592(-3.7)

-0.592(-3.7)

OD Highway Distance, JTW-1.60(-11.1)

OD Highway Distance, HBU-3.27(-3.3)

Socioeconomics

One-Vehicle Dummy-2.26(-8.8)

-3.60(-16.0)

-4.40(-9.1)

-3.37(-7.2)

0.00(Base)

0.00(Base)

Two or More Vehicles Dummy-3.56(-11.8)

-4.76(-17.7)

-4.40(-9.1)

-4.85(-8.5)

-0.0208(-0.2)

0.00(Base)

Vehicles/Member in Household-2.76(-12.4)

-2.76(-12.4)

-2.76(-12.4)

-2.08(-3.7)

-2.58(-13.3)

0.00(Base)

Population Density atAttraction Zone

0.000077(4.6)

Model StatisticsNumber of Observations 5550Initial Likelihood -7214.7Final Value of Likelihood -5057.5"Rho-Squared" w.r.t. Zero 0.299"Rho-Squared" w.r.t. Constants 0.013Value of IVT, JTW ($/hr) $6.00Value of IVT, HBU ($/hr) $2.03Transit OVT/IVT 2.0

t-statistics in parentheses* - constrained coefficient

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Table 3. Mode Choice Model for Home Based School Trips

Alternative

Variable TransitNon-

motorizedSchool

BusSharedRide

DriveAlone

Constant9.76(11.9)

10.5(13.7)

8.12(10.7)

7.96(10.8)

0.00(Base)

Level of Service

In-vehicle travel time -0.020* -0.020* -0.020* -0.020*

Out-of-vehicle time -0.060*

Cost-0.534(-2.9)

-0.534(-2.9)

-0.534(-2.9)

OD Highway Distance-1.04(-13.8)

Socioeconomics


-0.370(-1.2)

-0.489 (-2.0)

0.00(Base)

0.00(Base)


-1.05(-2.3)

-0.489(-2.0)

0.00(Base)

0.00(Base)


-9.50(-8.24)

-8.50(-8.96)

-7.49(-8.16)

0.00(Base)

Model Statistics




"Rho-Squared" w.r.t. Zero 0.321

"Rho-Squared" w.r.t. Constants 0.123

Value of IVT ($/hr) $2.25

Transit OVT/IVT 3.0t-statistics in parentheses* - constrained coefficient

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Table 4. Mode Choice Model for Home Based Shop Trips

Alternative

Variable

TransitAuto

Access

TransitWalk

Access

Non-motorize

dSharedRide

DriveAlone

Constant3.55(6.5)

6.83(14.2)

3.75(5.9)

1.65(8.3)

0.00(Base)

Level of service

In-vehicle travel time -0.020* -0.020* -0.020* -0.020*


Cost -0.400* -0.400* -0.400* -0.400*


Socioeconomics

One- or More Vehicle Dummy-1.16(-1.8)

-2.49(-4.6)

-1.18(-1.7)

0.00(Base)

0.00(Base)


-5.20(-6.8)

-6.87(-6.2)

-2.45(-10.0)

0.00(Base)

Population Density at ProductionZone

0.000030(3.1)

0.000030(3.1)

0.000042(3.7)

0.00(Base)

0.00(Base)

Employment Density atAttraction Zone

0.000079(1.2)

0.000079(1.2)

0.00031(4.2)

0.00(Base)

0.00(Base)

Model Statistics






Value of IVT ($/hour) $3.00

Transit OVT/IVT 3.0t-statistics in parentheses* - constrained coefficient

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Table 5. Mode Choice Model for Home Based Pickup/Dropoff Trips

Alternative

VariableNon-

motorized Shared Ride Drive Alone

Constant0.00

(Base)-2.03(-2.9)

-3.87(-5.4)

Level of service


SocioeconomicsVehicles/Member inHousehold

0.00(Base)

8.36(6.9)

10.3(8.3)

Area Type

Production zone CBD or urban0.00

(Base)-4.14(-3.1)

-4.25(-3.1)

Attraction zone CBD or urban0.00

(Base)3.82(2.8)

4.15(3.0)







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Table 6. Mode Choice Model for Home Based Social-Recreational Trips

Alternative

Variable TransitNon-

motorizedSharedRide Drive Alone

Constant8.11(21.9)

5.99(12.2)

2.21(9.3)

0.00(Base)

Level of service

In-vehicle travel time -0.020* -0.020* -0.020*


Cost-0.707(-5.2)

-0.707(-5.2)

-0.707(-5.2)


Socioeconomics


-2.31(-4.4)

0.00(Base)

0.00(Base)


-2.31(-4.4)

0.00(Base)

0.00(Base)


-4.04(-6.3)

-3.18(-10.7)

0.00(Base)

Model Statistics






Value of IVT ($/hour) $1.70Transit OVT/IVT 3.0


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Table 7. Mode Choice Model for Home Based Other Trips

Alternative

Variable

TransitAuto

AccessBus WalkAccess

TrolleyWalk

Access

Non-motorize

dSharedRide

DriveAlone

Constant4.43(15.4)

8.76(38.2)

7.23(15.5)

7.13(17.2)

2.54(20.0)

0.00(Base)

Level of ServiceIn-vehicle travel time -0.020* -0.020* -0.020* -0.020* -0.020*Out-of-vehicle time -0.060* -0.060* -0.060*

Cost-0.325(-4.2)

-0.325(-4.2)

-0.325(-4.2)

-0.325(-4.2)

-0.325(-4.2)


Socioeconomics

One Vehicle Dummy-1.77(-5.0)

-4.56(-15.5)

-3.34(-5.0)

-5.14(-12.0)

0.00(Base)

0.00(Base)


-5.51(-15.2)

-3.44(-4.4)

-5.14(-12.0)

0.00(Base)

0.00(Base)

Vehicles/Member inHousehold

-3.13(-8.6)

-3.13(-8.6)

-3.13(-8.6)

-2.88(-6.0)

-3.03(-19.6)

0.00(Base)


0.000041(1.6)

Model StatisticsNumber of Observations 3276Initial Likelihood -4425.9Final Value of Likelihood -3093.7"Rho-Squared" w.r.t. Zero 0.301"Rho-Squared" w.r.t.Constants 0.098Value of IVT ($/hr) $3.69Transit OVT/IVT 3.0t-statistics in parentheses* - constrained coefficient

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Table 8. Mode Choice Model for Non-Home Based Work Trips

Alternative

Variable

TransitAuto

AccessBus WalkAccess

TrolleyWalk

Access

Non-motorize

dSharedRide

DriveAlone

Constant0.058(0.0)

1.84(10.1)

1.86(6.4)

0.29(0.7)

-1.24(-10.4)

0.00(Base)

Level of service

In-vehicle travel time -0.020* -0.020* -0.020* -0.020* -0.020*

Out-of-vehicle time -0.060* -0.060* -0.060*

Cost-0.220(-2.2)

-0.220(-2.2)

-0.220(-2.2)

-0.220(-2.2)

-0.220(-2.2)


Employment Density atProduction Zone

-0.000015

(-3.4)

-0.000015

(-3.4)

-0.000015

(-3.4)0.00

(Base)0.00

(Base)0.00

(Base)

Employment Density atAttraction Zone

-0.000017

(-3.5)

-0.000017

(-3.5)

-0.000017

(-3.5)0.00

(Base)

-0.000011

(-1.6)

-0.000018

(-2.6)

Model Statistics




"Rho-Squared" w.r.t. Zero 0.210"Rho-Squared" w.r.t.Constants 0.0102Value of IVT ($/hr) $5.45Transit OVT/IVT 3.0


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Table 9. Mode Choice Model for Non-Home Based Non-Work Trips

Alternative

Variable

TransitAuto

Access

BusWalk

Access

TrolleyWalk

AccessNon-

motorizedSharedRide

DriveAlone

Constant0.772(3.2)

1.64(8.3)

1.46(4.2)

-0.834(-2.5)

0.389(4.2)

0.00(Base)

Level of service

In-vehicle travel time -0.020* -0.020* -0.020* -0.020* -0.020*

Out-of-vehicle time -0.060* -0.060* -0.060*

Cost-0.316(-3.8)

-0.316(-3.8)

-0.316(-3.8)

-0.316(-3.8)

-0.316(-3.8)



-0.00014(-2.4)

-0.000098

(-1.9)

-0.000078

(-1.3)0.00

(Base)

-0.000067

(-1.5)

-0.000075

(-1.6)

Model Statistics




"Rho-Squared" w.r.t. Zero 0.279"Rho-Squared" w.r.t.Constants -0.046Value of IVT ($/hr) $3.80Transit OVT/IVT 3.0


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Data analysis

Models for all trip purposes were successfully estimated and appear reasonable.However, it was desirable to constrain the coefficients for certain level of servicecoefficients (mainly the in-vehicle and out of vehicle time coefficients). This wasbecause some of the estimated coefficients were not within reasonable ranges givenexperience in other mode choice models from around the U.S.

FTA has been publicizing guidelines for travel models that will promote more accuratemodeling as well as help to ensure a “level playing field” for FTA New Starts projectevaluation. Guidelines affecting the mode choice model parameters include thefollowing1:

IVT COEFFICIENTS: FTA requires compelling evidence if Civt < -0.030 or Civt > -0.020.RATIO OF OVT/IVT COEFFICIENTS: FTA requires compelling evidence if Covt/Civt < 2.0 orCovt/Civt > 3.0.MODE SPECIFIC IVT COEFFICIENTS: FTA requires compelling evidence if using mode-specific Civt.VALUE OF TIME IN THE MODE CHOICE MODEL: Value of time should follow the following criteria:(Average income)/4 < Civt/Ccost < (average time)/3

It was decided that the model coefficients should be constrained to meet the FTAguidelines since there is no compelling evidence that travel behavior in Memphisshould be significantly different from these national norms.

The results of the mode choice model estimation were compared to models estimatedin other urban areas in the United States, as compiled by Cambridge Systematics.Table 10 presents the results of this comparison. The estimated (constrained) modelscompare favorably with the other models, as shown in the table.

1 Federal Transit Administration. “Travel Forecasting for New Starts.” Travel Forecasting for New StartsProposals, A Workshop Sponsored by the Federal Transit Administration, Minneapolis, Minnesota, June15-16, 2006

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Table 10. Comparison of Memphis Model Parameters to Other Urban AreasHome Based Work Memphis Average Range FTA GuidelineIVT (min) -0.025 -0.028 -0.01 to -0.05 -0.02 to -0.03OVT (min) -0.050 -0.054 -0.03 to -0.07 n/aCost ($) -0.250 -0.720 -0.2 to –1.3 n/aRatio: OVT/IVT 2.0 1.9 1.5 to 3 2 to 3Value of Time $6.00 $2.30 $2 to $5 Based on

income

Home Based Non-Work

Memphis Average Range FTA Guideline

IVT (min) -0.020 to -0.025

-0.017 -0.007 to -0.033

-0.02 to -0.03

OVT (min) -0.040 to -0.050

-0.054 -0.018 to -0.08 n/a

Cost ($) -0.350 to -1.020

-0.858 -0.2 to -1.3 n/a

Ratio: OVT/IVT 2.0 to 3.0 3.1 2 to 6 2 to 3Value of Time $1.70 to $3.69 $1.35 $0.5 to $5 Based on

income

Non-Home Based Memphis Average Range FTA GuidelineIVT (min) -0.020 -0.021 -0.01 to -0.05 -0.02 to -0.03OVT (min) -0.060 -0.076 -0.02 to -0.20 n/aCost ($) -0.220 to -

0.316-0.998 -0.2 to –1.3 n/a

Ratio: OVT/IVT 3.0 3.6 2 to 7 2 to 3Value of Time $3.80 to $5.45 $1.20 $0.2 to $5 Based on

income

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Use of Nested Model Structures

An advantage of nested structures is that similar modes, such as transit with autoaccess and transit with walk access, can be grouped as a subset, all branching from acommon “composite mode.” A nesting parameter, which represents the degree towhich the elemental modes are more strongly related to each other than to any modesin other parts of the model, is estimated for this composite mode.

In the nested logit model, the probability of choosing an alternative i in nest n is givenby:

P(i) = P(i|nest n) P (nest n)

where the probability of nest n is given by the logit formula presented in Section 1.1and the alternatives are all other nests of alternatives at the same nesting level. Theconditional probability P(i|nest n) is given by the logit formula where the alternativesover which the exponentiated utilities are summed include only the other alternativeswithin nest n.

The primary advantage of nested logit models over non-nested multinomial logitmodels is that the nested logit models reduce the intensity of the “independence ofirrelevant attributes” (IIA) property. The IIA property, which is characteristic of allmultinomial logit models as well as the lowest level nests in nested logit models,assumes that the relative shares of any two modes are independent of the availabilityof other modes. For example, assume there are three modes: auto, bus, and rail. Itmight be reasonable to assume that the ratio of the choice probabilities of bus and railis independent of whether auto is available. However, it would likely be unreasonableto assume that the ratio of the bus and auto shares is independent of the availabilityof rail; adding the rail mode to a market would likely draw more bus users than autotravelers.

The estimation of nested logit models was attempted for all trip purposes. For eachpurpose, the estimation resulted in a nest coefficient that was either out of theacceptable range (0 to 1) or was not significantly different from 1.0, implying amultinomial logit structure. For example, for the journey to work nested logit model,the estimate for the nest coefficient was 0.909, with a standard error of 0.198. The t-statistic to test for significant difference from 1.0 is 0.198/0.909 = 0.46. Thus theestimated nest coefficient is not significantly different from 1.0. Similar results wereobtained for the other trip purposes. Thus it was decided to use the multinomial logitspecifications for all trip purposes.

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Model Validation

There were several components to the mode choice model validation process. Theseincluded:

1. Development of the model validation targets (trips by mode for each tip purpose);2. Comparison of transit trip tables to the expanded trip table from the transit on-board survey;3. Adjustment of mode specific constants;4. Comparison of assigned transit volumes to transit ridership counts; and5. Repetition of Steps 3 and 4 until reasonable results are achieved.

These steps are described below.

Model Validation Targets

The mode choice validation targets were developed from a variety of sources, includingtransit ridership counts provided by MATA, the 1997 Memphis household travelsurvey, the 2004 transit on-board survey, and the 2000 MATA trolley survey. First,the transit boarding counts, which represent unlinked transit trips) were adjusted torepresent linked transit trips, which are the outputs of the mode choice model. Thiswas done by dividing the boarding counts by the transfer rate (number of unlinkedtrips per linked transit trip). The original estimate of 1.28 for the transfer rate wasobtained from the transit on-board survey.

Later surveys by MATA, however, indicated that the transfer rate was approximately1.40. This latter number was checked in two ways. First, the expanded trip tablefrom the on-board survey was assigned to the model transit network. The resultingtransfer rate was 1.36, indicating that the surveyed trips required more transfers thanwere reported by survey respondents. Second, the required minimum number oftransfers for each origin-destination pair reported in the on-board survey wasestimated by skimming the transit network, with the path builder settings set tominimize the number of transfers. The resulting numbers were compared to thenumber of transfers reported for each observation. For many observations, thenumber of reported transfers was less than the minimum number of transfersrequired. In these cases, the number of reported transfers was replaced with theminimum number required, and the overall transfer rate was recalculated to be 1.42.Based on this information, it was determined that a transfer rate of 1.40 was the mostreasonable estimate to use in developing the validation targets.

Table 11 shows the total linked and unlinked bus and trolley trips.

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Table 11. Linked and Unlinked Transit TripsBus Trolley Total

Unlinked Transit Trips 41,155 2,840 43,995Linked Transit Trips 29,396 2,029 31,425

The transit on-board survey data were used to split the transit trips by purpose, autoownership level, access mode, and transit submode. The total transit trips for eachpurpose and auto ownership level were subtracted from the total trips by autoownership level for the purpose (the outputs of the trip distribution model). Theremaining trips by purpose and auto ownership level were split among the non-transitmodes (non-motorized, drive alone, shared ride and—for home based school trips—school bus) using the percentages of trips from the household travel survey.

Table 12 shows the final validation targets by trip purpose, auto ownership level, andmode.

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Table 12. Model Validation TargetsJTW Chain HBSchool HBUniv HBShop HBPUDO

0-car households Trips Share Trips Share Trips Share Trips Share Trips Share

ALL TRANSIT 8,907 33.6% 373 0.5% 1,661 42.3% 1,112 9.7% - 0.0%

Transit - Bus with Walk Access 7,303 27.6% 1,405 35.8%

Transit - Trolley with Walk Access 504 1.9% 97 2.5%

Transit - Bus/Trolley with Walk Access 1,050 9.2%

Transit - Bus/Trolley with Auto Access 1,100 4.2% 159 4.1% 62 0.5%

Transit - All modes with all Access 373 0.5%

ALL NONMOTORIZED 3,050 11.5% 39,058 48.4% 32 0.8% 5,636 49.2% 22,716 40.5%

DRIVE ALONE - 0.0% - 0.0% - 0.0% - 0.0% - 0.0%

SHARED RIDE 14,530 54.9% 20,277 25.1% 2,231 56.9% 4,718 41.1% 33,441 59.5%

SCHOOL BUS - 0.0% 20,955 26.0% - 0.0% - 0.0% - 0.0%

TOTAL 26,487 100.0% 80,663 100.0% 3,924 100.0% 11,466 100.0% 56,157 100.0%

HBSR HBO TOTAL0-car households Trips Share Trips Share Trips Share

ALL TRANSIT 531 5.0% 3,702 14.7% 16,286 7.6%

Transit - Bus with Walk Access 3,014 11.9%

Transit - Trolley with Walk Access 208 0.8%

Transit - Bus/Trolley with Walk Access

Transit - Bus/Trolley with Auto Access 480 1.9%


ALL NONMOTORIZED 4,213 39.8% 6,737 26.7% 81,442 38.0%

DRIVE ALONE - 0.0% - 0.0%

SHARED RIDE 5,838 55.2% 14,811 58.7% 95,846 44.7%

SCHOOL BUS - 0.0% - 0.0% 20,955 9.8%

TOTAL 10,582 100.0% 25,250 100.0% 214,529 100.0%

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Table 12. Model Validation Targets (continued)JTW Chain HBSchool HBUniv HBShop HBPUDO


ALL TRANSIT 3,509 1.9% 392 0.4% 797 5.5% 315 0.5% - 0.0%

Transit - Bus with Walk Access 2,678 1.4% 594 4.1%


Transit - Bus/Trolley with Walk Access 252 0.4%

Transit - Bus/Trolley with Auto Access 646 0.3% 162 1.1% 63 0.1%


ALL NONMOTORIZED 5,150 2.8% 32,622 31.4% 119 0.8% 2,569 4.1% 848 1.2%

DRIVE ALONE 134,023 72.5% 1,646 1.6% 12,591 86.7% 34,848 55.6% 28,436 39.3%

SHARED RIDE 42,051 22.8% 42,528 40.9% 1,022 7.0% 24,912 39.8% 43,042 59.5%

SCHOOL BUS - 0.0% 26,701 25.7% - 0.0% - 0.0% - 0.0%

TOTAL 184,733 100.0% 103,889 100.0% 14,529 100.0% 62,644 100.0% 72,326 100.0%


ALL TRANSIT 204 0.4% 1,561 1.0% 6,778 1.1%







DRIVE ALONE 20,011 40.9% 58,361 37.2% 289,916 45.0%

SHARED RIDE 23,133 47.3% 92,641 59.1% 269,329 41.8%

SCHOOL BUS - 0.0% - 0.0% 26,701 4.1%

TOTAL 48,879 100.0% 156,719 100.0% 643,718 100.0%

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2+-car households Trips Share Trips Share Trips Share Trips Share Trips Share

ALL TRANSIT 1,583 0.3% 282 0.2% 872 2.3% 187 0.1% - 0.0%







DRIVE ALONE 482,356 84.2% 9,885 6.3% 31,209 82.6% 73,267 48.3% 38,195 34.7%

SHARED RIDE 85,025 14.8% 89,682 56.7% 5,394 14.3% 75,635 49.9% 70,730 64.3%

SCHOOL BUS - 0.0% 43,053 27.2% - 0.0% - 0.0% - 0.0%

TOTAL 572,729 100.0% 158,117 100.0% 37,784 100.0% 151,591 100.0% 110,081 100.0%

HBSR HBO TOTAL2+-car households Trips Share Trips Share Trips Share

ALL TRANSIT 200 0.1% 751 0.2% 3,875 0.2%

Transit – Bus with Walk Access 600 0.1%

Transit – Trolley with Walk Access 42 0.0%

Transit – Bus/Trolley with Walk Access

Transit – Bus/Trolley with Auto Access 109 0.0%

Transit – All modes with all Access 200 0.1%


DRIVE ALONE 59,800 39.7% 156,750 35.7% 851,462 52.6%

SHARED RIDE 82,807 55.0% 268,470 61.2% 677,743 41.8%

SCHOOL BUS - 0.0% - 0.0% 43,053 2.7%

TOTAL 150,623 100.0% 438,889 100.0% 1,619,815 100.0%

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ALL HOUSEHOLDS Trips Share Trips Share Trips Share Trips Share Trips Share

ALL TRANSIT 14,000 1.8% 1,047 0.3% 3,330 5.9% 1,613 0.7% - 0.0%





Transit - All modes with all Access 1,047 0.3%


DRIVE ALONE 612,745 78.3% 11,441 3.3% 44,496 79.1% 105,311 47.4% 76,247 32.0%

SHARED RIDE 141,822 18.1% 152,204 44.4% 7,894 14.0% 102,726 46.2% 137,998 57.8%

SCHOOL BUS - 0.0% 90,716 26.5% - 0.0% - 0.0% - 0.0%

TOTAL 782,888 100.0% 342,669 100.0% 56,237 100.0% 222,389 100.0% 238,562 100.0%

HBSR HBO NHBW NHBNW TOTALALL HOUSEHOLDS Trips Share Trips Share Trips Share Trips Share Trips Share

ALL TRANSIT 935 0.5% 6,014 1.0% 2,405 1.7% 2,086 0.4% 31,430 1.0%

Transit - Bus with Walk Access 4,781 0.8% 2,002 1.4% 1,603 0.3%

Transit - Trolley with Walk Access 330 0.1% 138 0.1% 111 0.0%




ALL NONMOTORIZED 18,899 9.2% 25,404 4.2% 8,919 6.3% 12,916 2.5% 205,293 6.6%

DRIVE ALONE 76,966 37.6% 208,926 34.3% 90,075 63.5% 176,856 33.5% 1,403,063 44.9%

SHARED RIDE 107,938 52.7% 369,118 60.6% 40,365 28.5% 335,301 63.6% 1,395,366 44.6%

SCHOOL BUS - 0.0% - 0.0% - 0.0% - 0.0% 90,716 2.9%

TOTAL 204,738 100.0% 609,462 100.0% 141,763 100.0% 527,159 100.0% 3,125,868 100.0%

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Comparison to Transit On-Board Survey

One validation test of the mode choice model is to compare the transit trip tableoutputs to the expanded trip table from the transit on-board survey. Because the2004 on-board survey focused on bus routes, the comparison was made for bus trips(although for some trip purposes trolley trips are not separated from bus trips).

Adjustment of Mode Specific Constants

An iterative process was used to adjust the mode (and auto ownership level) specificconstants to produce a better match between the model results and the validationtargets. For each mode-auto ownership level combination, the modeled trips werecompared to the validation target, and the constant was revised upward or downward.The model was rerun with the new constants, and the results were compared again.This process continued until the model results were close to the validation targets.

Table 13 shows the effective constants for each trip purpose by mode and autoownership level for the final validated mode choice model.

Because the destination choice model uses the logsums from the mode choice model,the validation of both the destination and mode choice models were performedtogether.

Comparison of Assigned Transit Volumes to Transit Ridership Counts

After an initial round of mode choice model validation, the transit trip tables wereassigned to the transit network, and the results were compared to the ridership countsprovided by MATA. Based on this comparison, it was discovered that ridership wasgenerally underestimated in areas with high concentrations of low income households,despite the presence of variables representing vehicle ownership in the mode choicemodels.

To address this issue, a new variable was added to the mode choice model utilityequations. This variable represents the percentage of low income (less than $10,000annual income in 1999 dollars) households in the district in which the trip isproduced. The coefficients for this variable were asserted as shown in Table 14. Thisnew variable was found to improve the results of the mode choice model and transitassignment.

The final results of the transit assignment validation are shown in TechnicalMemorandum # 11.

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Table 13. Calibrated Mode Choice Constants

Journey to WorkMode 0-Veh 1-Veh 2-Veh 3+-VehTransit auto access 0.2980 -2.0464 -4.3841 -4.3841Bus walk access 2.5975 -1.1174 -3.5222 -3.5222Trolley walk access 3.0327 -0.4509 -2.9337 -2.9337Non-motorized 4.1163 2.0530 0.0912 0.0912Shared ride 0.0000 -0.0324 -1.1427 -1.1427Drive alone n/a 0.0000 0.0000 0.0000

Home Based UniversityMode 0-Veh Dummy 1-Veh Dummy 2-Veh Dummy 3+-Veh DummyTransit auto access -0.1852 -0.3483 -2.6793 -2.6793Bus walk access 1.4425 -2.1784 -2.1951 -2.1951Trolley walk access 1.7426 -1.5287 -1.5135 -1.5135Non-motorized 0.6714 2.0853 2.6636 2.6636Shared ride 0.0000 -3.0447 -1.0553 -1.0553Drive alone n/a 0.0000 0.0000 0.0000

Home Based OtherMode 0-Veh Dummy 1-Veh Dummy 2-Veh Dummy 3+-Veh DummyTransit auto access -0.6614 -1.2423 -3.6336 -3.6336Bus walk access 1.0172 -0.4748 -2.1516 -2.1516Trolley walk access 1.4499 -0.0382 -1.7241 -1.7241Non-motorized 3.4690 1.8465 1.6979 1.6979Shared ride 0.0000 1.9340 1.4116 1.4116Drive alone n/a 0.0000 0.0000 0.0000

Home Based Social RecreationalMode 0-Veh Dummy 1-Veh Dummy 2-Veh Dummy 3+-Veh DummyTransit -0.2018 -0.4134 -2.1267 -2.1267Non-motorized 3.7680 4.1061 2.6959 2.6959Shared ride 0.0000 2.2021 1.8282 1.8282Drive alone n/a 0.0000 0.0000 0.0000

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Table 13. Calibrated Mode Choice Constants (continued)

Home Based SchoolMode 0-Veh Dummy 1-Veh Dummy 2-Veh Dummy 3+-Veh DummyTransit -1.5495 1.8213 -0.8906 -0.8906Non-motorized 2.7230 10.2679 5.6152 5.6152School bus -0.1416 7.0515 4.0329 4.0329Shared ride 0.0000 7.1970 4.6056 4.6056Drive alone n/a 0.0000 0.0000 0.0000

Home Based ShopMode 0-Veh Dummy 1-Veh Dummy 2-Veh Dummy 3+-Veh DummyTransit auto access -1.4214 -0.7794 -1.3548 -3.0954Transit walk access 0.6592 -0.2096 -2.2406 -2.3752Non-motorized 3.4943 4.0983 2.3483 1.4881Shared ride 0.0000 0.8276 0.7909 0.6015Drive alone n/a 0.0000 0.0000 0.0000

Home Based Pickup/DropOffMode 0-Veh Dummy 1-Veh Dummy 2-Veh Dummy 3+-Veh DummyNon-motorized 0.0000 0.0000 0.0000 0.0000Shared ride -3.0994 -2.9675 -1.6185 -1.2761Drive alone n/a -4.4776 -3.2733 -2.4206

Non-Home Based Non-WorkMode ConstantTransit auto access -3.6354Bus walk access -3.0345Trolley walk access -3.3875Non-motorized -0.6739Shared ride 0.3944Drive alone 0.0000

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Table 13. Calibrated Mode Choice Constants (continued)Non-Home Based WorkMode ConstantTransit auto access -3.3806Bus walk access -1.5036Trolley walk access -1.7695Non-motorized 0.4963Shared ride -1.0071Drive alone 0.0000

Table 14. Coefficients for Low Income VariableTrip Purpose 0-car 1+ carJTW 9.2 7.2HBU 10.3 7.3HBO 8.0 7.2HBSR 7.5 7.2HBSc 7.2 7.2HBSh 7.8 7.2NHBNW 7.2NHBW 7.2

Final Validated Model Results

The final validation results for the mode choice model are shown in Table 15. Thesecan be compared to the validation targets in Table 12.

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Table 15. Model Validation ResultsJTW Chain HBSchool HBUniv HBShop HBPUDO


ALL TRANSIT 8,666 32.7% 374 0.5% 1,610 41.4% 1,126 9.8% - 0.0%







DRIVE ALONE - 0.0% - 0.0% - 0.0% - 0.0% - 0.0%

SHARED RIDE 14,651 55.3% 20,686 25.6% 2,246 57.8% 4,680 40.8% 33,479 59.6%

SCHOOL BUS - 0.0% 21,031 26.1% - 0.0% - 0.0% - 0.0%

TOTAL 26,486 100.0% 80,664 100.0% 3,889 100.0% 11,466 100.0% 56,156 100.0%


ALL TRANSIT 546 5.2% 3,651 14.5% 15,973 7.4%







DRIVE ALONE - 0.0% - 0.0%

SHARED RIDE 5,809 54.9% 14,814 58.7% 96,365 44.9%

SCHOOL BUS - 0.0% - 0.0% 21,031 9.8%

TOTAL 10,582 100.0% 25,249 100.0% 214,492 100.0%

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Table 15. Model Validation Results (continued)JTW Chain HBSchool HBUniv HBShop HBPUDO


ALL TRANSIT 3,528 1.9% 394 0.4% 826 5.6% 314 0.5% - 0.0%






ALL NONMOTORIZED 5,251 2.8% 32,353 31.1% 134 0.9% 2,578 4.1% 805 1.1%

DRIVE ALONE 133,764 72.4% 1,650 1.6% 12,849 86.4% 34,871 55.7% 28,463 39.4%

SHARED RIDE 42,191 22.8% 42,618 41.0% 1,060 7.1% 24,883 39.7% 43,058 59.5%

SCHOOL BUS - 0.0% 26,873 25.9% - 0.0% - 0.0% - 0.0%

TOTAL 184,734 100.0% 103,888 100.0% 14,869 100.0% 62,646 100.0% 72,326 100.0%


ALL TRANSIT 206 0.4% 1,563 1.0% 6,831 1.1%







DRIVE ALONE 20,075 41.1% 58,353 37.2% 290,025 45.0%

SHARED RIDE 23,014 47.1% 92,551 59.1% 269,375 41.8%

SCHOOL BUS - 0.0% - 0.0% 26,873 4.2%

TOTAL 48,880 100.0% 156,717 100.0% 644,060 100.0%

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Table 15. Model Validation Results (continued)JTW Chain HBSchool HBUniv HBShop HBPD

2+-car households Trips Share Trips Share Trips Share Trips Share Trips Share

ALL TRANSIT 1,527 0.3% 283 0.2% 869 2.3% 188 0.1% - 0.0%







DRIVE ALONE 482,335 84.2% 9,894 6.3% 30,884 82.4% 73,270 48.3% 38,207 34.7%

SHARED RIDE 85,053 14.9% 89,487 56.6% 5,380 14.4% 75,607 49.9% 70,735 64.3%

SCHOOL BUS - 0.0% 43,388 27.4% - 0.0% - 0.0% - 0.0%

TOTAL 572,727 100.0% 158,117 100.0% 37,474 100.0% 151,592 100.0% 110,077 100.0%

HBSR HBO TOTAL2+-car households Trips Share Trips Share Trips Share

ALL TRANSIT 202 0.1% 718 0.2% 3,787 0.2%

Transit – Bus with Walk Access 607 0.1%


Transit – Bus/Trolley with Walk Access




DRIVE ALONE 59,631 39.6% 156,867 35.7% 851,088 52.6%

SHARED RIDE 82,786 55.0% 268,147 61.1% 677,195 41.8%

SCHOOL BUS - 0.0% - 0.0% 43,388 2.7%

TOTAL 150,626 100.0% 438,891 100.0% 1,619,485 100.0%

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Table 15. Model Validation Results (continued)JTW Chain HBSchool HBUniv HBShop HBPUDO

ALL HOUSEHOLDS Trips Share Trips Share Trips Share Trips Share Trips Share

ALL TRANSIT 13,542 1.7% 1,051 0.3% 3,309 5.9% 1,626 0.7% - 0.0%





Transit - All modes with all Access 1,051 0.3%


DRIVE ALONE 616,099 78.6% 11,544 3.4% 43,735 77.8% 108,140 47.9% 66,670 27.9%

SHARED RIDE 141,897 18.1% 152,793 44.6% 8,686 15.4% 105,168 46.6% 147,273 61.7%

SCHOOL BUS - 0.0% 91,291 26.6% - 0.0% - 0.0% - 0.0%

TOTAL 783,950 100.0% 342,669 100.0% 56,238 100.0% 225,699 100.0% 238,561 100.0%

HBSR HBO NHBW NHBNW TOTALALL HOUSEHOLDS Trips Share Trips Share Trips Share Trips Share Trips Share

ALL TRANSIT 955 0.5% 5,793 0.9% 3,218 2.2% 2,086 0.4% 31,580 1.0%

Transit - Bus with Walk Access 4,879 0.8% 2,930 2.0% 1,603 0.3%

Transit - Trolley with Walk Access 154 0.0% 158 0.1% 111 0.0%




ALL NONMOTORIZED 17,819 8.5% 24,178 3.9% 9,035 6.3% 12,916 2.5% 198,059 6.3%

DRIVE ALONE 79,704 37.9% 215,221 34.7% 91,106 63.3% 176,856 33.5% 1,409,075 44.8%

SHARED RIDE 111,607 53.1% 375,513 60.5% 40,422 28.1% 335,301 63.6% 1,418,660 45.1%

SCHOOL BUS - 0.0% - 0.0% - 0.0% - 0.0% 91,291 2.9%

TOTAL 210,085 100.0% 620,859 100.0% 143,939 100.0% 527,159 100.0% 3,148,665 100.0%

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Technical Memorandum #7 – Freight ModelThis memorandum covers the development of the following specific submodels relatedto the freight submodel:

• Internal truck trip generation• Internal truck trip distribution• Externalinternal truck trips• Externalexternal truck trips

This memorandum was prepared by the following Cambridge Systematics, Inc. staff:Edward Bromage and Thomas Rossi.

ContentsMethodology

Internal Truck Travel ExternalInternal/InternalExternal Trips ExternalExternal Truck Trips

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Methodology

Three trip types are simulated by the Freight Model: internal trips, externalinternal/internalexternal trips, and externalexternal trips.

Internal Truck Travel

Procedures described in Chapter 4 of the Federal Highway Administration’s QuickResponse Freight Manual1 (QRFM) were used to develop the internal truck model. TheQRFM process provides a methodology for estimating travel for three vehicleclassification types: fourtire commercial vehicles, single unit trucks with six or moretires, and combination trucks.

Trip Generation

The daily truck trip generation rates, from Table 4.1 of the QRFM, are shown in Table1. These rates are applied to the socioeconomic data at the zone level, resulting in thenumber of fourtire commercial vehicles, single unit trucks, and combination trucksgenerated for each zone. The trip generation rates shown in Table 1 are for tripdestinations (which, on an average day, are equal to trip origins).

Table 1: QRFM Trip Generation Rates

Commercial Vehicle Trip Destinations (or Origins)per Unit per Day

Generator (Unit) FourTireTrucks

Single UnitTrucks

CombinationTrucks

Total Trucks

Employment:Agriculture, Mining, and Construction 1.110 0.289 0.174 1.573Manufacturing, Transportation,Communications, Utilities, andWholesale Trade

0.938 0.242 0.104 1.284

Retail Trade 0.888 0.253 0.065 1.206Office and Services 0.437 0.068 0.009 0.514

Households 0.251 0.099 0.038 0.388

1 Cambridge Systematics, Inc., Comsis Corporation, and University of Wisconsin, Milwaukee. QuickResponse Freight Manual. Prepared for Federal Highway Administration, 1996.

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The employment categories used in the Memphis model do not precisely correspondwith the categories shown in this table. The Memphis employment categories areretail, industrial_MFG, wholesale, service, office, and government. Thecorrespondence between the Memphis and QRFM employment categories is shown inTable 2.

Table 2: Employment Categories CrossReference

QRFM Categories Memphis CategoriesAgriculture, Mining, and Construction NoneManufacturing, Transportation, Communications,Utilities, and Wholesale Trade

Industrial_MFG, Wholesale

Retail Trade RetailOffice and Service Service, Office, and GovernmentHouseholds Households

TimeofDay

The Memphis model simulates travel for four time periods: AM peak period, Midday,PM peak period, and Night offpeak. To convert the daily truck trips to trips by timeperiod flows, timeofday factors were derived from the vehicle classification count datacollected for this project. The factors are shown in Table 3.

Table 3: Time of Day Factors by Truck Type

Truck Type AM Peak Midday Peak PM Peak OffPeakFourTire Trucks 17.8% 29.6% 26.2% 26.4%Single Unit Trucks 17.4% 34.5% 25.2% 22.9%Combination Trucks 16.0% 33.0% 23.8% 27.2%

Trip Distribution

The quickresponse procedure uses the following standard gravity model for tripdistribution:

∑=

= n

j 1ijj

ijjiij

FD

FDOV

where:

Vij = trips (volume) originating at analysis area i and destined to analysis area j

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Oi = total trip originating at iDj = total trip destined at jFij = friction factor for trip interchange iji = origin analysis area number, i = 1, 2, 3 . . . nj = destination analysis area number, j = 1, 2, 3 . . . nn = number of analysis areas

The truck origins (Oi) and destinations (Dj) are the outputs of the trip generationprocess described above. The friction factors Fij from the QRFM for each vehicle typeare based on an exponential distribution and were used without change for theMemphis model. The equations for the three vehicle classifications are as follows(where tij represents the highway travel time between zones i and j):

Fourtire commercial vehicles:

Fij etij=

−0 08. *

Single unit trucks (6+ tires):

Fij etij=

−01. *

Combinations:

Fij etij=

−0 03. *

Truck Trip Assignment

The trip tables for the three truck classifications are assigned along with auto trips ina multiclass highway assignment procedure. This procedure is described inTechnical Memorandum #8(a), “Highway Assignment, Transit Assignment, andFeedback Procedures”.

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ExternalInternal/InternalExternal Trips

The methodology for computing externalinternal (EI) station truck flows requiresexternal station level computation of the volumes for the three truck types. For thebase year this process starts with the 2004 average daily traffic (ADT) counts. Thepercentage of trucks (either from vehicle classification counts or default values fromother locations of the same roadway functional classification if counts are notavailable) for each truck category are applied to the ADT to produce total externalstation truck trips by truck type. Finally, the percentage of truck trips that are EI(rather than externalexternal) is applied to produce EI trips by truck type.

Table 4 presents the information for this process for the external stations, whichincludes the percentage trucks by the percentage of truck trips that are EI. Table 5shows the final base year truck volumes by truck type for each external station.

Both internal and externalinternal trucks are distributed in the same gravity modeldistribution, as described above.

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Table 4. External Station Truck Count Summary

StationID Roadway Name 2004

ADT

%TotalTruck

%SingleUnitTruck

%Comb.Truck

%Truck

EI10001 Highway 59 1700 5.42 3.7 1.7 10010002 Highway 51 23480 10.8 2.8 8.0 93.4

10003 Highway 59 S/Mount Carmel 4900 6.5 1.8 4.7 97.0

10004 Austin Peay 2170 17.5 3.5 14.0 84.710005 Highway 79 2200 17.0 4.4 12.6 96.110006 Stanton Road N 630 5.42 3.7 1.7 76.510007 I40 E 31720 39.4 5.3 34.1 58.710008 Highway 59 E 3070 7.2 2.9 4.3 88.310009 Highway 64 14970 7.5 2.8 4.7 87.410011 Highway 57 7300 4.9 1.4 3.5 92.210012 Highway 178 22001 5.42 3.7 1.7 94.110013 Highway 78 25650 22.0 5.2 16.8 62.210014 Highway 305 S 3000 8.7 7.2 1.5 10010015 Highway 51 S 42001 7.4 5.5 1.9 10010016 I55 S 31000 18.3 3.4 14.9 78.110017 Pratt Road 750 5.42 3.7 1.7 10010018 Highway 304/713 2001 7.9 5.5 2.4 98.410019 Highway 61 290001 3.9 3.3 0.6 65.210020 Charleston Mason Road 500 3.02 2.0 1.0 10010022 Feathers Chapel Road 620 1.52 1.0 0.5 10010023 Macon Road 1000 3.0 1.4 1.6 10010024 Highway 72 14000 7.3 3.0 4.3 64.4

10025 Goodman RoadExtension 0 11.02 3.5 7.5 90.0

10026 Victoria Road 5001 5.42 3.7 1.7 89.310027 I40/I55 W 104220 28.0 6.0 22.0 69.610028 Stanton Road S 720 5.42 3.7 1.7 48.710029 Holly Springs Road 7501 3.02 2.0 1.0 10010030 Byhalia Road 5750 5.8 4.8 1.0 95.310031 Old Highway 61 7201 3.02 2.0 1.0 10010032 Route 3 10001 11.02 3.5 7.5 90.9

Notes for Table 4:1. Count is from 2003.2. No classification count available; default value computed from average of other

stations of same roadway type.

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Table 5. Base Year External Station Truck VolumesStationID Roadway Name

SingleUnitTrucks

CombinationTrucks

10001 Highway 59 63 2910002 Highway 51 573 163810003 Highway 59 S/Mount Carmel 88 23010004 Austin Peay 76 30410005 Highway 79 97 27710006 Stanton Road N 23 1110007 I40 E 1681 1081710008 Highway 59 E 89 13210009 Highway 64 419 70410011 Highway 57 102 25610012 Highway 178 81 3710013 Highway 78 1334 430910014 Highway 305 S 288 6010015 Highway 51 S 231 8010016 I55 S 1258 551310017 Pratt Road 28 1310018 Highway 304/713 220 9610019 Highway 61 957 17410020 Charleston Mason Road 10 510022 Feathers Chapel Road 6 310023 Macon Road 14 1610024 Highway 72 420 60210025 Goodman Road Extension 0 010026 Victoria Road 19 910027 I40/I55 W 6253 2292810028 Stanton Road S 27 1210029 Holly Springs Road 15 810030 Byhalia Road 276 5810031 Old Highway 61 14 710032 Route 3 35 75

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ExternalExternal Truck Trips

Based on the Tennessee and Mississippi statewide models and Freight AnalysisFramework data, a base year externalexternal truck trip table was developed. Theexternalexternal trips at each station are consistent with the average daily trafficvolumes, truck percentages, and EI percentages shown in Table 4. This trip tablecontains only combination trucks. The forecast year truck trip table was developedbased on growth in externalexternal truck trips in the statewide models. See section“Methodology – ExternalExternal and ExternalInternal Trips” in TechnicalMemorandum #3, “Trip Generation” for details on how the statewide model is used todetermine EE and EI trip splits. Appendix A of Technical Memorandum #3 providesthe EE truck percentages for each external station.

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Technical Memorandum #8(a)Highway Assignment, Transit Assignment, andFeedback ProceduresThis memorandum describes the proposed highway assignment, transit assignment,and feedback procedures for the Memphis MPO Travel Demand Model (TDM).

This memorandum was prepared by Craig Gresham and Zhiyong Guo of KimleyHornand Associates, Inc.

ContentsOverviewHighway Assignment ProcedureTransit Assignment ProcedureFeedback Loop Procedure

Appendix A – Model District Map

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Overview

The Memphis MPO Model is a compilation of a series of submodels. Each submodelwill be calibrated and validated not only as it is developed, but also in concert with itscomplementary submodels and as a part of the whole. This document focuses on theassignment process for highway trips and transit trips, along with the feedbackprocess used to run model feedback loops with congested travel times.

The flow chart in Figure 1 shows a graphical overview of where the highwayassignment, transit assignment, and feedback procedures fit in. After the mode choiceand destination choice models, daily trips by purpose and mode are factored into timeperiods for input into the highway and transit assignment procedures. After theassignment procedures are completed, the Memphis model uses a feedback loop torerun the model with congested travel times from the loaded model. The congestedtravel times are then used to better model mode choice, the impacts of congestion,transit use, HOV use, and more components. Several feedback loops will be used inthe model, which will more accurately reflect in the Memphis model the impact ofcongestion resulting from user choices.

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Figure 1. Model Process

Trip Generation

PostGenerationTimeofDay Modeling

Trip Distribution

Mode Choice

PostMode ChoiceTimeofDay Modeling

Trip Assignment(AM, Midday, PM, Off

Peak)

Feedback Loops withCongested Travel Times

Loaded Highwayand Transit Networks

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Highway Assignment Procedure

The highway assignment has two steps: a multimodal multiclass (MMA) allornothingassignment, and a multimodal multiclass (MMA) user equilibrium assignment. Theinitial allornothing assignment is used to “preload” through trips and largecommercial vehicle trips, which are less sensitive to travel time and do not reroutetrips based on congestion as often as trips such as an internal homebasedwork autotrip. A multimodal multiclass assignment, as described in Travel Demand Modeling inTransCAD 4.8, is “a generalized cost assignment that lets you assign trips byindividual modes or user classes to the network simultaneously.” This setup offersseveral advantages, including the flexibility to model HighOccupancy Vehicle (HOV)lanes, toll lanes, and passenger car equivalencies for trucks.

The two steps of assignments (preload and equilibrium) are applied for each of the fourtime periods (AM, midday, PM, night), which yields a total of eight assignment routinesfor the Memphis model. Volume Delay functions use for the assignment are based ontime and period capacity and are modified versions of the Bureau of Public Road (BPR)curves. The volumedelay curves have varied coefficients for different area types,functional classification, and link speed. These curves will be reviewed and adjustedduring the calibration process based on information gleaned from the travel timestudies carried out in 2004 by KimleyHorn.

Step 1. MMA AllorNothing Preload Assignment

The first step of the highway assignment procedure is to “preload” through trips andheavy (combinationunit) truck trips. This MMA assignment using an allornothingassignment, which assigns trips between origindestination pairs based on theshortest path established by the freeflow travel time. This assignment procedure isintended to reflect the insensitivity congestion has on through trips and heavy trucktrips, since they are typically much less likely to divert to another roadway than othertypes of trips, either due to lack of knowledge about the area, perceived inconvenience,or restrictions against heavy trucks.

Six trip tables are loaded during the preload assignment procedure for each timeperiod:

Externalexternal (EE) automobile singleoccupancy vehicle (SOV) EE automobile highoccupancy vehicle (HOV) trips EE singleunit (SU) truck trips EE combinationunit (CU) truck trips Internalexternal (IE) combinationunit truck trips Internalinternal (II) combinationunit truck trips

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Since there is no reflection of delay in the choice of path for these trips, no volumedelay function is required and only one assignment iteration is required.

Step 2. MMA User Equilibrium Assignment

The second step of the highway assignment procedure is to load all remaining tripsnot considered in the preload assignment. Preloaded trips are addressed in theassignment procedures as traffic that reduces capacity but cannot divert to anotherroute. The remaining trips are loaded using an MMA userequilibrium assignment,which assigns trips between origindestination pairs in an iterative fashion thataccounts for link congestion on route choice. The userequilibrium assignmentprocedure computes the link travel time, assigns link traffic based on shortest path,and then recalculates the link travel time. This step is repeated until the userequilibrium conditions are met: all used paths for each OD pair are minimal andequal; and any unused path for a given OD pair has a greater travel time than anyused paths for that OD pair. In TRANSCAD’s implementation, the convergence of userequilibrium is measured by the “relative gap,” which is an estimate of the “distance”between current solution and the user equilibrium solution. The relative gap is definedas follows:

Relative gap =

t tUE AONi i

links linkstUE

ilinks

x x

x∀ ∀

∀

−∑ ∑∑

Where:UEix = Current flow on link iAONix = Allornothing flow on link iUEtix = Current travel time on link i

The traffic assignment will stop when the current iteration relative gap is below a userspecified threshold or the maximum number of iterations is reached.

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Eight trip tables are loaded during the equilibrium assignment procedure for eachtime period:

Internalinternal (II) singleoccupancy vehicle (SOV) trips Internalinternal (II) highoccupancy vehicle (HOV) trips Internalexternal (IE) SOV trips Internalexternal (IE) HOV trips II light truck/commercial auto trips II singleunit (SU) truck trips IE singleunit (SU) truck trips Auto access transit trips (from origins to parkandride facilities)

The Memphis model uses the Bureau of Public Roads (BPR) formula as the volumedelay function to relate travel time to the volume/capacity ratio. The BPR formula isshown below,

where:

TN = Congested link travel timeT0 = Initial link travel time under freeflow conditionsV = Assigned traffic volumeC = Capacity (typically LOS C, D or E)

In the equation, the coefficient 0.15 is known as the alpha value and the exponent of 4is known as the beta value. Different functionally classified roads are known to havedifferent alpha and beta values. The values of 0.15 and 4 are recognized as the mostgeneric. The alpha and beta settings are based on the type of facility and its postedspeed. Settings are automatically applied in the GISDK code and have been developedbased on the coefficients presented in NCHRP Report 365. Table 1 lists the alpha andbeta settings, by functional classification, for the Memphis model. These alpha andbeta settings were revised based on the model performance as compared to counts andthe observed Memphis travel time data collected in the field in 2004.

+∗=

4

0 15.01CVTTN

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Table 1. Alpha and Beta Settings by Speed and Functional classification

Multilane SectionsPosted Speed<55 5565 >65Functional

classification Alpha Beta Alpha Beta Alpha BetaRural Interstate andFreeway 0.68 5.5 0.83 7.00 0.83 5.50

Urban Interstate andFreeway 0.68 5.5 0.83 7.00 0.88 9.80

Arterial, Collector,Local 0.71 2.10 0.83 2.70 1.00 5.40

TwoLane Sections, Ramps, and Frontage RoadsPosted Speed

<55 5565 >65Functionalclassification Alpha Beta Alpha Beta Alpha BetaArterial, Collector,Local 0.71 2.10 0.83 2.70 1.00 5.40

Frontage Road 0.71 2.10 0.83 2.70 1.00 5.40Ramp 0.71 2.10 0.83 2.70 1.00 5.40

The assignment module for Memphis loads the model at LOS E (which is the settingfor which both the alpha and beta settings in Table 1 were designed). The modelsettings allow the alpha and beta settings, along with the current number of iterationsand convergence criteria to be adjusted outside of the GISDK code. The calibratedMemphis model was loaded using LOS E, the maximum number of iterations allowedwas 50, and a convergence was set to 0.001.

Model Link Capacity

Hourly capacities were developed for the Memphis model in order to use collectedstreet data. This provides the most accurate representation of actual capacity (levelsof service A through E) on an individual link. These capacities — detailed in theTechnical Memorandum #8(b) – Capacity Development — are implemented usingan equation which takes into account functional classification, speed limit, lanes,signal density, median treatment, area type, average lane width, and average shoulderwidth. The capacity equations are built into the model process as a TransCAD lookuptable, so modifications to network attributes automatically update the capacity insubsequent runs.

Since the model is based on four multihour time periods, a conversion factor must beused to create a time period capacity for each of the four time periods. The capacity

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factors below are based on hourly traffic count data and the Memphis householdtravel survey. It is based on the following equation:

Capacity Factor = Total Time Period Volume/(PeakHour Time Period Volume * Hours in Time Period)

Table 2. Hourly Capacity Factor by TimeofDay

Time PeriodCapacityFactor

Duration(Hours)

AM 1.5 3Midday 2.7 5

PM 2.5 4Night 3.8 12

Observed FreeFlow Times

As part of the Memphis Model development, KimleyHorn used GPS devices andTransCAD to collect travel time data in 2004 on a sampling of corridors in theMemphis area to use in model development and validation. One product of the traveltime study is adjustment factors to apply against the posted speeds to create freeflowspeeds for the model. These factors are a simple ratio: if the speed limit is 65 mph,and the factor is 1.05, the freeflow speed is 68 mph (65 * 1.05). This was chosen overa direct adjustment (addition or subtraction of freeflow speed) because the ratios aremore sensitive as speeds get slower in the model. Table 3 shows the freeflow traveltime factors used in the model for distribution and assignment.

Table 3. FreeFlow Travel Time Factors(FreeFlow Speed/Posted Speed)

Area TypeFunctionalclassification CBD Urban Suburban Rural

Interstate/Freeway 1.05 1.05 1.05 1.05

Arterial(>=45 mph) 0.92 0.92 0.92 0.92

Arterial(<45 mph) 0.85 0.85 0.95 0.95

Collector/Local 0.80 0.70 0.70 0.90

Source: Based on Fall 2004 midday travel time observations, KimleyHorn

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Observed Congested Travel Times

Another product of the 2004 travel time study was congested travel times for the peakperiods (AM and PM). These congested travel times were used to develop congestedtravel time ratios. These are first used as an input to apply against the posted speedlimit to approximate the congested speeds for use in the mode choice and destinationchoice models. Subsequent feedback loops use actual congested speeds produced bythe model. As a part of the highway assignment calibration, the congestion speedtravel time factors shown in Table 4 is used as a point of comparison to substantiatethat the volumedelay settings and capacity equations are representing theappropriate level of congestion in the base year Memphis Model.

Table 4. Congested Speed Estimation Factors

Freeways Arterial Collector and LocalArea Type TimeofDayAll >=45 mph <45 mph All

AM 1.04 0.92 0.75 0.78PM 1.05 0.86 0.72 0.72

Midday 1.10 0.92 0.85 0.80CBD

Night 1.05 0.92 0.85 0.80AM 1.04 0.92 0.80 0.79PM 0.97 0.86 0.83 0.70

Midday 1.09 0.92 0.84 0.69Urban

Night 1.05 0.92 0.85 0.70AM 1.04 0.93 1.02 0.95PM 1.07 0.86 0.94 0.93


Night 1.05 0.92 0.95 0.90AM 1.04 0.93 1.02 0.95PM 1.07 0.86 0.94 0.93


Night 1.05 0.92 0.95 0.90 Source: Based on Fall 2004 PM travel time observations, KimleyHorn

Signal Penalty Information

For the Memphis Model, the locations of all of the known signalized locations on thehighway network were input into the network node data. A total of 927 signal

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locations were input to use in the highway network. These signal locations will beused to include turn delays at intersections in order to improve path choices taken bytravelers. In the model, left turns have the highest penalties, which are approximatelytwice that of right turns. Through penalties were the lowest from the model for thereason that these penalties are already partially addressed in the freeflow travel timefactors displayed in Table 3. Unsignalized intersections are not identified or used inthe model to penalize turns simply because of the sheer difficulty of identifying theseintersections for an entire metropolitan region. Table 5 shows the turn penalties bytimeofday and functional classifications used in the Memphis model.

Table 5. Signal Turn Penalties (in Minutes)

Turning Penalty (min.)Time of day Functional Classification

Left Right ThroughRamp * 0.500 0.500 0.050

Major Arterial 0.250 0.125 0.000Minor Arterial 0.250 0.125 0.025

AM/PMPeak

Collector / Local 0.250 0.125 0.050Ramp * 0.500 0.500 0.000


MD Offpeak

Collector / Local 0.225 0.115 0.045Ramp * 0.500 0.500 0.000


OP Offpeak

Collector / Local 0.200 0.100 0.040* Ramp penalties are implemented without considering signal locations.

Calibration Performance Reporting

The Memphis Model has a utility function which will be used in the model calibrationand validation to quickly report the performance measures for the 2004 model run. Asample window for the performance report is shown in Figure 2.

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Figure 2. Sample TransCAD Performance Report

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Transit Assignment Procedure

The transit assignment procedure is used to predict traveler’s choice of routes in thetransit network as a function of transit level of service and fare. For the MemphisTDM, the Pathfinder Method is used to assign the transit trips to the route system.The most important advantages of using Pathfinder method is that fare is used as partof the generalized cost function to determine the best path. In addition, mode to modetransfer penalties and prohibitions, limits on the number of transfers, and the bestpaths including parkandride access are also modeled in the Memphis TDM.

Transit Modes (Base year)

Four transit modes and modeled in the Memphis TDM:

Auto access transit Walk access transit Walk access bus Walk access trolley

For future year model, two additional modes specifically for light rail will be active inthe model. See Technical Memo #12 “Future Year Model Development” for details.

The OD tables for each mode are combined from the modalperson trip tablesgenerated by the Mode Split model, with each mode corresponding to the trip purposesin Table 7.

Table 7. Transit Mode and Trip Purpose Mapping

Transit Mode Trip PurposesAuto Access Transit JTW, HBO, HBSh, HBU, NHBW, NHBOWalk Access Transit HBSc, HBSh, HBSR

Walk Access Bus JTW, HBO, HBU, NHBW, NHBOWalk Access Trolley JTW, HBO, HBU, NHBW, NHBO

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The combined trip tables for each transit mode are then assigned to the route systemfor each time period.

ParkandRide (PNR) Trips

In Memphis TDM, four parkandride nodes are modeled: North End Terminal, CentralStation, Cleveland Station, and American Way Transit Center. The Auto Access Transittrips consist of two parts: transit trips and auto trips (to PNR facilities). The transittrip portion is assigned as a separate mode in transit assignment, as discussed inprevious section. To achieve more accuracy, for the auto trip portion, the Auto AccessTransit trip table is first being converted into an origintoparking node (OP) matrix.The OP matrix is then combined with the MMA trip tables, and assigned as a separateclass in the second step MMA User Equilibrium Highway Assignment procedure.

Transit Speed and InVehicle Travel Time (IVTT) Methodology

Transit speed methodology is adopted from the SEMCOG travel demand modeldeveloped by Cambridge Systematics. The transit speed is modeled as a function ofthe highway speed, area type, and highway functional classification. The transit speedis calculated as follows:

(1 1)transit highwayS k S= − × if highway cut offS S −≤ (Case 1)

0 ( ) 2transit highway cut offS S S S k−= + − × if highway cut offS S −> (Case 2)

Where:transitS = Transit speed

highwayS = Highway speed

cut offS − = Highway speed cutoff for transit speed calculation

0S = Transit speed lower bound

1k = Slope used for case 1


The parameter values used in transit speed calculation are listed in the Table 8.

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Table 8. Parameter Settings for Transit Speed Calculation

Area Type Functional classification cut offS − 0S 1k 2kInterstate 30.0 30.0 0.000 0.8

Major Arterial/Freeway 30.0 30.0 0.000 0.8

Minor Arterial/Collector 12.0 6.0 0.45 0.5CBD

Local or Transit ROW 10.0 6.0 0.4 0.4

Interstate 30.0 30.0 0.000 0.8


Minor Arterial/Collector 12.0 8.0 0.3 0.5Urban


Interstate 30.0 30.0 0.000 0.8


Minor Arterial/Collector 18.0 11.0 0.4 0.55Suburban


Interstate 30.0 30.0 0.000 0.8


Minor Arterial/Collector 18.0 12.0 0.389 0.6Rural


Fare Settings

In Memphis TDM, the transit fares are modeled as Zonal fares based on MATA’scurrent fare system. Special midday fares are used for the midday skimming andassignment procedure. Mode transfer fares are also modeled based on the currenttransfer fare rates.

The zonal fares are stored in the input file FareZones.mtx. Midday fares are stored inthe input file FareZones_Midday.mtx.

The mode transfer fares are stored in the input file mode_xfer.bin.

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Transit Pathfinding Parameters

The value of time and OVTT weight parameters are listed in Tables 9 and 10 below:

Table 9. Value of Time Settings for Transit Pathfinding

Value of Time AM MD PM OP

Drive to BusTrolley 0.1 0.05 0.1 0.05Walk to Bus 0.1 0.05 0.1 0.05

Walk to Trolley 0.1 0.05 0.1 0.05Walk to BusTrolley 0.05 0.05 0.05 0.05

Table 10. OVTT Weight Settings for Transit Pathfinding

IVTT Weights AM MD PM OP

Drive to Bus/Trolley 2 3 2 3Walk to Bus 2 3 2 3

Walk to Trolley 2 3 2 3Walk to Bus/Trolley 3 3 3 3

Other Pathfinder parameters are presented in Table 11.

Table 11. Global Parameter Settings for Transit Pathfinder Algorithm

Layover Time 5 minMax PACC 10 minMax Access 18 minMax Egress 18 min

Max Impedance 180 minTransfer Penalty Weight 1

Transfer Penalty 20 minMax Transfer Waiting 10 minMin Transfer Waiting 5 min

Max Transfer Walk Time 6 minMax Number of Transfers 3Path Combination Factor 0.5

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In addition, dwell times were modified to be applied by each timeofday period by each route,to provide a match between bus run times and the bus schedules.

To make the transit assignment procedure more efficient, the Transit Path Set (.tps)files are saved in the transit skimming procedure, and reused in the transitassignment procedure. This is a feature available only for the latest TRANSCADVersion 4.8.

Performance ReportingAt the systemwide level, for each timeofday period and daily, the following items arereported for the transit assignment:

• Total boardings• Total linked trips• Total number of transfers• Transfer rates

At district level, boardings, alightings, and number of transfers in each district arereported for each timeofday period and daily. The districts are developed based onthe planning districts in the region, taking into account for the expanded model area.The districts are shown in Appendix A. The transit OD matrices can also be combinedto district level for analysis use. See section 10.2 of the User’s Manual for details.

At route level, for each timeofday period and daily, the boardings, transfers, numberof stops in model, dwell times, bus run times, scheduled bus run times, anddifferences between model and schedules are reported.

At stop level, for each timeofday period and daily, the trolley line boardings arereported by each transit mode and total.

In addition, boarding counts are grouped by MATA routes, subgroups and groupsbased on the information provided by MATA. Boardings are then compared with thetargets in route, subgroup, and group level in separate tables.

Finally, the boarding counts by each route, timeofday period and transit mode arereported in a formatted table. This table can be easily imported into Excel for postanalysis.

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Feedback Loop Procedure

The objective of the feedback process is to execute the travel model system in anintegrated manner so that the time outputs from the traffic assignment model arereasonably consistent with the inputs assumed at the trip distribution and modechoice steps. The trip distribution, modal split, and trip assignment steps are repeateduntil a sufficient convergence — output times being close to input times — is achieved.In the Memphis TDM, the Method of Successive Averages (MSA) feedback loopprocedure is implemented.

In the MSA method, output volumes from trip assignment from previous iterations areweighted together to produce the current iteration’s link volumes. Adjusted congestedtimes are then calculated based on the normal volumedelay relationship. Thisadjusted congested time is then fed back to the skimming procedures.

The adjusted volume is calculated based on the following equation:

1 11 ( )n n n nMSAFlow MSAFlow Flow MSAFlown− −= + −

where:

n = current MSA iteration number

nMSAFlow = calculated MSA flow at iteration n

nFlow = resulting flow directly from trip assignment

The MSA flow and link cost created from the MMA assignment procedure is then “fedback” into the skimming procedure of the next MSA feedback iteration. The benefits ofthis process are that it can be applied with relatively ease of programming and thatconvergence is assured.

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Convergence Criteria

At the end of each feedback iteration, the MMA User Equilibrium AssignmentProcedure can return a calculated Root Mean Square Error (RMSE) statistic thatcompares volumes from the current feedback iteration to volumes from the lastfeedback iteration. The equation used to calculate RMSE is shown below:

1 2

1

( )

1

Ln ni i

in

x xRMSE

L

−

=

−=

−

∑

Where:i = link iL = total number of linksn = feedback iteration number

nRMSE = Root Mean Square Error for feedback iteration nnix = volume on link i , iteration n

The convergence is then checked against the predefined RMS Error threshold. If theconvergence criteria is not met the MSA flows and travel times are fed back to the nextiteration. This iterative process will continue until one of the following conditions hold:

1) nRMSE < RMSE threshold2) Current iteration n > maximum iteration allowed

The suggested thresholds for the Memphis model are:RMSE threshold = 3

Maximum iteration = 3

These thresholds are currently based on model runtime. As part of the modeldevelopment process, sensitivity tests will be used to evaluate the effects of additionalfeedback loops on model performance. Both of the two values can be easily changedin the model interface to meet the requirements for different scenarios.

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Appendix A Model District Map

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Technical Memorandum #8(b)Link Capacity DevelopmentThis memorandum details the link capacity development for the Memphis TravelDemand Model Update.

ContentsHighway Capacities

Highway Capacities

Daily and hourly capacities were developed for the Memphis Travel Demand ModelUpdate in order to utilize collected street data. This provides the most accuraterepresentation of actual capacity (levels of service A through E) on an individual link.These capacities are implemented using an equation which takes into account datasuch as functional classification, speed limit, lanes, median treatment, area type,average lane width, signal density, signal coordination, and average shoulder width.The capacity equations are built into the model process as a TransCAD lookup table,so modifications to network attributes automatically update the capacity insubsequent runs. In the equation, hourly capacities are developed and thenconverted to peak period and daily capacities by multiplying by time period factors.

The capacity setup for the Memphis Travel Demand Model Update has severalbenefits, including:

§ Better representation of capacity based on roadway attributes

§ Ability to load the Memphis Travel Demand Model Update with LOS D or E capacity

§ Hourly capacities are calculated and utilized in the timeofday model

§ Ability to easily recalculate capacities for future networks as improvements occur

§ Ability to make adjustments to capacity equations throughout the process

The equations were developed using the Highway Capacity Manual and analysisperformed by the Indiana Department of Transportation in 1997 for the Indiana StateHighway Congestion Analysis Plan [FHWA/IN/JHRP96/8 Opsuth and Whitford]. Theequations also incorporate the format of the roadway capacities presented in the

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Memphis Travel Demand Model Update Study Design Report and sample capacitiesdeveloped by Cambridge Systematics. The equations presented have been modified forthe Memphis Travel Demand Model Update.

As part of the capacity development, several modifications were applied to the capacityequation. First, the lane width/shoulder width portions of the equations werecollapsed to lookup tables. These only identify if a lane is of normal or narrow width,and general categories of shoulder width. The primary reason for this modification isto minimize the effect of inconsistencies in field data collection and the identification ofspecific shoulder and lane widths, since the data was collected by multiple teammembers. Second, signal density and signal coordination factors were developedspecifically for the Memphis model, using signal density information from theMemphis area, along with analysis performed using NCDOT's recently developed levelof service software. The NCDOT LOS software is based on the most recent HighwayCapacity Manual, and allows the users to test different attributes for roadways toidentify link capacity. The signal density and signal coordination factors are shown inTable 1 of the Appendix. The signal coordination factor is applied when a corridor isidentified to have signal coordination, and it applies a factor to increase both linkcapacity and freeflow link speed for these links.

The general form of the equation is:

SF = c * N * fw * fHV * Fp * FE * fd * FSD* FSC * FCLT * FPark * (v/c)i

Where the variables are:

SF = Maximum service flow for desired levelofservice

c = Capacity under ideal conditions (vehicles per hour per lane)

N = Number of lanes

fw = Factor due to lane and shoulder width

fHV = Factor due to percent heavy vehicles

Fp = Factor due to driver population

FE = Factor due to driving environment

fd = Factor due to directional distribution

FSD = Factor due to signal density

FSC = Factor due to signal coordination

FCLT = Factor for continuous leftturn lane (for undivided sections)

FPark = Factor for onstreet parking

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(v/c)i = Rate of service flow for levelsofservice A through E

The capacity equations 11 through 18 presented in the following section have beendeveloped to best represent the actual capacities in the Memphis Travel DemandModel Update model. Results of the capacity equations will be compared againstmodeled 2004 traffic conditions during model validation to verify the appropriatenessof the model settings. All capacities are developed by direction and then combined fortotal roadway capacity.

Following the capacity equations, generalized capacities are illustrated for all of thefunctional classifications. These equations assume a normal lane and shoulder widthand no parking. During model development, these capacities will be expanded to peakperiod and daily capacities. These initial expansion factors and generalized capacitiesare listed in Tables 2 and 3 (respectively) in the appendix.

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[11] Interstate Capacity Equations (Functional classification = 10 or 11)SF = c * N * fw * fHV * Fp * (v/c)i

Where:

c = 2 lanes = 2,200

3 or more lanes = 2,300

N = Number of lanes, by direction

fw =

Lane01’

Shoulder24’

Shoulder5’ and Wider

ShoulderNarrow Lane

(<=10 ft) 0.78 0.83 0.88Normal Lane

(>10 ft) 0.90 0.95 1.00

fHV = 0.88

Fp = Rural = 0.9

Urban = 0.92

(v/c)i = LOS A = 0.29

LOS B = 0.47

LOS C = 0.69

LOS D = 0.88

LOS E = 1.00

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[12] Freeway/Expressway Equations (Functional classification = 20 or 21)

SF = c * N * fw * fHV * Fp * (v/c)i

Where:

c = Rural = 2,200

Urban = 2,300


fw =

Lane01’

Shoulder24’


ShoulderNarrow Lane

(<=10 ft) 0.78 0.83 0.88Normal Lane

(>10 ft) 0.90 0.95 1.00

fHV = 0.88

Fp = Rural = 0.9

Urban = 0.92

(v/c)i = LOS A = 0.33

LOS B = 0.55

LOS C = 0.75

LOS D = 0.88

LOS E = 1.00

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[13] Principal Arterial Equations (Functional classification = 30 or 31)

SF = c * N * fw * fHV * Fp * FE * FSD * FSC * FCLT * FPark * (v/c)i

Where:

For Sections Divided with Median

c = Rural = 1,700

Urban = 1,500


fw =

Lane01’

Shoulder24’


ShoulderNarrow Lane

(<=9.5 ft) 0.77 0.83 0.88Normal Lane

(>9.5 ft) 0.89 0.95 1.00

fHV = 0.9

Fp = 0.95

Fe = Rural = 1.0, Urban = 0.9

FSD = See Table 1: Factors for Signal Density and Signal Coordination

FSC = See Table 1: Factors for Signal Density and Signal Coordination

FCLT = 1.0

FPark = 1.0

(v/c)i = LOS A = 0.30

LOS B = 0.50

LOS C = 0.70

LOS D = 0.84

LOS E = 1.00

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For Sections Not Divided with Median

c = Rural = 1,500

Urban = 1,300


fw =

Lane01’

Shoulder24’


ShoulderNarrow Lane

(<=9.5 ft) 0.78 0.82 0.86Normal Lane

(>9.5 ft) 0.92 0.96 1.00

fHV = 0.9

Fp = 0.95

Fe = Rural = 0.9, Urban = 0.8



FCLT = 1.08 (for sections with continuous leftturn lane)

FPark = 0.95 (for sections with onstreet parking)

(v/c)i = LOS A = 0.30

LOS B = 0.50

LOS C = 0.70

LOS D = 0.84

LOS E = 1.00

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[14] Minor Arterial Equations (Functional classification = 40 or 41)

SF = c * N * fw * fHV * Fp * FE * FSD * FSC * FCLT * FPark * (v/c)i

Where:


c = Rural = 1,600

Urban = 1,400


fw =

Lane01’

Shoulder24’


ShoulderNarrow Lane

(<=9.5 ft) 0.77 0.83 0.88Normal Lane

(>9.5 ft) 0.89 0.95 1.00

fHV = 0.9

Fp = 0.98

FE = Rural = 1.0, Urban = 0.9



FCLT = 1.0

FPark = 1.0

(v/c)i = LOS A = 0.30

LOS B = 0.50

LOS C = 0.70

LOS D = 0.84

LOS E = 1.00

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9


c = Rural = 1,350

Urban = 1,150


fw

Lane01’

Shoulder24’


ShoulderNarrow Lane

(<=9.5 ft) 0.81 0.86 0.93Normal Lane

(>9.5 ft) 0.94 1.00 1.05

fHV = 0.9

Fp = 0.98






(v/c)i = LOS A = 0.30

LOS B = 0.50

LOS C = 0.70

LOS D = 0.84

LOS E = 1.00

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[15] Collector Road Equations (Functional classification = 50 or 51)

SF = c * N * fw * fHV * FE * FSD * FSC * FCLT * FPark * (v/c)i

Where:


c = Rural = 1,350

Urban = 1,150


fw =

Lane01’

Shoulder24’


ShoulderNarrow Lane

(<=9 ft) 0.81 0.86 0.93Normal Lane

(>9 ft) 0.94 1.00 1.05

fHV = 0.92




FCLT = 1.0

FPark = 1.0

(v/c)i = LOS A = 0.31

LOS B = 0.52

LOS C = 0.72

LOS D = 0.83

LOS E = 1.00

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c = Rural = 1,150

Urban = 950


fw =

Lane01’

Shoulder24’


ShoulderNarrow Lane

(<=9 ft) 0.81 0.85 0.90Normal Lane

(>9 ft) 0.96 1.00 1.04

fHV = 0.92






(v/c)i = LOS A = 0.31

LOS B = 0.52

LOS C = 0.72

LOS D = 0.83

LOS E = 1.00

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[16] Frontage Road Equations (Functional classification = 60 or 61)

SF = c * N * fw * fHV * Fp * fd * FCLT * FPark * (v/c)i

Where:

c = 1,050


fw =

Lane01’

Shoulder24’


ShoulderNarrow Lane

(<=9 ft) 0.65 0.78 0.92Normal Lane

(>9 ft) 0.85 1.00 1.10

fHV = 0.92

Fp = 0.98

fd = 0.94



(v/c)i = LOS A = 0.31

LOS B = 0.52

LOS C = 0.72

LOS D = 0.83

LOS E = 1.00

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[17] Ramp Equations (Functional classification = 70 74)

SF = c * N * (v/c)i

Where:

c = 70 (Interstate>Interstate) = 1,800

71 (Principal>Interstate) = 1,000

72 (Arterial>Interstate) = 900

73 (Collector>Interstate) = 800

74 (All other ramps) = 1,100


(v/c)i = LOS A = 0.33

LOS B = 0.55

LOS C = 0.75

LOS D = 0.88

LOS E = 1.00

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[18] Local Road Equations (Functional classification = 80 or 81)

SF = c * N * fw * fHV * FE * fd * FSD * FSC * FCLT * FPark * (v/c)i

Where:

For MultiLane Sections

c = Rural = 1,000

Urban = 900


fw =

Lane01’

Shoulder24’


ShoulderNarrow Lane

(<=9 ft) 0.81 0.85 0.90Normal Lane

(>9 ft) 0.96 1.00 1.04

fHV = 0.97


Fd = 1.16 (for sections with median)





(v/c)i = LOS A = 0.31

LOS B = 0.52

LOS C = 0.72

LOS D = 0.83

LOS E = 1.00

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For Local TwoLane Sections

c = Rural = 900

Urban = 800

N = 1

fw =

Lane01’

Shoulder24’


ShoulderNarrow Lane

(<=9 ft) 0.65 0.78 0.92Normal Lane

(>9 ft) 0.85 1.00 1.10

fHV = 0.97


Fd = 0.94



FCLT = 1.0


(v/c)i = LOS A = 0.31

LOS B = 0.52

LOS C = 0.72

LOS D = 0.83

LOS E = 1.00

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Appendix

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Table 1: Factors for Signal Density and Signal Coordination

Functionalclassification Area Type Signals/Mile FSD FSC

12 1 1.05Suburban3+ 0.85 1.1512 1.1 1.134 1 1.15Urban5+ 0.95 1.2<7 1 1.179 0.9 1.15

PrincipalArterial

CBD10+ 0.8 1.212 1 1.05Suburban3+ 0.85 1.112 1.1 1.0534 1 1.1Urban5+ 0.9 1.15<7 1.05 1.0579 1 1.1

MinorArterial

CBD10+ 0.95 1.1512 1 1.05Suburban3+ 0.85 1.112 1.1 1.0534 1 1.05Urban5+ 0.9 1.1<7 1 1.0579 0.9 1.05

Collector

CBD10+ 0.75 1.11 1 1.05Suburban2+ 0.85 1.1<3 1.05 134 1 1Urban5+ 0.95 1.05<6 1.05 1.0568 1 1.05

Local

CBD9+ 0.95 1.1

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Table 2. Hourly Capacity Factor by TimeofDay

Time PeriodCapacityFactor

Duration(Hours)

AM 2.5 3Midday 3.5 5

PM 3.5 4Night 6 12

Table 3: Generalized Hourly Capacities by Level of Service

Rural Interstate (10)

Lanes Median LOS A LOS B LOS C LOS D LOS E4 Divided 2,020 3,280 4,810 6,130 6,9706 Divided 3,170 5,140 7,540 9,620 10,9308 Divided 4,230 6,850 10,060 12,820 14,570

Rural Freeway (20)

Lanes Median LOS A LOS B LOS C LOS D LOS E4 Divided 2,300 3,830 5,230 6,130 6,9706 Divided 3,610 6,010 8,200 9,620 10,9308 Divided 4,810 8,020 10,930 12,820 14,570

Rural Major Arterial (30)

Lanes Median LOS A LOS B LOS C LOS D LOS E2 Divided 870 1,450 2,030 2,440 2,9104 Divided 1,740 2,910 4,070 4,880 5,8106 Divided 2,620 4,360 6,100 7,330 8,7208 Divided 3,490 5,810 8,140 9,770 11,6302 Undivided 690 1,150 1,620 1,940 2,3104 Undivided 1,390 2,310 3,230 3,880 4,6202 Cont. LT 750 1,250 1,750 2,090 2,4904 Cont. LT 1,500 2,490 3,490 4,190 4,990

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Urban Interstate (11)

Lanes Median LOS A LOS B LOS C LOS D LOS E4 Divided 2,070 3,350 4,920 6,270 7,1206 Divided 3,240 5,250 7,710 9,830 11,1708 Divided 4,320 7,000 10,280 13,110 14,90010 Divided 5,400 8,750 12,850 16,390 18,620

Urban Freeway (20)

Lanes Median LOS A LOS B LOS C LOS D LOS E4 Divided 2,350 3,920 5,340 6,270 7,1206 Divided 3,690 6,140 8,380 9,830 11,1708 Divided 4,920 8,190 11,170 13,110 14,90010 Divided 6,140 10,240 13,970 16,390 18,620

Urban Major Arterial (31)

Lanes Median LOS A LOS B LOS C LOS D LOS E2 Divided 690 1,150 1,620 1,940 2,3104 Divided 1,390 2,310 3,230 3,880 4,6206 Divided 2,080 3,460 4,850 5,820 6,9308 Divided 2,770 4,620 6,460 7,760 9,2302 Undivided 530 890 1,240 1,490 1,7804 Undivided 1,070 1,780 2,490 2,990 3,5602 Cont. LT 580 960 1,340 1,610 1,9204 Cont. LT 1,150 1,920 2,690 3,230 3,840

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Rural Minor Arterial (40)

Lanes Median LOS A LOS B LOS C LOS D LOS E2 Divided 850 1,410 1,980 2,370 2,8204 Divided 1,690 2,820 3,950 4,740 5,6406 Divided 2,540 4,230 5,930 7,110 8,4708 Divided 3,390 5,640 7,900 9,480 11,2902 Undivided 640 1,070 1,500 1,800 2,1404 Undivided 1,290 2,140 3,000 3,600 4,2902 Cont. LT 690 1,160 1,620 1,940 2,3104 Cont. LT 1,390 2,310 3,240 3,890 4,630

Rural Collector (50)

Lanes Median LOS A LOS B LOS C LOS D LOS E2 Divided 770 1,290 1,790 2,060 2,4804 Divided 1,540 2,580 3,580 4,120 4,9706 Divided 2,310 3,880 5,370 6,190 7,4508 Divided 3,080 5,170 7,150 8,250 9,9402 Undivided 590 990 1,370 1,580 1,9004 Undivided 1,180 1,980 2,740 3,160 3,8102 Cont. LT 640 1,070 1,480 1,710 2,0604 Cont. LT 1,280 2,140 2,960 3,410 4,110

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Urban Minor Arterial (41)

Lanes Median LOS A LOS B LOS C LOS D LOS E2 Divided 670 1,110 1,560 1,870 2,2204 Divided 1,330 2,220 3,110 3,730 4,4506 Divided 2,000 3,330 4,670 5,600 6,6708 Divided 2,670 4,450 6,220 7,470 8,8902 Undivided 490 810 1,140 1,360 1,6204 Undivided 970 1,620 2,270 2,730 3,2502 Cont. LT 530 880 1,230 1,470 1,7504 Cont. LT 1,050 1,750 2,450 2,940 3,510

Urban Collector (51)

Lanes Median LOS A LOS B LOS C LOS D LOS E2 Divided 590 990 1,370 1,580 1,9004 Divided 1,180 1,980 2,740 3,160 3,8106 Divided 1,770 2,970 4,110 4,740 5,7108 Divided 2,360 3,960 5,480 6,320 7,6202 Undivided 430 730 1,010 1,160 1,4004 Undivided 870 1,450 2,010 2,320 2,8002 Cont. LT 470 790 1,090 1,250 1,5104 Cont. LT 940 1,570 2,170 2,510 3,020

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Frontage Road (60/61)

LOS A LOS B LOS C LOS D LOS E1 550 930 1,280 1,480 1,7802 1,100 1,850 2,560 2,950 3,560

Interstate/Freeway Ramps

LOS A LOS B LOS C LOS D LOS E70 590 990 1,350 1,580 1,80071 330 550 750 880 1,00072 300 500 680 790 90073 260 440 600 700 80074 360 610 830 970 1,100

Rural Local (80)

Lanes Median LOS A LOS B LOS C LOS D LOS E2 Divided 630 1,050 1,460 1,680 2,0304 Divided 1,260 2,110 2,920 3,360 4,0502 Undivided 460 770 1,060 1,230 1,4804 Undivided 1,080 1,820 2,510 2,900 3,4902 Cont. LT 490 830 1,150 1,320 1,6004 Cont. LT 1,170 1,960 2,720 3,130 3,770

Urban Local (81)

Lanes Median LOS A LOS B LOS C LOS D LOS E2 Divided 500 840 1,170 1,340 1,6204 Divided 1,000 1,690 2,330 2,690 3,2402 Undivided 360 610 840 970 1,1704 Undivided 870 1,450 2,010 2,320 2,7902 Cont. LT 390 660 910 1,050 1,2604 Cont. LT 940 1,570 2,170 2,500 3,020

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16

21

15

18

14

20

9

19

11

24

13

17

10

22 56

7

8

4

12

3

25

2

231

Model District Map

0 5 10 15

Miles

ID (Name)1 (CBD)2 (North Memphis)3 (Midtown and Depot)4 (East Memphis)5 (Southwest Memphis)6 (Hickory Hill)7 ( East Shelby County)8 (Collierville)9 (Northeast Shelby County)10 (Raleigh Bartlett)11 (Millington)12 (Frayser)13 (Northwest Shelby County)14 (East Desoto County)15 (West Desoto County)16 (South Desoto County)17 (Mashall County)18 (North Fayette County)19 (West Tipton County)20 (East Tipton County)21 (South Fayette County)22 (McKellar Lake)23 (University)24 (Shelby Farms Germantown)25 (Airport)

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Technical Memorandum #9Highway Validation Procedures and Goals andTransit Assignment Reasonableness CheckingProcedures

This memorandum describes the proposed highway assignment validation proceduresand goals and proposed transit assignment reasonableness checking procedures forthe Memphis MPO Travel Demand Model (TDM).

ContentsOverview

- Highway Assignment Validation and Procedures- Transit Assignment Reasonableness Checking Procedures

Overview

The Memphis Model is a compilation of a series of sub-models, each of which will becalibrated and validated as they are developed as well as in concert with theircomplementary sub-models and as a whole. The validation of each individualcomponent is described in the corresponding technical memorandum documenting thedevelopment of that component. This document focuses on those procedures andcriteria that will be used to validate and/or check the reasonableness of the MemphisTDM’s highway and transit assignments results. These procedures will be carried outat the system-wide, screenline, corridor, and link group level for the highwayassignment, and at the system-wide, screenline, transit schedule, sub-group andgroup level for the transit assignment.

In the Memphis Model Study Design, 2002, a series of highway assignment validationand reasonableness checking criteria are presented. The majority of these can befound in the FHWA Model Validation and Reasonableness Checking Manual, 1997written by Barton-Aschman and Cambridge Systematics.

For the transit assignment, there are no industry recognized targets for validationcriteria that we have identified nor utilized in our experience. However, there areseveral reasonableness checks that can be applied at the system, district/sub-region,and corridor level that we present in this document and will incorporate as part of ouroverall transit assignment review process.

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Highway Assignment Validation and Procedures

As stated, we will validate the highway assignment at varying levels of aggregation.This section presents the assignment measures that will be reviewed as well as thevalidation targets that will be employed

Vehicle Miles of Travel (VMT)

Regional, Household and per capita VMT will be computed and compared to HighwayPerformance Monitoring System (HPMS) data and other suggested ranges. A VMT perHousehold of 40-60 miles per day and a VMT per person of 17-24 miles per day forlarge urban areas have been suggested in the Model Validation and ReasonablenessChecking Manual, 1997. VMT will also be categorized by functional classification andcompared to suggested percent differences shown in Table 1.

Table 1. Percent Difference Targets for VMT by Functional Classification

Functionalclassification Target

Freeways 8-12%Major Arterials 18-22%Minor Arterials 27%

Collectors 33%Source: Christopher Fleet and Patrick De Corla-Souza, Increasing the Capacity of Urban Highways – The role ofFreeways, presented at the 69th Annual Meeting of the TRB, January 1990, (cited in FHWA, Model Validation andReasonableness Checking Manual, 1997)

Traffic Volumes

Coefficient of Determination (R2) is a useful measure to compare system wide observedtraffic counts versus estimated volumes. The Model Validation and ReasonablenessChecking Manual, 1997 suggests that the system wide R2 be greater that 0.88 at thesystem level. Percent Root Mean Square Error (%RMSE) is yet another measure usedto check the deviation of modeled volumes from observed traffic counts. A 35%%RMSE target has been established for the Memphis MPO model at the system level.

Following, traffic volumes will be disaggregated and assessed by functionalclassification and by volume groupings. Table 2 presents daily volume targets byfunctional classification for the entire functional classification category. Table 3presents validation targets grouped by daily volumes. Table 4 presents goals for thepercentage of link volumes that are to be within a percentage of the observed volume.This adds a measure of the variance of individual observed link volumes from

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individual modeled link volumes in addition to the comparison of aggregate linkvolumes.

Table 2. Percent Difference Volume Targets by Functional Classification

Functionalclassification

Target(+/-)

Freeway 7%Major Arterial 10%Minor Arterial 15%

Collector 25%

Table 3. Percent Difference Volume Targets by Daily Volume Groupings (totaledover entire group)

Volume GroupTarget(+/-)

<1,000 200%1,000-2,500 100%2,500-5,000 50%5,000-10,000 25%10,000-25,000 20%25,000-50,000 15%

>50,000 10%

Table 4. Percent of Links within a Specified Percent of Count by Functionalclassification


Target withinCount

Range Comparedto Counts

Freeway 75% 20%Freeway 50% 10%

Major Arterial 75% 30%Major Arterial 50% 15%Minor Arterial 75% 40%Minor Arterial 50% 20%

Note: Table 4 can be read as “75% of the freeway links need to be within 20% of counts, 50% of thefreeway links need to be within 10% of counts”.

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Screenlines and Cutlines

As a part of the model calibration/validation process, screenlines and cutlines weredeveloped to gauge how well the model replicates traffic between different areas withthe Memphis MPO area. Typically screenlines are placed only across roads withavailable traffic count and usually follow a natural barrier, such as a river or railroadtracks to minimize the number of crossings. Cutlines are typically placed acrosscorridors and sections of the model that need attention. Traffic volumes are summedat screenlines and cutlines to validate system wide traffic volumes (cutlines can bethought of as a more localized measure). The goal for any screenline or cutlinecomparison is to have 100% of the observed traffic replicated by the model. ForMemphis, a target of +/- 10% for screenlines and +/- 15% for cutlines has beenestablished. Figure 1 shows the maximum desirable deviation in total screenlinevolumes as suggested in the Model Validation and Reasonableness Checking Manual,1997. Figure 2 shows the screenlines and cutlines for the Memphis MPO Model.

Figure 1. Maximum Desirable Deviation in Total Screenline Volumes

Source: NCHRP 255 (cited in FHWA, Model Validation and Reasonableness Checking Manual, 1997)

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Figure 2. Screenlines and Cutlines

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Peak Period Assignment Reasonability Checks

The time of day period highway assignments will not be validated against targets, butthe volume measures presented in tables 2 and 3, the %RMSE and the R2 will bepresented for each time period as a check. Also, the performance of the time periodassignments will be compared at the screenlines and cutlines where hourly counts areavailable. Approximately 88% of the over 991 links where unique counts are availablehave hourly counts and approximately 92% of the 189 links that comprise the screenlines and cutlines have hourly counts available. A review of the traffic count databaseis presented later in the later in this memorandum.

Traffic Count Database

The Memphis MPO study area encompasses parts of five different counties: Shelby;Tipton; and Fayette Counties in Tennessee and Desoto and Marshall Counties inMississippi. Daily, hourly and classification counts acquired from multiple datasources were processed for 991 count locations in these counties.

A majority of the count locations had ADT’s for the base year of 2004, but 32 countlocations had data associated with 2000, 2002 or 2003. These ADT’s were factored upto base year (2004) by applying a growth percentage. Historic data in TN indicates thattraffic in Shelby, Tipton and Fayette counties grew by 0.4%, 1.5% and 2.9%respectively in the last decade. Historic growth percentages for Mississippi were noteasily available. Since the traffic in the Mississippi portion of the study area is mostclosely expected to behave like Tipton and Fayette counties, a 2.2% (average of 1.5%and 2.9%) growth rates was used to grow ADT’s in Mississippi. Additional informationregarding historic traffic and population growth rates in individual counties can befound in Memphis Travel Demand Model: Technical Memorandum #3b - Future YearExternal Trips

Like the ADT’s, the classification and time-of-day data also had to be extracted over amulti-year period, ranging from 1998 to 2004. Typically, time of day and classificationdata were available for only one of the years in the noted span. To obtain time periodcounts, the time-period percentages for the year in which the hourly link counts wereavailable were computed for the four time periods in the Memphis TDM and applied tothe actual or normalized 2004 link data. Likewise, for the truck classification counts,the truck percentages from the year in which the classification counts were availablewere applied to the 2004 link count to develop a 2004 classification count. Lastly, todevelop hourly classification counts, the hourly classification count (i.e. percentage oftrucks of a given category on link divided by the total number of trucks of thatcategory for the entire day) percentage for the year in which the data was available wasapplied to the 2004 link count. Table 5 gives the comparison of traffic counts and

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lane miles in model by functional classification. Table 6 gives the distribution ofclassification link counts and hourly link counts by year of availability.

Table 5. Distribution of Traffic Counts by Functional classification

Table 6. Distribution of Hourly and Vehicle Classification Traffic Counts byLatest Year of Availability

MS TN


Number ofCount

Locations

Percentage ofCount

Locations

Number ofCount

Locations

Percentageof CountLocations

Total LaneMiles inModel

% ofTotalLane

Miles inModel

Freeway 12 10% 63 7% 261 10%

Major Arterial 23 18% 162 19% 405 15%

Minor Arterial 30 24% 307 35% 634 24%

Collector 56 44% 309 36% 855 32%

Local 5 4% 24 3% 487 19%

Total 126 100% 865 100% 2643 100%

Number ofHourlyCounts

Percentage ofHourly Counts

Number ofClassification

Counts

Percentage ofClassification

Counts2004 301 34% 78 55%

2003 494 57% 27 19%

2002 - - 23 16%

2001 - - 1 1%

2000 78 9% 7 5%

1999 - - 1 1%

1998 - - 6 4%

Total 873 100% 143 100%

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Transit Assignment Reasonableness Checking Procedures

For the Memphis TDM, the base year transit assignment will be reviewed at thesystem-wide level and at the sub regional level. Also, line-by-line ridership will bepresented for both the peak and off-peak.

System Wide Validation TargetsAt the system wide level, the following items will be reported and compared toobserved data for the Transit assignment:

Total Linked Trips: A target of +/- 5% difference between modeled value andthe target is used.Total Boardings: A target of +/- 10% difference between modeled value and thetarget is used.Transfer Rate: This is the ratio of total boardings to total linked trips. A targetof +/- 10% difference between modeled value and the target is used.

See Technical memorandum #5 “Mode Choice” for the model target values and howthey are established.

Transit Route Schedules

The reported bus/trolley run times from the model will be compared with the routeschedules provided by MATA. Table 7 shows the scheduled bus scheduled run timesfrom MATA. By average, a target of +/- 5% difference between modeled values and thetargets is used.

Table 7. Route Schedule Targets

Route Name Target Time(min.)

2A 522C 522L 362W 324A 474C 497A 1028 20

10S 7110C 77

10RG 5010RL 5011C 36

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11F 2411T 4015 29

19M 5219NA 5619R 44

19RA 5619W 4420 6222L 7730 7531 56

32A 4732F 4732N 4433 27

34B 5034M 4834R 5535 5740 75

40B 6241 89

43B 6143H 6143S 5250G 5450W 5550Y 5552B 7152M 4552Q 6152R 45

52SE 4452SF 6353B 6053I 5553L 5553S 5653W 57

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56 5758B 7562G 6662W 7169 9280 25

80B 2581 5882 2393 42

Madison Trolley 23Main St Trolley 17

River front Trolley 34

Screenline Comparison

Two transit screenlines were developed by MATA for the transit assignment validationafter the 2006 peer review meeting. The first screenline encompasses the I-40/I-240Loop (excluding the Midtown segment) and is the same as the “I-40/I-240 LoopCutline” shown in Figure 6 of Technical Memorandum #1(a). The second screenlineencompasses the Parkway system — South Parkway, East Parkway and NorthParkway. The number of daily passengers on board at each point where a routecrosses a screenline was estimated by MATA, which was developed from NationalTransit Database sample data. A target of +/- 20% for screenlines has beenestablished.

Route Group, Sub-group, and Line Level Boardings

Boarding counts for validation were provided by MATA in three levels of aggregations.The boarding counts were first grouped by transit lines (41), then by sub-groups(15),and by groups(8). See Technical Memorandum #11 for the observed boarding values.Although there are no specific guidelines and targets for transit boarding validationfrom FTA or FHWA, a weighted average will be calculated for the % differences ingroup and sub-group level only. The weight used in the averaging process is theobserved boarding values from each group/sub-group. A target weighted average valueof 15% for group level boarding and 25% for sub-group level boarding are established.

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Technical Memorandum #10Base and Future Year Signalized IntersectionTools and Future Year Signal LocationForecasting Methodology

This document explains the methodology used to forecast future year signal locations,and identifies the tools developed to help calculate and update the signal densityvalues and signal coordination data.

ContentsOverviewSignal Density Grouping and Coordination Data Input

- Define a Group of Links as a New Signal Density Group- Update Link Density of an Existing Link Group- Update Coordination Data

Future Year Signal Forecasting Methodology and Tools- Flagging All Potential Signal Locations That Met the Signal Warrant 1

Analysis- Analyze a Particular Intersection- Accept/Reject All Signal Flags in Batch Mode

Other Utilities for the Signal Toolbox- Clear All Pending Signal Flags- Label the Links by Peak Volume or Names- Show Signal Locations in the Map- Select a Corridor Segment by End Points

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Overview

In the Memphis MPO Travel Demand Model, the impact of traffic signals on linkcapacity is considered in two factors: signal density and signal coordination, asdiscussed in detail in Technical Memorandum # 8(b). The tools developed for base andfuture year signals can be accessed from the Utility button of the Main Interface,which will activate the button identified below.

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By clicking the “Future Year Signal Tools” button, the “Future Signal Toolbox” willshow up as a floating toolbox.

The “Status Information” identified in the upper sectionof the toolbox shows the Target Year which the user iscurrently working on, and the total number of pendingfuture year signal flags exist (shown to the right).

Note that before launching the Future Signal Toolbox, a future year model run must besuccessfully completed and the network must contain the assigned future volumes in theappropriate field. You can change the target year using the drop down box, but it shouldbe set to the year consistent with your model volume. This will enable the toolbox tochange the attributes for the appropriate target year.

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The toolbox will launch a map showing the network with existing signals highlightedas green and pending signal flags shown as red, as shown in the following image takenfrom the model:

Three groups of tools are provided in the toolbox, explained in detail below.

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Signal Density Grouping and Coordination Data Input

The first group of tools (shown to the right) wasdeveloped to facilitate grouping links into signal densitygroup, updating the signal density values of link groups,and updating the signal coordination status along aparticular segment of a corridor. This set of tools can beused by both base year and future year scenario.

1. Define a Group of Links as a New Signal Density Group

(“New Group from Selection” Button)To define a new signal density group, a group of target links needs to be identified.Any standard TransCAD selection tools can be used for this purpose. Alternatively, the“Select Segment by End Points” tool also can be used to select a corridor segment, asdescribed in the next section. After the target links are identified, the user can select

the “New Group from Selection” button, which will bring up the following dialogbox:

The number of links selected and the total length of the selected segment will becalculated. The number of signals along the selected segment also will be displayed. Ifexisting density values are found on the first link, this also will be displayed forreference. Based on the length and number of signals, a calculated density value willbe displayed. The user can select the suggested value directly or give an appropriatevalue manually based on professional judgment. The model program also will suggest

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a unique name for the group based on the roadway names in the network. The usercan provide another name, and the uniqueness of the name will be checked when it issaved. The user also can select the “Unique Name” button on the interface to have theprogram suggest a name for the group that is guaranteed to be unique.

2. Update Link Density of an Existing Link Group

(“Click and Pick a Link Group” Button)By selecting this button and selecting a link from the network map, the same dialogbox will pop up with existing information shown if the link is associated with apredefined group. The user can provide new density values or new group names, thenselect the “Save” option to update the link density attributes.

3. Update Coordination Data

(“Set Signal Coordination” Button)The signal coordination data are not defined in groups, which allows more flexibilityfor the network settings. To set or change the signal density data, a particular corridorsegment needs to be identified. This can be performed by using any standardTransCAD selection tools or by using the “Select Segment by End Points” tool. With

the links selected, choosing the “Set Signal Coordination” button will bring up thefollowing dialog box:

By choosing “Yes” or “No” and “Save,” the signal coordination data will be updated.

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Future Year Signal Forecasting Methodology and Tools

The methodology used for forecasting future year signal locations is based on theManual on Uniform Traffic Control Devices (MUTCD, 2003 edition) and the City ofMemphis Design and Review Policy Manual (2002). The City of Memphis Design andReview Policy Manual (2002) is the only local reference available and used indeveloping the signal forecasting methodology. The signal design policy (section 201)in the City of Memphis Design and Review Policy Manual clearly states that:

Only Warrant 1 (eight-hour vehicular volume warrant), 4 (pedestrianvolume), and 7 (crash experience) in the MUTCD will be used to evaluatenew traffic signal installation.80% and 70% volume adjustment identified for Warrant 1 will not beapplied.The minimum volumes on the higher-volume minor-street approach (onedirection only) for the 100% level of Warrant 1, Condition B, shall beincreased by 30%.

Based on the MUTCD and the local design policy, only Warrant 1 is used to evaluatethe potential future signal locations. The following tables show the minimum volumesused for Warrant 1 condition A and B analysis.

Table 1. Warrant 1, Eight-Hour Vehicular VolumeCondition A — Minimum Vehicular Volumes

Number of lanes formoving traffic on

each approach

Vehicles per hour onmajor street (total of

both directions)

Vehicles per hour on higher-volume minor street approach

(one direction only)MajorStreet

MinorStreet 100% 100%

1 1 500 1502 or More 1 600 1502 or More 2 or More 600 200

1 2 or More 500 200Condition B — Minimum Vehicular Volume

Number of lanes formoving traffic on

each approach

Vehicles per hour onmajor street (total of

both directions)

Vehicles per hour on higher-volume minor street approach

(one direction only)MajorStreet

MinorStreet 100% 100%

1 1 750 982 or More 1 900 982 or More 2 or More 900 130

1 2 or More 750 130

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Based on the assignment results for a future year scenario, the eight-hour vehicularvolume is derived by averaging the AM and PM peak period volumes. Using theaverage hourly volumes on both major and minor streets, the conditions A and B ofWarrant 1 are checked, and a location is flagged as a potential signal location if eithercondition A or B is met. The model interface also provides tools to review the flaggedsignal locations, the analyst applies his or her judgment to review these flaggedlocations, and a decision is made to either accept or reject the proposed signals.

The “Warrant Analysis” box gives three buttons for the future year signal analysis,explained below:

1. Flagging All Potential Signal Locations That Met the Signal Warrant 1 Analysis

(“Flag All Potential Signals” Button)

By choosing the “Flag All Potential Signals” button, all intersections will be analyzedand all the potential signals will be flagged. After the analysis, the program will reportthe results in a message box similar to the following:

After the analysis, a map will be displayed with existing signals colored as Green andpotential signals colored as Red.

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2. Analyze a Particular Intersection

(“Click and Analyze One Node” Button)

If only one specific intersection needs to be analyzed, the user can select the “Clickand Analyze One Node” button and choose the one node from the map. This will bringup the Warrant 1 Analysis Report for this particular intersection, similar to thefollowing:

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The user can either accept the recommendation for or against signalization or reject it,based on the information provided and the judgment of the analyst. If signalization isaccepted, the intersection will be marked as “Signalized” for the target year and allmodel years beyond the target year. If this node is associated with any existing signaldensity groups, the signal density dialog boxes for each associated group will appear(shown below), and the user can update the new density values based on thesuggested value.

3. Accept/Reject All Signal Flags in Batch Mode

(“Accept/Reject Pending Flags” Button)

By selecting the “Accept/Reject Pending Flags” button, all pending signal flags will beselected. The Warrant 1 Analysis Report dialog box for each individual intersection willbe displayed in turn. The location of the intersection will be centered on the map. Thedecision-making process is the same as it was for analyzing a single intersection. Thisbutton allows the user to conveniently process all signal flags in batch.

Other Utilities for the Signal Toolbox

In addition to the other sections on the Signal Toolbox, four additional utility buttonsare provided in the toolbox for ease of analysis.

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1. Clear All Pending Signal Flags

(“Erase All Signal Flags” Button)

Selecting this button will clear all pending signal flags.

2. Label the Links by Peak Volume or Names

(“Label Links Using Name/AM Volume” Button)

While deciding whether to accept or reject a signalization recommendation, it could behelpful to review the AM peak period assigned volume. Selecting this button allows theuser to switch between the link labels to either link names or bidirectional AM peak-period assigned volumes.

3. Show Signal Locations in the Map

(“Show Signal Locations” Button)

Selecting this button will display only the existing and potential signal locations on amap.

4. Select a Corridor Segment by End Points

(“Select Segment by End Points” Button)

This tool provides a convenient method of selecting a segment of roadway usingstarting and ending points. By clicking this button, the user can specify the startingand ending points while the program will find the shortest path by length (shownbelow). This tool is designed to help selecting corridor segments for signal densitygrouping and updating coordination data. It also can be used in combination withother tools such as coding E+C links.

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Technical Memorandum #11Model Calibration and Assignment ValidationResults

This memorandum describes the results of the model calibration and subsequenthighway and transit assignment results for the Memphis model. The model has beenfully developed and is ready for use in travel demand modeling and forecasting.Discussed within this memorandum are both the final model calibration results andcalibration issues of which the MPO should be aware when forecasting using themodel. This memorandum briefly documents the effort by the model team since thepeer review meeting to review and calibrate the model in several areas, includingthrough data inputs, model process, and model settings. The primary purposes of thisreview were 1) to identify the potential sources of the global under-assignment of themodel, and 2) to identify the steps needed to bring the model into calibration. Severalof the calibration steps taken were based on input from the peer review committee,along with additional checks performed by the model team. The model run presentedin this memorandum was completed on 03/22/2007.

The sources of the targets used in model calibration are documented in “TechnicalMemorandum #9 - Highway Validation Procedures and Goals and Transit AssignmentReasonableness Checking Procedures,” submitted in March 2006.

ContentsModel Calibration Challenges and StrategiesModel Calibration Steps (Challenge 1)Model Calibration Steps (Challenge 2)Highway Assignment ResultsTransit Assignment Results

Model Calibration Challenges and Strategies

After the March 2006 peer review meeting, the model team undertook a thoroughreview of all model components for validity and accuracy. The most significantchallenge (Challenge 1) involved correcting a model that was loading 20% low when itappeared that all of the data used in the model looked valid. The initial step of model

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calibration involved identifying potential model calibration strategies, which weregenerated as a result of the peer review meeting with input from the peer review panel.The list of potential calibration strategies included:

Potential Calibration Strategies1. Adjust Internal Trip Rates2. Adjust Quick Response Freight Manual (QRFM) Truck Trip Rates3. Adjust Friction Factors/Average Trip Lengths4. Adjust Highway Performance Monitoring System (HPMS)/Traffic Count Data

System-Wide5. Revise Free-Flow Speed Factors by Functional classification6. Adjust Peak Period Capacity Factors (Hourly->Period)7. Revise Signal Density Factors by Functional classification8. Adjust Vehicle Occupancies9. Adjust Destination Choice/Mode Choice Model10.Adjust Special Generator Rates11.Adjust External Station Trip External-External (EE)/External-Internal- Internal

(EI) Splits

Based on this list of potential calibration strategies, the model team undertook asystematic review of the model and began model adjustments. A more specific list ofmodel review steps was identified to facilitate this process. The calibration sensitivitytest steps used by the team are shown in Table 1.

Table 1. Model Calibration/Sensitivity Review ProcessStep Description Notes

1 Centroid ConnectorsCheck link loadings, see if connectors needto be added/deleted

2 Review Zonal DensityJust a few checks to show zonal density isappropriate

3 Local Roads to Drop

Review network roads vs. Traffic AnalysisZone (TAZ) — drop inappropriate localroads

4 Network U-Turns Manually review network

5 Test Ramp PenaltiesTry 30 and 60 second ramp penalties to seeeffect on interstates

6 Modify Capacity Equations Review and adjust as needed

7Change Peak CapacityFactors

Reduce Time-of-Day capacity factors toincrease peak congestion

8 Adjust Free-Flow Speeds Based on congested speed comparison9 Add HOV Data

10Calculate Trip GenerationRates by Area Type

What is difference if we go urban/rural (orurban/suburban/rural)?

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Step Description Notes

11Trip Rates and Trip Length byDistrict

12 Special Generators Review Special Generator performance13 Increase Truck Trip Rates Based on traffic count comparison

14Implement Special GeneratorTruck Rates Need Special Generators first

15Extend/Weight IE TripLengths

Review length of IE trips & destination —make sure downtown is sufficientlyattractive

16 Adjust %EE/IE Trips Test effects of higher/lower % through trips17 District Flow Table What are district flows?

18Check percent Intrazonals byArea Type

19 Modify Vehicle OccupancyAdjust HOV/SOV splits in Mode ChoiceModel for non-home based (NHB) trips

20 Traffic CountsReview hourly calc process, compareagainst supplemental counts

21HPMS for SeasonalAdjustments

What is HPMS adjustment forMarch/April?

22 HMPS # of SamplesWhere are HPMS sample locations inMemphis area?

23Model vs. ObservedCongestion Times

Rerun with Time of Day (TOD) and free flowspeed (FFS) adjustment factors

The second challenge (Challenge 2) during the model calibration involved identifyinghow to fix the two issues related to the transit assignment: 1) high transfer rate(initially 1.88 versus the target transfer rate of 1.29), and 2) unbalanced transitassignment results, with under-assignment on North-South routes and over-assignment on East-West routes. Potential calibration strategies included:

1. Checking for and eliminating transit network coding errors2. Adjusting transit route headways3. Verifying and adjusting transit stop locations4. Adjusting walk access network5. Adjusting transit speed curve6. Adjusting Pathfinder transit skims/assignment parameters7. Reviewing and finalizing transfer rate/linked trips/boarding targets8. Expanding transit on-board survey and assigning the on-board survey OD

matrix to the network to identify issues9. Comparing model and expanded on-board survey OD matrix10. Recalibrating mode choice models

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The calibration steps, findings, and changes made on each step are discussed in thenext section.

Model Calibration Steps (Challenge 1)

This memorandum briefly documents the effort by the model team since the peer reviewmeeting to review the model in several capacities, including data inputs, model process, andmodel settings. The primary purposes of this review were 1) to identify the potential sources ofthe global under-assignment of the model, and 2) to identify the steps needed to bring themodel into calibration. The model will greatly benefit from the thorough review that has beenundertaken based on input from the peer review panel.

Highway Network/TAZ Data

Centroid Connector Review – Centroid connectors were reviewed to identify potential modelissues based on centroid connector coding. A relatively small number of centroid connectorswere adjusted, and no global issues were found.

Zonal Density Review – The TAZ density was reviewed to make sure that the number of TAZswas within the appropriate range based on standards for other areas. Based on informationprovided by ARC and other sources, it was found that the zonal density is appropriate toproperly model the Memphis area.

Area Standards:

0.5 – 0.8 TAZs per square mile; Memphis Model = 0.68

1 TAZ per 1,000 population; Memphis Model = 1.12

Minimize TAZs with >15,000 trips generated; Memphis Model = 6 TAZs

Local Road Density Review – One theory generated in the peer review meeting was that toomany local roads were included in the model, causing an under-assigning of volumes onparallel collectors and arterials. A few cases of this were found and corrected, but overall, theroad density matched the zonal density, and was not a likely source of overall model under-assignment and calibration.

Network Topology Review (U-Turns) – The model was reviewed to identify topology errorsthat were being interpreted as u-turns, which are prohibited, in effect disabling the links inquestion. Topology was corrected and this problem was eliminated.

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Ramp Penalty Implementation – Ramp penalties for traveling on and off of interstates wereimplemented to prevent short/local trips on freeways that would most likely stay on arterialand collector roads. A penalty of 30 seconds was added for each access/egress, which greatlyimproved the performance of the model by functional classification.

Capacity Equation Review – The model capacities were reviewed for appropriateness andhourly capacities were found to be reasonable, with some minor adjustments to betterdistinguish between arterials, collectors, and local roads. The only issue found was theexpansion from hourly to time period, discussed in further detail below.

Time-of-Day Capacity Review – The hourly capacity was expanded to time period by factors,which were based on the time period and the traffic count data for that period. It was foundthat the factors were high, thus creating artificially high capacities. The time-of-day factorswere changed, as shown in Table 2, and model performance with regard to travel times,congestion, and assignment by functional classification all markedly improved.

Table 2. Original and Revised Time-of-Day Factors

Time PeriodOriginalFactor

RevisedFactor

AM 2.5 1.5Midday 3.5 2.7

PM 3.5 2.5Night 6 3.8

Total Daily 15.5 10.5

Free-Flow Speed Adjustments – The free-flow speeds were created by applying a factor to thespeed limit by facility and area type. It was found that the free-flow speeds were inadvertentlycreating a strong bias toward freeways. The factors were damped so that the free-flow speedswere closer to the original speed limits, which removed the strong bias toward the freeways.

HOV Lane Data – HOV lanes in the model area are now modeled as separate links parallel tothe associated Interstate links. High occupancy vehicles (2+) will have the opportunity to weavein and out of the HOV links in each major connection points (ramp connection points or pointswhere number of lanes change). The highway assignment procedure was revised toaccommodate this travel opportunity. Separate highway skims for HOV and non-HOVnetworks are now calculated by the model and used in varies models. Assigned HOV lanevolumes are correctly summarized in the model calibration reports.

Trip Generation

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Trip Generation by Area Type – The peer review panel recommended examining whether tripgeneration rates should vary by area type. Since the model was under-assigning trips in theurban area (inside I-240), a theory was postulated that the trip rates in the urban area should behigher than suburban and rural areas, and that lumping together all of the area types with asingle trip generation rate was causing the under-assignment of urban trips. The possibility ofthis theory was examined, and it was found that the observed overall urban trip rates wereactually lower than suburban and rural trip rates. If lower trip rates in the urban area wereused, the model would actually perform worse in the urban area; therefore, the trip generationrates by area type will not be used in the model.

Special Generators – The travel associated with special generators had been estimated prior tothe peer review panel meeting but had not yet been implemented in the model. The specialgenerator module has now been implemented.

Truck Trips – The truck trip rates were reviewed in light of the assignment results for truckscompared to truck counts. The original rates were based on the Quick Response FreightManual and adjusted to reflect local conditions. These rates were further revised followingexamination of the validation results.

Review External Trips – Sensitivity analysis was performed on the percentage of IE/EE trips atthe external stations, and it was found that the overall number of trips and performance of themodel improved as the percent of IE trips increased. This is partially due to the NHB-NonResident (NR) trips generated by IE trips (discussed below), and partially due to IE trips havingmore destinations in the central area, where the model was most frequently under-assigning.The percentage of trips that were IE, which was based primarily on the statewide model, wasincreased by 10% as part of the calibration process.

Non-Home Based Trips by Non-Residents – The model generates additional non-home basedtrips for internal-external travelers, which make other trips inside the study area beforeeventually leaving the study area. The standard is 0.4-0.6 NHB-NR trips per IE trip, and as partof the calibration process, the factor was increased from 0.5 to 0.6.

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Trip Distribution

Show Calibration Adjustments for Destination Choice – As discussed at the peer reviewmeeting, calibration adjustments were incorporated into the logit destination choice model toprovide for consistency with attraction control totals. The adjustments were put in place for alltrip purposes. The program was revised to report the adjustments to a text report:Attraction_Scaler_Report.txt. This text file can be imported to Excel for review.

District Level Validation of Trip Distribution – A new set of districts has been defined formodel validation reporting. The program was revised to report the trip distribution results atthe district level in a text report: DistrictReport.txt. District level validation results werereported in Technical Memorandum #4: Trip Distribution.

Intrazonal Trip Distribution Review – The number of intrazonal trips were reviewed by trippurpose, and it was concluded that the percent of trips that are intrazonal was reasonable,comparable to survey data, and was not a potential cause of global under assignment of trips.

Revalidation – The trip distribution model was completely revalidated to reflect the othermodel changes, including the mode choice model changes, which affect the trip distributionmodels via the logsum variables. The revalidated model was documented in TechnicalMemorandum #4.

Mode Choice

Incorporate Revised Ridership Targets from MATA into Mode Choice – MATA providedupdated ridership totals for use in model validation. The mode choice validation targets wererevised to be consistent with the new information from MATA, and the mode choice modelrevalidated to reflect the revised targets. The mode choice model revalidation was documentedin Techincal Memorandum #6.

Corrected Vehicle Occupancy Rates in Mode Choice – The peer review meeting included adiscussion concerning whether the vehicle occupancy rates for journey to work trip chains werecorrect. These rates were found to be correct, based on the information from the householdsurvey. However, an error in the vehicle occupancy rates for non-home based trips was foundand corrected, and the mode choice validation targets revised accordingly. This revision wasreflected in the revised mode choice model validation.

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Assignment/Validation

Check Traffic Counts (HPMS, seasonal adjustments, etc.) – After a review of the traffic countinformation, it was determined that the traffic counts were not regionally under-/over-reported. Seasonal adjustments of average annualized daily traffic (AADT) were reviewed, butit was found if an adjustment was implemented, traffic counts would be higher, and the modelwould be further away from calibration. Also, HPMS data was reviewed and it was found thatit was based on 430 locations in the region, which is more than adequate to properly estimatevehicle miles traveled (VMT) in the area.

Interstate Volume-Delay Curve Revisions – Volume-delay curves were reviewed forappropriateness and for performance against observed travel time data. Lack of observedcongestion was noted on interstates, which was partly due to time-of-day factoring, and alsodue to the volume-delay curves slowly increasing congestion when V/C>1.0. Volume-delaycurves were implemented that have similar performance before V/C>1.0, and then aggressivelyreduce speeds as congestion increases, which is a more realistic assumption. The parameters ofthe revised curves are shown in Table 3.

Table 3. Volume-Delay Curve Revisions

Functionalclassificatio

n

ALPHA>=70 MPH

BETA>=70 MPH

ALPHA55-69 MPH

BETA55-69 MPH

ALPHA<=55 MPH

BETA<=55 MPH

1 0.83 5.5 0.40 5 0.56 3.6OriginalV/C Curves 11 0.88 9.8 0.40 5 0.56 3.6

1 0.83 5.5 0.83 7 0.68 5.5RevisedV/C Curves 11 0.88 9.8 0.83 7 0.68 5.5

Model Calibration Steps (Challenge 2)

The steps documented in this section focus on resolving the two major issues encounteredduring the transit assignment validation. The first issue involved the transfer rate being toohigh. It had an initial value of 1.88, compared with the initial target value of 1.29. The secondissue involved the assignment results revealing that the North-South routes were severelyunder-assigned and the East-West routes were heavily over-assigned. Ten strategies wereidentified by the model team, as discussed in the Model Challenges and Strategies section. Thissection briefly summarizes the findings and changes made on each of the strategies to bring themodel into calibration.

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Transit Network Reviews

Eliminate Transit Network Coding Errors – In this step, each individual transit route wasreviewed. Some routes with minor coding errors were discovered and corrected. For under-assigned routes, more stops were added to allow better access from the adjacent centroids. Acouple of routes serving Saturday and Sunday only were removed from the network, and route7A and 7B were combined as per MATA’s suggestion. In addition, routes not serving a certaintime period were still found to have significant boardings, although the headway was coded as9999. This problem was corrected by building the transit network by each time-of-day periodwith the actual serving routes only. In addition, each of the access modes were reviewed and itwas determined that the WalktoBus mode should only use bus routes. This problem wascorrected by building separate transit networks for bus-only routes and all routes. The bus-onlytransit network was then used for the WalktoBus mode assignment. An algorithm wasdeveloped to automatically generate a transfer wait time table so that penalties could be appliedto further eliminate transfers between parallel routes.

Adjust Transit Route Headways – Coordinating with MATA, route headways were reviewedagain. It was found that the methodology used to determine headways was not consistentlyapplied due to the joint considerations with actual waiting time and, in some case, unequaldispatching intervals or very limited dispatches (e.g., one or two buses only during each timeperiod). The model team determined that the actual waiting time could be capped by themaximum waiting time applied in the assignment parameters. The headways were thenadjusted to closely match the number of buses serving each time-of-day period.

Verify and Adjust Transit Stop Locations – Actual number of stops and their locations werecompared with the bus stop inventory data provided by MATA. It had been theorized that thecoding of too many stops in the network was one of the reasons for excessive transfers. Thecomparison results show that MATA actually had more stops than what was coded in themodel. Since the model already had stops at most major intersections and centroid/walkconnector locations, it was decided that it was not necessary to add more stops since the modelcould not benefit from it. For all under-assigned routes, stop locations were reviewed carefullyand stops were added whenever applicable.

Adjust Walk Access Network – The walk network (including network links, walk links, andwalk connectors) was reviewed, and connection errors were corrected. In addition, more walkconnectors were added for under-assigned routes wherever applicable to improve theaccessibility. Only limited walk connectors were removed for over-assigned routes whereverdetermined redundant or unreasonable.

Adjust Transit Speed Curves – As discussed in the Highway and Transit Assignment

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Procedures memo (Technical Memorandum #9), the transit vehicle travel speed is a function ofhighway congested travel speed borrowed from the SEMCOG model. The transit speed wasreviewed and no correlation was found between travel speed and the unbalanced transitassignment. However, to better match the transit speed with the schedule, the speed curve wasreviewed and adjusted to speed up the transit vehicles in local/collector streets and slow themdown in the arterial and freeways. The adjusted transit curve parameters are listed in Table 4:

(1 1)transit highwayS k S if highway cut offS S (Case 1)

0 ( ) 2transit highway cut offS S S S k if highway cut offS S (Case 2)

Where:transitS = Transit speed

highwayS = Highway speed

cut offS = Highway speed cut-off for transit speed calculation

0S = Transit speed lower bound



Table 4. Parameter Settings for Transit Speed Calculation

Area Type Functional classification cut offS 0S 1k 2kInterstate 30.0 30.0 0.000 0.8


Minor Arterial/Collector 12.0 6.0 0.45 0.5CBD


Interstate 30.0 30.0 0.000 0.8


Minor Arterial/Collector 12.0 8.0 0.3 0.5Urban


Interstate 30.0 30.0 0.000 0.8


Minor Arterial/Collector 18.0 11.0 0.4 0.55Suburban


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Area Type Functional classification cut offS 0S 1k 2kInterstate 30.0 30.0 0.000 0.8


Minor Arterial/Collector 18.0 12.0 0.389 0.6Rural


Express Bus Fares – Originally, the model did not apply express bus fares to express bus routes.This has been corrected by introducing a separate fare matrix for the express bus and specifiesthe fare matrix index in the assignment procedure.

Adjust Pathfinder Transit Skims/Assignment Parameters

After eliminating most of the network issues, the skims/assignment parameters for thePathfinder assignment procedure was carefully reviewed and adjusted to verify compliancewith FTA guidelines and to apply more restrictions on the transit transfer. It was determined tobe more appropriate to apply value of time and the OVTT (out-of-vehicle travel time) weightsby time-of-day and by access mode. The value of time and OVTT weight parameters are listedin Tables 5 and 6 below:

Table 5. Value of Time Settings for Transit Pathfinding

Value of Time AM MD PM OP

Drive to BusTrolley 0.1 0.05 0.1 0.05Walk to Bus 0.1 0.05 0.1 0.05

Walk to Trolley 0.1 0.05 0.1 0.05Walk to BusTrolley 0.05 0.05 0.05 0.05

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Table 6. OVTT Weight Settings for Transit Pathfinding

IVTT Weights AM MD PM OP

Drive to Bus/Trolley 2 3 2 3Walk to Bus 2 3 2 3

Walk to Trolley 2 3 2 3Walk to Bus/Trolley 3 3 3 3

Other Pathfinder parameters are presented in Table 7.

Table 7. Global Parameter Settings for Transit Pathfinder Algorithm

Layover Time 5 minMax PACC 10 minMax Access 18 minMax Egress 18 min

Max Impedance 180 minTransfer Penalty Weight 1

Transfer Penalty 20 minMax Transfer Waiting 10 minMin Transfer Waiting 5 min

Max Transfer Walk Time 6 minMax Number of Transfers 3Path Combination Factor 0.5

In addition, dwell times were modified to be applied by each time-of-day period by each route,to provide a match between bus run times and the bus schedules.

Review and Finalize Transfer Rate/Linked Trips/Boarding Targets

Boarding Target – The boarding target was the first target to be determined. By working closelywith MATA, the boarding target of 43, 995 unlinked trips were established. This includes41,155 trips for bus, and 2,840 trips for trolley.

Transfer Rate Target – To determine the transfer rate target, four sources of information werereferenced:

1. Transit on-board survey: The transfer rate directly inferred from the transit on-boardsurvey responses was 1.29.

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2. MATA 2006 transit transfer analysis: This data provided by MATA was based on thetransfer study conducted in 2006. The estimated transfer rate was 1.34.

3. Assigning expanded transit on-board survey OD matrix to the model network: Thetransfer rate from this assignment exercise was 1.36.

4. After ruling out other transit network issues, the theory was formed that the transit on-board survey was under-reporting number of transfers. To verify this, the top 30 surveyrecords carrying the most significant weights were examined and showed that 35% ofthe records were under-reporting transfers, and only 6% of the records were over-reporting transfers. For all the OD pairs checked using the model network during thisvalidation process, it was found that the model produced more reliable answers onnumbers of transfers required for each OD pair — no unreasonable paths were found bythe model. Based on the findings, the model team decided to establish a lower boundfor the transfer rate using the transit on-board survey OD matrix and the minimumnumber of transfers required in the model network. This was accomplished by settingthe Pathfinder parameters so that the total number of transfers was minimized. Thenumber of transfers in each survey record was then replaced by the minimum numberof transfers from the model if the survey was under reporting number of transfers. Thelower bound established by this exercise was 1.43.

Based on all the information discussed above, the team decided that 1.40 was a reasonabletarget to use for the mode choice revalidation.

Linked Trips Target – After the transfer rate target was established, the total number of linkedtrips could be derived from the unlinked trips and transfer rate targets. The target for linkedtransit trips was 31,425.

Transit Trip OD Distribution Review

The steps listed in this category were undertaken to 1) identify problems on transit networkregarding transfer rate and 2) explore causes and solutions for the unbalanced North-South andEast-West assignment problem.

Expand Transit On-Board Survey – The transit on-board survey was expanded to create an ODmatrix directly based on the on-board survey. This OD matrix was then assigned to the modeltransit network. The OD matrix was then aggregated to 25 districts, which were based onexisting planning districts. At the district level, the trip table was compared with the districttrip table generated by the model. The comparison results showed that in low-income districtssuch as the CBD, North Memphis, Southwest Memphis, and Frayser, the model was under-predicting trips. Also, the correlation between the low-income districts and the North-South

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under assignment problem appeared to be strong. The vehicle availability model results werethen reviewed. Although the vehicle availability model results matched the survey targets wellat the regional level, there were indications that districts where transit trips were under-predicted coincided with districts where zero-car households were under-predicted. Figure 1shows this correlation graphically.

By reviewing the survey data, it was found that the 1998 household survey broke down theincome levels from $0 to $15,000. The theory was put forward that the biased 0-car householdforecast at the district level was a result of the survey data not being able to capture thedifference between the poor families and the “super” poor families with income level of lessthan $10,000. The transit on-board survey also showed that 34% of the MATA riders have lessthan $6,000 income, and another 31.8% riders have income level between $6,000 and $18,000.

To capture the “super” poor family distribution, data was obtained from the Census 2000database for households with incomes of less than $10,000. This data was then presentedgeographically, as shown in Figure 2. Figures 1 and 2 illustrate a strong correlation betweenthe very low-income level household distribution and the model’s East-West over-assignmentand North-South under-assignment problem.

Recalibrate Mode Choice Models – Based on the results of the previous tasks, the model teamdecided to introduce a new district level variable to the mode choice model: the percentage ofhouseholds with income of less than $10,000. The variable values were derived from theCensus 2000 data, and could be forecasted for future year by assuming that the fraction of<$10,000 income households over <$15,000 income households does not change for eachdistrict. The recalibrated mode choice model is presented in the updated TechnicalMemorandum #6 – Mode Choice.

After the recalibration and revalidation of the mode choice model, the assignment resultsshowed significant improvements. The North-South under-assignment problem was eliminatedand the East-West over-assignment problem was reduced.

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Figure 1. Map of Under-/Over-Assigned Routes Overlaid on Top of District Level0-Car Household differences (Model Compared to Survey)

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Figure 2. Percent of Households with Income of less that $10,000 (Census Data)

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Highway Assignment Results

As discussed in the model validation memo (Technical Memorandum #9), the highwayassignment was validated at varying levels of aggregation. This section presents thehighway assignment results in the model.

Vehicle Miles of Travel (VMT)

Regional, household, and per capita VMT have been computed and compared to HPMSdata and other suggested ranges. Based on HPMS data, the areawide daily target forthe Memphis area was 26,980,000 vehicle miles of travel. This computes to a targetVMT per capita of 24.5 and VMT per household of 64. A VMT per household of 40-60miles per day and a VMT per person of 17-24 miles per day for large urban areas havebeen suggested in the Model Validation and Reasonableness Checking Manual, 1997.The target VMTs for the Memphis model were higher than the target VMTs presentedin the reasonableness manual, but are in line with data gathered at other largemetropolitan areas, such as Atlanta and Charlotte.

VMT is categorized by functional classification and compared with suggested percentdifferences, shown in Table 8. Overall, the model was within 1% of the total VMTtarget. Regionwide, the current model produces a VMT per capita of 24.4 and VMT perhousehold of 64.5, estimates which are at the established targets.

Table 8. Percent Difference Targets for VMT by Functional Classification

Functionalclassification Target

VMTModel VMT %

Difference Target

Freeways 8,781,000 9,004,400 3% 8%Principal Arterials 8,420,700 8,494,600 1% 18%

Minor Arterials 7,124,900 6,852,100 -4% 27%Collectors 2,653,000 2,530,400 -5% 33%

Total 26,980,700 26,881,500 0% 10%

Table 8 shows that the model is performing very well in terms of VMT estimation byfunctional classification regionwide.

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Traffic Volumes

Coefficient of determination (R2) is a useful measure to compare system-wide observedtraffic counts with estimated volumes. The Model Validation and ReasonablenessChecking Manual, 1997 suggests that the system-wide R2 be greater than 0.88 at thesystem level. The current model has an R2 of 0.917, which is above this target. Thecurrent root mean square error (%RMSE) is 30%, which is within the desired range(35%).

Table 9 presents daily volume targets by functional classification for the entirefunctional classification category. Table 10 presents validation targets grouped bydaily volumes. Table 11 presents goals for the percentage of link volumes that are tobe within a percentage of the observed volume. This adds a measure of the variance ofindividual observed link volumes from individual modeled link volumes in addition tothe comparison of aggregate link volumes.

The results of these comparisons show that, on aggregate, the model effectivelyestimates model volumes both by functional classification and volume group. Theonly targets that were not met in the volume comparisons are shown in red in Table11 — the percent of links within the range of counts for Major and Minor Arterials.This indicates that while the majority of these counts were overall within acceptableranges, there was a large amount of variance at each particular location. Significanteffort was spent on the link-level calibration to help bring these measures within thedesired range, and the actual results reflect this effort, even though the targets werenot achieved. In link-level calibration, individual counts location with highdiscrepancy between counts and model volume was thoroughly reviewed. The effortincludes reviewing centroid connector locations, link and corridor coding qualitycheck, free-flow and congested speed comparison, parallel facility coding qualitycheck, and analysis of origin-destination demand utilizing target links.

Table 9. Percent Difference Volume Targets by Functional Classification


CountsTargetCountTotal

ModelCountTotal

%Difference

TargetFreeways 134 5,251,790 5,336,848 2% 7%

Principal Arterials 211 4,931,870 4,831,840 -2% 10%Minor Arterials 328 3,749,905 3,667,137 -2% 15%

Collectors 341 1,231,849 1,234,304 0% 25%Total 1,012 15,165,414 15,070,129 -1% 5%

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Table 10. Percent Difference Volume Targets by Daily Volume Groupings

Volume Group

TargetCountTotal

ModelCountTotal

%Difference Target

<1,000 23,824 54,191 127% 200%1,000-2,500 201,914 291,317 44% 100%2,500-5,000 690,068 817,290 18% 50%5,000-10,000 1,404,109 1,360,340 -3% 25%10,000-25,000 4,615,086 4,541,419 -2% 20%25,000-50,000 5,504,167 5,478,767 0% 15%

>50,000 2,726,246 2,526,805 -7% 10%

Table 11. Percent of Links within a Specified Percent of Count by Functionalclassification


Counts withinRange

Target withinCount

RangeCompared to

CountsFreeway 78% 75% 20%Freeway 54% 50% 10%

Major Arterial 76% 75% 30%Major Arterial 46% 50% 15%Minor Arterial 70% 75% 40%Minor Arterial 41% 50% 20%

Note: Table 11 can be read as “75% of the freeway links need to be within 20% of counts, 50% of thefreeway links need to be within 10% of counts.”

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As a part of the model calibration/validation process, screenlines and cutlines weredeveloped to gauge how well the model replicates traffic between different areas withinthe Memphis MPO area. The goal for any screenline or cutline comparison is to have100% of the observed traffic replicated by the model. For Memphis, a target of +/-10% for screenlines and +/- 15% for cutlines has been established. Refer to Figure 6of Technical Memorandum #1(a): Network and TAZ Development for screenlineand cutline locations. Table 12 shows the current screenline and cutline performance.All screenlines fell within the desired targets, while only two cutlines were greater than15% high. These two cutlines have relatively low volumes and few counts, so theresults were deemed to be within an acceptable level of calibration.

Table 12. Screenline/Cutline Comparison


Target CountTotal

Model CountTotal Counts % Difference

Screenline 1 276,861 278,795 27 1%Screenline 2 764,201 733,915 54 -4%Screenline 3 805,834 766,220 47 -5%

Cutline 1(240 Loop) 1,306,195 1,299,837 54 0%

Cutline 2 162,841 151,910 9 -7%Cutline 3 72,168 90,415 5 25%Cutline 4 74,900 69,883 6 -7%Cutline 5 31,680 29,026 3 -8%Cutline 6 12,970 16,470 1 27%

Time-of-Day Period Results

Table 13 compares the target counts with model assigned volumes by each time-of-day period. Although the AM and Midday volumes were slightly out of the 5%(established for daily volume validation) range by 4% and 2%, the results show thatthe model performs reasonably well in each time-of-day period at a system-wide level.

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Table 13. Time-of-day Comparison

Time-of-dayPeriod

Target CountTotal

Model CountTotal % Difference

AM 1,807,599 1,970,714 +9%Midday 2,870,178 2,661,964 -7%

PM 3,079,521 3,242,762 +5%Off-Peak 2,810,584 2,684,726 -4%

Daily Total 15,165,414 15,070,129 -1%

Model Districts

Table 14 summarizes the counts and model assigned volumes to district level usingthe 25 districts defined in the model validation process. The results show that themodel performed reasonably well in each districts, with the exception of the“Northwest Shelby County” district, which has only 5 counts on low volume roadwaysavailable for comparison.

Table 14. District Assignment Results Comparison

Index District NameTargetCountTotal

ModelCountTotal

Numberof

Counts

%Difference

1 CBD 795,401 748,154 62 -6%2 North Memphis 378,883 404,667 23 7%3 Midtown and Depot 1,320,203 1,415,819 72 7%4 East Memphis 1,678,638 1,722,476 86 3%5 Southwest Memphis 1,432,591 1,410,697 91 -2%6 Hickory Hill 1,779,464 1,691,806 93 -5%7 East Shelby County 52,535 55,671 10 6%8 Collierville 369,261 368,631 32 0%

9 Northeast ShelbyCounty 264,212 315,630 38 19%

10 Raleigh Bartlett 996,933 921,950 65 -8%11 Millington 345,709 357,995 43 4%12 Frayser 475,941 476,082 33 0%

13 Northwest ShelbyCounty 8,558 12,593 5 47%

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Index District NameTargetCountTotal

ModelCountTotal

Numberof

Counts

%Difference

14 East Desoto County 551,763 546,688 47 -1%15 West Desoto County 303,178 291,351 34 -4%16 South Desoto County 258,447 238,657 42 -8%17 Marshall County 0 0 0 0%18 North Fayette County 132,531 154,532 13 17%19 West Tipton County 47,412 42,622 12 -10%20 East Tipton County 98,946 112,997 20 14%21 South Fayette County 97,525 112,804 20 16%22 McKellar Lake 12,967 14,086 2 9%23 University 296,052 351,074 26 19%

24 Shelby FarmsGermantown 2,325,938 2,224,703 104 -4%

25 Airport 1,142,326 1,078,444 41 -6%

Transit Assignment Results

As discussed in the model validation memo (Technical Memorandum #9), the transitassignment will be validated at the system-wide level and at the sub-regional level.Line-by-line ridership data also is presented in this section.

System-Wide Measures

At the system-wide level, the target for the total number of linked transit trips was31,425. The total number of linked transit trips generated by the mode choice modelwas 32,201, which is only 2.5% higher than the target. As discussed in the previoussection, the revised transfer rate target was 1.40. In comparison, the transfer rateresulting from the transit assignment was 1.41, which is only 0.7% higher thanestablished target transfer rate. Table 15 summarizes the system-wide transitassignment results by each access mode, and Table 16 shows the model performanceby comparing the linked trips, transfer rate, and total boardings with the establishedtarget values.

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Table 15. System-Wide Transit Assignment Results Summary

Mode Boarding # Trips TransferRate

Drive Access Transit 4675 4314 1.08Walk Access Bus 33324 22515 1.48

Walk Access Trolley 709 723 0.98Walk Access Transit 5042 3420 1.47

Total 43750 30971 1.41

Table 16. System-Wide Performance Measures

PerformanceMeasures

ObservedValue

ModelValue

%Difference Target

Total Number of LinkedTrips 31,425 30,971 -1.4% 5%

Transfer Rate 1.40 1.41 +0.7% 10%

Total Number ofBoardings 43,995 43,750 -0.6% 10%

Screenlines

As part of the peer review panel recommendations, MATA defined two transitscreenlines for the model transit assignment validation. The first screenlineencompasses the I-40/I-240 Loop (excluding the Midtown segment) and is the same asthe “I-40/I-240 Loop Cutline” shown in Figure 6 of Technical Memorandum #1(a).The second screenline encompasses the Parkway system — South Parkway, EastParkway and North Parkway. The number of daily passengers on board at each pointwhere a route crosses a screenline was estimated by MATA, which was developed fromComprehensive Operational Analysis sample data. Tables 17 and 18 show thescreenline comparisons for the two screenlines defined above respectively. The resultsshow that the modeled total passenger on board crossing two screenlines werereasonably close to the targets, with less than 1% difference for the Parkwayscreenline, and less than 16% difference for the I-40/I-240 screenline.

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Table 17. Parkway Screenline Comparison

Passenger On-boardScreenlineLocation Crossing Locations

Model Value Observed Value

South Parkway Swift Street 583 1293South Parkway Florida Street 663 605South Parkway Third Street 1266 848South Parkway Fourth Street 288 396South Parkway Lauderdale Street 248 383South Parkway Mississippi Blvd. 547 259South Parkway I-240 161 18South Parkway Bellevue Blvd. 3159 2755South Parkway Pillow Street 299 702South Parkway Cooper Street 142 200South Parkway Lamar Avenue 2524 3341South Parkway East Parkway 1310 681East Parkway Southern Avenue 372 324East Parkway Central Avenue 1017 731East Parkway Union Avenue 640 321East Parkway Poplar Avenue 456 2522North Parkway North Parkway 461 582North Parkway Watkins Street 3350 1786North Parkway Bellevue Blvd. 927 747North Parkway I-40 206 0North Parkway Third Street (north of Auction) 717 796

Screenline Total = 19335 19290% Difference = 0.23%

Table 18. I-40/I-240 Screenline Comparison

Passenger On-boardScreenlineLocation Crossing At


I-55 Hornlake Rd 256 186I-55 Highway 61 1220 1431

I-55/I-240 I-55 0 0I-240 Elvis Presley 2622 1640I-240 Airways Blvd 464I-240 Plough Blvd 480 239

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Passenger On-boardScreenlineLocation Crossing At


I-240 Larmar Ave 385 850I-240 Getwell Rd 504 217I-240 Perkins Rd 779 419I-240 Mendenhall / Mt Moriah Rd 624 308I-240 385 0 0I-240 Quince Rd 11 192I-240 Park Ave 83 576I-240 Poplar Ave 264 571I-240 Shady Grove Rd 77 0I-240 Walnut Grove Rd 151 0

I-240/I-40 I-40 0 0I-40 Summer Ave 505 654I-40 Covington Pike 272 152I-40 Austin Peay / Jackson 441 506I-40 New Allen 184 318I-40 Hollywood 192 447I-40 Watkins 1129 836

Highway 300 Highway 51 / Thomas 1125 617Highway 300 Whitney /Second 0 0

Screenline Total = 11769 10159% Difference = 15.85%

Transit Route Schedules

In this validation process, the model reported transit route run times were comparedto the route schedule run times provided by MATA. Table 19 shows the percentagedifference of run times between the model and the actual MATA schedules. Theresults show that by average, the bus run times were within 5% difference comparedwith schedule, and the trolley lines were on target. There were three outliers (route11C, 11F, and 15) with a difference of greater than 25% between model run time andschedule. By schedule, these three routes travel in faster speed zones than otheraverage routes (23.5 mph for route 11C, 29.9 mph for route 11F and 20.6 mph forroute 15, whereas the average bus speed by schedule is only 16.3 mph).

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Table 19. Modeled Transit Run Times Compared to Schedules

RouteName

ModelTime (min.)

Target Time(min.)

%Difference

2A 52 52 -0.1%2C 52 52 0.1%2L 36 36 0.3%2W 33 32 4.1%4A 47 47 0.6%4C 50 49 1.2%7A 102 102 0.3%8 20 20 0.5%

10S 71 71 0.6%10C 77 77 0.3%

10RG 46 50 -7.2%10RL 46 50 -7.8%11C 51 36 40.8%11F 39 24 64.2%11T 47 40 18.5%15 37 29 28.4%

19M 60 52 15.5%19NA 58 56 3.5%19R 45 44 2.3%

19RA 57 56 1.6%19W 44 44 0.1%20 63 62 2.2%22L 81 77 5.4%30 79 75 4.7%31 56 56 0.0%

32A 48 47 2.8%32F 47 47 0.1%32N 44 44 0.6%33 30 27 9.6%

34B 52 50 3.0%34M 48 48 -0.1%34R 56 55 1.9%35 57 57 -0.1%40 75 75 0.1%

40B 67 62 8.8%41 93 89 4.8%

43B 61 61 0.1%43H 61 61 -0.3%

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RouteName

ModelTime (min.)

Target Time(min.)

%Difference

43S 52 52 -0.1%50G 55 54 2.5%50W 55 55 0.3%50Y 55 55 -0.1%52B 71 71 -0.4%52M 45 45 -0.1%52Q 61 61 0.0%52R 45 45 0.2%

52SE 45 44 1.1%52SF 63 63 0.1%53B 60 60 0.1%53I 55 55 0.0%53L 55 55 0.2%53S 56 56 0.0%53W 63 57 10.9%56 57 57 0.4%

58B 75 75 0.0%62G 68 66 2.9%62W 75 71 5.8%69 92 92 0.5%80 31 25 22.6%

80B 28 25 11.6%81 58 58 0.2%82 28 23 21.3%93 42 42 0.1%

Madison 23 23 0.0%Main 17 17 0.0%

River front 34 34 0.0%Average Absolute % Differences for Bus

Routes 4.60%

Average Absolute % Differences for TrolleyLines 0.00%

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Route Group Level Boardings

The total number of boardings were first compared and validated in route group level,using the boarding counts and groups provided by MATA. A total of eight (8) routegroups were defined, as shown on Table 20.

Table 20. Route Group Level Boarding Comparison

Group Index Group Name ModelBoarding

TargetBoarding

%Difference

1 Southwest 15175 15611 -2.8%2 Southeast 4114 3735 10.2%3 East 6742 6173 9.2%4 North 8476 8458 0.2%5 N-S Crosstowns 4787 4920 -2.7%6 E-W Crosstowns 2420 1855 30.4%7 Connectors 528 403 31.1%8 Trolley 1508 2840 -46.9%

The results show that the boarding differences for groups 1 through 5 were withinreasonable range of less than 15%. A weighted average of % differences (using targetboarding values as the weight for each % difference number in absolute value) iscalculated as 8.1%, which is within the target of 15% established in the validationprocedure.

For groups with larger differences, possible reasons include the following:

1) East-West crosstown routes were 30.4% higher than the target boarding. The over-assigned Route 69 (Winchester-Shelby Drive) makes the greatest contribution to thedifference. For Route 69, majority of the boarding/alighting occurs in the AmericanWay transfer center, the airport, and south Memphis area west of airport. These areasare high transit demand areas served by multiple competing routes, which mightcontribute to the over-assignment problem.

2) Although the Connector routes were overloaded by 31.1%, the absolute differencewas relatively small, with only 164 boardings.

3) Trolley lines were under-assigned by 47%. The major contributors for the under-assignment problem were the Riverfront and Main Street trolley lines for the followingtwo reasons:

The competing bus services serving Front Street (6 bus lines) and 3rd Street (8 bus

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lines) in the downtown area may receive the majority of downtown riders. Thecombined trunkline headways of these bus services might be more attractive thanthe trolley service.One major part of the trolley riders are tourists. Tourist trips are not explicitlymodeled by the Memphis Travel Demand Model.

Route Sub-Group Level Boardings

Table 21 shows the sub-group level boardings. A total of 15 route sub-groups weredefined by MATA. In Table 21, the column “Group Index” corresponds to the groupindex in Table 20.

Table 21. Route Sub-Group Level Boarding Comparison

Sub-groupIndex

GroupIndex Sub-Group Name Model

BoardingTarget

Boarding%

Difference

1 1 Third St Corridor 2453 3582 -31.5%2 1 South Memphis 2688 2980 -9.8%

3 1 Bellevue/Elvis PresleyCorridor 4335 4624 -6.3%

4 1 Lamar Corridor 5460 4360 25.2%5 1 Whitehaven Night 239 65 267.9%6 2 Midtown/UM 1569 1006 56.0%7 2 Quince/Park 2545 2729 -6.7%8 3 Poplar Corridor 5517 4368 26.3%9 3 Summer Corridor 1225 1805 -32.1%10 4 Jackson/Chelsea 5232 5919 -11.6%11 4 Watkins/Thomas 3244 2539 27.8%12 5 N-S Crosstowns 4787 4920 -2.7%13 6 E-W Crosstowns 2420 1855 30.4%14 7 Connectors 528 403 31.1%15 8 Trolley 1508 2840 -46.9%

A weighted average of % differences (using target boarding values as the weight foreach % difference number in absolute value) is calculated as 21.6%, which is withinthe target of 25% established in the validation procedure.

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Line Level Boardings

MATA transit line level boarding figures are shown on Table 22. In Table 22, thecolumn “Sub-Group Index” gives the sub-group ID in which the route belongs to.Similarly, the column “Group Index” indicates to which group the route belongs.

Table 22. Line Level Boarding Comparison

IndexSub-

GroupIndex

GroupIndex Line Group Name Model

BoardingTarget

Boarding%

Difference

1 1 1 11-Tulane/Hodge[11T,11S] 746 920 -19.0%2 1 1 15-Presidents Island [15] 78 113 -31.4%3 1 1 19-Third [19W, 19R] 650 1313 -50.5%4 1 1 53-Florida [53I,53L,53W] 980 1236 -20.7%5 2 1 4-Walker [4A,4C] 1992 2004 -0.6%6 2 1 2-Lauderdale [2L,2W] 696 976 -28.7%

7 3 1 20-Bellevue/Winchester

[20] 1902 1698 12.0%

8 3 1 43-Elvis Presley[43B,43H,43S] 2432 2926 -16.9%

9 4 1 7-Air Park [7A, 7B] 324 288 12.4%10 4 1 10-Lamar [10C,10S] 1228 1708 -28.1%11 4 1 17-McLemore [34M,34N] 685 673 1.8%12 4 1 56-Union [56] 3224 1691 90.6%

13 5 1 89-

WalkerHomes/Westwood 82 28 191.6%14 5 1 90-Neely/Shelby Drive 158 37 325.6%15 6 2 2-Medical Center[2A,2C] 1569 1006 56.0%16 7 2 52-Park [52Q,52B,52SF] 2371 2699 -12.2%17 7 2 58-Fox Meadows B [58B] 175 30 482.0%18 8 3 22-Poplar [22] 90 137 -34.5%

19 8 3 34-Union/WalnutG

[34R,34B] 1408 735 91.5%20 8 3 41-Collierville [41] 187 306 -38.7%21 8 3 50-Poplar [50G,50W,50Y] 3832 3190 20.1%22 9 3 53-Summer [53B,53S] 1225 1805 -32.1%23 10 4 8-Chelsea [8] 1366 1468 -7.0%

24 10 4 19-Vollintine

[19RA,19NA,19M] 986 1280 -22.9%25 10 4 40-Raleigh [40,40B] 725 447 62.2%

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IndexSub-

GroupIndex

GroupIndex Line Group Name Model

BoardingTarget

Boarding%

Difference

26 10 4 52-Jackson

[52M,52R,52SE] 2155 2724 -20.9%27 11 4 10-Watkins [10RG,10RL] 2155 1545 39.5%28 11 4 11-Thomas [11F,11C] 1089 994 9.6%29 12 5 30-Perkins [30] 1260 644 95.6%30 12 5 31-Crosstown [31] 2195 2901 -24.3%

31 12 5 32-E Parkway[32A,32F,32N] 1061 1289 -17.7%

32 12 5 33-Highland [33] 198 32 517.5%

33 12 5 82-Germantown Parkway

[82] 73 54 35.3%34 13 6 35-Southgate [35] 454 549 -17.3%

35 13 6 62-Frayser/E. Memphis

[62G,62W] 544 346 57.2%36 13 6 69-Winchester [69] 1422 960 48.1%37 14 7 80-Cordova [80] 53 12 337.6%

38 14 7 81-Shelby Drive/Hickory Hill [81] 151 194 -22.0%

39 14 7 93-Hickory Hill/Winchester

[93] 325 197 64.7%40 15 8 Main Street Trolley 416 1269 -67.2%41 15 8 Riverfront Trolley 165 1052 -84.4%

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Technical Memorandum #12Future Year Model Development and ResultsThis memorandum details the future year model development for the Memphis TravelDemand Model Update.

ContentsFuture Year TAZ Attributes Forecasting Methodology

- Economic and Demographic Data Forecasts- School and University Enrollment Forecasts- Special Generator Demand Forecasts

Future Year External Trip Forecasting Methodology- Development of Base Year (2004) External Trips- Development of Future Year (2030) External Trips- Model Application

Future Year Light Rail Mode and Mode Choice Model Adjustments- Mode Choice Model Adjustments and Assumptions- Transit Path Building

Future Year Highway Network Development- Future year projects- Future Year Signal Locations- Heavy Vehicle Restricted Routes- Future year HOV lanes- Ramp Types

Future Year Transit Network Development- Fixed Guideway Corridors- Local Bus Routes- Express Bus Routes- Park-and-ride and Passenger Transit Terminals

Horizon Year 2030 Highway Assignment Results- Vehicle Miles of Travel (VMT) by Functional Classification- Time-of-Day Period Results- Model Districts- Screenlines and Cutlines

Horizon Year 2030 Transit Assignment Results- System-Wide Measures- Route Group Level Boardings- Route Sub-Group Level Boardings

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- Line Level Boardings- Route Level Boardings

Appendix A — External Station LocationsAppendix B — Future Year Transit Route AttributesAppendix C — Future Year Transit Park-And-Ride Facilities and PassengerTerminalsAppendix D — Revised Income Forecasts for Sub-County Areas inMetropolitan MemphisAppendix E — Horizon Year 2030 Route Level Boardings

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Future Year TAZ Attributes Forecasting Methodology

Economic and Demographic Data Forecasts

Methodologies for forecasting future year demographic and economic data aredescribed in detail in the Final Report “Economic and Demographic Forecasts for theMemphis-Shelby County MPO Travel Demand Study Area and 45 Sub-County Areas”.For horizon year 2030, all the demographic and economic attributes were distributedin TAZ level. The following interim years were incorporated in the Memphis model:2008, 2010, 2011, 2014, 2017, and 2020. For each of the interim year, the attributeswere forecasted to the Sub-County Area (SCA) level first. Then the values weredistributed from SCA level to each TAZ based on the base year distribution and byholding the SCA level values as the control total.

School and University Enrollment Forecasts

School (K-12) and University Enrollment Forecasts are both inputs that must bemanually forecast for input into the model. Since no long-term forecasts are availablefor school systems in the area (like most places, they have 5 or 10-year forecasts atbest), growth for educational enrollment was tied closely to the population forecast,along with observed growth at the University of Memphis. Growth was then allocatedto TAZs using the existing enrollment in each zone as a guide.

School (K-12) Forecasts

School forecasts were prepared by assuming there would be growth in enrollment thatis proportional to growth in population in the area. Forecasts were prepared by modeldistrict, since these most closely replicate school districts, are divided by County, andrepresent a good level of detail between the entire model and the TAZ. To forecast theschool enrollment, the steps below were used. Table 1 shows the school enrollmentforecasts by district.

School Forecasting Process1) Determined an enrollment to population ratio for each district2) Multiplied this enrollment/population ratio by the 2030 projected population to

get a preliminary 2030 enrollment number3) In cases where population growth was negative, kept the same school enrollment

number as the base year

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4) In cases where population growth was positive, transferred the number of newstudents (enrollment growth) to each zone in the district by a weighted average(larger schools got more of the growth then smaller schools)

Table 1. School Enrollment Forecast by District

District 2004Population

2030Population

2004School

Enrollment

2004Enrollment

toPopulation

Ratio

2030School

Enrollment

1 19,024 14,845 4,061 0.21 4,0612 32,049 34,168 7,119 0.22 7,5843 80,705 76,621 14,413 0.18 14,4134 101,181 101,771 30,327 0.30 30,4875 106,423 109,137 24,339 0.23 24,9436 121,361 131,143 19,033 0.16 20,5597 13,586 48,955 3,953 0.29 14,2418 42,451 79,418 8,647 0.20 16,1729 22,817 54,873 4,448 0.19 10,69410 104,706 135,597 20,331 0.19 26,31411 20,793 40,841 5,434 0.26 10,66812 44,255 44,392 9,272 0.21 9,29613 8,423 12,002 939 0.11 1,33714 60,399 147,660 10,722 0.18 26,20815 47,998 86,721 9,875 0.21 17,83816 22,427 51,118 3,582 0.16 8,16317 8,046 23,150 1,612 0.20 4,63718 9,481 27,083 679 0.07 1,93919 19,452 34,674 6,425 0.33 11,45120 15,432 33,675 1,270 0.08 2,77021 9,284 31,936 790 0.09 2,71622 0 0 0 0.00 023 23,427 15,689 2,760 0.12 2,76024 148,150 199,408 30,117 0.20 40,52225 21,669 22,315 5,092 0.23 5,240

Total 1,103,539 1,557,192 225,240 0.20 315,013

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University Growth

University growth differs from K-12 school growth in that it is less affected by changesin growth in the surrounding area. Therefore, this forecast was produced by reviewinghistorical growth data and applying a trend to prepare a model forecast. Historicalenrollment data was only available for the University of Memphis, so the trendobserved for this University was applied to all College Enrollment in the area(Including other Universities, Colleges, and Technical Schools). Table 2 shows theforecasted University of Memphis and Model Enrollment data.

Table 2. University Enrollment Forecast

YearUniversity

ofMemphis

Enrollment

ModelUniversityEnrollment

(StudyArea)

1997 19,8511998 20,1001999 20,3012000 19,9862001 20,3322002 19,7972003 19,9112004 20,668 43,8562005 21,106 44,7852006 21,390 45,3882008 22,327 47,3762010 23,203 49,2352011 23,641 50,1642014 24,955 52,9532017 26,270 55,7432020 27,584 58,5312030 31,965 67,8272035 34,156 72,477

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Special Generator Demand Forecasts

There are three unique special generators in the Memphis area: The Memphis Airport,Federal Express Hub, and Graceland.

Memphis Airport

Forecasts for the Memphis Airports were based on the Terminal Area Forecast (TAF)prepared by the Federal Aviation Administration (FAA). Two forecasts prepared byFAA for the Memphis airport were used to assist in modeling forecast: 2020 TotalAirport Operations and 2020 Passenger Enplanements. To prepare the modelforecasts, it was assumed that growth would continue to 2035 at the same rate thatwas prepared by FAA up to 2020.

The Memphis model incorporates the number of person-trips (regardless of mode)generated by the airport into the model stream. These trips represent all person-tripsentering and leaving the airport. Table 3 shows the forecasted airport person-tripsthat are used in the Memphis model.

Table 3. Memphis Airport Forecast

YearAirport

OperationsForecast

Enplane-ments

Forecast

ModelForecast(Person-

Trips)2004 430,065 5,243,950 32,000

2008 480,031 5,853,592 35,719

2010 507,150 6,184,498 37,7382011 521,279 6,356,901 38,7902014 566,072 6,903,485 42,1242017 614,714 7,497,066 45,7442020 667,535 8,141,685 49,6772030 878,641 10,718,256 65,3922035 1,008,046 12,297,846 75,026

Federal Express Hub

Federal Express, which is headquartered in Memphis, has a central hub locatedadjacent to the airport. The model has a special generator based on what units?

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Since it is a private entity and very little historical and forecasted data is availablefrom the company, it was assumed that its operations would grow proportionally tothe operations forecasted at the airport. Table 4 shows the forecasted data for theFederal Express Hub.

Table 4. Federal Express Forecast

YearAirport

OperationsForecast

Fedex Trips(in Person)

2004 430,065 4602008 480,031 5132010 507,150 5422011 521,279 5582014 566,072 6062017 614,714 6582020 667,535 7142030 878,641 9402035 1,008,046 1,078

Graceland

Graceland trips were forecasted based on growth data provided by Graceland on theexpected growth. Available data indicates that weekday Graceland trips will grow byapproximately 0.89% per year over the next thirty years. Table 5 shows the forecastedGraceland attraction data.

Table 5. Graceland Forecast

YearGracelandForecast

(Person Trips)2004 1,3002008 1,3472010 1,3712011 1,3832014 1,4202017 1,4582020 1,4972030 1,6362035 1,710

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Future Year External Trip Forecasting Methodology

While internal trips are determined by the model and based on trip rates and theforecasted housing and employment, external trips must be forecasted manually.These are treated as a model input. To develop the Memphis MPO external stationtrips, first the base year (2004) traffic counts must be developed and categorized byfunctional classification, vehicle class, and internal-external/external-external tripbreakdown. Then, using data such as traffic count history, population growth (bycounty, state, and proximity to model), and functional classification, traffic is thenforecast to 2030. This forecast is based on the growth rates developed using thesupporting information gathered.

The external trips will be applied in the model using separate distribution models forinternal-external (IE) and external-external (EE) trips. IE trips will be distributedusing the process documented in Technical Memorandum #4 – Trip Distribution:Intrazonal Travel, Terminal Times, and Destination Choice Models. EE trips willbe distributed through a model using K-factors based on observed through trip data.This EE trip methodology is described in the last portion of this memo.

Development of Base Year (2004) External Trips

The base year external trips were developed from the base year traffic counts at theexternal stations. These base year traffic counts were taken from Tennessee andMississippi DOT traffic counts for 2003 and/or 2004. See the section “Methodology –Internal External Trips” of the Technical memo # 3 “Trip Generation” for details.

Development of Future Year (2030) External Trips

The future year external trips were developed by applying a growth rate to the baseyear external trips. The growth rate varied based on a number of different criteriaincluding state, functional class of roadway, historic count data, and historicpopulation growth by census tract inside and outside the model boundary.

Historic traffic count data was only available for Tennessee. It was observed that theaverage traffic count growth in Tennessee between 1996 and 2005 was 1.6% (growthrate per year). In Shelby County it grew by 0.4%. Fringe county portions (Fayette andTipton Counties) of the model grew by 1.5% (Tipton) and 2.9% (Fayette). Table 6shows the historic traffic count growth that was observed in the model.

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Table 6. Historical Traffic Count Growth Rates

1 Growth rate per year

Looking at population growth from the U.S. Census Bureau, Tennessee had thehighest growth rate between 1990 and 2000 at 1.6%. Mississippi grew at a pace of1.0%, while Arkansas grew at 1.3%. Inside the model, Shelby County, which has amajority of the population, grew at the lowest rate in the region at 0.8%. The fringecounties, especially in Mississippi, grew at much higher rates. Urban sprawl ispartially responsible for these higher growth rates in the rural areas; the other primaryfactor is the lower rural population that the growth is being calculated against. Figure1 and Table 7 show the observed model growth in the model area as well assurrounding counties.

Location 1986 - 20051 1996 - 20051

Tennessee 2.7% 1.6%

Shelby County 1.9% 0.4%Tipton County 2.6% 1.5%

Fayette County 3.6% 2.9%

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Figure 1. Historical Population Growth (1990 – 2000)

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Table 7. Historical Population Growth

1 Growth rate per year

The functional classification, location, growth in population, and historic traffic countswere used to determine growth rates for each of the external stations. On a subjectivelevel, the existing volumes and the surrounding area as well as anticipateddevelopment were also taken into consideration when developing the rates.

Growth rates for stations in Tennessee are based on the historical traffic counts from1986-2004 (or 1995-2004) by assuming exponential growth rates for each individualstations. For Tennessee locations where the historical data is not available, an averagerate by functional class in Tennessee is used. Mississippi growth rates are based onopportunity for growth outside Desoto County where growth has recently been high(4.7%), while Tunica (1.2%), Tate (1.7%) and Marshall (1.4%) have been low.Functional class 7 and 8 for Mississippi was factored down based on lower growthrates in neighboring Tennessee on similarly classified roads. All low volume roads (<1000) were given a growth rate of 3%. Goodman Road extension future ADT based onplanned loop connection and location relative to existing traffic counts. In addition, I-69 future ADT is based on the I-69 Environmental Impact Study Reports.

Table 8 shows the future year external trips forecast for the model. An external stationlocation map in Appendix A shows all the external station locations geographically.

Location 1970 - 20031 1980 - 20001 1990 - 20001

StatesTennessee 1.2% 1.1% 1.6%

Mississippi 0.8% 0.6% 1.0%Arkansas 1.1% 0.8% 1.3%

CountiesShelby 0.7% 0.7% 0.8%Tipton 2.0% 2.2% 3.2%

Fayette 1.1% 0.7% 1.2%Haywood 0.0% -0.1% 0.2%

Desoto 3.8% 3.5% 4.7%

Marshall 1.2% 0.9% 1.4%Tate 1.0% 1.2% 1.7%

Tunica -0.5% -0.2% 1.2%

Crittenden 0.2% 0.1% 0.2%

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Table 8. Future Year External Trips

1 2003 ADT

TransCADID Name Functional

Class State County 2004ADT

PercentGrowth

2010ADT

2020ADT

2030ADT

10001 Highway 59 7 TN Tipton 1,700 2.2% 1,900 2,400 2,95010002 Highway 51 2 TN Tipton 23,480 2.6% 23,900 30,900 39,900

10003 Highway 59South/Mount Carmel 6 TN Tipton 4,900 2.5% 5,700 7,250 9,300

10004 Austin Peay 6 TN Tipton 2,170 0.9% 2,300 2,500 2,700

10005 Highway 79 6 TN Haywood 2,200 1.5% 2,400 2,750 3,20010006 Stanton Road North 7 TN Haywood 630 3.0% 750 1,000 1,350

10007 I-40 East 1 TN Haywood 31,720 2.5% 36,800 47,100 60,30010008 Highway 59 East 6 TN Fayette 3,070 1.3% 3,300 3,800 4,300

10009 Highway 64 2 TN Fayette 14,970 3.0% 17,900 24,000 32,300

10011 Highway 57 6 TN Fayette 7,300 0.4% 7,500 7,800 8,10010012 Highway 178 7 MS Marshall 2,2001 1.7% 2,450 2,900 3,400

10013 Highway 78 2 MS Marshall 25,650 2.2% 29,250 36,350 45,150

10014 Highway 305 South 6 MS Tate 3,0001 1.4% 4,350 5,000 5,75010015 Highway 51 South 7 MS Tate 4,2001 1.7% 4,650 5,500 6,500

10016 I-55 South 1 MS Tate 31,0001 2.2% 42,150 52,400 65,15010017 Pratt Road 7 MS Tunica 7501 3.0% 900 1,200 1,600

10018 Highway 304/713 7 MS Tunica 2,0011 1.7% 4,450 5,250 6,200

10019 Highway 61 2 MS Tunica 29,0001 2.2% 33,050 41,100 51,050

10020 Charleston MasonRoad 8 TN Tipton 500 3.0% 600 800 1,100

10022 Feathers Chapel Road 9 TN Fayette 620 3.0% 750 1,000 1,350

10023 Macon Road 7 TN Fayette 1,000 1.2% 1,050 1,200 1,350

10024 Highway 72 2 MS Marshall 14,000 2.5% 16,250 20,800 26,600

10025 Goodman RoadExtension 2 MS Marshall NA NA NA NA 13,000

10026 Victoria Road 7 MS Marshall 5001 3.0% 600 800 1,100

10027 I-40/I-55 West 1 AR Crittenden 104,220 2.0% 117,350 143,050 174,40010028 Stanton Road South 7 TN Fayette 720 3.0% 850 1,150 1,550

10029 Holly Springs Road 8 MS Marshall 7501 3.0% 900 1,200 1,600

10030 Byhalia Road 7 MS Marshall 5,750 1.7% 6,350 7,550 8,90010031 Old Highway 61 8 MS Tunica 7201 3.0% 850 1,150 1,550

10032 Route 3 6 MS Tunica 1,0001 1.4% 1,100 1,250 1,450

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Model Application

The Memphis model will apply external trips at multiple points in the process: tripgeneration, distribution, and assignment.

In trip generation, the trips generated are based on the Average Daily Traffic (ADT) ateach station. External-internal and external-external trips are split from the ADTbased on the assumptions described in Technical Memorandum #3 – Trip Generation.

External-external trips are distributed based on the distribution in the Tennesseestatewide model. These trips were used to develop an EE trip gravity model whichdistributes trips based on their location, EE trip volume, and a K-factor matrix. Table9 shows the K-factors used in the gravity model. Since it was desired to minimize theeffect of the addition and improvements of roads into the distribution of through trips,EE trips are distributed based on shortest length rather than travel time. However,during trip assignment, EE trips are assigned based on travel time, which could affectthe path of the EE trip, but not the overall number of through trips. Using the gravitymodel to distribute the through trips is useful because the addition of the GoodmanRoad Extension as an external station can easily be handled in the model – only minoradjustments to the gravity model are necessary.Trip distribution for external-internal trips is also performed using a gravity model,albeit a more traditional friction-factor based distribution.

For trip assignment, external-external Single-Unit (SU) and Combination-Unit (CU)truck trips are treated as an all-or-nothing preload assignment, based on travel time.External-external auto trips and Internal-external auto/truck (SU,CU) trips areassigned along with internal auto and truck (SU,CU) trips based on a multi-class userequilibrium assignment, with the preloaded EE truck trips affecting the availablecapacity.

Table 9. K-factors for the Trip Distribution Gravity Model

Station ID Type of TripsFrom To AUTO_SOV AUTO_HOV SU CU10002 10016 25.05 24.99 22.45 37.3710002 10019 5.83 5.82 5.66 50.2410002 10025 0.20 0.20 0.20 0.2010002 10027 0.20 0.20 0.49 0.5210003 10004 96.40 97.25 212.11 454.9310003 10025 1.00 1.00 1.00 1.0010004 10003 96.39 97.24 212.11 455.1010004 10008 154.10 130.52 273.02 697.13

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Station ID Type of TripsFrom To AUTO_SOV AUTO_HOV SU CU10004 10009 1.03 1.04 12.40 25.6010004 10016 0.09 0.09 7.44 10.5910004 10019 0.08 0.08 3.20 34.0010004 10027 0.03 0.03 0.28 0.3610005 10008 859.72 837.14 399.79 688.7410005 10027 0.01 0.01 0.60 0.5110006 10028 146.68 145.51 366.16 3290.1310007 10016 1.40 1.40 1.00 1.6010007 10019 3.20 3.20 3.99 4.0510007 10025 1.00 1.00 1.00 1.0010007 10027 1.16 1.15 1.05 1.0710007 10028 0.09 0.08 1.63 2.2910007 10032 5.14 5.14 12.83 15.5910008 10004 154.61 130.95 273.55 697.9410008 10005 862.69 840.02 400.62 689.7110008 10025 0.20 0.20 0.20 0.2010009 10004 1.03 1.04 12.40 25.6010009 10011 26.72 26.71 151.65 329.7610009 10019 0.64 0.64 1.15 11.3110009 10025 0.20 0.20 0.20 0.2010009 10027 1.43 1.43 1.12 1.0110011 10009 26.68 26.67 151.29 328.8310011 10019 0.50 0.51 4.99 60.1310011 10025 0.20 0.20 0.20 0.2010012 10025 0.20 0.20 0.20 0.2010012 10027 2.16 2.16 1.51 1.2210013 10019 0.11 0.11 1.12 3.5310013 10025 0.20 0.20 0.20 0.2010013 10027 1.53 1.53 1.24 1.1310013 10030 12.75 12.75 9.02 16.4610014 10025 0.20 0.20 0.20 0.2010015 10025 0.20 0.20 0.20 0.2010016 10002 25.03 24.97 22.40 37.2510016 10004 0.09 0.09 7.42 10.5610016 10007 1.40 1.40 1.00 1.6010016 10025 1.00 1.00 1.00 1.0010016 10027 1.02 1.02 1.09 0.9610017 10025 0.20 0.20 0.20 0.2010018 10025 0.20 0.20 0.20 0.2010019 10002 5.81 5.81 5.64 50.05

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Station ID Type of TripsFrom To AUTO_SOV AUTO_HOV SU CU10019 10004 0.08 0.08 3.19 33.8910019 10007 3.20 3.20 3.98 4.0510019 10009 0.64 0.64 1.14 11.2810019 10011 0.50 0.51 5.00 60.2710019 10013 0.11 0.11 1.12 3.5210019 10024 3.00 2.99 3.51 41.1910019 10025 1.00 1.00 1.00 1.0010019 10027 0.36 0.36 0.60 0.4810020 10025 0.20 0.20 0.20 0.2010022 10025 0.20 0.20 0.20 0.2010023 10025 0.20 0.20 0.20 0.2010024 10019 2.99 2.99 3.51 41.1210024 10025 0.20 0.20 0.20 0.2010024 10027 1.57 1.57 1.27 1.1310025 10007 1.00 5.13 12.79 15.5310026 10025 0.20 0.20 0.20 0.2010027 10002 0.20 0.20 0.49 0.5210027 10004 0.03 0.03 0.28 0.3610027 10005 0.01 0.01 0.60 0.5110027 10007 1.16 1.15 1.05 1.0710027 10009 1.43 1.43 1.11 1.0110027 10012 2.15 2.16 1.50 1.2110027 10013 1.53 1.53 1.24 1.1210027 10016 1.02 1.02 1.09 0.9610027 10019 0.36 0.36 0.60 0.4810027 10024 1.57 1.57 1.27 1.1210027 10025 1.00 1.00 1.00 1.0010027 10028 0.00 0.00 0.11 0.2410028 10006 147.59 146.41 367.16 3293.1010028 10007 0.09 0.08 1.62 2.2810028 10027 0.00 0.00 0.11 0.2510030 10013 12.62 12.63 8.92 16.2610032 10007 5.13 5.13 12.79 15.53

Future Year Light Rail Mode and Mode Choice Model Adjustments

The 2004 base year scenario includes a single rail mode—the existing trolley. Themodel may be used to analyze future scenarios in which new rail options are analyzed.

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The technologies and operating characteristics may be significantly different thanthose of the existing trolley lines. Consequently, a “new mode” was added to the modechoice model that can be used for the analysis of any new transit modes that might beconsidered using the model.

Mode Choice Model Adjustments and Assumptions

The existing mode choice models are multinomial logit models for all trip purposes.The program includes an additional “new mode” for which there are no transit servicesin the base year scenario. The new mode is simply an additional option in the modechoice model for each trip purpose. Walk and auto access options for the new modeare included for any trip purpose where the walk and auto access submodes areseparated for the existing transit service. The set of modes by trip purpose includingthe new modes is as follows:

JTW/HBU, HBO, NHBW, NHBO: Transit Auto Access, Bus Walk Access, Trolley Walk Access, Non-motorized, Shared Ride, Drive Alone, New Mode Walk Access, New Mode Auto AccessHBSc: Transit (excluding new mode), Non-motorized, School Bus, Shared-ride, Drive alone, New ModeWalk AccessHBSh: Transit Auto Access, Transit Walk Access, Non-motorized, Shared-ride, Drive Alone, New ModeWalk Access, New Mode Auto AccessHBPD: Non-motorized, Shared Ride, Drive AloneHBSR: Transit (excluding new mode), Non-motorized, Shared Ride, Drive Alone, New Mode WalkAccess

The same program can be used for the base and forecast year scenarios. Themultinomial logit models produce zero trips for any unavailable modes, including thenew modes in the base year (and other scenarios that may not have any new modes).

For each trip purpose, the utility functions for the new modes are identical to those forthe transit mode that includes the existing trolley mode. For HBSc and HBSR this isthe transit mode. For HBSh trips, the new mode with auto access uses the utilityfunction from the transit auto access mode, and the new mode with walk access usesthe utility function from the transit walk access mode. For all other trip purposes withtransit available, the new mode with auto access uses the utility function from thetransit auto access mode, and the new mode with auto access uses the utility functionfrom the trolley auto access mode.

The use of the same utility functions as for other transit modes is an obvious choicebecause, for each trip purpose, the level of service (time and cost) coefficients are thesame for all modes. The alternative specific constants are also the same as thecalibrated constants by vehicle availability level for the other transit modes becausethere is no data with which to estimate different constants (since the new mode, by

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definition, does not exist now). This is consistent with FTA guidelines for New Startsridership forecasts, which would likely be followed for any forecasts of “new modes.” Itis a simple matter to incorporate changes to model parameters (such as making theconstant for the new mode slightly higher than for existing transit) that might beconsistent with the evolving FTA guidance. The existing coefficients for the new mode,like the coefficients for other transit modes, are stored in a separate parameter filethat can be edited as needed.

Transit Path Building

Any lines for the new mode for a particular scenario should be coded in TransCAD. Aswith all other transit network coding, the network data should reflect the expectedtransit operating characteristics of the new lines (headway, speed, stop locations, etc.).

The path building procedures for the new mode are essentially the same as for theexisting transit modes. The path building parameters are documented in TechnicalMemorandum # 11, Model Calibration. There are no additional restrictions ontransferring or park-and-ride access for the new mode.

Any trip that uses both the new mode and another transit mode is defined as a “newmode” trip, in path building and mode choice.

Future Year Highway Network Development

Future year projects

Future year highway improvement projects are modeled as a project table in theMemphis model to give maximum flexibility for the end users. The default project tableprovided with the Model includes all the founded projects in the current 2026 LongRange Transportation Plan (LRTP). See section 6.0 “Network Alternatives” in the User’sManual for detailed description of the project table structure. Common modelmaintenance tasks such as adding, deleting, and modifying the projects are fullyautomated for the Memphis Model. Please refer to section 7.0 “Project Coding Tools” ofthe User’s Manual for how to complete these tasks.

Future Year Signal Locations

In the Memphis Model, new signalized intersections will have impact on the roadwaycapacity. As described in Technical Memo #8(b) “Link Capacity Development”, signaldensity and signal coordination are the two factors that should be changed when thenew signals or the new coordinated signal system are expected to be in place. TheModel provides signal location forecasting tools specifically for this purpose. Technical

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Memo #10 “Base and Future Year Signalized Intersection Tools and Future Year SignalLocation Forecasting Methodology” described this tools in detail.

The model project team forecasted future year signal locations for horizon year 2030only. Coordinated signal systems were also forecasted based on the informationavailable from the City of Memphis.

Heavy Vehicle Restricted Routes

Heavy Vehicle Restricted Routes for base year were coded into the base year networkbased on the information from the City of Memphis. No data is available for futureyear truck restricted routes. The Model reserves space in the future year network foreach interim year and horizon year to add heavy vehicle restricted routes if needed.

Future year HOV lanes

Current 2026 LRTP only identified potential corridors and roadway segments to beable to add HOV lanes. Based on the conversation with the MPO, the decision wasmade that no future HOV facility was coded in the model for future year.

New HOV lanes were added to the model using the following steps:1) Modify the link layer by adding new links parallel to the main line roadway.2) Add logical access points where drivers can waive in and out of the HOV lanes.3) Add the base year link attributes for the new HOV links to be the same as the

mainline roadway.4) Assign number of lanes for the HOV links, and subtract this number of lanes

from the mainline roadway accordingly.5) Add the HOV link ID to the HOV_LINK_ID field of the mainline link field.6) Start a fresh run of the model.

Ramp Types

The Model defines five type of ramps based on the roadway functional classifications ofthe roadways. For all LRTP projects, the ramp types were coded in the network forfuture year. The Model reserves space for each interim and horizon years for the ramptype code. If the functional classification of the roadways changes in future year, theramp type can be easily changed in the network.

Future Year Transit Network Development

Similar to the highway projects, all future year transit facilities are coded with a bornyear and a retire year (if applicable). Based on the target year specified in the main

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model interface, the Model will automatically pick up the active transit routes andpassenger terminals based on the born year, or drop the routes based on the retireyear and the disable bit coded in the route table.

Born year, headways, and routing of the future year transit routes were based on theinformation provided by MATA. Dwell times are assumed as 0.5 minute for fixedguideways, 0.15 minute for future local bus routes, and 1 minute for future expressbus routes. Same dwell times as base year are assumed for all feeder bus and turnback routes. For future year, light-rail routes were added with exclusive right-of-way.See Appendix A for base year transit route attributes, and Appendix B for future yeartransit route attributes.

Fixed Guideway Corridors

Based on the meeting results with the MPO and MATA, only the Downtown AirportLine Fixed Guideway (North Alternative) is enabled in the model by default. Inaddition, feeder bus routes and turn back routes were also coded and enabled in thefuture year transit network for the Downtown Airport line. Other fixed guidewaycorridors modeled but not enabled in the transit network includes:

Downtown Airport West AlternativeCollierville Line 1997 AlternativeCollierville Line Alternative (tie into Downtown Airport Line)Millington LineMississippi Line

No feeder bus routes and turn back routes were coded for all the fixed guidewaycorridors currently not enabled.

For Downtown Airport lines, the segment from Downtown to Pauline Street was addedas shared right-of-way similar to Madison Line Trolley. The segment from PaulineStreet to Park Avenue was added using the existing highway links, although it hassemi-exclusive right-of-way in the median. All other light-rail routes were added withexclusive right-of-way.

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Local Bus Routes

The following new local bus routes are modeled in the future year transit network:

Airport CirculatorBartlett LocalHornlake LocalSouthaven LocalCollierville LocalOliver Branch LocalMillington LocalCordova Local

Express Bus Routes

The following new express bus routes are modeled in the future year transit network:

Arlington ExpressSouthaven ExpressCollierville ExpressOliver Branch ExpressMillington Express

Park-and-ride and Passenger Transit Terminals

For future year, additional park-and-ride facilities for both express bus and light-railwere added. In addition, passenger transit terminals were also modeled in the futureyear transit network. Appendix B shows a list of park-and-ride facilities coded in thenetwork and their attributes.

Horizon Year 2030 Highway Assignment Results

This section presents the highway assignment results for the horizon year 2030 model.As described in previous sections, the results are based on the 2030 demographic andeconomic forecasts, and the 2030 network with all projects in the 2026 Long RangeTransportation Plan. For horizon year 2030, the Vehicle Miles of Travel (VMT) issummarized by roadway functional classifications, time-of-day, and districts. The VMTis then compared with the base year 2004 results. Assigned traffic volumes acrossscreenlines and cutlines are also compared with the base year 2004 results.

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Vehicle Miles of Travel (VMT) by Functional Classification

The 2030 model predicted VMT per capita of 29.5 and VMT per household of 74.6.VMTs by functional classification are summarized in Table 10. Overall, the regionalVMT increased by 64% from 2004 to 2030.

Table 10. Year 2030 VMT by Functional Classification


2004Model VMT

2030Model VMT

%Difference

Freeways 8,997,121 15,982,998 77.6%Principal Arterials 8,496,497 13,508,706 59.0%

Minor Arterials 6,856,829 10,140,809 47.9%Collectors 2,529,160 4,552,880 80.0%

Total 26,879,606 44,185,393 64.4%

Time-of-Day Period Results

Table 11 compares the 2004 model VMT with year 2030 model VMT by each time-of-day period.

Table 11. Time-of-day VMT Comparison

Time-of-dayPeriod

2004 ModelVMT

2030 ModelVMT % Difference

AM 4,865,568 7,890,558 62.2%Midday 6,879,618 11,506,473 67.3%

PM 7,899,582 12,908,988 63.4%Off-Peak 7,235,950 11,880,699 64.2%

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Model Districts

Table 12 summarizes the year 2030 model VMT to district level using the 25 districtsdefined in the model validation process.

Table 12. District VMT Comparison

Index District Name2004ModelVMT

2030ModelVMT

%Difference

1 CBD 638,946 805,397 26.1%2 North Memphis 455,794 583,818 28.1%3 Midtown and Depot 1,692,367 1,967,605 16.3%4 East Memphis 3,032,792 3,587,265 18.3%5 Southwest Memphis 1,970,975 2,508,231 27.3%6 Hickory Hill 2,508,316 3,728,479 48.6%7 East Shelby County 305,606 1,166,024 281.5%8 Collierville 780,025 1,533,160 96.6%

9 Northeast ShelbyCounty 1,129,657 2,443,231 116.3%

10 Raleigh Bartlett 1,481,938 2,172,331 46.6%11 Millington 770,436 1,168,137 51.6%12 Frayser 783,280 1,131,806 44.5%

13 Northwest ShelbyCounty 63,976 557,494 771.4%

14 East Desoto County 1,441,727 3,025,043 109.8%15 West Desoto County 1,092,794 2,043,303 87.0%16 South Desoto County 816,496 2,403,046 194.3%17 Marshall County 300,362 879,234 192.7%18 North Fayette County 694,576 1,335,952 92.3%19 West Tipton County 198,301 828,039 317.6%20 East Tipton County 378,351 654,464 73.0%21 South Fayette County 347,612 1,164,043 234.9%22 McKellar Lake 55,796 58,106 4.1%23 University 544,798 635,641 16.7%

24 Shelby FarmsGermantown 3,830,398 5,765,054 50.5%

25 Airport 1,508,006 1,911,815 26.8%

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Table 13 shows the year 2030 projected traffic volumes on the screenlines andcutlines.

Table 13. Screenline/Cutline Volume Comparison


2004 ModelTotal

2030 ModelTotal Counts % Difference

Screenline 1 279,119 460,336 27 64.9%Screenline 2 734,688 1,103,021 54 50.1%Screenline 3 765,391 1,042,442 47 36.2%

Cutline 1(240 Loop) 1,299,488 1,582,901 54 21.8%Cutline 2 152,136 236,341 9 55.3%Cutline 3 90,579 116,454 5 28.6%Cutline 4 69,820 141,381 6 102.5%Cutline 5 29,055 36,452 3 25.5%Cutline 6 16,495 29,740 1 80.3%

Horizon Year 2030 Transit Assignment Results

This section presents the transit assignment results from the horizon year 2030model. The results are compared with the observed base year data.

System-Wide MeasuresTable 14 summarizes the system-wide transit assignment results by each accessmode, and Table 15 compares the year 2030 model results with the base yearobserved values by linked trips, transfer rate, and total boardings.

Table 14. Year 2030 System-Wide Transit Assignment Results Summary

Mode Boarding # Trips TransferRate

DrivetoRailBusTrolley 342 316 1.08DrivetoBusTrolley 6,579 6,336 1.04

WalktoRailBusTrolley 6,361 3,938 1.62WalktoBusTrolley 3,383 2,173 1.56

WalktoTrolley 704 717 0.98WalktoBus 22,709 14,267 1.59

Total 40,078 27,747 1.44

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Table 15. System-Wide Performance Measures

PerformanceMeasures

2004ModelValue

2030ModelValue

%Difference

Total Number of LinkedTrips 31,425 27,747 -11.7%

Transfer Rate 1.40 1.44 +2.9%

Total Number ofBoardings 43,995 40,078 -8.9%

The Year 2030 model shows that the total number of linked trips will decrease by11.7% from base year (2004). The results seemed to be counter-intuitive initially, andso the model team conducted a comprehensive investigation on various components ofthe models and their input data. Specifically, the following items were examined:

1. Checking for and eliminating future year transit network coding errors2. Checking and eliminating transit network and path building errors3. Checking and eliminating errors in feedback process.4. Reviewing and eliminating errors in utility calculations and mode choice

models.5. Checking and eliminating errors in future year demographic and economic

forecasts

After the review, the only error found by the model team was in the utility calculationfor the HBPD trip purposes. This error would produce incorrect results for thisparticular trip purposes only, and it was corrected. However, transit modes are notavailable in the pick-up drop-off trip purposes, so there is no significant impact on thetransit side.The model team determined that there is no indication of other errors for items 1through 4 above. The major reasons for decreasing transit ridership are summarizedas follows:

1. Demographic and economic growth distribution:

Although the region’s economic growth over the forecast period exceeds that of thenation as a whole, the areas with fast growth rate are the outlying districts, suchas Fayette/Tipton/Desoto Counties, which are also areas with limited transitaccess. On the other hand, the areas with the most convenient transit access, suchas central Shelby County, are predicted to lose employment, households, andpopulation. See Chapter 3 of the “Economic and Demographic Forecasts for the

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Memphis and Shelby County MPO, Final Report” for details. The change in thehousehold and employment distribution is one of the major reasons for theprojected decline in transit ridership.

With the new model ready to use, future year transit service should be examined tosee if adjustments could be made to better serve the rapidly developing areas.

2. Income growth (in constant 1999 dollars):

Transit ridership is closely related to the income distribution of households. Firstof all, household income distribution is an input to the vehicle availability model.With real income increasing, the percentage of zero-car households, whichrepresents the most significant segment of the transit rider population, is expectedto decrease. Next, during the mode choice model validation process, a new variablewas added to the mode choice model utility equations. This variable represents thepercentage of low income (less than $10,000 annual income in 1999 dollars)households in the district in which the trip is produced. This new variable wasfound to improve the results of the mode choice model and transit assignment. SeeTechnical Memorandum #6, Mode Choice for more detailed information on this newvariable.

The demographic and economic forecasts indicated that there is 1.25% per yearincrease in real income (in constant 1999 dollars) in the Memphis region. However,the model team discovered that the methodology used to forecast incomedistribution in households needed to be adjusted. The adjustment addresses theneed to allow for rising income inequality, with higher-income households andindividuals gaining at faster rates than their lower-income counterparts. AppendixD of this Memo documents this adjustment in detail.

It should be noted that even after the adjustment, real income is projected toincrease from 2004 to 2030. For example, the percentage of households havingreal annual incomes (in 1999 dollars) of less than $45,000 is projected to decreasefrom 53% in 2004 to 42% in 2030 (prior to the adjustment, the 2030 percentagewas projected to be 35%). This increase in income continues to result in shifts for2030 from transit to auto, relative to 2004.

With the adjusted household income distribution, the Year 2030 model results stillshow an 11.7% decrease in total linked transit trips. This is due to the decrease inpopulation and employment in the CBD, and the expected increase in real incomelevels.

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Route Group Level Boardings

The total number of boardings in route group level is reported in Table 16. Twoadditional groups are defined for year 2030: Light Rail and Future Bus Routes.

Table 16. Route Group Level Boardings

Group Index Group Name 2030 ModelBoarding

2004ObservedBoarding

%Difference

1 Southwest 12,207 15,611 -21.82 Southeast 2,335 3,735 -37.53 East 5,209 6,173 -15.64 North 6,797 8,458 -19.65 N-S Crosstowns 4,034 4,920 -186 E-W Crosstowns 2,299 1,855 23.97 Connectors 627 403 55.68 Trolley 1,392 2,840 -519 Light Rail 1,088 - -10 Future Bus Routes 4,091 - -

Note that the future feeder bus routes and turn back routes for light-rail lines are keptin their base year group and subgroups.

Route Sub-Group Level Boardings

Table 17 shows the sub-group level boardings. Three additional sub-groups are addedfor future year: Light Rail, Future Local Bus, and Future Express Bus. A total of 18route sub-groups were listed. In Table 17, the column “Group Index” corresponds tothe group index in Table 16.

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Table 17. Route Sub-Group Level Boardings

Sub-groupIndex

GroupIndex Sub-Group Name

2030Model

Boarding


%Difference

1 1 Third St Corridor 1,801 3,582 -49.72 1 South Memphis 1,891 2,980 -36.5

3 1 Bellevue/Elvis PresleyCorridor 2,853 4,624 -38.3

4 1 Lamar Corridor 5,256 4,360 20.65 1 White Heaven Night 405 65 523.76 2 Midtown/UM 495 1,006 -50.87 2 Quince/Park 1,840 2,729 -32.68 3 Poplar Corridor 4,330 4,368 -0.99 3 Summer Corridor 879 1,805 -51.310 4 Jackson/Chelsea 4,248 5,919 -28.211 4 Watkins/Thomas 2,549 2,539 0.412 5 N-S Crosstowns 4,034 4,920 -1813 6 E-W Crosstowns 2,299 1,855 23.914 7 Connectors 627 403 55.615 8 Trolley 1,392 2,840 -5116 9 Light Rail 1,088 - -

17 10 Future Local BusRoutes 3,863 - -

18 10 Future Express BusRoutes 229 - -

Line Level Boardings

Transit line level boarding figures are shown on Table 18. In Table 18, the column“Sub-Group Index” gives the sub-group ID in which the route belongs to. Similarly, thecolumn “Group Index” indicates to which group the route belongs.

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Table 18. Line Level Boardings

IndexSub-

GroupIndex

GroupIndex Line Group Name

2030Model

Boarding


%Difference

1 1 1 11-Tulane/Hodge[11T,11S] 578 920 -37.12 1 1 15-Presidents Island [15] 46 113 -59.53 1 1 19-Third [19W, 19R] 492 1,313 -62.54 1 1 53-Florida [53I,53L,53W] 686 1,236 -44.55 2 1 4-Walker [4A,4C] 1,526 2,004 -23.86 2 1 2-Lauderdale [2L,2W] 365 976 -62.67 3 1 20-Bellevue/Winchester [20] 1,443 1,698 -158 3 1 43-ElvisPresley [43B,43H,43S] 1,409 2,926 -51.89 4 1 7-Air Park[7A, 7B] 554 288 92.410 4 1 10-Lamar [10C,10S] 1,366 1,708 -2011 4 1 17-McLemore [34M,34N] 1,235 673 83.512 4 1 56-Union [56] 2,101 1,691 24.313 5 1 89-WalkerHomes/Westwood 155 28 452.714 5 1 90-Neely/Shelby Dr 251 37 577.515 6 2 2-Medical Center[2A,2C] 495 1,006 -50.816 7 2 52-Park[52Q,52B,52SF] 1,684 2,699 -37.617 7 2 58-FoxMeadowsB[58B] 156 30 418.518 8 3 22-Poplar [22] 76 137 -44.419 8 3 34-Union/WalnutG[34R,34B] 1,125 735 53.120 8 3 41-Collierville[41] 179 306 -41.621 8 3 50-Poplar[50G,50W,50Y] 2,950 3,190 -7.522 9 3 53-Summer[53B,53S] 879 1,805 -51.323 10 4 8-Chelsea [8] 933 1,468 -36.524 10 4 19-Vollintine[19RA,19NA,19M] 705 1,280 -44.925 10 4 40-Raleigh[40,40B] 821 447 83.626 10 4 52-Jackson[52M,52R,52SE] 1,789 2,724 -34.327 11 4 10-Watkins[10RG,10RL] 1,622 1,545 528 11 4 11-Thomas[11F,11C] 927 994 -6.829 12 5 30-Perkins [30] 1,028 644 59.630 12 5 31-Crosstown [31] 1,410 2,901 -51.431 12 5 32-E Parkway[32A,32F,32N] 1,192 1,289 -7.532 12 5 33-Highland[33] 168 32 426.133 12 5 82-GermantownPkwy[82] 236 54 336.434 13 6 35-Southgate [35] 430 549 -21.7

35 13 6 62-Frayser/EMemphis[62G,62W] 619 346 78.8

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IndexSub-

GroupIndex

GroupIndex Line Group Name

2030Model

Boarding


%Difference

36 13 6 69-Winchester[69] 1,250 960 30.237 14 7 80-Cordova [80] 143 12 109238 14 7 81-ShelbyDr/HickoryHill [81] 130 194 -32.939 14 7 93-HickoryHill/Winchester[93] 354 197 79.740 15 8 Main St Trolley 346 1,269 -72.741 15 8 Riverfront Trolley 139 1,052 -86.842 15 8 Madison St Trolley 907 519 74.743 16 9 Light Rail 1,088 - -44 17 10 Future Local Bus Routes 3,863 - -45 18 10 Future Express Bus Routes 229 - -

Route Level Boardings

Boarding figures for each transit route (by direction) are shown on Appendix E of thisMemo.

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Appendix A — External Station Location Map

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Appendix B — Future Year Transit Route Attributes

Route_Name ALT BORNYEAR ModeFareMatrixIndex AM_Headway MD_Headway PM_Headway OP_Headway

AM_DwellTime

MD_DwellTime

PM_DwellTime

OP_DwellTime

4A_2015 DA NewStart 2015 2 0 30 50 30 60 0.01 0.01 0.01 0.014A_R_2015 DA NewStart 2015 2 0 30 50 30 60 0.01 0.01 0.01 0.014C_2015 DA NewStart 2015 2 0 30 50 30 60 0.01 0.01 0.01 0.014C_R_2015 DA NewStart 2015 2 0 30 50 30 60 0.01 0.01 0.01 0.017_2015 DA NewStart 2015 2 0 15 30 30 9999 0.10 0.10 0.10 0.107_R_2015 DA NewStart 2015 2 0 15 30 30 9999 0.10 0.10 0.10 0.1010S_2015 DA NewStart 2015 2 0 15 30 60 120 0.13 0.13 0.13 0.1310S_R_2015 DA NewStart 2015 2 0 15 30 60 120 0.13 0.13 0.13 0.1310C_2015 DA NewStart 2015 2 0 15 30 60 120 0.17 0.17 0.17 0.1710C_R_2015 DA NewStart 2015 2 0 15 30 60 120 0.17 0.17 0.17 0.1720_2015 DA NewStart 2015 2 0 30 45 30 9999 0.07 0.07 0.07 0.0720_R_2015 DA NewStart 2015 2 0 30 45 30 9999 0.07 0.07 0.07 0.0731_2015 DA NewStart 2015 2 0 15 30 15 60 0.12 0.12 0.12 0.1231_R_2015 DA NewStart 2015 2 0 15 30 15 60 0.12 0.12 0.12 0.1232A_2015 DA NewStart 2015 2 0 15 30 60 9999 0.06 0.06 0.06 0.0632A_R_2015 DA NewStart 2015 2 0 15 30 60 9999 0.06 0.06 0.06 0.0632F_2015 DA NewStart 2015 2 0 15 9999 60 9999 0.18 0.18 0.18 0.1832F_R_2015 DA NewStart 2015 2 0 15 9999 60 9999 0.18 0.18 0.18 0.1832N_2015 DA NewStart 2015 2 0 15 30 15 9999 0.01 0.01 0.01 0.0132N_R_2015 DA NewStart 2015 2 0 15 30 15 9999 0.01 0.01 0.01 0.0134M_2015 DA NewStart 2015 2 0 15 30 15 9999 0.03 0.03 0.03 0.0334M_R_2015 DA NewStart 2015 2 0 15 30 15 9999 0.03 0.03 0.03 0.0334N_2015 DA NewStart 2015 2 0 9999 9999 9999 90 0.01 0.01 0.01 0.0143B_2015 DA NewStart 2015 2 0 30 9999 30 9999 0.17 0.17 0.17 0.1743B_R_2015 DA NewStart 2015 2 0 30 9999 30 9999 0.17 0.17 0.17 0.1743H_2015 DA NewStart 2015 2 0 30 9999 30 9999 0.19 0.19 0.19 0.1943H_R_2015 DA NewStart 2015 2 0 30 9999 30 9999 0.19 0.19 0.19 0.1943S_2015 DA NewStart 2015 2 0 60 9999 60 60 0.23 0.23 0.23 0.2343S_R_2015 DA NewStart 2015 2 0 60 9999 60 60 0.23 0.23 0.23 0.2352B_2015 DA NewStart 2015 2 0 15 9999 48 9999 0.04 0.04 0.04 0.0452B_R_2015 DA NewStart 2015 2 0 15 9999 48 9999 0.04 0.04 0.04 0.0452Q_2015 DA NewStart 2015 2 0 15 30 48 9999 0.19 0.19 0.19 0.1952Q_R_2015 DA NewStart 2015 2 0 15 30 48 9999 0.19 0.19 0.19 0.1952SF_2015 DA NewStart 2015 2 0 15 30 48 45 0.19 0.19 0.19 0.1952SF_R_2015 DA NewStart 2015 2 0 15 30 48 45 0.19 0.19 0.19 0.1956_2015 DA NewStart 2015 2 0 25 40 25 60 0.14 0.14 0.14 0.1456_R_2015 DA NewStart 2015 2 0 25 40 25 60 0.14 0.14 0.14 0.1469_2015 DA NewStart 2015 2 0 50 50 50 9999 0.09 0.09 0.09 0.0969_R_2015 DA NewStart 2015 2 0 50 50 50 9999 0.09 0.09 0.09 0.09FGC Collierville 1997 IB LRTP 2020 4 3 15 30 15 30 0.50 0.50 0.50 0.50FGC Collierville 1997 OB LRTP 2020 4 3 15 30 15 30 0.50 0.50 0.50 0.50

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AM_DwellTime

MD_DwellTime

PM_DwellTime

OP_DwellTime

FGC Millington IB LRTP 2030 4 3 15 30 15 30 0.50 0.50 0.50 0.50FGC Millington OB LRTP 2030 4 3 15 30 15 30 0.50 0.50 0.50 0.50FGC Mississippi IB LRTP 2025 4 3 15 30 15 30 0.50 0.50 0.50 0.50FGC Mississsippi OB LRTP 2025 4 3 15 30 15 30 0.50 0.50 0.50 0.50FGC DtnAirport ALT2West OB DA-ALT2 West 2015 4 3 10 20 10 30 0.50 0.50 0.50 0.50FGC DtnAirport ALT2West IB DA-ALT2 West 2015 4 3 10 20 10 30 0.50 0.50 0.50 0.50FGC DtnAirport ALT2North IB DA-ALT2 North 2015 4 3 10 20 10 30 0.50 0.50 0.50 0.50FGC DtnAirport ALT2North OB DA-ALT2 North 2015 4 3 10 20 10 30 0.50 0.50 0.50 0.50FGC Collierville-DA IB LRTP 2020 4 3 15 30 15 30 0.50 0.50 0.50 0.50FGC Collierville-DA OB LRTP 2020 4 3 15 30 15 30 0.50 0.50 0.50 0.50FBR Collierville Local EB FutureBusRoutes 2014 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Collierville Local WB FutureBusRoutes 2014 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Bartlett Local OB FutureBusRoutes 2010 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Cordova Local OB FutureBusRoutes 2023 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Bartlett Local IB FutureBusRoutes 2010 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Cordova Local IB FutureBusRoutes 2023 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Millington Local NB FutureBusRoutes 2025 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Millington Local SB FutureBusRoutes 2025 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Hornlake Local SB FutureBusRoutes 2012 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Hornlake Local NB FutureBusRoutes 2012 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Southaven OB LocalSB FutureBusRoutes 2016 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Southaven OB LocalNB FutureBusRoutes 2016 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Southaven Local SB FutureBusRoutes 2012 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Southaven Local NB FutureBusRoutes 2012 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Oliver Branch LocalSB FutureBusRoutes 2016 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Oliver Branch LocalNB FutureBusRoutes 2016 2 0 45 90 45 9999 0.15 0.15 0.15 0.15FBR Arlington Express EB FutureBusRoutes 2010 2 1 30 9999 30 9999 1.00 1.00 1.00 1.00FBR Arlington Express WB FutureBusRoutes 2010 2 1 30 9999 30 9999 1.00 1.00 1.00 1.00FBR Southaven ExpressSB FutureBusRoutes 2012 2 1 30 9999 30 9999 1.00 1.00 1.00 1.00FBR Southaven ExpressNB FutureBusRoutes 2012 2 1 30 9999 30 9999 1.00 1.00 1.00 1.00FBR Collierville ExpressEB FutureBusRoutes 2014 2 1 30 9999 30 9999 1.00 1.00 1.00 1.00FBR Collierville ExpressWB FutureBusRoutes 2014 2 1 30 9999 30 9999 1.00 1.00 1.00 1.00FBR Oliver Branch FutureBusRoutes 2016 2 1 30 9999 30 9999 1.00 1.00 1.00 1.00

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AM_DwellTime

MD_DwellTime

PM_DwellTime

OP_DwellTime

Express SBFBR Oliver BranchExpress NB FutureBusRoutes 2016 2 1 30 9999 30 9999 1.00 1.00 1.00 1.00FBR Millington ExpressNB FutureBusRoutes 2030 2 1 30 9999 30 9999 1.00 1.00 1.00 1.00FBR Millington ExpressSB FutureBusRoutes 2030 2 1 30 9999 30 9999 1.00 1.00 1.00 1.00FBR Airport CirculatorClockwise FutureBusRoutes 2008 2 0 15 30 15 60 0.15 0.15 0.15 0.15FBR Airport Circulator CT-Clockw FutureBusRoutes 2008 2 0 15 30 15 60 0.15 0.15 0.15 0.15

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Appendix C — Future Year Transit Park-and-Ride Facilities and Passenger Terminals

Node ID Born Year Name Location Functional classification125950 2008 Brooks Road (SIT) Airways Blvd and Brooks Rd Transit Center / Rail Access Park-and-Ride125945 2010 Paul Barrett Parkway I-40 and Paul Barrett Parkway Express Bus Park-and-Ride125946 2010 Canada Road I-40 and Canada Road Express Bus Park-and-Ride125947 2010 Germantown Parkway I-40 and Germantown Parkway Express Bus Park-and-Ride125948 2010 Whitten Road I-40 and Whitten Road Express Bus Park-and-Ride125962 2011 Raleigh Austin Peay and Jones Rd Transit Center125961 2012 Shelby Dr (I-55) Shelby Dr and Mill Branch Express Bus Park-and-Ride125949 2012 Goodman Road I-55 and Goodman Road Express Bus Park-and-Ride125964 2014 Southwest Highway 61 and Mitchell Transit Center125969 2014 Collierville (Hacks Cross) SR-385 and Hacks Cross Rd Express Bus Park-and-Ride125970 2014 Collierville (Hwy 72) SR-385 and Highway 72 Express Bus Park-and-Ride125951 2015 Defense Depot Airways Blvd south of Frisco Ave Light Rail Park-and-Ride125952 2015 Park Ave Park Ave and Lamar Ave Light Rail Park-and-Ride125953 2015 Carnes Lamar Ave and Carnes Ave Light Rail Park-and-Ride125954 2015 Rozelle St Lamar Ave and Rozelle St Light Rail Park-and-Ride125968 2016 Olive Branch Highway 78 and Goodman Rd Express Bus Park-and-Ride125963 2017 Frayser Frayser Rd and Watkins Rd Transit Center125955 2020 Germantown Road Poplar Pike and Germantown Rd Light Rail Park-and-Ride125956 2020 Byhalia Road Byhalia Rd south of Poplar Ave Light Rail Park-and-Ride125965 2020 East Memphis Poplar Ave and Kirby Parkway Transit Center125966 2023 Cordova Walnut Grove Rd and Germantown Parkway Transit Center125957 2025 Raines Rd Raines Rd east of Tulane Rd Light Rail Park-and-Ride125958 2025 Goodman Road Goodman Rd west of Highway 51 Light Rail Park-and-Ride125967 2025 Millington Highway 51 and Paul Barrett Parkway Express Bus Park-and-Ride125959 2030 Fite Road Fite Rd east of Highway 51 Light Rail Park-and-Ride125960 2030 Naval Station West Union east of Highway 51 Light Rail Park-and-Ride

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Appendix D — Revised Income Forecasts for Sub-County Areas inMetropolitan Memphis

The household modeling and forecasting tasks executed during 2004-06 to supportMemphis area transportation planning were conducted entirely in terms of relativeincome. Households were assigned to income quintiles defined on a regional basis (sothat each category always contained 20% of the region’s households), and allhousehold descriptions and forecasts for sub-county areas referenced these relativeincome groups rather than dollar-based categories. Only as a last step – reported inAppendix F of the consultant documentation – were the household forecasts convertedto a dollar basis. This memo and the accompanying Excel file (“Revised IncomeForecasts032807.xls”) adjust the approach to this conversion,

The adjustment addresses the need to allow for rising income inequality.Distributions of income have become increasingly unequal throughout the U.S., withhigher-income households and individuals gaining at faster rates than their lower-income counterparts, for more than a quarter-century. This trend is a result oftechnological change and the forces now referenced as globalization. It can beexpected to continue for decades to come, and some of its drivers (e.g., off-shoring ofservices via the Internet) may even accelerate.

The past conversion of Memphis-area household forecasts from relative income to aconstant-dollar basis assumed that real income at all levels would escalate beyond2004 at a rate of 1.25% per year. Instead the calculations should have used differentescalation rates for the various income quintiles, with values significantly lower for thebottom groups and higher for at least the top quintile. This text explains thedevelopment of new escalation rates and summarizes the forecasts resulting from theirapplication.

The data utilized here consist of income statistics from the Bureau of EconomicAnalysis (BEA); household income distributions from the 1980 and 2000 decennialcensuses; and household statistics for 2005 from the Census Bureau’s AmericanCommunity Survey (ACS). Descriptions of historical trends in income distributiontend to vary with the data source consulted. For example, studies based on IRSstatistics have suggested that inequality has increased across the board, with middle-income households moving ahead of households at the bottom while both fall behindthose at the top, whereas census-based calculations mainly highlight differencesbetween the top and the rest (as shown below). In any case it has been considerednecessary here to work from census statistics because they were the source of the2004 household tabulations incorporated in the project baseline and used fortransportation model calibration.

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The first steps focus upon national trends. Table 1 on the next page describes U.S.income and income per household according to the BEA, the decennial census and theACS (the source of the census figures on the third line). The years cited in the tableare those in which the census and ACS surveys were taken, but the income figuresactually pertain to earlier periods as explained by the various footnotes. The BEAstatistics have been prepared on a comparable basis.

Table 1. U.S. Income and Income Per Household in Constant 2005 Dollars Income in Billions of 2005 $ Income Per Household

Census BEA Cen/BEA Census BEAIncome

1980 1/ 4,391 5,574 0.7879 54,572 69,2642000 2/ 7,008 9,217 0.7603 66,401 87,3342005 3/ 6,949 10,017 0.6937 62,556 90,174

Annual % Change1980-00 0.99% 1.17%2000-05 4/ -1.08% 0.58%1980-05 5/ 0.54% 1.04%

Notes: 1/ Income received in 1979 by households present on April 1, 1980. 2/ Income received in 1999 by households present on April 1, 2000. 3/ Income rec'd from mid-'04 to mid-'05 by households present on 7/1/05. 4/ Interval of 5.5 years; see notes 2/ and 3/ above. 5/ Interval of 25.5 years; see notes 1/ and 3/ above.

BEA income is the quantity that enters the national income and product accounts andthus represents the definitive measure. The population census understates income bysubstantial margins due to the vagaries of survey responses. Between 1980 and 2000the share of BEA income not covered by the census rose from 21.2% to 24%. (Some ofthe shortfall involved income received by persons not living in households, but thispart was far too small to account for the downtrend in coverage.) Then the ACSestimate for 2005 fell more than 30% below BEA income, indicating that the ACSfigures were not comparable even with the decennial census data.

BEA income per household rose at a compound rate of 1.17% per year between 1980and 2000. Then due to the recent recession it increased by only 0.58% per yearduring 2000-05. The corresponding rates of change according to decennial censusand ACS data were 0.99% and –1.08%. The latter figure was clearly an aberrationcaused by the decline in income coverage. Thus the message of Table 1 is that, whilecensus data must be used to describe individual household groups, any quantitiesextrapolated into the future must be pegged to the overall income trend established by

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BEA data. The appropriate peg is considered to be the bottom figure in Table 1,describing average annual growth in real income per household over the quarter-century to 2005. This inflation-adjusted national figure is 1.04% per year.

Still focusing on the U.S. as a whole, Table 2 on the next page describes income perhousehold for quintile groups (each containing one-fifth of the nation’s householdsranked by income). The figures are all based on decennial census and ACS statistics.The table’s lower part shows the implied annual rates of change for various intervals.

Table 2. Estimation of Long-Term Rates of Change in U.S. Income Per Household

U.S. Income Per Household by Quintile in Constant 2005 Dollars Lower Lower-M Middle Upper-M Upper All HH

Income Per HH*1980 10,405 27,730 45,661 66,890 122,174 54,5722000 11,473 30,549 49,845 75,867 164,274 66,4012005 10,467 27,861 46,702 71,899 155,853 62,556

2005 adjusted 11,888 31,642 53,041 81,657 177,004 71,046Annual % Chg.*

1980-00 0.49% 0.49% 0.44% 0.63% 1.49% 0.99%2000-05 -1.65% -1.66% -1.18% -0.97% -0.95% -1.08%1980-05 0.02% 0.02% 0.09% 0.28% 0.96% 0.54%1980-05 adj. 0.52% 0.52% 0.59% 0.79% 1.46% 1.04%

* See notes on years and intervals in previous table.

Based on the decennial censuses, U.S. income per household rose during 1980-2000by just under 0.5% per year for households in the three lowest quintiles, whileincreasing three times as fast for the top quintile and twice as fast overall. (See thefirst row of percentages.) The 2005 figures in the third row of Table 2 are all lowerthan the income-per-household levels in 2000 due to the underreporting problem justdiscussed, but the present methodology assumes that they are still meaningful inrelative terms. The chosen procedure is to scale up these figures proportionally sothat their total reflects the 1.04% quarter-century growth rate that has been chosen asa peg. The resulting annual growth rates for quintile groups resemble the census-based rates for 1980-2000 except that the values for middle-income and upper-middle-income households are about 0.15% higher and the overall rate is 0.05%higher. These figures – shown in the last line of Table 2 – constitute our revisedforecast of U.S. annual gains in real income per household by quintile over the next 35years.

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Table 3 below focuses upon metropolitan Memphis (specifically the MSA as definedbefore 2003). The contents resemble the national statistics in Table 1 except that theyreference 2004 and omit ACS income data, which are unreliable at the local level.

Table 3. Metropolitan Memphis Income in Constant 2005 Dollars

Income in Billions of 2005 $ Income Per Household Census BEA Cen/BEA Census BEA

Income1980 1/ 16,050 21,260 0.7550 50,315 66,5572000 2/ 26,732 37,737 0.7084 62,974 88,9602004 3/ 40,673 90,899

Annual % Change1980-00 1.13% 1.46%2000-04 4/ 0.43%1980-04 5/ 1.25%

* See notes on years and intervals in Table 1.

During the 1980-2000 period and the quarter-century ending in 2005, income perhousehold increased more rapidly in metro Memphis than the U.S. by margins of0.14% to 0.29% per year (depending on the interval and data source). The bottomfigure in Table 3 is the income escalator of 1.25% per year formerly used for allhouseholds taken collectively. The present approach yields a slightly lower overallgrowth rate, however, as explained below.

Table 4 develops income-per-household estimates for metro Memphis in 2040 throughlinkages to national data. The first two lines repeat the first lines of Table 2,describing national income levels for household quintiles in 1980 and 2000, while thethird and fourth lines provide equivalent data for metro Memphis. The next two linesdescribe what is called the Memphis-U.S. gap, calculated for each year as unity minusthe ratio of metro Memphis income per household to U.S. income per household. Forevery quintile besides the top group, the Memphis-U.S. gap decreased between 1980and 2000.

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Table 4. Estimation of Metro Memphis Income Per Household in 2040

Income Per Household and Related Measures in 2005 Dollars Lower Lower-M Middle Upper-M Upper All HH

Income Per HouseholdUnited States 1980 1/ 10,405 27,730 45,661 66,890 122,174 54,572 2000 1/ 11,473 30,549 49,845 75,867 164,274 66,401Metro Memphis 1980 1/ 8,545 23,612 40,789 62,094 116,534 50,315 2000 1/ 10,058 28,661 47,403 72,644 156,104 62,974

Memphis-U.S. Gap 2/ 1980 0.1787 0.1485 0.1067 0.0717 0.0462 2000 0.1233 0.0618 0.0490 0.0425 0.0497

Ratio of Gap Values:2000 to 1980 3/ 0.6903 0.4161 0.4591 0.5924 1.0772Est. 2040 to 2000 4/ 0.4765 0.1731 0.2108 0.3510 1.0000

Est. Memphis-U.S. Gap in 2040 5/ 0.0588 0.0107 0.0103 0.0149 0.0497

Est. Income/HH in 2040United States 6/ 14,273 37,926 65,149 107,375 294,423 103,829Metro Memphis 13,435 37,520 64,476 105,775 279,780 100,197

Annual % Chg. In Memphis Inc/HH, 2000-40 0.73% 0.68% 0.77% 0.94% 1.47% 1.17%

Notes: 1/ See first two notes in Table 1. 2/ Equals unity minus Memphis income per HH divided by U.S. income per HH. 3/ Equals ratio of preceding figures. 4/ Equals preceding figure squared. 5/ Equals preceding figure times Memphis-U.S. gap in 2000. 6/ Equals adjusted U.S. income per household in 2005 from the fourth line of Table 2, escalated to 2040 using the annual percent changes shown in the last line of Table 2.

The forecast calculations assume that the Memphis-U.S. gaps for the bottom fourquintiles will continue to close, but in an asymptotic rather than linear fashion. Theseventh line of Table 4 shows the ratio of the 2000 gap to the 1980 gap for eachquintile. Since these ratios apply to a 20-year historical interval, further asymptoticreductions in the gaps over the next 40 years can be estimated by applying the ratiostwice more. Thus the table’s eighth line shows the squares of the ratios (except for thetop quintile, where a unity value is entered to hold the gap constant rather thanletting it increase). Applying these figures to the 2000 gaps in the table’s sixth lineyields the estimated 2040 gaps presented in the ninth line.

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The tenth line of Table 4 offers forecasts of U.S. income per household obtained byescalating the figures in the fourth line of Table 2 from 2005 to 2040 using the annualrates of change shown at the bottom of that table. The eleventh line of Table 4 thengives 2040 income-per-household forecasts for metro Memphis derived by using theMemphis-U.S. gaps in the ninth line to discount the U.S. values in the tenth line.Lastly, the bottom line of Table 4 shows the resulting annual rates of change inincome per household from 2000 to 2040. These rates are around 0.7% per year forthe two lowest quintiles and range up to 1.47% for the top quintile, with an overallaverage of 1.17% per year.

New forecasts for sub-county areas (SCAs) in the Memphis transportation planningdistrict have been obtained by applying the quintile-specific escalation rates shown inthe last line of Table 4 to all areas and all time intervals between 2004 and 2040. Afully rigorous procedure was available for translating households from quintiles to realincome categories; its only problem was the use of a single escalation rate rather thanquintile-specific rates. The results obtained by using the rates developed in Table 4are presented for all 45 SCAs in the second worksheet of the file “Revised IncomeForecasts 032807.xls” and summarized in Table 5 below. Small changes in escalationrates make a big difference when compounded over 36 years. The former forecastscalled for essentially no increase in the number of households with incomes below$30,000 in 1999 dollars, whereas the revised forecasts call for gains of around 25% inthat income range.

Table 5. Forecast Comparison: Households by Income in Constant 1999 Dollars Actual Forecast 2040 % Change, 2000-40 % Chg.

In 2004 Former Revised Former Revised Forecast

Under $15,000 68,633 68,091 87,023 -0.8% 26.8% 27.8%$15,000 to $29,999 78,493 81,213 97,490 3.5% 24.2% 20.0%$30,000 to $44,999 74,877 83,166 94,479 11.1% 26.2% 13.6%$45,000 to $59,999 57,885 76,368 77,537 31.9% 34.0% 1.5%$60,000 to $74,999 45,202 73,910 67,981 63.5% 50.4% -8.0%$75,000 to $99,999 42,986 92,821 68,892 115.9% 60.3% -25.8%$100,000 to $149,999 29,851 102,836 77,592 244.5% 159.9% -24.5%$150,000 and over 18,882 83,661 91,075 343.1% 382.3% 8.9% Total 416,809 662,068 662,068 58.8% 58.8% 0.0%

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Appendix E — Horizon Year 2030 Route Level Boardings

RouteID Route Name Boarding Transfer IVTT

(min.) # StopsDwellTime(min.)

TotalTime(min.)

1066 2C 269 104 39.7 63 0.17 50.41805 2C_R 227 17 39.2 62 0.18 50.41839 2L 100 57 31.3 60 0.05 34.31739 2L_R 91 0 27.5 59 0.12 34.41838 2W 103 54 29.7 60 0.05 32.71737 2W_R 71 1 25.8 55 0.08 30.41746 4A_2015 381 198 45.4 62 0.05 48.51747 4A_R_2015 423 54 43.2 64 0.08 48.31750 4C_2015 314 154 45.1 64 0.05 48.41751 4C_R_2015 408 33 45.4 68 0.05 48.81793 7_2015 336 123 61.9 45 0.06 64.81792 7_R_2015 218 33 62.8 40 0.05 651465 8 456 171 14.8 39 0.11 19.31466 8_R 477 21 17.4 41 0.05 19.51756 10S_2015 416 164 40.0 41 0.07 42.91757 10S_R_2015 299 39 38.5 41 0.06 40.81754 10C_2015 376 132 41.1 43 0.1 45.41755 10C_R_2015 276 46 41.3 44 0.1 45.51785 10RG 607 177 38.2 63 0.17 49.11784 10RG_R 469 106 38.6 55 0.05 41.41707 10RL_R 276 30 38.7 52 0.05 41.31878 11C 230 183 41.7 36 0.05 43.51877 11C_R 146 8 50.5 37 0.05 52.41885 11F 332 208 34.8 42 0.05 36.91884 11F_R 218 49 35.5 41 0.05 37.61879 11S 149 82 21.6 27 0.05 22.91880 11S_R 66 1 18.4 25 0.05 19.61667 11T 152 68 44.1 66 0.05 47.41873 11T_R 211 24 44.6 56 0.05 47.41711 15 22 20 35.8 47 0.05 38.11712 15_R 24 0 30.0 45 0.05 32.21852 19M 160 84 54.9 64 0.05 58.11853 19M_R 165 40 57.5 67 0.05 60.81534 19NA 74 63 55.2 52 0.05 57.81808 19NA_R 64 5 54.3 54 0.05 57

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TotalTime(min.)

1741 19R 121 98 42.2 59 0.06 45.71871 19R_R 187 36 43.6 59 0.05 46.51532 19RA 103 52 53.2 54 0.06 56.21807 19RA_R 139 40 53.7 54 0.05 56.41743 19W 98 57 39.6 54 0.1 44.81872 19W_R 86 18 39.3 57 0.1 451773 20_2015 674 281 64.1 83 0.05 68.21783 20_R_2015 770 208 59.0 80 0.06 63.81581 22L 53 43 81.3 71 0.05 84.81815 22L_R 23 16 68.8 71 0.12 77.31399 30 550 346 73.1 66 0.05 76.41400 30_R 478 259 73.4 64 0.05 76.61622 31_2015 741 278 42.1 76 0.16 54.21623 31_R_2015 670 199 40.4 74 0.19 54.31568 32A_2015 126 61 24.3 32 0.15 29.11569 32A_R_2015 281 120 24.2 30 0.05 25.71570 32F_2015 30 8 24.6 33 0.14 29.11571 32F_R_2015 4 1 24.5 31 0.14 28.81572 32N_2015 278 80 24.3 33 0.08 26.81573 32N_R_2015 474 133 24.2 33 0.06 26.31575 33 83 73 25.7 32 0.05 27.31798 33_R 86 74 28.9 38 0.05 30.81765 34B 422 258 46.4 74 0.05 50.11781 34B_R 341 93 46.4 72 0.05 50.21713 34M_2015 438 94 38.3 70 0.13 47.41714 34M_R_2015 584 98 37.8 66 0.14 47.31753 34N_2015 147 106 36.1 61 0.05 39.11752 34N_R 66 38 37.0 55 0.05 39.71762 34R 201 149 51.5 82 0.05 55.61780 34R_R 160 71 48.3 74 0.07 53.71654 35 243 131 47.9 57 0.1 53.51655 35_R 187 93 45.5 54 0.16 53.91882 40 414 299 64.1 63 0.16 74.31883 40_R 295 41 63.7 61 0.17 74.31429 40B 81 75 68.0 44 0.05 70.21806 40B_R 31 19 64.6 42 0.05 66.71894 41 122 112 84.9 70 0.08 90.11893 41_R 57 39 88.6 75 0.06 93.1

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TotalTime(min.)

1869 43B_2015 172 50 50.3 74 0.12 59.21847 43B_R_2015 359 47 54.0 75 0.08 59.71848 43H_2015 137 51 49.7 76 0.12 58.81849 43H_R_2015 127 3 51.8 76 0.1 59.41851 43S_2015 413 238 37.6 63 0.14 46.41850 43S_R_2015 202 51 41.2 66 0.12 49.11583 50G 1,183 342 47.6 68 0.07 52.61814 50G_R 1,475 113 48.6 72 0.06 53.11584 50W 172 53 47.0 67 0.09 53.11816 50W_R 114 35 46.4 67 0.1 52.71586 50Y 4 4 37.9 59 0.25 52.71817 50Y_R 2 2 39.7 62 0.21 52.71609 52B_2015 222 96 37.4 43 0.06 401610 52B_R_2015 189 18 37.8 43 0.05 39.91881 52M 193 57 32.5 57 0.2 43.71886 52M_R 181 25 35.8 61 0.13 43.81605 52Q_2015 251 105 23.5 31 0.12 27.21606 52Q_R_2015 119 26 20.0 29 0.17 251887 52R 639 351 35.8 57 0.13 43.11889 52R_R 465 29 35.2 56 0.14 42.91888 52SE 144 99 35.5 55 0.13 42.61890 52SE_R 168 13 39.6 61 0.05 42.91615 52SF_2015 463 168 23.5 35 0.16 291613 52SF_R_2015 442 73 24.3 36 0.15 29.71809 53B 333 196 45.8 72 0.18 591810 53B_R 190 7 46.6 75 0.17 58.91718 53I 67 48 45.9 62 0.12 53.41870 53I_R 55 23 46.3 64 0.11 53.31861 53L 72 44 40.6 62 0.21 53.61874 53L_R 48 8 40.2 59 0.23 53.91812 53S 69 22 40.2 64 0.23 54.91811 53S_R 286 20 45.8 74 0.12 54.91875 53W 188 137 57.3 64 0.05 60.51876 53W_R 257 117 59.7 72 0.05 63.31763 56_2015 1,241 422 50.8 82 0.06 561716 56_R_2015 861 195 42.6 80 0.17 56.21760 58B 97 72 68.1 48 0.14 74.81761 58B_R 58 41 64.3 43 0.25 75.1

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TotalTime(min.)

1592 62G 91 69 61.7 72 0.05 65.31593 62G_R 91 77 62.7 75 0.05 66.41594 62W 230 188 69.6 68 0.05 731595 62W_R 207 140 70.7 73 0.05 74.41858 69_2015 583 273 89.5 92 0.08 97.21859 69_R_2015 667 310 89.1 94 0.08 96.31704 80 93 42 37.1 19 0.05 381508 80_R 32 8 26.3 9 0.05 26.81705 80B 11 7 30.7 18 0.05 31.61706 80B_R 8 1 26.4 11 0.05 271900 81 74 46 52.2 24 0.33 60.31899 81_R 56 24 48.7 24 0.46 59.81867 82 95 39 26.2 18 0.05 27.11866 82_R 141 67 24.8 23 0.05 261855 89 120 50 30.5 30 0.05 321854 89_R 34 6 30.4 26 0.05 31.71505 90 95 23 41.3 42 0.05 43.41504 90_R 155 102 41.2 52 0.05 43.81758 93 219 140 36.2 36 0.17 42.31759 93_R 135 17 34.6 29 0.27 42.31358 Trolley Madison St I 574 0 10.8 11 1.37 25.91359 Trolley Madison St O 333 25 11.0 12 1.25 26771 Trolley Main St NB 157 1 7.7 12 0.93 18.9772 Trolley Main St SB 190 9 7.7 14 0.8 18.9770 Trolley Riverfront L 139 1 15.9 15 1.45 37.71797 FGC DtnAirport ALT2 513 217 22.9 14 0.5 29.91795 FGC DtnAirport ALT2 576 271 22.9 14 0.5 29.91208 FBR Collierville Loc 174 78 38.8 43 0.15 45.31209 FBR Collierville Loc 132 19 39.7 43 0.15 46.11549 FBR Bartlett Local O 285 169 42.9 59 0.15 51.8

1547 FBR Cordova LocalOB 430 264 59.4 77 0.15 70.9

1550 FBR Bartlett Local I 217 60 43.4 59 0.15 52.2

1548 FBR Cordova LocalIB 322 72 60.0 77 0.15 71.6

1836 FBR Millington Local 61 24 24.1 35 0.15 29.41837 FBR Millington Local 49 5 24.4 35 0.15 29.7

1002 FBR Hornlake LocalS 106 57 22.9 21 0.15 26

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TotalTime(min.)

1003 FBR Hornlake LocalN 72 6 23.5 21 0.15 26.7

1004 FBR Southaven OBLoc 127 80 46.9 33 0.15 51.8

1007 FBR Southaven OBLoc 116 22 47.9 37 0.15 53.4

1006 FBR Southaven Local 182 124 20.4 20 0.15 23.41008 FBR Southaven Local 105 18 20.6 20 0.15 23.61895 FBR Oliver Branch Lo 54 36 47.6 50 0.15 55.11897 FBR Oliver Branch Lo 74 13 48.2 52 0.15 561443 FBR Arlington Expres 28 15 47.0 5 1 521444 FBR Arlington Expres 47 6 46.6 5 1 51.6

1799 FBR SouthavenExpres 31 19 21.9 3 1 24.9

1800 FBR SouthavenExpres 29 0 26.7 3 1 29.7

1801 FBR Collierville Exp 18 14 45.1 4 1 49.11802 FBR Collierville Exp 26 12 51.4 4 1 55.4

1896 FBR Oliver BranchEx 29 23 42.3 3 1 45.3

1898 FBR Oliver BranchEx 21 9 45.1 3 1 48.1

1252 FBR Airport Circulat 744 143 70.4 53 0.15 78.31253 FBR Airport Circulat 613 148 67.0 52 0.15 74.8

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TRAVEL DEMAND MODEL DOCUMENTATION - Memphis Urban

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