GEOG 111/211A Transportation Planning UTPS (Review from last time) Urban Transportation Planning...

GEOG 111/211A Transportation Planning

UTPS (Review from last time) • Urban Transportation Planning System

– Also known as the Four - Step Process

– A methodology to model traffic on a network

– Developed in 1962 (Chicago)

• Four Steps:– Trip Generation Estimate Person Trips for each

TAZ

– Trip Distribution Distribute Person Trips from TAZ to TAZ

– Mode Choice Convert Person Trips to Vehicle Trips

– Traffic Assignment Assign Vehicles to the Network

Oct/Nov 2004


Survey Data – interviews of persons about their behavior

Models of behavior – extract key aspects to capture most variation

Use models – incorporate models into a computerized map

If no survey available?

Discuss options in class!


The Four Steps:• Trip Generation = Estimate Person Trips for each TAZ• Trip Distribution = Distribute Person Trips from TAZ to

TAZ• Mode Choice = Convert Person Trips to Vehicle Trips• Traffic Assignment = Assign Vehicles to the Network

• Pre 4-step = Land Use and Demographics?• Post 4-step = Emissions, Traffic Simulation, Link by Link

Evaluation


Key Concepts of UTPS• TAZ: Traffic Analysis Zone

– A TAZ is an arbitrary subdivision of the study area

– TAZs are used in trip generation and trip distribution

– TAZs may be any shape or size, but US Census Blocks, Block Groups, and Tracts are often used

Block Block Group Tract

i.e., a city block


Key Concepts of UTPS• Centroid

– Every TAZ (Gate and Internal Zone) has a centroid, usually placed roughly at the geographic center of the TAZ

– All trips to or from a TAZ are assumed to start or end at the centroid

• Discussion– Why do we use TAZs and centroids to model trips?


Key Concepts of UTPS• Gate TAZs

– TAZs placed outside the Study Area where major roads cross the boundaries of the study area

– Used to model External Trips (i.e., trips with an origin or destination or both outside the study area)

– Gate TAZs represent all areas outside of the study area

(Study Area)

Gate TAZ

Network


Gate TAZ

Centroid


Every zone is a node (the centroid) with an identifier and type


Trip Generation

Additional suggested reading material: Ortuzar & Willumsen, third edition,

Chapter 4.


Trip Generation Objectives• Estimate amount of trip making going out of a TAZ • Estimate amount of trip making going into a TAZ• Account for differences among TAZs due to person

and household characteristics• Account for differences among TAZs due to

business (establishments) characteristics• Develop functions to predict future amount of trip

making


Trip Generation Usual Process• Collect Data, usually by Surveys and Census

– Sociodemographic Data and Travel Behavior Data

• Create Trip Generation Models• Estimate the number of Productions and Attractions

for each TAZ, by Trip Purpose • Balance Productions and Attractions for each Trip

Purpose– Total number of Productions and Attractions must be

equal for each Trip Purpose


Trip Generation Models• Regression Models

– Explanatory Variables are used to predict trip generation rates, usually by Multiple Regression

• Trip Rate Analysis– Average trip generation rates are associated with different

trip generators or land uses

• Cross - Classification / Category Analysis– Average trip generation rates are associated with different

trip generators or land uses as a function of generator or land use attributes

• Models may be TAZ, Household, or Person - Based


Usual Unit of Analysis• TAZ - zonal rates (Number of trips as a function of a

zone’s population characteristics)• Household rates (Number of trips as a function of

household characteristics)• Person rates (Number of trips as a function of person

characteristics)

• NEW (PennState Research)! Multilevel rates (Number of trips as a function of person & household & TAZ characteristics)


Units and Models• TAZ-based models = productions and attractions

converted to origins and destinations

• Household and/or person - based models = origins and destinations

• Establishment - based = attractions need to convert to destinations


Common Trip Definitions in CE422• Trip: a one - way movement from one place to another

• HB = Home Based: a trip where the home of the traveler is either the origin or the destination of the trip

• HBW = Home Based Work: trips between home and work

• HBNW = Home Based Non-Work: trips between home and shopping, also called HBS (Home Based Shopping)

• HBO = Home Based Other: trips between home and a non - work / shopping location

• NHB = Non Home Based: trips where neither end of the trip is the home of the traveler


Related Definitions

Home

Work

School

1.Home-based school trip

2.NonHome-based work trip

3. Home-based work trip

1+2+3=Tour or Trip Chain (home-based)


Productions - Attractions

ResidentialArea

Non-ResidentialArea

Non-ResidentialArea

Non-ResidentialArea

Production

Production

Production

Attraction

Attraction

Attraction

Attraction

Production

All Home - Based Trips

Non - Home - Based Trips

= Origin

= DestinationSee also OW-p. 124


Trip Balancing Methods• Hold Productions Constant

– Attractions are multiplied by the ratio of the sum of non-gate productions to the sum of non - gate attractions

– Most common form of trip balancing

• Hold Attractions Constant– Productions are multiplied by the ratio of the sum of non-gate

productions to the sum of non - gate attractions

• Hold Neither Productions or Attractions Constant– Not used very often

Note: Gate Productions and Attractions are not included in this balancing process


Examples• http://tmip.fhwa.dot.gov/clearinghouse/docs/Time-D

ay/ - discussion of time-of-day issues

• http://www.psrc.org/datapubs/index.htm (this is the metropolitan plan where models are used)

• http://tmip.fhwa.dot.gov/clearinghouse/ <the ultimate web site for GEOG 111/211A>

All sites verified October 2004


Gate Trip Estimation• Gate Trips Must be Modeled Separately

– Gates have specific traffic volumes associated with them

– Gates do not have sociodemographic data

– Gates may represent trips with extremely variable trip lengths

• Gate Trip Modeling– Correlate percentages of traffic volumes to different trip

purposes (e.g., X% * Total daily volume observed = trips for commuting)


ITE Trip Generation Manual• Trip Rate Analysis Model

– Univariate regression for trip generation

– Primarily for businesses (attraction rates)

– Explanatory variables are usually number of employees or square footage

– Models developed using data from national averages and numerous studies from around the US

Copies of the ITE Trip Generation Manual may be Found in the Hammond and PTI Libraries


TAZ Issues

• Data availability limited by privacy issues• Larger TAZs, with complete data, are no longer

necessarily homogeneous• Model accuracy decreases with larger TAZs

TAZ Scale Modeling Accuracy Data Availability

Block Good Poor

Block Group Not Good Excellent

Tract Poor Good


Model Formulation and Surveys• Privacy

– May limit data collection efforts

– Private information must remain secure

• Response Rate– Good survey should have at least 85% response rate

• Representative Sample Size– Pop. representation most important

• Model Stability and Transferability– Over time, behavior may change

– Behavior is not necessarily the same from place to place


Trip Generation Example• Similar to the lab exercise• From the Puget Sound Region in 1989• Subsistence (work + school trips)• These are one way trips (origins) instead of

productions


Sample Descriptives

Descriptive Statistics

1621 1.00 7.00 2.7378 1.17211621 .00 5.00 .4349 .79831621 .00 3.00 .2295 .55811621 .00 8.00 2.3146 1.11261559 15.00 90.00 46.9602 14.21441621 .00 1.00 .6539 .47591621 .00 7.00 .8421 .87401559

HHSIZETOT6_17TOT1_5NUMVEHAGEEMPLOYSFREQ1Valid N (listwise)

N Minimum Maximum MeanStd.

Deviation

Class: What do you observe?


Trip Generation Linear RegressionModel for Subsistence Trips

Coefficientsa

.409 .120 3.413 .0019.99E-03 .035 .013 .283 .7782.50E-02 .042 .023 .595 .552

-.116 .049 -.075 -2.380 .017-8.5E-03 .019 -.011 -.437 .662-4.4E-03 .002 -.071 -2.685 .007

.986 .043 .536 22.838 .000

(Constant)HHSIZETOT6_17TOT1_5NUMVEHAGEEMPLOY

Model1

B Std. Error

UnstandardizedCoefficients

Beta

Standardized

Coefficients

t Sig.

Dependent Variable: SFREQ1a.

Class: Interpret the model


Goodness of fitANOVAb

385.290 6 64.215 124.186 .000a

802.521 1552 .5171187.811 1558

RegressionResidualTotal

Model1

Sum ofSquares df

MeanSquare F Sig.

Predictors: (Constant), EMPLOY, TOT1_5, TOT6_17, NUMVEH, AGE, HHSIZEa.

Dependent Variable: SFREQ1b.

Model Summary

.570a .324 .322 .7191Model1

R R SquareAdjusted R

Square

Std. Errorof the

Estimate

Predictors: (Constant), EMPLOY, TOT1_5, TOT6_17,NUMVEH, AGE, HHSIZE

a.


Let’s Improve the Model

If (age < 20) Teen = 1 .If (age >= 20 and age < 35) Young=1.If (age >= 35 and age < 65) Midage=1.If (age >= 65 and age < 75) Senior=1.If (age >=75) VSenior=1.


Descriptives of the New Vars

Descriptive Statistics

1621 .00 1.00 .1777 .38241621 .00 1.00 1.73E-02 .13031621 .00 1.00 .6268 .48381621 .00 1.00 .1154 .31961621 .00 1.00 2.47E-02 .15521621

YOUNGTEENMIDAGESENIORVSENIORValid N (listwise)

N Minimum Maximum MeanStd.

Deviation


Linear RegressionCoefficientsa

.221 .094 2.355 .0191.037 .043 .565 24.120 .000.659 .163 .098 4.050 .000

-.133 .100 -.058 -1.321 .187-4.2E-02 .094 -.023 -.451 .652

-.118 .106 -.043 -1.106 .269-.197 .146 -.035 -1.343 .179

(Constant)EMPLOYTEENYOUNGMIDAGESENIORVSENIOR

Model1

B Std. Error


Beta

Standardized

Coefficients

t Sig.

Dependent Variable: SFREQ1a.


Leisure Trip GenerationCoefficientsa

2.298 .191 12.057 .000-9.9E-02 .087 -.032 -1.129 .259-4.8E-02 .331 -.004 -.146 .8841.38E-02 .204 .004 .068 .946-4.8E-02 .190 -.016 -.253 .800

-.170 .216 -.037 -.786 .432-.545 .298 -.058 -1.831 .067

(Constant)EMPLOYTEENYOUNGMIDAGESENIORVSENIOR

Model1

B Std. Error


Beta

Standardized

Coefficients

t Sig.

Dependent Variable: LFREQ1a.

The same model as for subsistence did not work!!!!!


New model for leisureCoefficientsa

1.682 .178 9.425 .000.339 .166 .052 2.045 .041

-8.0E-05 .000 -.027 -1.050 .294.261 .045 .144 5.771 .000

7.94E-02 .064 .031 1.241 .2152.64E-02 .033 .020 .795 .427-8.8E-02 .073 -.030 -1.206 .228-1.4E-03 .001 -.034 -1.384 .1679.96E-02 .046 .054 2.171 .030

(Constant)LICENSEWKDISTTOT6_17TOT1_5NUMVEHSEXSTUDENTS1

Model1

B Std. Error


Beta

Standardized

Coefficients

t Sig.

Dependent Variable: LFREQ1a.


Goodness of fitANOVAb

95.414 8 11.927 5.814 .000a

3306.909 1612 2.0513402.323 1620

RegressionResidualTotal

Model1

Sum ofSquares df

MeanSquare F Sig.

Predictors: (Constant), S1, SEX, STUDENT, TOT1_5, TOT6_17, LICENSE,WKDIST, NUMVEH

a.

Dependent Variable: LFREQ1b.

Model Summary

.167a .028 .023 1.4323Model1

R R SquareAdjusted R

Square

Std. Errorof the

Estimate

Predictors: (Constant), S1, SEX, STUDENT, TOT1_5,TOT6_17, LICENSE, WKDIST, NUMVEH

a.


Compare frequencies

LFREQ1

12.010.08.06.04.02.00.0

1000

800

600

400

200

0

Std. Dev = 1.45

Mean = 2.2

N = 1621.00

SFREQ1

7.06.05.04.03.02.01.00.0

700

600

500

400

300

200

100

0

Std. Dev = .87

Mean = .8

N = 1621.00

Class: Which one is easier to estimate?


Traditional Trip Generation• Input: social and economic characteristics• Output: productions/attractions, origins/destinations

by zone• Key concepts: trip generation by purpose maybe

more accurate but some purposes easier to predict (trips to work)

• Other: Goods movement productions/attractions are handled in a similar way (Freight Forecasting Manual exists)


Post-MTC Lawsuit Models• Level of service = “quality” of transportation system

measured in travel time from an origin to a destination

• Trip generation also function of level of service• New models for induced demand = new demand for

travel after improvements in level of service• Activity-based models to reflect “scheduling” of

persons, coordination of activities• Multilevel models to reflect within group

coordination


In the Lab - Check• TAZ population and productions• Businesses and attractions• What do you expect the relationship to be?• Does the relationship make sense?


Summary• Collect data using surveys• Derive a model using statistics• Use the model to predict number of trips generated

in each zone• Apply this at each centroid representing a zone• Have all this ready for the next step – trip

distribution

If you cannot run a survey – use equations from ITE trip generation manual or other studies – check for similarities/verify results!

GEOG 111/211A Transportation Planning UTPS (Review from last time) Urban Transportation Planning...

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