Building the 4Ds into

Travel Demand Models

Don Hubbard, AICP, PE

Senior Associate

Topics Covered• What are the 4Ds and why are they

important?

• Insensitivity of conventional models to changes in 4D characteristics

• Adjusting a model to reflect the 4Ds – a case study from Sacramento

• Potential improvements to the methodology

• Conclusions – What does it all mean?

What are the 4Ds and Why

are They Important?

What are the 4Ds?National research has found that certain characteristics of the built environment tend affect travel behavior in predictable ways. These characteristics are:

• Density in terms of dwelling units or jobs per acre

• Diversity of land uses within any given area• Design of the pedestrian and bicycling

environment• Destinations; proximity to regional activity

centers

Why Are They Important?

Environmental

Characteristic

Elasticity

VT Per Capita

Elasticity

VMT per Capita

Density 4% to 12% 1% to 17%

Diversity 1% to 11% 1% to 13%

Design 2% to 5% 2% to 13%

Destinations 5% to 29% 20% to 51%

Sources: 4D National Syntheses, Twin Cities, Sacramento, Location Efficiency

Because they affect per-capita auto use

… and (on a different level) because they have entered public debate

• Growing consensus that highway construction does not solve congestion problems in the long term

• Prominent groups (APA, FHWA) are supporting Smart Growth as a way to reduce the demand for road space as an alternative to increased supply

• Transportation planners are being asked to analyze these potential reductions

Insensitivity of Conventional

Models to Changes in 4D

Characteristics

“Blind Spots” in Conventional Models – Walking Trips

• Walking trips must use road links, and only roads big enough to be in the traffic model

• Sidewalk completeness and other aspects of sidewalk condition (shade, aesthetics, etc.) are ignored

• Intra-zonal and adjacent-TAZ trips (the most important for walk mode) are handled very abstractly

“Blind Spots” in Conventional Models – Land Use

• No consideration is given to the distances between land uses within a given TAZ

• Interactions between different non-residential land uses (e.g. offices and restaurants) not well represented

• Density is ignored (a TAZ with a dense development in one corner is treated the same as a TAZ with the same population spread evenly throughout its area)

Consequences of the“Blind Spots”

• Model tests of Smart Growth policies usually understate benefits; things that cannot be measured tend to be ignored

• Planners are frustrated by model results that do not match field experience

• But if they reject the model’s results, then land use planning and transportation planning proceed on separate tracks

Blind spots in models affect real-life choices; correcting the models will reduce the distortions in the project mix

Adjusting a Model to Reflect

the 4Ds – A Case Study in

Sacramento

4D Applications

Already Done

• Atlanta (Atlantic Steel Site)

• Twin Cities

• East Bay (TBart)

On-Going Projects

• Humboldt County

• San Luis Obispo COG

Background• SACOG initiated a public visioning process

for the long-term future of the Sacramento Region

• Smart Growth policies were prominently featured in the debate

• However, the regional model (SACMET) was insensitive to 4D characteristics

• The model needed to be augmented to enable quantitative forecasts of the effects of smart growth policies in different scenarios

Approach Used

• 4D adjustments were computed as elasticities (each % change in neighborhood characteristics resulted in a certain % change in travel behavior)

• % changes based on differences from a Base Case

• These adjustments were applied to outputs from the SACMET model

Adjustment

Methodology

Overview of Travel Forecasting With 4D Post-Processor

Differences in TAZ Characteristics

4D Elasticities from

Household Survey Data

4D Post-Processor

VT & VMT Adjustments

Expressed in %

The 4D adjustment component is shown in blue

Adjusted VT & VMT Forecast for Scenario 1

Difference in VT & VMT Between Base Case and Scenario 1

Land Use Software

Conventional Traffic Model

Inputs fromWorkshop or Staff

Key

Software

Data

Base Case

Network Changes for Scenario 1

Base Case

Land Use Assumptions for Scenario 1

Base Case

Land Use files for Scenario 1

Network Editing Software

Base Case

Base Case

VT & VMT Forecasts for Scenario 1

Network files for Scenario 1

Data Sources

• VT & VMT data came from a large (4,000 HH) travel diary survey

• Households, jobs, and developed acres came from a parcel database (400,000+ parcels)

• Sidewalk coverage and route directness came from aerial photographs

4D Elasticities from Net Res. Net Emp. Job-HH Jobmix Index HBW Non_HBW

household surveys Density Density Diversity Diversity Design Destinations Destinations

HBO -0.119 -0.044 -0.032 -0.041

HBW -0.117 -0.059 0.000 -0.375

NHB -0.339 -0.462 0.000 -0.822

HBO -0.133 -0.16 -0.030 -1.405

HBW -0.238 -0.26 0.000 -1.234

NHB -0.444 -0.459 0.000 -1.318

VT

VM

T

Regression Analysis

Different formulas were used for different trip purposes

Some values were not statistically significant

NHB proved to be the most elastic

HBW was the least elastic

Problems Encountered• Some of the relationships were not

statistically significantDrop any below 90% significance

• Elasticity approach does not work well at the extremes (vacant-to-non-vacant)Use floor and ceiling values to keep results within actual experience in region

• Adjustment process very labor-intensiveAutomate data processing

PLACE3S Used for Land Use Data

Parcel-level data collected for all of Sac County

Four land use scenarios:• Current Trends• Increased Density• Density with Smart Growth• Land Use BalanceTwo transportation networks (current trends and Smart Growth)

Scenario Themes• Base Case – Continuation of current trends;

• Density (only) – Build to highest densities allowed under existing general plans for residential uses

• Density with Smart Growth - Most regional growth goes to compact, centrally-located transit- and bike-friendly communities. A widespread BRT system replaces limited LRT line extensions

• Land Use Balance - Sac County broken into ten areas, each with a good balance of residential, retail, and non-retail land uses

In each case regional population will approximately double (from 1.2 million to 2.4 million)

Comparison of Transportation Results

Scenario TotalVT/Day

TotalVMT/Day

Current Trends +140% +120%

Density Only +114% +89%

Dense & Smart Growth +91% +62%

Land Use Balance +111% +74%

Results – Regional Auto Use

When population doubles, there will be a big increase in auto use under any scenario

But 4D model shows smart growth policies could reduce the growth significantly

% Change from Existing

Scenario Down-town

SacCounty

TotalRegion

Existing 5.5% 1.1% 0.9%

Current Trends 7.5% 1.1% 0.8%

Density Only 14.1% 2.6% 1.6%

Dense & Smart Growth 16.3% 5.4% 3.4%

Results – Transit Mode

4D characteristics (especially density) make a big difference in transit use that a conventional model might miss

Scenario SacCounty

TotalRegion

Existing 6.6% 6.4%

Current Trends 5.1% 4.8%

Density Only 11.6% 8.9%

Dense & Smart Growth 23.5% 18.0%

Land Use Balance 13.9% 10.6%

Results – Non-Motorized Modes

Again, density and walkable design have major impacts on the walking mode that would not be detectable using a conventional model

Mode Split for Sac County

Scenario Auto TransitNon-

Motorized

Existing 92.2% 1.1% 6.6%

Current Trends 93.8% 1.1% 5.1%

Density Only 84.9% 2.4% 12.5%

Dense & Smart Growth 71.1% 5.4% 23.5%

Land Use Balance 83.0% 3.0% 13.9%

4D model does not forecast the demise of the auto mode, even under the most aggressive scenario.

But it does suggest that a more balanced mode split is achievable in Sacramento

Potential Improvements to

the Methodology

Base the regressions on fixed radii rather than TAZs

TAZ boundaries tend to separate land use types

Each TAZ’s land use seems to be poorly balanced

Fixed radii more accurately reflect field conditions

Also, less arbitrary and more transferable

Shift the Adjustment to an Earlier Stage of the

Model

TAZ 3-DCharacteristics

3-D Elasticities

Compute Raw 3-D Ajustments

Compute Final 3-D Ajustments

Ceiling & Floor Values

Post-processing means that link volumes cannot reflect 4D affects

3D Adjustment

Upstream adjustment means that the assigned

volumes reflect the adjustments

TAZ 3-DCharacteristics

3-D Elasticities

Compute Raw 3-D Ajustments

Adjusted NHB P&AAdjusted HBO

Adjusted NHB P&A

3D Adjustment

Compute Final 3-D Ajustments

Ceiling & Floor Values

Highway Network

Balanced P & A Trip

Ends

Test ScenarioTrip-Gen Rates

TripGeneration

NetworkSkimming

Skim Matrix

Trip Distribution

OD Matrix

Trip Assignment

LinkVolumes

NHB P&AHBO P&A

NHB Productions &Atractions

Model With 4D Adjustment

Total VT and VT/HH

Total VMT and VMT/HH

Base CaseLand Uses

Develop Elasticities for the Attraction End of the Trip

Conditions at both ends affect travel behavior

…… TAZ 18 TAZ 19 TAZ 20 …..4D Adjustment 0.99 1.02 0.96

TAZ 61 1.04TAZ 62 0.93 0.95TAZ 63 0.96

Destination

Ori

gin …

……

…

Develop a 5th D Related to Transit Accessibility

• The Sacramento Case had a very simple mode split component– Reductions in VT were assumed to shift to transit or

walk/bike

– Split between transit and walk/bike based on split in SACMET model (which differs for each TAZ)

• Next generation will use elasticities based on characteristics of the neighborhoods around stations

Develop Elasticities for Other Cities

• Sacramento had the first elasticities by trip purpose; difficult to gauge transferability

• Regional differences may be significant (especially the effect of winter on walking)

• City size may also be significant

• Ultimately would like to have values usable (as a first estimation) without local surveys

Conclusions

What Does It All Mean?• Conventional traffic models were not intended

to measure the issues inherent in Smart Growth

• Tests using conventional models tend to show that Smart Growth policies would have few benefits; but field experience suggests otherwise

• We now have a method to make forecasts more sensitive to Smart Growth policies

• So cities are now able to have more enlightened analysis of Smart Growth

Any Questions?

Comments?

Offers of Data Sets?