David B. Goldstein, Ph.D., NRDC John Holtzclaw, Sierra Club Todd Litman, Victoria Transport Policy Institute

1. Overcoming Barriers to Smart Growth:Surprisingly Large Role of Better Transportation Modelingbased on a paper presented at theACEEE Summer Study August 2006 David B. Goldstein, Ph.D., NRDC John Holtzclaw, Sierra Club Todd Litman, Victoria Transport Policy Institute

2. The Efficiency Opportunity • Smart Growth can reduce travel by 50% and more. • Ten year’s savings potential in the U.S. from new housing construction is: • 110 million CO2 emissions annually (6% of total greenhouse gas emissions) • $2.2 trillion of present value savings • 600,000 barrels/day of oil saved • California savings are about 12% of this

3. Key Physical Attributes of Smart Growth

4. Sprawl Vs. Smart Growth

5. Images of Smart Growth

6. Images of Smart Growth

7. Images of Dumb Growth

8. Images of Dumb Growth

9. Physical Realities and Policy Options • We know what smart growth should look like and we know what dumb growth looks like. • We know the difference of land use and in terms of transportation infrastructure. • What is less understood is the policy changes necessary to get us there. • Models are important to evaluate specific policy choices at the project, local, county, regional, and statewide levels.

10. Transportation and Land Use Policy is Complicated • Decisions are made by a complex combination of government regulation and market forces, and by government incentives and policies at the local, regional, state, and national level. • While transportation infrastructure investments are made by definable government agencies, their decisions are influenced by a breathtaking variety of laws and political influences.

11. Transportation and Land Use Policy is Complicated--II • The one place things come together is in transportation models. • Models are used as part of the justification and design of highway and transit projects. • Models are also used on EIS’s of building projects

12. Weaknesses of the Models • Transportation models are significantly flawed in how they treat smart growth • Models systematically underestimate the reductions in traffic from transit, density, and other smart growth variables • Models also underestimate the induced traffic from new highway capacity

13. Specific Failings of Models: Changes in density • Suburban green fields smart growth • Suppose a suburban area adjacent to current sprawl is developed with the same number of units as sprawl, but more compact and transit oriented • Actual traffic generation will be 30%-50% lower than from existing development; and travel from the existing development will be reduced • But the models will predict no change in travel behavior per household: the new residents will drive just as much in the model as the old ones

14. Specific Failings of Models II • Consider an urban infill development that doubles the density of an existing neighborhood and also increases transit service level substantially • In reality, peak hour traffic on neighboring streets will decline, and total travel (for the doubled number of households) will increase by only 20% or less • But models will predict that travel from the neighborhood doubles

15. Specific Failings of Models: Transit Expansion • Suppose that a new transit service is introduced in a high-density area • In reality, automobile traffic will decline by about 5 times as many passenger kilometers as are observed on the transit system • But models will predict one-for-one switches of mode with no decrease in total travel

16. Specific Failings of Models IV • Thus, if the choice is between more expensive neighborhood-focused transit that reduces overall travel and cheaper transit down freeway rights of way that does not reduce travel, cost-effective analysis based on current models will always prefer the cheaper option. • But more careful analysis sometimes will favor the more expensive option

17. Summary • In all of these cases, models make erroneous predictions that lead to misallocation of investment dollars • Residentially focused transit looks less cost-effective than it is • Compared to highways, and • Compared to non-housing focused transit • Highway capacity needs to serve smart growth developments are over-predicted

18. Policy Impact on Housing Decisions • Transportation models are used frequently in Environmental Impact Reports on new housing developments • In many areas, predictions of significant traffic impacts will generate neighborhood opposition that can stop projects from being built • Alternately, opposition will reduce density when the project goes ahead. • If this opposition is based on spurious predictions of traffic impacts, smart growth projects will be hard to build

19. These problems happen in the real world • The SF Planning Dept. notes that most neighborhood opposition to new housing projects is really opposition to new cars, not to new neighbors • Several smart growth projects in Marin County, CA were defeated due to neighborhood opposition based on traffic concern. • A major downtown housing projects with 2,000 units on the doorstep of extensive transit service was predicted to generate 450 additional rush hour trips, none of which is ever likely to materialize

20. Traffic Models can be Improved • Several metropolitan planning agencies in the United States and Canada have embarked on successful efforts to overcome these problems • These improvements do not require radical changes in model structure or detail of data inputs