Travel, Land Use & Smart Growth: What Don’t We Know?

1. Travel, Land Use & Smart Growth:What Don’t We Know? • Caltrans Research Connection • Randy Crane, UCLA • September 2004

2. Outline • Land Use, Smart Growth & Travel • Research questions, large and small • Research examples: Smart growth, VMT, and commute length • Summary and next moves

3. Popular Arguments

4. Got Smart Growth?

5. Big Questions • Urban Form: Sprawl and traffic • Neighborhood Design: The influence of neighborhood features on trip taking, mode choice, physical activity,….

6. Big Answers • The New Urbanism: Increase densities & transit access, mix land uses more, village scale, discourage cars,.... • Smart Growth: The above + public participation and broader urban growth management strategies

7. Two Research Examples • Will smart growth reduce VMT? • Will smart growth shorten commutes?

8. Example 1:Will Smart Growth reduce traffic?

9. Summary of Smart Growth Argument • Auto travel is increasing faster than population growth, and suburbanization, sprawl and car subsidies are to blame • In particular, more roads do not relieve congestion but transit and “smart growth” do, since both common sense and UC Berkeley studies indicate that: • Induced demand fills up roads as fast as they’re built • Higher residential densities reduce car ownership, trips/person & VMT/person • TOD and mixed land uses increase transit use, reduce parking & VMT

10. Bad Neighborhood vs Good Neighborhood

11. Problems with Early Research • Many studies indicate a lower auto mode share & VMT in higher density, mixed use areas. • But, empirical strategies for estimating and evaluating the impacts of accessibility on travel behavior were primitive. For example, • mode choice or VMT or #trips = f(density, access, pedestrian friendliness, demographics,....) • Behavioral story ad hoc; the role of conventional choice variables such as relative prices, resources, etc., unspecified.

12. Why a Problem? • These unresolved modeling issues, which center on the lack of a consistent behavioral framework, greatly limit the usefulness of empirical results. • For example, where are the demand elasticities, what is the performance interpretation of accessibility, how can any given set of results be transferred outside the data at hand?

13. Research Strategy • What we want to know: How does each neighborhood and community design feature, alone and in conjunction with others, influence travel behavior? • Consider the evidence on this question to date: Descriptive, simulation, behavioral. • A “behavioral” approach

14. Behavioral Model • Q: What influences travel and how do land use, density, and access reflect behavioral variables? • Model the demand for trips but make the built environment explicit. For example, say consumers make trip decisions to maximize U(a,w,x) • subject to y = x + apa + apw . The solutions to this problem are the trip demand functions a(pa,pw,y) and w(pa,pw,y). • (where a is the number of trips by automobile for each purpose, w is the number of trips by walking for each purpose, x is a composite of the time spent on other activities, pa is time per trip for travel by automobile, pw is time per trip for walking, y is the total time available for travel.)

15. Apply to Data • Empirical Specification: Determine how the land use measures map into the parameters (pi, mi, ti). Determine how trip purposes map into the variables (a, w). Find data corresponding to these measures. • Estimation: Specify a functional form for demand and estimate a(pa,pw,y), w(pa,pw,y), etc., as appropriate to the data.

17. VMT Evidence • Compact development, mixed uses, and open circulation pattern reduce length of a typical trip • But shorter trips are taken more often, so VMT could rise (or fall). • There is no clear evidence that higher densities will systematically change travel patterns beyond increasing congestion.

18. Mode Choice Evidence • Does compact development or transit-based housing improve ridership? Depends. • Will cities build transit-based housing? Cities see transit stations as economic development anchors and sources of labor, not as a way to get residents to work or shopping elsewhere

19. Completely Open questions • Trip generation of compact development vs. trip length • Impact of higher densities on total travel, mode choice, congestion, and air quality • Impact of transit-based housing or commercial development on transit ridership • Determinants of walking and biking

20. Smart Growth/Traffic Summary • There is no clear evidence that higher densities will systematically change travel patterns beyond increasing congestion. • Shorter trips are taken more often, VMT could rise • Cities see transit stations as economic development anchors and sources of labor, not as a way to get residents to work or shopping elsewhere • The evidence that TOD raises transit use or that the built environment influences travel behavior at the margin is weak to none

21. Example 2:Will Smart Growth shorten commutes?

22. Background • 1. The CW: Increasingly, people are driving hours to work because of sprawl • 2. The evidence: Little regarding whether houses & jobs are growing farther apart -- or which industries/occupations are dispersing most • 3. Leading to the question: Since there are arguments both ways, how does the commute vary with job sprawl?

23. What is Sprawl? • “Smart Growth America” measures sprawl as: • One part density • One part mixed use • One part “centeredness” • One part “accessibility”

24. The Fort Lauderdale & Tucson areas are roughly average in the sum of these measures, with FL relatively high in access and low in centeredness, & Tucson high in mixed uses & centers. Are their traffic outcomes then similar? • (15 VMT/cap vs 20 VMT/cap)

25. %Population & Employment in Suburbs, 1948-1990

26. Commute Times in California, 1990-2000 (Census)

27. Basic Explanations • Monocentric model:suburban residents drive further, but are compensated by lower land rents • Basic extensions: multiple employment centers, commute patterns more complex, both rents & wages compensate

28. Recent Extensions • Wheaton (2002): Firms benefit from both clustering (via agglomeration economies) and from shorter commutes (via lower wages) • ➥ Shorter commutes if jobs decentralize • ➥Consistent with Gordon, Kumar, and Richardson (1989)

29. Crane (1996): If area polycentric, job location may change ➥Residential choice is a gamble • ➥ Rational workers will hedge bets by locating to minimize average expected commute costs • ➥ Implies longer commutes than Wheaton • ➥ Results should vary by occupation (job mobility) & life-cycle (moving costs), as in Wachs, et al. (1993)

30. Empirical Strategy • Specification: Explain individual commute as a function of household demand/supply factors (e.g., resources, dual earners, travel costs, tastes, etc.) • … plus regional employment deconcentration, and individual occupation and life-cycle factors • Estimation issues: Note that wages, land costs, and car access are potentially endogenous

31. Data • American Housing Survey: ~50,000 surveys in most metropolitan areas every two years, current sample in use since 1985. • ➥ We use only urban part for reported SMSAs • ➥ 11,000-15,000 per year, for 64,000+ total observations over 12 years • BEA county employment data by 1 digit SIC

32. Results: Model of Commute Length

33. Industry Detail Results • All these things considered, employment sprawl is associated with shorter (in distance) commutes • However, results differ by industry: • Construction & Wholesale => shorter • Manf & Govt. => longer • Retail & Service => same

34. Commuting Conclusions • No definitive answers. Some support for argument that both firms & workers value shorter commutes & locate accordingly, but results differ by industry • ➥ Need better detail on employment sprawl • Less support for argument that job mobility, with life-cycle effects, lengthens commutes • ➥ Need data on occupation & moving costs

35. Overall Summary • Urban Form and Land Use certainly influence transportation behavior, but we do not have confidence about the details • Smart Growth is a sincere, hopeful effort to provide more consistent and participatory planning, but its specific transportation claims are mainly speculative • Better data and better empirical methods in understanding the interaction of urban design & transportation are on the way. In the meantime, a case by case approach is advised.

36. Questions?