Advertisement

Transportation

, Volume 34, Issue 5, pp 535–556 | Cite as

Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach

  • Xinyu Cao
  • Patricia L. Mokhtarian
  • Susan L. Handy
Article

Abstract

Suburban sprawl has been widely criticized for its contribution to auto dependence. Numerous studies have found that residents in suburban neighborhoods drive more and walk less than their counterparts in traditional environments. However, most studies confirm only an association between the built environment and travel behavior, and have yet to establish the predominant underlying causal link: whether neighborhood design independently influences travel behavior or whether preferences for travel options affect residential choice. That is, residential self-selection may be at work. A few studies have recently addressed the influence of self-selection. However, our understanding of the causality issue is still immature. To address this issue, this study took into account individuals’ self-selection by employing a quasi-longitudinal design and by controlling for residential preferences and travel attitudes. In particular, using data collected from 547 movers currently living in four traditional neighborhoods and four suburban neighborhoods in Northern California, we developed a structural equations model to investigate the relationships among changes in the built environment, changes in auto ownership, and changes in travel behavior. The results provide some encouragement that land-use policies designed to put residents closer to destinations and provide them with alternative transportation options will actually lead to less driving and more walking.

Keywords

Longitudinal analysis Smart growth Built environment Land use Self-selection 

Notes

Acknowledgments

The data collection was funded by the UC Davis-Caltrans Air Quality Project and analysis was supported by grants from the Robert Wood Johnson Foundation and the University of California Transportation Center. Thanks to Ted Buehler, Gustavo Collantes, and Sam Shelton for their work on the implementation of the survey. Comments from several anonymous referees improved the paper, and conversations with David Ory helped clarify some of the ideas and interpretations presented here.

References

  1. Anderson, T.W., Amemiya, Y.: The asymptotic normal distribution of estimators in factor analysis under general conditions. Ann. Stat. 16(2), 759–771 (1988)Google Scholar
  2. Bagley, M.N., Mokhtarian, P.L.: The impact of residential neighborhood type on travel behavior: a structural equations modeling approach. Ann. Regional Sci. 36, 279–297 (2002)CrossRefGoogle Scholar
  3. Ben-Akiva, M., Atherton, T.J.: Methodology for short-range travel demand predictions: analysis of carpooling incentives. J. Transp. Econ. Policy 11, 224–261 (1977)Google Scholar
  4. Bentler, P.M., Dudgeon, P.: Covariance structure analysis: statistical practice, theory, and directions. Annu. Rev. Psychol. 47, 563–592 (1996)CrossRefGoogle Scholar
  5. Boarnet, M.G., Anderson, C.L., Day, K., McMillan, T., Alfonzo, M.: Evaluation of the California Safe Routes to School legislation: urban form changes and children’s active transportation to school. Am. J. Prev. Med. 28, 134–140 (2005)CrossRefGoogle Scholar
  6. Byrne, B.M.: Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. Lawrence Erlbaum Associates, Inc., Mahwah, New Jersey (2001)Google Scholar
  7. Cao, X.: The causal relationship between the built environment and personal travel choice: evidence from Northern California. PhD Dissertation, Department of Civil and Environmental Engineering, University of California, Davis (2006)Google Scholar
  8. Cao, X., Mokhtarian, P.L., Handy, S.L.: Examining the Impacts of Residential Self-selection on Travel Behavior: Methodologies and Empirical Findings. Research Report UCD-ITS-RR-06-18, Institute of Transportation Studies, University of California, Davis, November. Available via http://pubs.its.ucdavis.edu/download_pdf.php?id=1057 (2006a)Google Scholar
  9. Cao, X., Handy, S.L., Mokhtarian, P.L.: The influences of the built environment and residential self-selection on pedestrian behavior: evidence from Austin, TX. Transportation 33(1), 1–20 (2006b)CrossRefGoogle Scholar
  10. Cao, X., Mokhtarian, P.L., Handy, S.L.: Cross-sectional and quasi-panel explorations of the connection between the built environment and auto ownership. Environ. Plann. A. 39, 830–847 (2007)CrossRefGoogle Scholar
  11. Cervero, R., Duncan, M.: Walking, bicycling, and urban landscapes: evidence from San Francisco Bay Area. Am. J. Public Health 93(9), 1478–1483 (2003)CrossRefGoogle Scholar
  12. Chatman, D.G.: How the built environment influences non-work travel: theoretical and empirical essays. PhD Dissertation, Department of Urban Planning, University of California, Los Angeles (2005)Google Scholar
  13. Crane, R.: The influence of urban form on travel: an interpretive review. J. Plann. Lit. 15(1), 3–23 (2000)CrossRefGoogle Scholar
  14. Crane, R., Crepeau, R.: Does neighborhood design influence travel? A behavioral analysis of travel diary and GIS data. Transport. Res. D. 3(4), 225–238 (1998)CrossRefGoogle Scholar
  15. Ewing, R., Cervero, R.: Travel and the built environment: a synthesis. Transport. Res. Rec. 1780, 87–113 (2001)CrossRefGoogle Scholar
  16. Finkel, S.E.: Causal Analysis with Panel Data. Sage University Paper Series on Quantitative Application in the Social Sciences, 07-105. Thousand Oaks, CA (1995)Google Scholar
  17. Handy, S.L.: Methodologies for exploring the link between urban form and travel behavior. Transport. Res. D. 1(2), 151–165 (1996)CrossRefGoogle Scholar
  18. Handy, S., Cao, X., Mokhtarian, P.: Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transport. Res. D. 10(6), 427–444 (2005)CrossRefGoogle Scholar
  19. Handy, S., Cao, X., Mokhtarian, P.: Self-selection in the relationship between built environment and walking? Evidence from Northern California. J. Am. Plann. Assoc. 72(1), 55–74 (2006)Google Scholar
  20. Handy, S.L., Mokhtarian, P.L., Buehler, T.J., Cao, X.: Residential Location Choice and Travel Behavior: Implications for Air Quality. Davis, CA, University of California, Davis – Caltrans Air Quality Project: 54. Available via http://aqp.engr.ucdavis.edu/Documents/Final_report_editted_updated1.pdf (2004)Google Scholar
  21. Kitamura, R., Mokhtarian, P.L., Laidet, L.: A micro-analysis of land use and travel in five neighborhoods in the San Francisco Bay Area. Transportation 24, 125–158 (1997)CrossRefGoogle Scholar
  22. Krizek, K.: Residential relocation and changes in urban travel: does neighborhood-scale urban form matter? J. Am. Plann. Assoc. 69(3), 265–281 (2003)Google Scholar
  23. Lei, M., Lomax, R.G.: The effect of varying degrees of nonnormality in structural equation modeling. Struct. Equation Model. 12(1), 1–27 (2005)CrossRefGoogle Scholar
  24. MacCallum, R.C., Browne, M.W., Sugawara, H.M.: Power analysis and determination of sample size for covariance structure modeling. Physiol. Methods 1(2), 130–149 (1996)Google Scholar
  25. Meurs, H., Haaijer, R.: Spatial structure and mobility. Transport. Res. D. 6(6), 429–446 (2001)CrossRefGoogle Scholar
  26. Micceri, T.: The unicorn, the normal curve, and other improbable creatures. Psychol. Bull. 105, 156–166 (1989)CrossRefGoogle Scholar
  27. Morrow-Jones, H.A., Irwin, E.G., Roe, B.: Consumer preference for neotraditional neighborhood characteristics. Hous. Policy Debate 15(1), 171–202 (2004)Google Scholar
  28. Mueller, R.O.: Basic Principles of Structural Equation Modeling – An Introduction to LISREL and EQS. Springer-Verlag Inc., New York (1996)Google Scholar
  29. Raykov, T., Marcoulides, G.A.: A First Course in Structural Equation Modeling. Lawrence Erlbaum Associates, Inc., Mahwah, New Jersey (2000)Google Scholar
  30. Schwanen, T., Mokhtarian, P.L.: Does dissonance between desired and current neighborhood type affect individual travel behaviour? An empirical assessment from the San Francisco Bay Area. In: Proceedings of the European Transport Conference (ETC), Strasbourg, France, October 8–10, 2003Google Scholar
  31. Schwanen, T., Mokhtarian, P.L.: What affects commute mode choice: neighborhood physical structure or preferences toward neighborhoods? J. Transport Geogr. 13(1), 83–99 (2005a)CrossRefGoogle Scholar
  32. Schwanen, T., Mokhtarian, P.L.: What if you live in the wrong neighborhood? The impact of residential neighborhood type dissonance on distance traveled. Transport. Res. D. 10(2), 127–151 (2005b)CrossRefGoogle Scholar
  33. Stevens, J.: Applied Multivariate Statistics for Social Sciences. Lawrence Erlbaum Associates, Inc., Mahwah, New Jersey (1996)Google Scholar
  34. Transportation Research Board and Institute of Medicine: Does the Built Environment Influence Physical Activity? Examining the Evidence – Special Report 282. Washington, DC. Available via http://trb.org/publications/sr/sr282.pdf (2005)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Xinyu Cao
    • 1
  • Patricia L. Mokhtarian
    • 2
  • Susan L. Handy
    • 3
  1. 1.Upper Great Plains Transportation InstituteNorth Dakota State UniversityFargoUSA
  2. 2.Department of Civil and Environmental EngineeringUniversity of CaliforniaDavisUSA
  3. 3.Department of Environmental Science and PolicyUniversity of CaliforniaDavisUSA

Personalised recommendations