Advertisement

Transportation

, Volume 34, Issue 5, pp 575–588 | Cite as

The impact of urban form on automobile travel: disentangling causation from correlation

  • Colin Vance
  • Ralf Hedel
Article

Abstract

A longstanding question within the field of transportation demand management is the strength of the relationship between urban form and mobility behavior. Although several studies have identified a strong correlation between these variables, there is as yet scant evidence to support policy interventions that target land use as a means of influencing travel. To the contrary, some of the more recent research has cast skepticism on the proposition that the relationship is causative, recognizing the possibility that households endogenously self-select themselves into communities that support their preferences for particular transportation modes. Focusing on individual automobile travel, the present study seeks to contribute to this line of inquiry by estimating econometric models on a panel of travel-diary data collected in Germany between 1996 and 2003. Specifically, we employ the two-part model (2PM)—a procedure involving probit and OLS estimators—to assess the determinants of the discrete decision to use the car and the continuous decision of distance traveled. Beyond modeling variables that capture the urban form features that are commonly suggested to influence mobility behavior, including mixed use and public transit, this study employs instrumental variables to control for potential endogeneity emerging from the simultaneity of residential and mode choices. Unlike much of the work to date, our results suggest that urban form has a causative impact on car use, a finding that is robust to alternative econometric specifications.

Keywords

Individual travel behavior Instrumental variables Urban form Two-part model 

Notes

Acknowledgments

We wish to thank Elmar Brockfeld for assistance in creating the GIS measures of road density. We would also like to express our gratitude to Manuel Frondel and to three anonymous reviewers for their comments on an earlier draft.

References

  1. Angrist, J.D., Krueger, A.B.: Split-sample instrumental variables estimates of the return to schooling. J. Bus. Econ. Stat. 13(2), 225–235 (1995)CrossRefGoogle Scholar
  2. BBR (Bundesministerium für Raumordnung, Bauwesen und Städtebau): Raumordnungspolitischer Orientierungsrahmen. Leitbild für die räumliche Entwicklung der Bundesrepublik Deutschland, Berlin (1993)Google Scholar
  3. Bednarz, R., Ralston, B.: The importance of scale in measuring the secondary effects in regression analysis. Prof. Geogr. 34, 424–431 (1982)CrossRefGoogle Scholar
  4. Bento, A.M., Cropper, M.L., Mobarak, A.M., Vinha, K.: The impact of urban spatial structure on travel demand in the United States. Rev. Econ. Stat. 87(3), 466–478 (2005)CrossRefGoogle Scholar
  5. Boarnet, M.G., Sarmiento, S.: Can land-use policy really affect travel behavior? A study of the link between non-work travel and land-use characteristics. Urban Stud. 35(7), 1155–1169 (1998)CrossRefGoogle Scholar
  6. Bryk, A.S., Raudenbush, S.W.: Hierarchical Linear Models. Sage, Newbury Park, CA (1992)Google Scholar
  7. Cervero, R., Kockelman, K.: Travel demand and the 3Ds: density, diversity, and design. Transp. Res. Part D 2(3), 199–219 (1997)CrossRefGoogle Scholar
  8. Crane, R., Crepeau, R.: Does neighborhood design influence travel? A behavioral analysis of travel diary and GIS data. Transp. Res. Part D 3(4), 225–238 (1998)CrossRefGoogle Scholar
  9. Dee, T.: Are there civic returns to education? J. Public Econ. 88(9), 1697–1720 (2004)CrossRefGoogle Scholar
  10. Dow, W.H., Norton, E.C.: Choosing between and interpreting the Heckit and two-part models for corner solutions. Health Serv. Outcomes Res. Methodol. 4(1), 5–18 (2003)CrossRefGoogle Scholar
  11. Dresden (Landeshauptstadt Dresden).: Integriertes Stadtentwicklungskonzept. Stadtplanungsamt der Landeshauptstadt Dresden, Dresden (2002)Google Scholar
  12. Duan, N., Manning, W., Morris, C., Newhouse, J.: Choosing between the sample-selection model and the multi-part model. J. Bus. Econ. Stat. 2, 283–289 (1984)CrossRefGoogle Scholar
  13. Friedman, B., Gordon, S.P., Peers, J.B.: Effect of neotraditional design on travel characteristics. Transp. Res. Rec. 1466, 63–70 (1994)Google Scholar
  14. Hall, A.R., Rudebusch, G.D., Wilcox, D.W.: Judging instrument relevance in instrumental variables estimation. Int. Econ. Rev. 37(2), 283–298 (1996)CrossRefGoogle Scholar
  15. Handy, S.: Methodologies for exploring the link between urban form and travel behavior. Transp. Res. Part A 1(2), 151–165 (1996)CrossRefGoogle Scholar
  16. Hautzinger, H., Stock, W.: Wer Fährt Eigentlich Wie Viel? Volumen und Struktur der Fahrleistung Deutscher Kraftfahrzeuge 1993/2002. Int. Verkehrswesen 57, 368–373 (2005)Google Scholar
  17. Hay, J., Olsen, R.: Let them eat cake: a note on comparing alternative models of the demand for medical care. J. Bus. Econ. Stat. 2, 279–282 (1984)CrossRefGoogle Scholar
  18. Hesse, M., Trostorff, B.: Raumstrukturen, Siedlungsentwicklung und Verkehr—Interaktionen und Integrationsmöglichkeiten. Working paper, IRS Erkner. http://www.irs-net.de/download/berichte_5.pdf. Cited 14 Nov 2006 (2000)
  19. Holtzclaw, J.: Explaining Urban Density and Transit Impacts on Auto Use. Report to Natural Resources Defense Council. Sierra Club, San Francisco (1990)Google Scholar
  20. Khattak, A.J., Rodriguez, D.: Travel behavior in neo-traditional neighborhood developments: a case study in USA. Transp. Res. Part A 39(6), 481–500 (2005)Google Scholar
  21. Krizek, K.J.: Neighborhood services, trip purpose, and tour-based travel. Transportation 30(4), 387–410 (2003)CrossRefGoogle Scholar
  22. Lassen, D.: The effect of information on voter turnout: evidence from a natural experiment. Am. J. Polit. Sci. 49(1), 103–118 (2005)Google Scholar
  23. Lueng, S.F., Yu, S.: On the choice between sample selection and two-part models. J. Econom. 72, 197–229 (1996)CrossRefGoogle Scholar
  24. Maas, C.J.M., Hox, J.J.: Robustness issues in multilevel regression analysis. Stat. Neerl. 58, 127–137 (2004)CrossRefGoogle Scholar
  25. Miguel, E., Satyanath, S., Sergenti, E.: Economic shocks and civil conflic: an instrumental variables approach. J. Polit. Econ. 112, 725–753 (2004)CrossRefGoogle Scholar
  26. Milligan, K., Moretti, E.N., Oreopoulos, P.: Does education improve citizenship? Evidence from the US and the UK. J. Public Econ. 88(9–10), 1667–1695 (2004)CrossRefGoogle Scholar
  27. MOP (The German Mobility Panel homepage).: German Federal Ministry of Transport, Building and Housing, Berlin. http://mobilitaetspanel.ifv.uni-karlsruhe.de/ENGLISH/MOP_Frameset.htm. Cited 14 Nov 2006 (2006)Google Scholar
  28. Newey, W.: Efficient estimation of limited dependent variable models with endogenous explanatory variables. J. Econom. 36(3), 231–250 (1987)CrossRefGoogle Scholar
  29. Newman P., Kenworthy J.: Cities and Automobile Dependence: An International Sourcebook. Gower Publishing, Aldershot (1989)Google Scholar
  30. Shannon C.E., Weaver W.: The Mathematical Theory of Communication. Urbana, University of Illinois Press (1949)Google Scholar
  31. Siedentop, S., Stein, S., Wolf, U., Lanzendorf, M., Hesse, M.: Mobilität im suburbanen Raum. Neue verkehrliche und raumordnerische Implikationen des räumlichen Strukturwandels, Dresden/Berlin/Leipzig (2005)Google Scholar
  32. Stock, J.H., Wright, J.H., Yogo, M.: A survey of weak instruments and weak identification in generalized method of moments. J. Bus. Econ. Stat. 20, 518–529 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI Essen)EssenGermany
  2. 2.Institute for Transport ResearchGerman Aerospace Center (DLR e.V.)BerlinGermany

Personalised recommendations