Using propensity score matching technique to address self-selection in transit-oriented development (TOD) areas

  • Arefeh Nasri
  • Carlos Carrion
  • Lei Zhang
  • Babak Baghaei


Many studies have investigated the effects of transit-oriented development (TOD) on travel behavior, especially on transit ridership. However, most studies do not explicitly and effectively address the issue of residential self-selection in their analyses. The aim of this paper is to use cross-sectional data and propensity score matching (PSM) technique to quantify the contribution of residential self-selection to the analysis of mode choice in TOD areas across the metropolitan areas of Washington, D.C. and Baltimore, MD. The authors use PSM because it does not make substantive assumptions to the structure of the self-selection problem (e.g., explicit modeling of outcome and treatment). The results of PSM indicate that, even though the self-selection effect is considerable in the analysis of mode choice in TOD areas (about 7.65% in Washington, D.C. and 5.05% in Baltimore), living in TOD still has a significant impact on encouraging transit and other active modes of transportation.


Propensity score matching Self-selection Transit-oriented development Travel behavior Built environment 



This research is funded partially by the National Transportation Center at the University of Maryland. The authors would like to thank the MWCOG for providing household travel survey data for this analysis. Findings presented in this paper do not necessarily represent the official views of the sponsoring agency. The authors are solely responsible for all statements in the paper.


  1. Arrington, G.B., Cervero, R.: TCRP Report 128: Effects of TOD on Housing, Parking, and Travel, p. 3. Transportation Research Board of the National Academies, Washington, DC (2008)Google Scholar
  2. Austin, P.C.: A comparison of 12 algorithms for matching on the propensity score. Stat. Med. 33(6), 1057–1069 (2014). CrossRefGoogle Scholar
  3. Bagley, M.N., Mokhtarian, P.L.: The impact of residential neighborhood type on travel behavior: a structural equations modeling approach. Ann. Reg. Sci. 36(2), 279–297 (2002)CrossRefGoogle Scholar
  4. Bhat, C.R., Guo, J.Y.: A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transp. Res. Part. B Methodol. 41(5), 506–526 (2007)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(2), 134–140 (2005)CrossRefGoogle Scholar
  6. Boer, R., Zheng, Y., Overton, A., Ridgeway, G.K., Cohen, D.A.: Neighborhood design and walking trips in ten US metropolitan areas. Am. J. Prev. Med. 32(4), 298–304 (2007)CrossRefGoogle Scholar
  7. Calthorpe, P.: The Next American Metropolis: Ecology, Community, and the American Dream. Princeton Architectural Press, Princeton (1993)Google Scholar
  8. Cao, X.J.: Exploring causal effects of neighborhood type on walking behavior using stratification on the propensity score. Environ. Plan. A 42(2), 487–504 (2010)CrossRefGoogle Scholar
  9. Cao, X.J.: 10 The effects of neighbourhood type and self-selection on driving: a case study of Northern California. In: International Handbook on Transport and Development, vol. 149 (2015)Google Scholar
  10. Cao, X., Fan, Y.: Exploring the influences of density on travel behavior using propensity score matching. Environ. Plan B Plan. Des. 39(3), 459–470 (2012)CrossRefGoogle Scholar
  11. Cao, X.J., Schoner, J.: The influence of light rail transit on transit use: an exploration of station area residents along the Hiawatha line in Minneapolis. Transp. Res. Part A Policy Pract. 59, 134–143 (2014)CrossRefGoogle Scholar
  12. 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 (2006a)CrossRefGoogle Scholar
  13. Cao, X., Mokhtarian, P.L., & Handy, S.L.: Impacts of the built environment and residential self-selection on nonwork travel: seemingly unrelated regression approach. In Transportation Research Board 85th Annual Meeting (No. 06-1595) (2006b)Google Scholar
  14. Cao, X., Mokhtarian, P.L., Handy, S.L.: Examining the impacts of residential self-selection on travel behaviour: a focus on empirical findings. Transp. Rev. 29(3), 359–395 (2009)CrossRefGoogle Scholar
  15. Cao, X.J., Xu, Z., Fan, Y.: Exploring the connections among residential location, self-selection, and driving: propensity score matching with multiple treatments. Transp. Res. Part A Policy Pract. 44(10), 797–805 (2010)CrossRefGoogle Scholar
  16. Cervero, R.: Transit-based housing in California: evidence on ridership impacts. Transp. Policy 3, 174–183 (1994)CrossRefGoogle Scholar
  17. Cervero, R.: Transit-oriented development in the United States: experiences, challenges, and prospects, vol. 102, Transportation Research Board (2004)Google Scholar
  18. Cervero, R.: Transit-oriented development’s ridership bonus: a product of self-selection and public policies. Environ. Plan. A 39(9), 2068–2085 (2007)CrossRefGoogle Scholar
  19. Cervero, R.: Effects of TOD on housing, parking, and travel. Transit Cooperative Research Program (TCRP) Report 128 (2008)Google Scholar
  20. Chatman, D.G.: How the built environment influences non-work travel: theoretical and empirical essays. Doctoral Dissertation: University of California, Los Angeles (2005)Google Scholar
  21. Chatman, D.G.: Deconstructing development density: quality, quantity and price effects on household non-work travel. Transp. Res. Part A Policy Pract. 42(7), 1008–1030 (2008)CrossRefGoogle Scholar
  22. Chatman, D.G.: Does TOD need the T? On the importance of factors other than rail access. J. Am. Plan. Assoc. 79(1), 17–31 (2013)CrossRefGoogle Scholar
  23. Chatman, D.G.: Estimating the effect of land use and transportation planning on travel patterns: three problems in controlling for residential self-selection. J. Transp. Land Use 7(3), 47–56 (2014)CrossRefGoogle Scholar
  24. Ewing, R., Cervero, R.: Travel and the built environment: a synthesis. Transp. Res. Rec. J. Transp. Res. Board 1780, 87–114 (2001)CrossRefGoogle Scholar
  25. Ewing, R., Cervero, R.: Travel and the built environment: a meta-analysis. J. Am. Plan. Assoc. 76(3), 265–294 (2010)CrossRefGoogle Scholar
  26. Faghri, A., Venigalla, M.: Measuring travel behavior and transit trip generation characteristics of transit-oriented developments. Transp. Res. Rec. J. Transp. Res. Board 2397, 72–79 (2013)CrossRefGoogle Scholar
  27. Hammond, D.: Residential location and commute mode choice. Doctoral dissertation: University of Wales, Cardiff (2005)Google Scholar
  28. Handy, S.L., Clifton, K.J.: Evaluating neighborhood accessibility: possibilities and practicalities. J. Transp. Stat. 4(2/3), 67–78 (2001)Google Scholar
  29. Handy, S., Cao, X., Mokhtarian, P.: Correlation or causality between the built environment and travel behavior? Evidence from Northern California. Transp. Res. Part D Transp. Environ. 10(6), 427–444 (2005)CrossRefGoogle Scholar
  30. Handy, S., Cao, X., Mokhtarian, P.L.: Self-selection in the relationship between the built environment and walking: empirical evidence from Northern California. J. Am. Plan. Assoc. 72(1), 55–74 (2006)CrossRefGoogle Scholar
  31. Hickman, R., Bonilla, D., Givoni, M., Banister, D.: International Handbook on Transport and Development. Edward Elgar Publishing, Cheltenham (2015)CrossRefGoogle Scholar
  32. Huang, X., Cao, X., Cao, J.: The association between transit access and auto ownership: evidence from Guangzhou, China. Transp. Plan. Technol. 39(3), 269–283 (2016)CrossRefGoogle Scholar
  33. Lee, M.: Micro-econometrics for Policy, Program and Treatment Effects. Oxford University Press, Oxford (2005)CrossRefGoogle Scholar
  34. Lund, H.: Reasons for living in a transit-oriented development, and associated transit use. J. Am. Plan. Assoc. 72(3), 357–366 (2006)CrossRefGoogle Scholar
  35. Lund, H., Willson, R.W., Cervero, R.: A re-evaluation of travel behavior in California TODs. J. Archit. Plan. Res. 1, 247–263 (2006)Google Scholar
  36. Meurs, H., Haaijer, R.: Spatial structure and mobility. Transp. Res. Part D Transp. Environ. 6(6), 429–446 (2001)CrossRefGoogle Scholar
  37. Mokhtarian, P.L., Cao, X.: Examining the impacts of residential self-selection on travel behavior: a focus on methodologies. Transp. Res. Part B Methodol. 42(3), 204–228 (2008)CrossRefGoogle Scholar
  38. Mokhtarian, P.L., van Herick, D.: Quantifying residential self-selection effects: a review of methods and findings from applications of propensity score and sample selection approaches. J. Transp. Land Use 9(1), 9–28 (2016)CrossRefGoogle Scholar
  39. Naess, P.: Tempest in a teapot: the exaggerated problem of transport-related residential self-selection as a source of error in empirical studies. J. Transp. Land Use 7(3), 57–79 (2014)CrossRefGoogle Scholar
  40. Nasri, A., Zhang, L.: The analysis of transit-oriented development (TOD) in Washington, DC and Baltimore metropolitan areas. Transp. Policy 32, 172–179 (2014)CrossRefGoogle Scholar
  41. Nixon, H., Boarnet, M., Houston, D., Spears, S., & Lee, J.: Changes in Transit Use and Service and Associated Changes in Driving Near a New Light Rail Transit Line (No. CA-MTI-1108) (2015)Google Scholar
  42. Oakes, J.M., Johnson, P.J.: Propensity score matching for social epidemiology. Methods Soc. Epidemiol. 1, 370–393 (2006)Google Scholar
  43. Parady, G., Takami, K., Harata, N.: Connection between built environment and travel behavior: propensity score approach under a continuous treatment regime. Transp. Res. Rec. J. Transp. Res. Board 2453, 137–144 (2014)CrossRefGoogle Scholar
  44. Park, K., Ewing, R., Scheer, B.C., & Khan, S.S.A.: Travel behavior in TODs versus non-TODs: using cluster analysis and propensity score matching. Presented at the Transportation Research Board 96th Annual meeting (No. 17-00899) (2017)Google Scholar
  45. Parker, T. et al.: Statewide Transit-Oriented Development Study: Factors for Success in California (Sacramento: California, Department of Transportation, 2002)Google Scholar
  46. Peikes, D.N., Moreno, L., Orzol, S.M.: Propensity score matching: a note of caution for evaluators of social programs. Am. Stat. 62(3), 222–231 (2008)CrossRefGoogle Scholar
  47. Rosenbaum, P.R., Rubin, D.B.: The central role of the propensity score in observational studies for causal effects. Biometrika 70(1), 41–55 (1983)CrossRefGoogle Scholar
  48. Scheiner, J., Holz-Rau, C.: Travel mode choice: affected by objective or subjective determinants? Transportation 34(4), 487–511 (2007)CrossRefGoogle Scholar
  49. Shatu, F.M., Kamruzzaman, M.: Investigating the link between transit-oriented development and sustainable travel behavior in Brisbane: a case-control study. J. Sustain. Dev. 7(4), 61–70 (2014)CrossRefGoogle Scholar
  50. Shen, Q., Chen, P., Pan, H.: Factors affecting car ownership and mode choice in rail transit-supported suburbs of a large Chinese city. Transp. Res. Part A Policy Pract. 94, 31–44 (2016)CrossRefGoogle Scholar
  51. Stevens, M.R.: Does compact development make people drive less? J. Am. Plan. Assoc. 83(1), 7–18 (2017)CrossRefGoogle Scholar
  52. Zamir, K., Nasri, A., Baghaei, B., Mahapatra, S., Zhang, L.: Effects of transit-oriented development on trip generation, distribution, and mode share in Washington, DC, and Baltimore, Maryland. Transp. Res. Rec. J. Transp. Res. Board 2413, 45–53 (2014)CrossRefGoogle Scholar
  53. Zhang, M.: Can transit-oriented development reduce peak-hour congestion? Transp. Res. Rec. J. Transp. Res. Board 2174, 148–155 (2010)CrossRefGoogle Scholar
  54. Zhang, L., Hong, J.H., Nasri, A., Shen, Q.: How built environment affects travel behavior: a comparative analysis of the connections between land use and vehicle miles traveled in US cities. J. Transp. Land Use 5(3), 40–52 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Arefeh Nasri
    • 1
  • Carlos Carrion
    • 1
  • Lei Zhang
    • 1
  • Babak Baghaei
    • 1
  1. 1.Department of Civil and Environmental EngineeringUniversity of MarylandCollege ParkUSA

Personalised recommendations