Does TOD Need the T?

Abstract

Problem, research strategy, and findings: Transit-oriented developments (TODs) often consist of new housing near rail stations. Channeling urban growth into such developments is intended in part to reduce the climate change, pollution, and congestion caused by driving. But new housing might be expected to attract more affluent households that drive more, and rail access might have smaller effects on auto ownership and use than housing tenure and size, parking availability, and the neighborhood and subregional built environments.

I surveyed households in northern New Jersey living within two miles of 10 rail stations about their housing age and type, access to off-street parking, work and non-work travel patterns, demographics, and reasons for choosing their neighborhoods. The survey data were geocoded and joined to on-street parking data from a field survey, along with neighborhood and subregional built environment measures. I analyzed how these factors were correlated with automobile ownership and use as reported in the survey.

Auto ownership, commuting, and grocery trip frequency were substantially lower among households living in new housing near rail stations compared to those in new households farther away. But rail access does little to explain this fact. Housing type and tenure, local and subregional density, bus service, and particularly off- and on-street parking availability, play a much more important role.

Takeaway for practice: Transportation and land use planners should broaden their efforts to develop dense, mixed-use, low-parking housing beyond rail station areas. This could be both more influential and less expensive than a development policy oriented around rail.

Research support: Data collection and initial research were funded under contract with the New Jersey Department of Transportation.

Keywords:

• transit-oriented development
• rail transit
• auto use
• parking
• sustainability

Acknowledgments

A number of people contributed to the research study upon which this article draws. Project manager Stephanie DiPetrillo designed the physical layout of the paper survey, identified much of the “new TOD” portion of the survey sample, managed the field survey of on-street parking, and helped write a report on the project from which some of the “Study design” section was extracted. Marc Weiner coordinated the household survey, with the able assistance of Orin Puniello. The mailings and data entry were carried out by ABT/SRBI, under the direction of Chintan Turakhia and David Ciemnecki. Marc and Chintan also advised on the questionnaire and sampling design. Dan Tischler coded verbatim occupational responses into standard occupational classifications. Nick Klein carried out the construction of most spatial measures in GIS, with initial work by Nicholas Tulach and Kyeongsu Kim. The grocery store counts were done by Matt Brill. The parking audits, and management of parking data, were carried out by Nick Klein, Lewis Thorwaldson, Katie Thielman, Milan Patel, Rodney Stiles, Liz Thompson, Charu Kukreja, Andrew Besold, Aaron Sugiura, Michael Parenti, and Graydon Newman. Thanks to Mike Manville, Robert Noland, Robert Cervero, and three anonymous reviewers for their very helpful comments on previous drafts.

Notes

1. Housing age was reported by survey respondents and supplemented with information about the year of development for known multifamily projects. Almost 20% of respondents reported that they did not know the age of the unit they were living in or did not answer the question; only 6% of those were in multifamily units known to be new. The remaining units are assumed to be at least eight years old.

2. Alternative methods such as structural equations, nested logit, or two-stage least squares could be used to control for the potential endogeneity of residential location, public transit, population density, parking, or other dependent variables (e.g., Bailey et al., 2008; Cervero & Murakami, 2010; Deka, 2002; Salon, 2009). Such efforts require plausibly exogenous instruments and historical data, which are not present in this dataset, but could be the subject of future research.

3. Multicollinearity generally did not present problems in these data, with the exception of the variable for on-street parking and, in the models restricted to near-station households, the subregional built environment variables. For example, for the 14 models presented here, the variance inflation factor on distance to rail averaged 1.99 with a range of 1.72 to 2.29. When independent variables of interest were statistically insignificant in the presence of variance inflation, I removed other collinear variables to see if significance occurred once variance inflation was reduced. Statistical significance was generally unaffected, except for the spatial variables; as a result the set of spatial variables varies slightly for each of the model sets, except that Models 4 and 5 in each set are kept consistent with Model 2.

4. The carpooling model does a poor job of explaining the likelihood of carpooling; distance to rail is not significant, nor are many of the other built environment variables. I ran other variants of this modal categorization but results were very similar. Detailed results are available upon request.