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

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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.

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Notes

  1. Our data were collected at one point in time, but respondents were asked to retrospectively report a number of characteristics for a previous point in time as well as for the current time. So the data are different from longitudinal data, which are typically measured at two time points.

  2. Total effects include direct effects and indirect effects. For example, if the variable X influences the variable Y2 without any mediating variables, this influence (X → Y2) represents a direct effect from X to Y2; by contrast, if X influences Y2 through Y1, this influence (X → Y1 → Y2) represents an indirect effect from X to Y2. Standardized effects, as in single-equation regression, are based on the coefficients of a model in which all variables have been standardized to remove scale dependence.

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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.

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Authors and Affiliations

  1. Upper Great Plains Transportation Institute, North Dakota State University, P.O. Box 5074, Fargo, ND, 58105, USA

    Xinyu Cao

  2. Department of Civil and Environmental Engineering, University of California, Davis, CA, 95616, USA

    Patricia L. Mokhtarian

  3. Department of Environmental Science and Policy, University of California, Davis, CA, 95616, USA

    Susan L. Handy

Authors
  1. Xinyu Cao
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  2. Patricia L. Mokhtarian
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  3. Susan L. Handy
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Correspondence to Xinyu Cao.

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Cao, X., Mokhtarian, P.L. & Handy, S.L. Do changes in neighborhood characteristics lead to changes in travel behavior? A structural equations modeling approach. Transportation 34, 535–556 (2007). https://doi.org/10.1007/s11116-007-9132-x

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