Exploring the influence of built environment on tour-based commuter mode choice: A cross-classified multilevel modeling approach

Ding, C., Y. Wang, and C. Liu

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Understanding travel behavior and its relationship to urban form is vital for the sustainable
planning strategies aimed at automobile dependency reduction. The objective of this study
is twofold. First, this research provides additional insights to examine the effects of built
environment factors measured at both home location and workplace on tour-based mode
choice behavior. Second, a cross-classified multilevel probit model using Bayesian
approach is employed to accommodate the spatial context in which individuals make
travel decisions. Using Washington, D.C. as our study area, the home-based work
(Home-work) tour in the AM peak hours is used as the analysis unit. The empirical data
was gathered from the Washington-Baltimore Regional Household Travel Survey
2007–2008. For parameter estimation, Bayesian estimation method integrating Markov
Chain Monte Carlo (MCMC) sampling is adopted. Our findings confirmed the important role
that the built environment at both home location and work ends plays in affecting commuter
mode choice behavior. Meanwhile, a comparison of different model results shows
that the cross-classified multilevel probit model offers significant improvements over the
traditional probit model. The results are expected to give a better understanding on the
relationship between the built environment and commuter mode choice behavior.