Exploring the influence of built environment on tour-based commuter mode choice: A cross-classified multilevel modeling approach
Authors: Ding, C., Y. Wang, and C. Liu
Report
Synopsis: Understanding travel behavior and its relationship to urban form is vital for the sustainableplanning strategies aimed at automobile dependency reduction. The objective of this studyis twofold. First, this research provides additional insights to examine the effects of builtenvironment factors measured at both home location and workplace on tour-based modechoice behavior. Second, a cross-classified multilevel probit model using Bayesianapproach is employed to accommodate the spatial context in which individuals maketravel 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 datawas gathered from the Washington-Baltimore Regional Household Travel Survey2007–2008. For parameter estimation, Bayesian estimation method integrating MarkovChain Monte Carlo (MCMC) sampling is adopted. Our findings confirmed the important rolethat the built environment at both home location and work ends plays in affecting commutermode choice behavior. Meanwhile, a comparison of different model results showsthat the cross-classified multilevel probit model offers significant improvements over thetraditional probit model. The results are expected to give a better understanding on therelationship between the built environment and commuter mode choice behavior.
Authors: Ding, C., Y. Wang, and C. Liu
Report
Synopsis: Understanding travel behavior and its relationship to urban form is vital for the sustainableplanning strategies aimed at automobile dependency reduction. The objective of this studyis twofold. First, this research provides additional insights to examine the effects of builtenvironment factors measured at both home location and workplace on tour-based modechoice behavior. Second, a cross-classified multilevel probit model using Bayesianapproach is employed to accommodate the spatial context in which individuals maketravel 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 datawas gathered from the Washington-Baltimore Regional Household Travel Survey2007–2008. For parameter estimation, Bayesian estimation method integrating MarkovChain Monte Carlo (MCMC) sampling is adopted. Our findings confirmed the important rolethat the built environment at both home location and work ends plays in affecting commutermode choice behavior. Meanwhile, a comparison of different model results showsthat the cross-classified multilevel probit model offers significant improvements over thetraditional probit model. The results are expected to give a better understanding on therelationship between the built environment and commuter mode choice behavior.