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The objective of travel distance models is to better understand travel behavior so that policies can be implemented for reducing travel and with that the externalities of transport such as air pollution, congestion, and crashes. Hierarchical Bayesian models offer a flexible framework to analyze travel behavior by allowing the study of short term decisions of the activity and travel choices as well as long term decisions of residential and employment location. Since travel distance is censored at zero for a significant fraction of the observations, parameter estimates obtained by conventional regression methods are biased. Consistent parameter estimates can be obtained by using the Tobit model. The purpose of this paper is to demonstrate the application of fully Bayesian Tobit hierarchical models to the analysis of travel distance, aiming to accommodate the multilevel and censored nature of the data. Results show that the hierarchical Tobit Model performs significantly better than the non-hierarchical model as measured by the Deviance and Deviance Information Criteria. Further, the highly significant variance at the individual and location levels, demonstrates the importance of using a multilevel approach. The distance traveled increases with years of study and job qualification. In addition, all the members of the household travel less than the householder and women travel less than men. Industry sectors also show significant differences in travel time: workers in the secondary and tertiary sectors travel farther than workers in the primary sector. Land price is significantly correlated with distance traveled in both residence and employment locations. [ABSTRACT FROM AUTHOR]