Graduation Year


Document Type




Degree Granting Department

Civil Engineering

Major Professor

Ram M. Pendyala, Ph.D.

Committee Member

Jian J. Lu, Ph.D., P.E.

Committee Member

Edward Mierzejewski, Ph. D., P.E.


travel behavior, travel demand models, causal relationship, simultaneous equation system, econometric models


Modeling travel demand by time of day is gaining increasing attention in travel demand forecasting practice. This is because time of day choice has important implications for mode choice and for quantifying potential modal and time of day shifts in response to traffic congestion and peak period travel demand management strategies. In this context, understanding the causal relationship between time of day (departure time) choice and mode choice behavior would be useful in the development of time of day based travel demand modeling systems both within the four-step modeling paradigm and within newer tour-based and activity-based microsimulation paradigms. This thesis investigates the relationship between departure time choice and mode choice for non-work trips as work trips tend to be constrained with respect to time of day choice. Two alternative causal structures are considered in this thesis: one structure in which departure time choice is determined first and mode choice is subsequently influenced by departure time choice and a second structure in which mode choice is determined first and affects departure time choice. These two causal structures are analyzed in a recursive bivariate probit modeling framework that allows random error covariance. The estimation is performed separately for worker and non-worker samples drawn from the 1999 Southeast Florida Regional Household Travel Survey. For workers, model estimation results show that the causal structure in which departure time choice precedes mode choice performs significantly better. For non-workers, the reverse causal relationship in which mode choice precedes departure time choice is found to be a more suitable joint modeling structure. These two findings can be reasonably explained from a travel behavior perspective and have important implications for advanced travel demand model development and application.