A limited dependent variable structural equations model of travel behavior for transportation policy evaluation

An exploratory analysis of commuters' activity and travel patterns was carried out using activity-based travel survey data collected in the Washington, D.C. metropolitan area. It was conjectured that a deeper understanding of reasons behind commuter travel could lead to the development of robust structural equations model systems that are applicable to predict commuters' responses to hypothetical pricing scenarios. An in-depth descriptive analysis was performed to determine the extent to which activity and travel behavior differ across different socio-demographic factors. Structural equations modeling methodology was adopted to determine the structural relationships among commuters' demographics, activity patterns, trip generation, and trip chaining information.;Three types of model systems were first estimated: one that models relationships between travel and activity participation, another that captures trade-offs between in-home and out-of-home activity durations, and a third one that models the generation of complex work trip chains. The maximum likelihood method of estimation was employed to obtain consistent and efficient parameter estimates. The models performed well with respect to various goodness-of-fit measures despite the relatively small sample of 656 commuters in the data set.;The model estimation results show that strong relationships do exist among commuters' socio-demographics, activity participation, and travel behavior. The finding that significant trade-offs exist between in-home and out-of-home activity participation is noteworthy in the context of in-home vs. out-of-home substitution effects. These findings were found to be very useful in addressing issues of induced travel demand.;The activity-based framework was further extended to the modeling of commuter responses to hypothetical pricing scenarios. These approaches were found to offer powerful analytical tools for policy evaluation that cannot be produced with conventional travel demand models. It was found that trip chaining patterns of individuals play an important role in determining their behavioral adjustments in the wake of hypothetical value pricing scenarios. The incorporation of activity engagement behavior of commuters together with socio-demographics was found to be critical in analyzing stated responses to pricing-based TCMs.