A new approach for travel demand modeling: linking Roy's Identity to discrete choice

摘要

The variety of choice alternatives in travel contexts has led to significant simplifications of behavior in models of these complex decisions. Typically, several demand submodels are run independently, producing relatively disconnected estimates of trip generation, destination choice, mode, and time of day. This work relies on nested behavioral models for cost minimization and applications of Roy's Identity to the ensuing comprehensive cost values. The end result is a behaviorally grounded model of travel demand across any number of choice dimensions. These are subject to a general budget constraint based on time and money limitations. Unlike existing models, the model produces rigorous welfare measures recognizing all aspects of travel choice. For purposes of illustration, the model was calibrated using Austin, TX travel-diary data and a modified-translog indirect utility specification. Results indicate that Austinites are less flexible about mode choice than destination choice for non-work trips and that the elasticity of trip generation with respect to travel times and costs is very low. In addition, welfare analyses using equivalent variation measures were performed under various network and policy scenarios, including congestion pricing. These strictly accommodate the welfare impacts of network and land use changes on trip-generation and other travel choices; the resulting estimates suggest flexibility in trip-making.