Framework for Travel Time Learning and Behavioral Adaptation in Route and Departure Time Choice

Abstract

Understanding the dynamics of individual travel choice behavior is both a considerable research challenge and of considerable practical importance. Of particular interest are the day-to-day dynamics of commuters’ route and departure time choice decisions, since these processes influence peak-period conditions. Two key mechanisms underlying these behavioral dynamics are the process by which commuters update their perceptions of network travel times (which will be referred to as learning) and the process by which they adjust their behavior as a result of this learning. A new theoretical framework for modeling the day-to-day dynamics of drivers’ route and departure time choice decisions in a commuting context is presented as well as estimation results based on data collected in a series of web-based experiments involving travelers’ repeated decisions in hypothetical transport networks over a period of 15 simulated days. The estimation results indicate that triggering, updating, and adaptation are indeed distinct processes and that the proposed framework constitutes a fruitful direction for future research.