Modeling Learning in Route Choice

Abstract

Performing the same trip many times, travelers can learn about available routes from their experiences. Two types of learning found in psychological learning theory appear to play a role in day-to-day route choice: implicit (reinforcement-based) and explicit (belief-based). Memory decay also plays a major role. Although much progress had been made in modeling learning in route choice, a model that captures both learning types and for which the parameters are empirically underpinned was not found. Such a model thus is developed, and a large data set from experimental research is used to validate it and to estimate its parameters. The developed model uses a Markov formulation for the day-to-day updating of a person's belief about travel time (i.e., perceived travel time) on a route. Reinforcement (and inertia) is modeled by including the latest 10 route choices in the model. Results indicate that 20% of perceived travel time is from the most recent experience; therefore, formulations that use either the mathematical mean of all past experienced travel times or only the most recent travel times are not accurate. Furthermore, the reinforcement–inertia part of the model can make up a significant part of the route utility and therefore should be a standard component in route choice models. In sum, the results validate the theoretical and mathematical model.