Drivers' route choice and learning mechanism under bounded information environment

In this paper, we propose the theory of Rational Beliefs as the model of expectations that extends the theory of rational expectations to the postulated environments. Under the rational beliefs paradigm, drivers do not have structural knowledge of traffic conditions and they choose their routes based only on personal experiences and decision-making rules. We found that if drivers have different decision-making rules and experiences, then they form different beliefs of traffic conditions (e.g. average travel time) even though they have the same public information and use the same routes. Under the rational beliefs model, drivers are not motivated to renew their beliefs because the beliefs are compatible with their experiences. Therefore, the heterogeneity of beliefs does not disappear even though they have long-term learning. In order to investigate how drivers form their beliefs of traffic conditions under bounded information environments, numerical experiment is carried out.