Modelling of route choice behaviours of car-drivers under imperfect information

摘要

The conventional models for describing car-drivers' route choice behaviours in traffic networks have treated the decision-makers as a non-atomic quantity who are homogeneous in the preference function and always take rational behaviours. Those behavioural assumptions require that each driver knows the minimum-cost route in spite of the deterministic or probabilistic. The action hypothesis in a route choice is not realistic and is insufficient to analyze the influence that traffic information gives over action choice. In this study, we treat each driver as a discrete decision-maker and assume as a heterogeneous agent with bounded rationality. Each agent does not know the minimum-cost route on the network, and only knows the route information that he or she has experienced. This assumption motivates us to propose a behavioural model in which regret-matching is combined with reinforcement learning. We show that even in such a situation there exist adaptive learning rules that lead drivers to rational choices in the long run.