Evolution of Day-to-Day Route Choice Behaviors under Different Memory-Based Learning Strategies

Owing to the uncertainty of the traffic system and incomplete travel information, travelers usually make route-choice decisions relying on their own experience. In this paper, we assume that commuters make their route-choice decisions based on the perceived cost in a logit-based manner, and different memory-based learning strategies on previous travel time, such as smoothed adaptive pattern and peak-end adaptive pattern (anchoring on highest travel time or lowest travel time), are proposed to obtain the perceived cost. A numerical example is also given for comparing the impact of different learning strategies on flow evolution and further illustrating the model. The results show that the peak-end adaptive pattern could capture the commuters’ risk attitude in the route choice process and thus provide a more actual traffic flow, which is obviously helpful to traffic control.