A study of route selection behaviour based on Equate-to-Differentiate theory under the influence of preference reversal

Abstract:
The route preference reversal phenomenon will lead to inaccurate prediction of route selection considering preferences, which in turn leads to imbalance in the distribution of traffic flows on the road network considering path preferences. To deal with this problem, based on the Equate-to-Differentiate theory, to describe the dynamic change pattern of travelers’s preferences by combining the experience-weighted attractiveness (EWA) learning model and the cumulative reinforcement learning model, then, constructing a model of route selection behavior considering preference reversal. On this basis, Comparison and analysis of the evolution of network traffic flow with considering route preference reversal or not. The algorithm validation shows that: In considering the travelers route preference reversal, road network traffic flow is more balanced under the dynamic update of both preferences.