A Priori Lane Selection Strategy for Reinforcement Learning of Dynamic Expressway Tolling

Abstract

Dynamic tolling of toll roads is a way to dynamically adjust the toll rates according to the changing road traffic conditions in order to alleviate traffic congestion and improve commuting efficiency. Aiming at the dynamic toll collection problem of Chinese expressway, we design a reinforcement learning simulation environment for China’s expressway network and propose a reinforcement learning dynamic toll model based on a priori lane selection strategy that adapts to the characteristics of the network and travelers’ travel habits. Experiments show that the reinforcement learning-based dynamic tolling can increase the total revenue by more than 10% compared with the fixed- rate tolling scheme and keep the congestion rate at a low level. In addition, the ablation experiments demonstrate that the priori knowledge-based lane selection model can better weigh the "total revenue", "system throughput" and "total system travel time" of the optimized road network under the joint optimization objective