A reinforcement learning approach for distance-based dynamic tolling in the stochastic network environment

This paper proposes a novel dynamic tolling model based on distance and accounts for uncertain traffic demand and supply conditions. The distance-based tolling controller is modeled as an intelligent agent interacting within the stochastic network environment dynamically by taking actions, which are to decide different distance-based tolling rates of vehicles. The distance-based tolls are determined according to various metrics, for example, total traffic flow throughput, delay time, vehicular emissions, which are set as objectives in the modeling framework. The optimal tolling rate is determined by an R-Markov Average Reward Technique based reinforcement learning algorithm. In the numerical case study, we test the proposed tolling scheme on a benchmark test network - the Sioux Falls network - where specified links are candidate toll links. The result shows that the total travel time of tolling links reduces by 25% over simulation runs.