Reinforcement learning for road pricing: a review and future directions

Demand for mobility is growing, and traffic on roads has increased substantially, leading to suboptimal traffic flow and congestion. Road pricing can encourage vehicles to change their behavior by charging for road use. Because traffic is not static, dynamic road pricing can help dynamically control traffic. Reinforcement Learning is an effective approach to optimizing the performance of a system. It has already been applied to control traffic signals and has recently found an application in dynamic road pricing for traffic optimization. We survey recent solutions and find that the methods proposed demonstrate the usefulness of reinforcement learning for road pricing. We compared how common challenges in reinforcement learning were approached in the works. Challenges which remain little explored are generalizability and scalability of solution approaches. Approaches to partial observability, credit assignment and non-stationarity are not in all cases taking full account of existing solutions for these common challenges. We further note the need for standardized benchmarks to allow comparisons between the performance of the provided solutions.