Reinforcement Learning and Mechanism Design for Routing of Connected and Autonomous Vehicles

The data provided by Connected and Autonomous Vehicles (CAVs) is a powerful tool, providing insight into user incentives and preferences, and combined with existing road data sources, provides a number of new research avenues for intelligent traffic systems. In this paper, we propose the use of Reinforcement Learning (RL) for adaptive pricing of travel systems such as trains, buses and tollroad, in simulations which consider multiple transport providers and traffic management systems, known as the multi-market pricing problem. We also propose two research directions for this problem, the use of incentives when user preferences are included and development of detection and prevention of unintentional collusion between RL pricing agents.