Adaptive Microtolling in Competitive Online Congestion Games via Multiagent Reinforcement Learning

Efficient urban traffic management remains a critical challenge, yet traditional congestion games fail to capture the dynamic and competitive nature of real-world transportation systems. We introduce the Multi-Market Routing Problem (MMRP), an online and oligopolistic extension that models competition amongst route providers utilising adaptive microtolling strategies to influence driver behaviour and mitigate congestion. We formally define the MMRP, highlighting the computational complexity of solving the MMRP, and use an adapted version of Proximal Policy Optimisation (PPO) to improve update stability in multiagent environments to address this problem in online settings. Our empirical analysis demonstrates that our PPO-based approach not only matches the performance of existing benchmarks but also significantly enhances equity, reduces travel times for users, and increases profitability for providers.