Learning Joint Rebalancing and Dynamic Pricing Policies for Autonomous Mobility-on-Demand

Rapid urbanization in the past decades has significantly escalated mobility demand and imposed higher service quality standards. As a promising solution to address this challenge, Autonomous Mobility-on-Demand (AMoD) systems offer tailored mobility services while facilitating centralized control. In this paper, we formulate the joint rebalancing and dynamic pricing problem in AMoD systems as a reinforcement learning problem over graph elements. By proposing a hierarchical policy, we exploit the benefits of both optimization and reinforcement learning methods. Through experiments conducted with real-world data from New York City and San Francisco, we demonstrate that, by leveraging the joint policy, the two control mechanisms can work in tandem to effectively address the limitations inherent to independent dynamic pricing or rebalancing policies under different scenarios. Crucially, we show that our approach is effective when learning from both (1) online interaction with the transportation system and (2) offline from static historical data, thus avoiding the potentially expensive interaction needed for training. The success of our proposed framework in improving system performance under different problem settings underscores its potential as a viable solution for controlling real-world transportation systems.