Non-myopic Matching and Rebalancing in Large-Scale On-Demand Ride-Pooling Systems Using Simulation-Informed Reinforcement Learning

Ride-pooling, also known as ride-sharing, shared ride-hailing, or microtransit, is a service wherein passengers share rides. This service can reduce costs for both passengers and operators and reduce congestion and environmental impacts. A key limitation, however, is its myopic decision-making, which overlooks long-term effects of dispatch decisions. To address this, we propose a simulation-informed reinforcement learning (RL) approach. While RL has been widely studied in the context of ride-hailing systems, its application in ride-pooling systems has been less explored. In this study, we extend the learning and planning framework of Xu et al. (2018) from ride-hailing to ride-pooling by embedding a ride-pooling simulation within the learning mechanism to enable non-myopic decision-making. In addition, we propose a complementary policy for rebalancing idle vehicles. By employing n-step temporal difference learning on simulated experiences, we derive spatiotemporal state values and subsequently evaluate the effectiveness of the non-myopic policy using NYC taxi request data. Results demonstrate that the non-myopic policy for matching can increase the service rate by up to 8.4% versus a myopic policy while reducing both in-vehicle and wait times for passengers. Furthermore, the proposed non-myopic policy can decrease fleet size by over 25% compared to a myopic policy, while maintaining the same level of performance, thereby offering significant cost savings for operators. Incorporating rebalancing operations into the proposed framework cuts wait time by up to 27.3%, in-vehicle time by 12.5%, and raises service rate by 15.1% compared to using the framework for matching decisions alone at the cost of increased vehicle minutes traveled per passenger.