T2BR: A Hierarchical Repositioning Approach for Autonomous Mobility on Demand Systems

Abstract

Autonomous mobility-on-demand (AMoD) systems face persistent challenges due to the spatio-temporal mismatch between vehicle supply and passenger demand, which results in low fulfillment rates and inefficient fleet utilization. Existing repositioning strategies primarily follow two paradigms. Region-level approaches direct idle vehicles to high-demand areas using coarse-grained policies but often fail to provide effective guidance within the target region. In contrast, route-level methods offer fine-grained control by generating paths on the road network, yet they frequently lack global planning and overlook broader supply-demand dynamics. To address the limitations of both paradigms, we propose a novel top-to-bottom repositioning (T2BR) framework that hierarchically integrates decision-making at multiple levels. At the regional level, reinforcement learning is employed to optimize inter-regional movements of idle vehicles based on long-term platform objectives. At the route level, Monte Carlo Tree Search is utilized to generate context-aware paths that facilitate efficient passenger pickups within target regions. This hierarchical structure allows for dynamic, adaptive, and spatially coordinated repositioning decisions. Comprehensive evaluations using real-world operational data from Manhattan demonstrate that the proposed T2BR framework significantly improves key performance metrics, including order fulfillment rate, platform revenue, and vehicle utilization, when compared to existing baseline methods. These results highlight the effectiveness of our approach in enhancing the operational efficiency of AMoD systems.