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Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach
March 21, 2024, 4:43 a.m. | Jinhua Si, Fang He, Xi Lin, Xindi Tang
cs.LG updates on arXiv.org arxiv.org
Abstract: The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive enhancements. Nevertheless, its online operations suffer the inherent complexities due to the coupling of vehicle resource allocation among cities and pooled-ride vehicle routing. To tackle these challenges, this study proposes a two-level framework designed to facilitate online fleet management. Specifically, a novel multi-agent feudal …
abstract agent arxiv city complexities cs.ai cs.lg cs.sy demand development eess.sy hierarchical multi-agent operations pooling reinforcement reinforcement learning responsive routing service services travel type
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