Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach

arXiv:2307.06742v2 Announce Type: replace-cross
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