Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning

arXiv:2311.05780v2 Announce Type: replace-cross
Abstract: Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available vehicles to ride requests, rebalancing idle vehicles to areas of high demand, and charging vehicles to ensure sufficient range. While this problem can be posed as a linear program that optimizes flows over a space-charge-time graph, the size of the resulting optimization problem does not allow for real-time implementation in realistic settings. In this work, we present the …

abstract arxiv autonomous charging control cs.lg cs.ro cs.sy decisions demand eess.sy electric graph mobility operators real-time reinforcement reinforcement learning systems type vehicles via