Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control

arXiv:2302.03963v2 Announce Type: replace-cross
Abstract: Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution. However, the success of these systems heavily depends on efficient and effective fleet control strategies. In this context, we study online control algorithms for autonomous mobility-on-demand systems and develop a novel hybrid combinatorial optimization enriched machine learning pipeline which learns online dispatching and rebalancing policies from optimal full-information solutions. We …

abstract arxiv autonomous cities context control cs.lg demand math.oc mobility optimization pollution strategies study success systems transportation type urban