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Autonomous mobility-on-demand systems are a viable alternative to mitigatemany transportation-related externalities in cities, such as rising vehiclevolumes in urban areas and transportation-related pollution. However, thesuccess of these systems heavily depends on efficient and effective fleetcontrol strategies. In this context, we study online control algorithms forautonomous mobility-on-demand systems and develop a novel hybrid combinatorialoptimization enriched machine learning pipeline which learns online dispatchingand rebalancing policies from optimal full-information solutions. We test ourhybrid pipeline on large-scale real-world scenarios with different vehiclefleet sizes and various request densities. We show that our approachoutperforms state-of-the-art greedy, and model-predictive control approacheswith respect to various KPIs, e.g., by up to 17.1% and on average by 6.3% interms of realized profit.

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