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

Author

Listed:
  • Kai Jungel

    (TUM School of Management, Technical University of Munich, 80333 Munich, Germany)

  • Axel Parmentier

    (CERMICS, École des Ponts, 77455 Marne-la-Vallée, France)

  • Maximilian Schiffer

    (TUM School of Management, Technical University of Munich, 80333 Munich, Germany; and Munich Data Science Institute, Technical University of Munich, 80333 Munich, Germany)

  • Thibaut Vidal

    (CIRRELT & SCALE-AI Chair in Data-Driven Supply Chains, Department of Mathematics and Industrial Engineering, École Polytechnique de Montréal, Montréal, Quebec H3T 1J4, Canada)

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 test our hybrid pipeline on large-scale real-world scenarios with different vehicle fleet sizes and various request densities. We show that our pipeline outperforms greedy and model-predictive control approaches with respect to various key performance indicators (KPIs), for example, by up to 17.1% and on average by 6.3% in terms of realized profit, and on average by 4.7% in terms of satisfied customers.

Suggested Citation

  • Kai Jungel & Axel Parmentier & Maximilian Schiffer & Thibaut Vidal, 2026. "Learning-Based Online Optimization for Autonomous Mobility-on-Demand Fleet Control," INFORMS Journal on Computing, INFORMS, vol. 38(3), pages 745-765, May.
    • Handle: RePEc:inm:orijoc:v:38:y:2026:i:3:p:745-765
    DOI: 10.1287/ijoc.2024.0637
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