Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
This addresses fleet control challenges for autonomous mobility services, offering incremental improvements over existing methods.
The paper tackled the problem of efficient fleet control for autonomous mobility-on-demand systems by developing a hybrid combinatorial optimization and machine learning pipeline, which outperformed state-of-the-art methods with up to 17.1% and an average of 6.3% higher realized profit in real-world scenarios.
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 approach outperforms state-of-the-art greedy, and model-predictive control approaches with respect to various KPIs, e.g., by up to 17.1% and on average by 6.3% in terms of realized profit.