Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
arxiv(2023)
摘要
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
terms of realized profit.
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关键词
online optimization,learning-based,mobility-on-demand
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