用大模型实时调整共享电单车调度策略，应对突发需求
Bridging Policy and Real-World Dynamics: LLM-Augmented Rebalancing for Shared Micromobility Systems
- 结合基础调度与大模型自反思机制，动态优化策略
- 在芝加哥真实数据上提升需求满足率和系统收益
- 适合需要快速响应突发事件的智慧交通系统
共享微出行服务如电动滑板车和自行车已成为城市交通的重要组成部分，但其效率高度依赖于车辆的有效再平衡。现有方法或基于平均需求模式优化，或采用鲁棒优化与强化学习处理预设不确定性，却忽视了突发事件（如需求激增、车辆故障、政策干预）的影响，或牺牲常态下的表现。本文提出AMPLIFY框架，通过大语言模型增强的策略自适应机制，实现对突发场景的实时响应。该框架由基础再平衡模块与基于大模型的自适应模块构成，后者根据系统上下文、需求预测和基础策略进行自我反思式调整。在芝加哥真实电单车数据上的评估表明，该方法显著提升了需求满足率与系统收入，验证了大模型驱动自适应在应对微出行系统不确定性中的潜力。
原文摘要 · Abstract (English)
Shared micromobility services such as e-scooters and bikes have become an integral part of urban transportation, yet their efficiency critically depends on effective vehicle rebalancing. Existing methods either optimize for average demand patterns or employ robust optimization and reinforcement learning to handle predefined uncertainties. However, these approaches overlook emergent events (e.g., demand surges, vehicle outages, regulatory interventions) or sacrifice performance in normal conditions. We introduce AMPLIFY, an LLM-augmented policy adaptation framework for shared micromobility rebalancing. The framework combines a baseline rebalancing module with an LLM-based adaptation module that adjusts strategies in real time under emergent scenarios. The adaptation module ingests system context, demand predictions, and baseline strategies, and refines adjustments through self-reflection. Evaluations on real-world e-scooter data from Chicago show that our approach improves demand satisfaction and system revenue compared to baseline policies, highlighting the potential of LLM-driven adaptation as a flexible solution for managing uncertainty in micromobility systems.
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