用AI智能调度共享单车，大幅减少车辆闲置和调度距离
SmartFlow Reinforcement Learning and Agentic AI for Bike-Sharing Optimisation
- 分层设计：用强化学习学策略，用规则模块优化路线和派车
- 调度后网络失衡降低95%以上，车队利用率高且行驶距离少
- 用大模型生成可执行指令，让算法结果能被工作人员理解执行
SmartFlow是一个多层级框架，融合强化学习与代理型AI，解决城市共享单车的动态调车问题。其架构将战略、战术和通信功能分离，提升清晰度与可扩展性。战略层采用深度Q网络（DQN）在纽约Citi Bike高保真仿真环境中训练，将调车问题建模为马尔可夫决策过程，学习鲁棒调度策略；战术层基于此策略，优化多段行程并安排准时调度，以最小化车队行驶距离。多次随机运行评估显示，SmartFlow使网络失衡降低超过95%，同时实现高卡车利用率和低行驶里程。通信层由基于大语言模型（LLM）的具身代理实现，将物流计划转化为清晰可操作的指令，确保可解释性与执行可行性。该系统实现了机器智能与人工运营的无缝衔接，显著减少空驶时间，提升车辆可用率，降低运营成本，为复杂城市交通网络中的可解释性智能物流提供范例。
原文摘要 · Abstract (English)
SmartFlow is a multi-layered framework that integrates Reinforcement Learning and Agentic AI to address the dynamic rebalancing problem in urban bike-sharing services. Its architecture separates strategic, tactical, and communication functions for clarity and scalability. At the strategic level, a Deep Q-Network (DQN) agent, trained in a high-fidelity simulation of New Yorks Citi Bike network, learns robust rebalancing policies by modelling the challenge as a Markov Decision Process. These high-level strategies feed into a deterministic tactical module that optimises multi-leg journeys and schedules just-in-time dispatches to minimise fleet travel. Evaluation across multiple seeded runs demonstrates SmartFlows high efficacy, reducing network imbalance by over 95% while requiring minimal travel distance and achieving strong truck utilisation. A communication layer, powered by a grounded Agentic AI with a Large Language Model (LLM), translates logistical plans into clear, actionable instructions for operational staff, ensuring interpretability and execution readiness. This integration bridges machine intelligence with human operations, offering a scalable solution that reduces idle time, improves bike availability, and lowers operational costs. SmartFlow provides a blueprint for interpretable, AI-driven logistics in complex urban mobility networks.
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