用强化学习动态调度共享自动驾驶接驳车，提升低密度区通达性。
Semi-on-Demand Transit Feeders with Shared Autonomous Vehicles and Reinforcement-Learning-Based Zonal Dispatching Control
- 基于强化学习的分区调度，实时响应需求变化
- 服务16%更多乘客，平均成本高13%
- 适合研究智能公交与最后一公里接驳的学者
本文提出一种半按需运行的共享自动驾驶车辆（SAV）接驳服务，结合固定线路与按需响应优势，提升低密度区域可达性。车辆从枢纽出发后先执行固定停靠，再在预设灵活路线区域内提供按需接送。采用深度强化学习模型，通过近端策略优化算法动态分配车辆至细分区域，应对实时需求波动。基于德国慕尼黑真实公交线路的仿真结果显示，经过有效训练后，该服务相比传统固定线路服务可多服务16%的乘客，平均综合成本提高13%；其中强化学习带来的效率提升使乘客数增加2.4%，成本仅上升1.4%。研究展示了将SAV接驳与机器学习技术融合于公共交通的潜力，为多模式交通系统中的首末段难题提供了新思路。
原文摘要 · Abstract (English)
This paper develops a semi-on-demand transit feeder service using shared autonomous vehicles (SAVs) and zonal dispatching control based on reinforcement learning (RL). This service combines the cost-effectiveness of fixed-route transit with the adaptability of demand-responsive transport to improve accessibility in lower-density areas. Departing from the terminus, SAVs first make scheduled fixed stops, then offer on-demand pick-ups and drop-offs in a pre-determined flexible-route area. Our deep RL model dynamically assigns vehicles to subdivided flexible-route zones in response to real-time demand fluctuations and operations, using a policy gradient algorithm - Proximal Policy Optimization. The methodology is demonstrated through agent-based simulations on a real-world bus route in Munich, Germany. Results show that after efficient training of the RL model, the semi-on-demand service with dynamic zonal control serves 16% more passengers at 13% higher generalized costs on average compared to traditional fixed-route service. The efficiency gain brought by RL control brings 2.4% more passengers at 1.4% higher costs. This study not only showcases the potential of integrating SAV feeders and machine learning techniques into public transit, but also sets the groundwork for further innovations in addressing first-mile-last-mile problems in multimodal transit systems.
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