用强化学习同时优化行人与车辆通行效率，大幅减少等待时间。
Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
- 单智能体强化学习框架，联合优化8个路口信号灯。
- 行人平均等待时间减少67%，车辆减少52%，总等待时间降幅超50%。
- 在未见交通条件下仍表现稳定，适合城市智慧交通系统部署。
强化学习（RL）在自适应交通信号控制方面具有巨大潜力。现有基于RL的方法虽能有效缓解车辆拥堵，但普遍以车辆为中心，忽视行人通行需求与安全问题。本文提出一种深度强化学习框架，用于真实城市路段上8个交通信号灯的自适应控制，同时优化行人与车辆的通行效率。采用来自Wi-Fi日志和视频分析的真实行人与车辆需求数据训练单智能体策略。结果表明，相比传统定时信号，本方法可使行人平均等待时间减少最高67%，车辆减少52%，两类用户总等待时间分别降低最多67%与53%。此外，模型在训练中未见的交通需求下仍具泛化能力，验证了强化学习在服务所有道路使用者方面的应用潜力。
原文摘要 · Abstract (English)
Reinforcement learning (RL) holds significant promise for adaptive traffic signal control. While existing RL-based methods demonstrate effectiveness in reducing vehicular congestion, their predominant focus on vehicle-centric optimization leaves pedestrian mobility needs and safety challenges unaddressed. In this paper, we present a deep RL framework for adaptive control of eight traffic signals along a real-world urban corridor, jointly optimizing both pedestrian and vehicular efficiency. Our single-agent policy is trained using real-world pedestrian and vehicle demand data derived from Wi-Fi logs and video analysis. The results demonstrate significant performance improvements over traditional fixed-time signals, reducing average wait times per pedestrian and per vehicle by up to 67% and 52% respectively, while simultaneously decreasing total wait times for both groups by up to 67% and 53%. Additionally, our results demonstrate generalization capabilities across varying traffic demands, including conditions entirely unseen during training, validating RL's potential for developing transportation systems that serve all road users.
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