中文标题#
基于强化学习的城市行人和车辆交通优化
英文标题#
Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
中文摘要#
强化学习（RL）在自适应交通信号控制方面具有重要的前景。 虽然现有的基于 RL 的方法在减少车辆拥堵方面表现出有效性，但它们主要关注以车辆为中心的优化，忽视了行人通行需求和安全挑战。 在本文中，我们提出了一种深度 RL 框架，用于对现实世界城市走廊中的八个交通信号进行自适应控制，同时优化行人和车辆的效率。 我们的单智能体策略使用从 Wi-Fi 日志和视频分析中得出的真实世界行人和车辆需求数据进行训练。 结果表明，与传统的固定时间信号相比，性能有显著提升，行人和车辆的平均等待时间分别减少了最多 67% 和 52%，同时两者总等待时间也分别减少了最多 67% 和 53%。 此外，我们的结果展示了在不同交通需求下的泛化能力，包括训练期间完全未见过的情况，验证了 RL 在开发服务所有道路使用者的交通系统方面的潜力。
英文摘要#
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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