使用强化学习在城市环境中联合优化行人与车辆交通
Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
AI总结：
本文提出深度强化学习框架，利用真实需求数据训练单智能体策略，联合优化城市走廊八个信号的行人与车辆效率，并显著降低等待时间且具备跨需求泛化能力。
摘要：
强化学习（RL）对自适应交通信号控制具有显著前景。尽管现有的基于RL的方法在减少车辆拥堵方面表现出有效性，但它们主要聚焦于以车辆为中心的优化，导致行人的出行需求和安全挑战未得到解决。本文提出一种深度RL框架，用于自适应控制一条真实城市走廊沿线的八个交通信号，联合优化行人与车辆的效率。我们的单智能体策略使用来自Wi-Fi日志和视频分析的真实行人与车辆需求数据进行训练。结果表明，相较于传统的固定配时信号，其性能有显著提升，将每名行人和每辆车的平均等待时间分别最多减少67%和52%，同时将两类群体的总等待时间分别最多降低67%和53%。此外，结果还展示了在不同交通需求（包括训练期间完全未见过的状况）下的泛化能力，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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
