中文标题#
基於強化學習的城市行人和車輛交通優化
英文标题#
Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
中文摘要#
強化學習（RL）在自適應交通信號控制方面具有重要的前景。 雖然現有的基於 RL 的方法在減少車輛擁堵方面表現出有效性，但它們主要關注以車輛為中心的優化，忽視了行人通行需求和安全挑戰。 在本文中，我們提出了一種深度 RL 框架，用於對現實世界城市走廊中的八個交通信號進行自適應控制，同時優化行人和車輛的效率。 我們的單智能體策略使用從 Wi-Fi 日誌和視頻分析中得出的真實世界行人和車輛需求數據進行訓練。 結果表明，與傳統的固定時間信號相比，性能有顯著提升，行人和車輛的平均等待時間分別減少了最多 67% 和 52%，同時兩者總等待時間也分別減少了最多 67% 和 53%。 此外，我們的結果展示了在不同交通需求下的泛化能力，包括訓練期間完全未見過的情況，驗證了 RL 在開發服務所有道路使用者的交通系統方面的潛力。
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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