用深度强化学习让红绿灯兼顾车流与行人公平性，缓解拥堵。
Balancing Efficiency and Fairness in Traffic Light Control through Deep Reinforcement Learning
- 基于实时需求动态调节车流与人流信号时长。
- 实验显示拥堵降低且两类用户服务更公平。
- 适合智能交通、城市规划者关注的实用方案。
城市交通拥堵对现代城市的流动性与可持续性构成重大挑战。传统信号灯控制难以适应动态路况，导致效率低下。本文提出一种新型深度强化学习代理用于交通灯控制，通过显式整合车辆与行人流量的公平性考量，克服了以往仅关注车辆的局限。该方法根据实时需求动态平衡两类交通流，而非固定策略。实验表明，所提代理在有效减少拥堵的同时，保障了不同道路使用者之间的服务公平性。本研究为智慧城市中的智能交通管理提供了可落地、可适应的解决方案，推动更高效、包容的城市出行体验。
原文摘要 · Abstract (English)
Urban traffic congestion presents a significant challenge for modern cities, which impacts mobility and sustainability. Traditional traffic light control systems often fail to adapt to dynamic conditions, leading to inefficiencies. This paper proposes a novel deep reinforcement learning agent for traffic light control that addresses this limitation by explicitly integrating fairness considerations for both vehicular and pedestrian traffic. Unlike prior work, our approach dynamically balances these flows based on real-time demand, moving beyond systems focused solely on vehicles. Experimental results demonstrate that our agent effectively reduces congestion while ensuring equitable service for both the categories of road users. This research contributes to a practical and adaptable solution for intelligent traffic management within the framework of smart cities, paving the way for more efficient and inclusive urban mobility.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。