Traffic light control falls into two main categories: Agnostic systems that do not exploit knowledge of the current traffic state, e.g., the positions and velocities of vehicles approaching intersections, and holistic systems that exploit knowledge of the current traffic state. Emerging fifth generation (5G) wireless networks enable Vehicle-to-Infrastructure (V2I) communication to reliably and quickly collect the current traffic state. However, to the best of our knowledge, the optimized traffic light management without and with current traffic state information has not been compared in detail. This study fills this gap in the literature by designing representative Deep Reinforcement Learning (DRL) agents that learn the control of multiple traffic lights without and with current traffic state information. Our agnostic agent considers mainly the current phase of all traffic lights and the expired times since the last change. In addition, our holistic agent considers the positions and velocities of the vehicles approaching the intersections. We compare the agnostic and holistic agents for simulated traffic scenarios, including a road network from Barcelona, Spain. We find that the holistic system substantially increases average vehicle velocities and flow rates, while reducing CO ₂ emissions, average wait and trip times, as well as a driver stress metric.

中文翻译：

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通过强化学习优化交通灯管理：具有当前V2I交通状态知识的交通状态不可知代理与整体代理

交通信号灯控制分为两个主要类别： 不可知论者 不利用当前交通状态知识的系统，例如，接近交叉路口的车辆的位置和速度，以及 整体的利用当前流量状态知识的系统。新兴的第五代（5G）无线网络使车辆到基础设施（V2I）通信能够可靠，快速地收集当前交通状况。但是，据我们所知，没有详细比较没有和有当前交通状态信息的优化交通信号灯管理。本研究通过设计代表性的深度强化学习（DRL）代理来学习文献中的控制方法，从而填补了文献中的空白。多不带有当前交通状态信息的交通灯。我们的不可知论者主要考虑所有交通信号灯的当前阶段以及自上次更改以来的到期时间。此外，我们的整体代理会考虑接近交叉路口的车辆的位置和速度。我们比较了模拟交通场景的不可知论者和整体论者，包括西班牙巴塞罗那的道路网络。我们发现，整体系统大大提高了平均车辆速度和流量，同时减少了CO ₂排放，平均等待和出行时间以及驾驶员压力指标。