Traffic congestion in dense urban centers presents an economical and environmental burden. In recent years, the availability of vehicle-to-anything communication allows for the transmission of detailed vehicle states to the infrastructure that can be used for intelligent traffic light control. The other way around, the infrastructure can provide vehicles with advice on driving behavior, such as appropriate velocities, which can improve the efficacy of the traffic system. Several research works applied deep reinforcement learning to either traffic light control or vehicle speed advice. In this work, we propose a first attempt to jointly learn the control of both. We show this to improve the efficacy of traffic systems. In our experiments, the joint control approach reduces average vehicle trip delays, w.r.t. controlling only traffic lights, in eight out of eleven benchmark scenarios. Analyzing the qualitative behavior of the vehicle speed advice policy, we observe that this is achieved by smoothing out the velocity profile of vehicles nearby a traffic light. Learning joint control of traffic signaling and speed advice in the real world could help to reduce congestion and mitigate the economical and environmental repercussions of today's traffic systems.

翻译：密集城市中心的交通拥堵给经济与环境带来了沉重负担。近年来，车连万物通信技术的普及使基础设施能够获取详细的车辆状态信息，从而用于智能交通信号灯控制。反之，基础设施也可向车辆提供驾驶行为建议（如建议车速），进而提升交通系统的运行效率。已有研究将深度强化学习分别应用于交通信号灯控制或车辆速度建议，而本文首次提出联合学习这两类控制策略。实验表明，该方法能有效提升交通系统效能：在十一个基准场景中的八个场景中，相较于仅控制交通信号灯，联合控制方法可降低车辆平均行程延误时间。通过分析车辆速度建议策略的定性行为，我们发现该效果是通过平滑交通灯附近车辆的速度曲线实现的。在现实世界中应用交通信号与速度建议的联合控制学习，有望缓解当前交通系统带来的拥堵问题及其经济、环境负面影响。