基于雾计算和强化学习的交通灯智能协同控制研究

针对路口交通拥堵现象，结合雾计算和强化学习理论，提出了一种FRTL（fog reinforcement traffic light）交通灯控制模型，该模型根据实时的交通流信息进行交通灯智能协同控制。雾节点将收集到的实时交通流信息上传到雾服务器，雾服务器在雾平台实现信息共享，雾平台结合处理后的共享数据和Q学习制定交通灯控制算法。算法利用检测到的实时交通数据计算出合适的交通灯配时方案，最终应用到交通灯上。仿真结果表明，与传统的分时段控制方式和主干道控制方式（ATL）相比，FRTL控制方法提高了路口的吞吐量，减少了车辆平均等待时间，达到了合理调控红绿灯时间、缓解交通拥堵的目标。

Research on intelligent coordinated control of traffic light based on fog computing and reinforcement learning

For intersection traffic congestion phenomenon, combined fog computing and reinforcement learning theory, this paper proposed a traffic light control model FRTL. The model performed intelligent coordinated control of traffic lights based on real-time traffic flow information. The fog node uploaded the collected real-time traffic flow information to the fog server, and the fog server realized information sharing on the fog platform, and the fog platform combined the processed shared data and Q learning to formulate a traffic light control algorithm. The algorithm used the detected real-time traffic data to calculate a suitable traffic light timing scheme, which was finally applied to the traffic light. The simulation results show that compared with the traditional time-phase control method and the main road control method(ATL), the FRTL control method improves the throughput of intersections, reduces the average waiting time of vehicles, and achieves the goal of properly regulating traffic lights and alleviating traffic congestion.