车联网环境下连续信号交叉口协同控制模型

智能交通信号控制技术是缓解交通拥堵的重要手段。为解决传统强化学习算法应用到连续多交叉口的局限性问题，提出了1种基于上下层神经网络的连续交叉口交通信号控制模型。控制模型由下层神经网络选择当前状态下可能的最优控制策略，再由上层神经网络根据各路口车均延误进行二次调整，将最终控制策略应用到多交叉口的相位配时中。以典型连续3个交叉口为例，通过SUMO仿真平台对模型进行仿真验证，在低与高饱和度下，该控制模型分别对车均延误降低了23.6%和26%，排队长度降低了8.4%和9.4%。实验数据表明，该模型可有效提高连续交叉口道路通行能力，为缓解城市交通拥堵提供了1种有效技术手段。 

A Cooperative Control Model of Continuous Signal Intersections for Connected Vehicles

Intelligent traffic signal control is an essential means to alleviate traffic congestion. A continuous traffic signal control model based on the upper and lower neural networks is proposed to solve the limitation of the traditional reinforcement learning algorithm at continuous multiple intersections. In this model, the local optimal control strategy in the current state is selected by the lower neural network. Then, the secondary adjustment can be made by the upper neural network according to the delay of vehicles at intersections. A global control strategy is applied to the phase timing of multiple intersections. The model is verified by the SUMO simulation platform, taking three typical continuous intersections as case studies. The average vehicle delay reduces by 23.6% and 26% under low and high saturation, and the queue length reduces by 8.4% and 9.4%. The results show that the road capacity of continuous intersections can be improved based on the proposed model, which provides an effective technical method to alleviate urban traffic congestion.