The invention discloses a road intersection signal lamp split control method, and relates to the field of traffic signal control, and the method comprises the steps: extracting the traffic flow, the average vehicle speed and the average queuing length of each entrance lane of an intersection through a detector to represent a traffic state, and uploading data when a signal period is finished; based on a deep reinforcement learning algorithm, taking a split scheme as an action and taking the minimum average delay time as a target, performing action value estimation on a real-time traffic flow state by using a deep full-connection neural network, and selecting the action with the maximum value to control signal timing of a next period; improving the stability of network convergence through offline training and freezing training, improving the convergence capability of the network by using exploration rate cosine attenuation and introducing a zero-reward delay factor, and finally, copying the decision network to an actual signal control machine. According to the method, factors of conventional detector data and a countdown display are fully considered, and compared with a phase control method, the method is more practical, and traffic congestion can be reduced.
本发明公开了一种道路交叉口信号灯绿信比控制方法,其涉及交通信号控制领域,包括:通过检测器提取交叉口各进口道的车流量、平均车速和平均排队长度表征交通状态,在信号周期结束时上传数据;基于深度强化学习算法,以绿信比方案为动作,以平均延误时间最小为目标,使用深度全连接神经网络对实时交通流状态进行动作价值估计,选择价值最大的动作控制下一周期的信号配时;通过离线训练以及冻结训练的方式提高网络收敛的稳定性,使用探索率余弦衰减以及引入零奖赏延误因子提高网络的收敛能力,最后拷贝决策网络到实际信号控制机上。本发明充分考虑了常规检测器数据和倒计时显示器的因素,相对控制相位的方法来说更具有实用性,可以减少交通拥堵。
Road intersection signal lamp split control method, device and equipment
一种道路交叉口信号灯绿信比控制方法、装置和设备
2022-01-21
Patent
Elektronische Ressource
Chinesisch
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