The invention discloses an intelligent traffic signal control method. The method comprises the steps of obtaining road network traffic flow operation data and evaluating a current traffic jam value; constructing and training a traffic signal control model; and using the trained traffic signal control model to determine the road network state and obtain an optimal traffic signal control strategy capable of reducing road network traffic congestion. According to the method, traffic road network state data is processed through the convolutional neural network, the space-time dependence characteristics of traffic flow are effectively extracted, and meanwhile, the problem of huge signal phase combination space is solved in a value function iterative approximation mode by adopting an Actor-Critic reinforcement learning method, so that an intelligent traffic signal control model is established. The model is high in portability and can be suitable for various types of road traffic conditions.
本发明公开了一种智能交通信号控制方法,包括获取路网车流运行数据并评价当前交通拥堵值;构建并训练交通信号控制模型;利用训练好的交通信号控制模型判断路网状态并获得能减少路网交通拥堵的最佳交通信号控制策略。本发明通过卷积神经网络对交通路网状态数据进行处理,有效提取交通流的时空依赖特征,同时采用Actor‑Critic的强化学习方法以价值函数迭代逼近的方式解决信号相位组合空间巨大的问题,从而建立智能交通信号控制模型。模型的移植性强,能够适用于各种形式的道路交通状况。
Intelligent traffic signal control method
一种智能交通信号控制方法
2021-10-08
Patent
Electronic Resource
Chinese