The invention discloses a traffic signal and direction-variable lane cooperative control method based on multi-agent learning, and the method comprises the steps: making a decision through the iteration of a traffic signal control module and a lane guiding control module, and carrying out the operation at the same time to control the traffic; the lane guiding control module adopts a dual depth Q network algorithm to enhance a learning model, an intelligent agent selects actions based on traffic flow and traffic signal states to maximize the lane utilization rate, and an award is calculated based on lane queuing length difference and the maximum direction demand traffic flow ratio; the traffic signal control module agent selects an action based on the observed lane density and the current lane state and obtains an award according to the traffic flow satisfaction of the selected phase. According to the method, the lane utilization rate is improved by controlling lane guidance, and the dynamically changing traffic flow is controlled in space and time dimensions by using a reinforcement learning framework; higher stability and efficiency are shown, and the traffic capacity of the intersection is improved.
本发明公开了一种基于多智能体学习的交通信号与可变方向车道的协同控制方法,通过交通信号控制模块和车道导向控制模块迭代做出决策,并同时运行以控制交通;车道导向控制模块采用双重深度Q网络算法增强学习模型,智能体基于交通流量和交通信号状态选择动作,以最大化车道利用率,奖励基于车道排队长度差异和最大方向需求交通流量比率计算;交通信号控制模块智能体基于观察到的车道密度和当前车道状态选择动作,并根据所选相位的交通流满意度获得奖励。本发明通过控制车道导向来改善车道利用率,使用强化学习框架,在空间和时间维度上控制动态变化的交通流量;表现出更高的稳定性和效率,并提高了交叉口的通行能力。
Cooperative control method for traffic signal and direction-variable lane based on multi-agent learning
基于多智能体学习的交通信号与可变方向车道的协同控制方法
2025-05-30
Patent
Electronic Resource
Chinese
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