According to the urban road network traffic signal timing optimization control method, importance sampling is introduced on the basis of a traditional A2C algorithm, an adjacent intersection experience sharing method is achieved, adjacent intelligent agents are made to learn through off-line data sharing, the data utilization rate is increased, the system convergence efficiency is improved, and in the aspect of reward function setting, the method is more efficient and more efficient. A hybrid reward function model of an intersection dynamic density method is provided, the EMV priority is emphasized while the social vehicle passing efficiency is maximized, the problem of deep reinforcement learning reward sparseness in a traffic network environment is solved, and through simulation experiments based on a synthetic network and a Monago traffic network, the probability of reward sparseness is lowered. According to the method, the average delay of the intersection and the average travel time of the vehicles can be effectively reduced, so that the throughput of the vehicles is improved, compared with social vehicles, the EMV extraction delay rate is lower than that of the social vehicles, and the algorithm shows better signal priority, stability, practicability and safety when the road resource utilization rate is lower.
本发明城市路网交通信号配时优化控制方法,在传统A2C算法的基础上引入重要性采样实现了邻近交叉口经验共享方法,通过离线数据共享使邻近智能体进行学习,提高数据利用率,提高系统收敛效率,在奖励函数设置方面,提出了一种交叉口动态密度法的混合奖励函数模型,在最大化社会车辆通行效率的同时,强调EMV优先级,解决了交通路网环境中深度强化学习奖励稀疏的问题,通过基于合成网络以及摩纳哥交通网络中仿真实验,可以有效地减少交叉口的平均延误和车辆的平均行程时间,从而提高车辆的通过量,与社会车辆相比,EMV提取延迟率低于社会车辆,在道路资源利用率较低的时候,算法表现出较好的信号优先性、稳定性、实用性、安全性。
Urban road network traffic signal timing optimization control method
城市路网交通信号配时优化控制方法
2023-12-22
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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