摘要
随着城市化进程的加速,交通拥堵成为影响城市效率和居民生活质量的主要问题之一。特别是在交叉口车流量高峰期,不合理的信号配时方案设置常常导致严重的交通堵塞和排队溢流。为减少高峰期交叉口二次排队车辆数和最大排队长度,提高交通流的通行效率,通过深度强化学习中的DQN(Deep Q Network,深度Q网络)算法来优化交叉口信号灯配时方案。首先构建交叉口仿真模型作为DQN算法的学习环境;其次接受动作并反馈回报;最后提取交通运行体征指标来评价DQN算法在交叉口信号控制中的优化效果。研究结果显示,相比韦伯斯特信号配时方法,DQN算法可以将最大排队长度降低14%,交叉口车均延误降低16%,证明DQN算法下的信号控制方案能更有效地适应交通流变化,提高交叉口的车流运行效率。
With the acceleration of urbanization,traffic congestion has become one of the major issues impacting the urban efficiency and the life quality of the residents.Particularly during the peak traffic flow at intersections,the unreasonable signal timing schemes frequently result in severe traffic congestion and queue overflows.In order to reduce the number of vehicles experiencing secondary queuing and the maximum queue length during peak hours at the intersections,and enhance the efficiency of traffic flow,the DQN(Deep Q Network)algorithm in the deep intensive learning is used to optimize signal timing schemes at intersections.Firstly,the simulation model of intersection is established as the learning environment of DQN algorithm.Secondly,the actions are accepted and the feedback is given.Finally,the traffic operation indicators are extracted to assess the optimization effects of the DQN algorithm in intersection signal control.The research result shows that compared to the Webster signal timing method,the DQN algorithm can reduce the maximum queue length by 14%and the intersection vehicle delay by 16%,demonstrating that signal control schemes under the DQN algorithm can more effectively adapt to fluctuations in traffic flow,thus improving the operational efficiency of vehicle flow at intersections.
作者
李琦
冷荣梦
LI Qi;LENG Rongmeng(Shanghai SEARI Intelligent System Co.,Ltd.,Shanghai 200063,China)
出处
《城市道桥与防洪》
2025年第12期1-6,共6页
Urban Roads Bridges & Flood Control
基金
国家科技部重点研发计划“自主式交通计算技术”(2023YFB4301900)。
关键词
信号优化
深度强化学习
DQN算法
排队溢出
signal optimization
deep intensive learning
DQN algorithm
queue overflow
