基于深度强化学习的单路口交通信号控制  

Deep Reinforcement Learning for Traffic Signal Control of Isolated Signalized Intersections

作　　者：刘皓 吕宜生[2] LIU Hao;LYU Yisheng(University of Chinese Academy of Sciences,Beijing 100049,China;The State Key Laboratory for Management and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)

机构地区：[1]中国科学院大学,北京100049 [2]中国科学院自动化研究所,复杂系统管理与控制国家重点实验室,北京100190

出　　处：《交通工程》2020年第2期54-59,共6页Journal of Transportation Engineering

摘　　要：合理的交通信号灯控制方案能减少交叉口处的排队长度,缓解交通拥堵问题.路口交通流具有非线性、时变性、不确定性等特点,对其建模困难,从而导致无法借助其精确的数学模型来优化交通信号控制方案.本文将深度强化学习方法应用到交通信号控制问题,深度强化学习Agent以减少路口处的排队车辆总数为目标,通过观察交叉口处所有入口车道的状态进行相位控制;使用SUMO仿真平台对本文提出的控制方法进行了仿真实验.实验结果表明,相较于定时控制方法,本文提出的基于深度强化学习的控制方法能显著减少交叉口处的排队车辆数,缓解交通拥堵.Well-designed traffic signal control strategies can help decrease vehicle queue length and alleviate traffic congestion at intersections.It is hard to model traffic dynamics at intersections accurately due to its nonlinearity,time-varying,and uncertainty.Thus it is extremely difficult to optimize traffic signal timing plans mathematically.In this paper,deep reinforcement learning is employed to examine traffic signal control problems.The traffic Agent observes traffic states in all the approach lanes at the intersection,and decides the control plan which aims to reduce the total vehicle queue length at the intersection.The proposed method is tested with the SUMO simulation platform.Experimental results show that the proposed method can reduce the queue length at the intersection significantly compared with the fixed-time signal control method.

关 键 词：深度强化学习 深度Q网络 交通信号控制 智能交通系统 

分 类 号：U491[交通运输工程—交通运输规划与管理]