A Single Intersection Signal Control Method Based on Improved DQN Algorithm
In order to improve the efficiency of single intersection signal control,aiming at the problems of inaccurate traffic state description and low sampling efficiency of experience pool in Deep reinforcement learning algorithm,an improved DQN signal control algorithm is proposed.Considering the vehicle length,the distance between cell and stop line and the number of detectors,the state space with non-uniform division of cell length is constructed to accurately characterize the traffic state.The dynamic greedy strategy is proposed to optimize the iterative process to improve the learning efficiency of the algorithm.Based on SUMO modeling,the experimental results show that the improved DQN algorithm can obtain better signal control effect.Compared with the traditional DQN algorithm,the cumulative delay and average queue length of vehicles in off-peak hours are reduced by 83.63％and 83.48％respectively,and the two indexes in peak hours are reduced by 94.88％and 94.87％respectively.
traffic engineeringintelligent traffictraffic signal controldeep reinforcement learningdeep Q network
万方数据
维普
