Green wave traffic light adaptive control algorithm based on deep reinforcement learning
Aiming at the problem that the traditional traffic signal light system cannot provide dynamic and flexible timing scheme for the urban main road traffic,a hybrid drive adaptive green wave control algorithm based on deep reinforcement learning(DRL)was proposed.The algorithm combines the deep reinforcement learning algorithm with the MAXBAND algorithm to reduce the computational overhead of the algorithm while realizing adaptive dynamic traffic control.The MAXBAND green wave algorithm is used to determine the traffic light period and phase difference of the main road,the DQN algorithm was used to optimize the green signal ratio,the joint state and joint reward were used to solve the dimension explosion problem,and a new reward function was introduced for the DQN algorithm in the traffic signal control problem for multi-agent coordination.The simulation results showed that the proposed algorithm could be used for signal timing more flexibly,and can deal with the congestion of the main road more effectively than the traditional green wave algorithm and the traditional DQN control algorithm in the three scenarios of undersaturation,saturation and oversaturation.
green wavedeep reinforcement learningadaptive traffic signal controlCVISjoint strategySUMO
万方数据
维普
