Abstract: The intersection is the core and hub of the urban road network.Reasonable optimization of the signal control at the intersection can greatly improve the operational efficiency of the urban transportation system.Using real-time traffic information as input and dynamically adjusting the phase time of the traffic signal becomes the important direction of current research.This paper proposed a traffic signal control method based on double deep Q-learning network with Dueling Architecture (D3QN).The deep learning network is combined with the traffic signal control machine to form an intelligent agent for adjusting the signal control strategy of the intersection.Then the DTSE (Discrete Traffic State Coding) method is used to transform the traffic state of the intersection into a two-dimensional matrix composed of the position and velocity information of the vehicle.Then high-level features are captured by deep neural network,which makes accurate perception of traffic state come true.On this basis,an adaptive traffic signal control strategy is realized through reinforcement learning.Finally,the traffic micro-simulator (SUMO) is used for simulation experiments,the timing control and induction control methods are used as control experiments.The results show that the proposed method achieves better control effect and is therefore feasible and effective.

Key words: Deep learning, Deep reinforcement learning, Intelligent transportation, Reinforcement learning, Traffic signal control