The adaptive traffic signal control method is adopted to effectively control the traffic lights at the urban road junctions, with the rapid growth of the traffic flow in Shenzhen. Shenzhen traffic police asked for a real-time, distributed and adaptive control on the basis of the self-developed smooth signal control. Joint innovation has developed the reinforcement learning based on the deep neural network. Through online learning of various traffic loads, and the real-time reasoning, the information control period, phase, phase sequence, signal cycle, split and phase difference are calculated. This paper reviews the reinforcement learning model used in the traffic signal control, and makes an evaluation on the spot.

traffic signal control  /  reinforcement learning  /  artificial intelligence  /  pass efficiency