Joint Control of Lane Allocation and Traffic Light for Changeable-Lane Intersection Based on Reinforcement Learning

Deep reinforcement learning based intelligent traffic light is an increasing tendency for improving the traffic condition due to its promising control performance. The current works about intelligent traffic lights tend to control the signal phase or phase time under fixed-lane condition. However, the fixed-lane condition cannot fit the different vehicle flow very well, thus the traffic condition cannot be enhanced further. In this paper, for improving the traffic condition further, we extend the existing intelligent traffic lights to the two-dimension control problem where the agent needs to control not only the traffic light signal, but also the direction of changeable lanes. As a result, the traffic light signal and lane allocation satisfy the need of current vehicle flow simultaneously. Furthermore, for the joint problem of lane allocation and the control of traffic light control, we develop a Q-network structure to build its deep reinforcement learning model. The simulation results demonstrate that the performance of the proposed method outperforms that of the existing intelligent traffic light with fixed lane.