A Deep Reinforcement Learning-Based Method for Signal Duration Control at Intersections with Asymmetric Traffic Flows

At the intersection with asymmetric traffic flow, a single neural network or other control methods cannot make a choice in time to ensure that the intersection with a large traffic flow and the intersection with a long queue length can obtain more traffic time. In order to solve this problem, a signal length control method for asymmetric traffic flow intersections based on deep reinforcement learning is proposed. Using deep Q-learning, the traffic signal control problem is transformed into a reinforcement learning problem. The state of traffic intersection is defined as traffic cycle time, asymmetric traffic flow parameters, asymmetric traffic flow parameters, the green signal ratio of the signal, and the control action of a traffic signal is defined as changing the phase and duration of the signal. Through the deep Q-learning model, a neural network model is trained to predict the long-term cumulative return (i.e., Q value) of each action under different conditions, that is, asymmetric traffic flow, and select the optimal control action according to the Q value, so as to realize the signal light duration control of asymmetric traffic flow intersections. Through experimental verification, when the discount factor of the model is 0.5, the learning speed and stability of the optimal agent can be obtained, which effectively reduces the occurrence of traffic congestion and greatly improves the traffic safety of vehicles, which is of great significance for improving urban traffic conditions.