Traffic Signal Optimization at T-Shaped Intersections Based on Deep Q Networks

In this paper traffic signal control strategies for T-shaped intersections in urban road networks using deep Q network (DQN) algorithms are proposed. Different DQN networks and dynamic time aggregation were used for decision-makings. The effectiveness of various strategies under different traffic conditions are checked using the Simulation of Urban Mobility (SUMO) software. The simulation results showed that the strategy combining the Dueling DQN method and dynamic time aggregation significantly improved vehicle throughput. Compared with DQN and fixed-time methods, this strategy can reduce the average travel time by up to 43% in low-traffic periods and up to 15% in high-traffic periods. This paper demonstrated the significant advantages of applying Dueling DQN in traffic signal control strategies for urban road networks.