Traffic Signal Phasing Optimisation using Enhanced Q-network (EDQN)

Traffic signal control is the key to managing urban traffic flows and volumes. However, traditional traffic signal control methods typically struggle to deal with real-time traffic fluctuations, leading to frequent congestion. In contrast, reinforcement learning (RL), a machine learning approach that learns and optimises behaviour from its environment, offers an adaptive way to adjust signal control strategies. This paper proposes a new RL method based on Enhanced Deep Q-networks (EDQN) to optimise traffic signal phasing at a single intersection, giving additional neural networks to reduce further overestimation on Q-values compared with other methods. To demonstrate the optimisation of the enhanced model, we define the average queue length of all vehicles that wait in the lane before entering the intersection as the evaluation metric. The results of simulation with light, medium and heavy traffic volumes show that this model achieves over 70% optimisation in reducing queue length.