Traffic light optimization (TLO) using reinforcement learning for automated transport systems

Current traffic light systems follow predefined timing sequences, causing the light to turn green even when no cars are waiting, while the side road with waiting vehicles may still face a red light. Reinforcement learning can help by training an intelligent model to analyze real-time traffic conditions and dynamically adjust signal lights based on actual demand and necessity. If the traffic light becomes intelligent and autonomous then it can significantly reduce the time wasted everyday commuting due to previously determined traffic light timing sequences. In our previous work, we used fuzzy logic to control the traffic light where the time was fixed but in this paper, the waiting time becomes a variable that changes depending on other road variables like vehicles, pedestrians, and times. Moreover, we trained an agent in this work using reinforcement learning to optimize the traffic flow in junctions with traffic lights. The trained agent worked using the greedy method to improve traffic flow to maximize the rewards by changing the signals appropriately. We have two states and there are only two actions to take for the agent. The results of the training of the model are promising. In normal situations, the average waiting time was 9.16 seconds. After applying our fuzzy rules, the average waiting time was reduced to 026 seconds, and after applying reinforcement learning, it was 0.12 seconds in a simulator. The average waiting time was reduced by 97~98%. These models have the potential to improve real-world traffic efficiency by approximately 67~68%.