An Adaptive Traffic Signal Management System Incorporating Reinforcement Learning

Although there has been a lot of study done on traffic monitoring, control, modeling, and the use of artificial intelligence to handle these problems, there are still a lot of critical problems that need to be solved. In existing traffic signal research, a major problem is longer delays and queues, causing significant traffic congestion. Our research aims to minimize these issues, successfully reducing delays and queues. This has led to improved traffic flow and a significant decrease in congestion. Reinforcement learning for traffic signal control uses RL techniques to create intelligent agents that make real-time decisions about changing traffic signals at intersections. Its goal is to enhance traffic flow, reduce congestion, and improve overall transportation efficiency by optimizing signal phases dynamically. Reinforcement property state contain the current circumstances at an intersection, actions correspond to the choices made by the traffic signal controller, and rewards reflect the controller's performance and are in line with traffic objectives like minimizing delays, reducing congestion, cutting down on travel times, and improving safety. We scrutinize Deep Q learning by reason of Deep Q Learning gives Q value, By estimating and updating Q-values, the agent can learn which actions are more beneficial in different traffic conditions. We are able to cut the queue length by 9.7% to shorten wait times and relieve traffic. Get a happy outcome of traffic signal control and improve rewards by 9.7%. This research study showcases the promising capabilities of reinforcement learning in enhancing the synchronization of traffic light controllers, effectively mitigating the adverse consequences of traffic congestion within urban environments. The adoption of this innovative approach holds the promise of fostering a more sustainable and streamlined transportation system for the future.