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A reliable transit system is essential and offers a lot of advantages. However, traffic has always been an issue in major cities, and one of the main causes of congestion in these places is intersections. To reduce traffic, a reliable traffic control system must be put in place. This research sheds light on how to consider dynamic traffic at intersections and minimize traffic congestion using an end-to-end deep reinforcement learning approach. The goal of the model is to reduce waiting times at these crossings by controlling traffic in various scenarios after receiving the necessary training.