Dynamic Traffic Management Using Reinforcement Learning

Traffic congestion has become a major problem in this rapidly growing world.Everyone operating a vehicle, as well as the traffic police in charge of managing the traffic, finds it difficult to become stuck in heavy traffic.For this a set, predetermined timing for traffic flow for each direction at the junction is utilized by traditional traffic light controllers.However, the concept of a fixed time traffic signal controller does not work well in places with uneven traffic.A dynamic traffic control system is therefore required, which regulates the traffic signals in accordance with the volume of traffic.This paper proposes a model that uses reinforcement learning (RL) along with deep neural networks (DNN) to manage discretions (signal status) for an environment with the help of Simulation of Urban MObility (SUMO).A simulation of real-world environment consisting a network of Four-way crossroad junction that contains 4 arriving lanes and 4 exiting lanes is used to train the agent.The main objective of this research study is to construct a model that can independently determine the best course of action and aims to provide better traffic management that will decrease the average waiting time, cause lower congestion, and provide a smooth flow of traffic.