Simulation of Smart Traffic Light By Using Image Processing and Reinforcement Learning

Vehicle travel is on the rise across the world, particularly in metropolitan cities. As a result, simulating and optimizing traffic control algorithms is required to better handle this growing demand. In this research, we investigate the simulation and optimization of traffic light controllers in a city and provide a reinforcement learning-based approach. The algorithm that is used is deep Q-learning. There are four processes in this study. The first is data collection. Next, a simulation is built. Then, a reinforcement learning model is trained and tested in the model. Last, the results are compared between the traditional traffic lights and traffic lights that were applied with the reinforcement learning model. The results obtained in this study are queue length of vehicles in front of traffic light and delay time of vehicles after given green signal in two scenarios, which are an environment that uses traditional traffic light and an environment that uses traffic light that applied with reinforcement learning model. From the result, the environment that applied with reinforcement learning agent has shorter time delay and queue length of vehicles. Queue length of vehicles is reduced from 58 to 18 and time delay is reduced from 3900 seconds to 380 seconds.