Journals Proceedings

Abstract

Existing traffic light controls are ineffective and causes a handful of problems such as congestion and pollution. The purpose of this study is to investigate the application of deep reinforcement learning on traffic control systems to minimize congestion at a targeted traffic intersection. The traffic data was extracted, analyzed and simulated based on the Poisson Distribution, using a simulator, Simulation of Urban Mobility (SUMO). In this research, we proposed a deep reinforcement learning model, which combines the capabilities of convolutional neural networks and reinforcement learning to control the traffic lights to increase the effectiveness of the traffic control system. The paper explains the method we used to quantify the traffic scenario into different matrices which fed to the model as states which reduces the load of computing as compared to images. After 2000 iterations of training, our deep reinforcement learning model was able to reduce the cumulative waiting time of all the vehicles at the Pulai Perdana intersection by 47.31% as compared to a fixed time algorithm and can perform even when the traffic is skewed in a different direction. When the traffic is scaled down to 50% and 20 %, the agent continues to improve the waiting time by 69.5% and 68.36 % respectively.