Traffic Light Control Using Reinforcement Learning

Abstract

Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people's general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability.