Traffic Signal Control Systems Incorporate Reinforcement Learning Techniques

Abstract

An effective transportation system must include intelligent traffic light regulation. An intelligent traffic light control system should dynamically respond to real-time traffic, unlike traditional traffic lights, which are typically operated using manual instructions. Q-reinforcement learning is a technology that is increasingly being applied to traffic light regulation, and recent experiments have yielded promising results. In this study, an adaptive traffic signal scheduling strategy is designed utilizing Q-learning (QL) to minimize the number of vehicles blocking an intersection.