A Review of Deep Reinforcement Learning for Traffic Signal Control

Abstract

Traffic signal control plays a vital role in effectively managing traffic flow and alleviating congestion in urban areas. Traditional methods for controlling traffic signals often rely on fixed timing plans or predefined algorithms, which may not be adaptable to changing traffic conditions. Reinforcement Learning is gaining traction as a favored data-centric method for adapting traffic signal control in intricate urban traffic networks. This article represents a conceptual review of recent studies and techniques that showcase the effectiveness of Deep Reinforcement Learning (DRL) in enhancing the performance of traffic signal control. These improvements include reducing travel time, fuel consumption, and emissions. Additionally, we will delve into different algorithms and learning systems explored in research papers, such as multi-agent reinforcement learning and Deep Q Networks (DQN).