Deep Reinforcement Learning for Adaptive Traffic Signal Control in Smart Cities: An Intelligent Infrastructure Perspective

Abstract

The rise of smart cities has necessitated the development of advanced traffic management systems that can adapt to dynamic urban traffic conditions. Traditional traffic signal control systems often fall short in responding to real-time fluctuations, leading to increased congestion and reduced efficiency. Deep Reinforcement Learning (DRL) offers a promising solution by enabling adaptive traffic signal control through continuous learning and optimization. This paper explores the application of DRL for adaptive traffic signal control, focusing on how it can enhance traffic flow and reduce congestion in smart cities. We discuss the fundamental principles of DRL, including the roles of agents, states, actions, and rewards, and explain how these elements are used to develop adaptive traffic control strategies. We examine various DRL algorithms such as Q-learning, Deep Q-Networks (DQNs), and Policy Gradient methods, and their applications in traffic signal control. Additionally, we address the challenges associated with implementing DRL in real-world traffic systems, including the need for accurate traffic modeling, efficient training, and scalability. Our findings demonstrate that DRL can significantly improve the adaptability and performance of traffic signal control systems, contributing to the development of more efficient and responsive urban traffic networks.