Reinforcement Learning Algorithms for Optimizing Traffic Flow in Smart City Infrastructures

Abstract

The rapid growth of urban populations and increasing vehicular traffic necessitate innovative solutions for optimizing traffic flow within smart city infrastructures. This paper explores the application of reinforcement learning (RL) algorithms to address these challenges by dynamically adjusting traffic signal timings, rerouting vehicles, and managing congestion. We present an overview of various RL techniques, such as Q-learning, Deep Q Networks (DQN), and policy gradient methods, emphasizing their ability to adapt to real-time traffic conditions and improve system efficiency. The study evaluates RL-based approaches through simulations and case studies, demonstrating their potential to reduce travel times, lower emissions, and enhance the overall sustainability of urban transport networks. Moreover, we discuss the integration of RL algorithms with emerging technologies such as the Internet of Things (IoT), autonomous vehicles, and edge computing to create responsive, data-driven traffic management systems. The findings suggest that reinforcement learning can significantly enhance traffic optimization in smart cities, providing a scalable and flexible framework for future intelligent transportation systems.