Deep Reinforcement Learning for IoT-Based Smart Traffic Management Systems

The increasing complexity of urban traffic networks demands more intelligent and adaptive solutions for traffic management. This paper presents a novel approach utilizing Deep Reinforcement Learning (DRL) in conjunction with Internet of Things (IoT) technology to develop a smart traffic management system aimed at optimizing traffic flow in real-time. IoT devices, including sensors, cameras, and connected vehicles, provide comprehensive, high-resolution traffic data, which is processed by a DRL-based algorithm to dynamically control traffic signals and manage congestion. The proposed system continuously learns from evolving traffic patterns, adapting its decision-making to optimize key metrics such as vehicle throughput, travel time, and energy consumption. Compared to traditional traffic management systems, the DRL-based approach exhibits superior performance in handling dynamic traffic environments, reducing bottlenecks, and minimizing delays. Extensive simulations demonstrate that the integration of DRL with IoT enhances the system’s ability to manage complex, real-world traffic scenarios more efficiently. This research contributes to the growing body of work in smart city development, offering an innovative framework for the deployment of intelligent traffic management systems. The study highlights the potential of DRL and IoT technologies to transform traffic management, promoting sustainable urban mobility and improving overall traffic efficiency..