Traffic Management System Using AI-Based Embedded Systems

Abstract Rapid urbanization and the exponential growth of vehicular traffic have significantly intensified congestion, resulting in increased travel delays, fuel consumption, environmental pollution, and road safety risks. Conventional traffic control systems primarily rely on fixed-time or semi-adaptive signalling mechanisms, which are insufficient to handle dynamic and unpredictable traffic conditions. In recent years, Artificial Intelligence (AI) integrated with embedded systems has emerged as a promising solution for intelligent traffic management. This review paper presents a comprehensive analysis of AI-enabled embedded traffic management systems, focusing on adaptive signal control, real-time traffic monitoring, congestion prediction, and emergency vehicle prioritization. The study reviews state-of-the-art AI techniques, including computer vision, machine learning, deep learning, and reinforcement learning, deployed on embedded and edge computing platforms. Key challenges such as latency, scalability, data availability, and privacy concerns are critically analysed, along with recent solutions such as edge-based AI processing and privacy-preserving data handling.The review highlights that AI-driven traffic management systems significantly improve traffic flow efficiency, reduce waiting time, minimize fuel consumption and emissions, and enhance emergency response performance. The paper concludes that the integration of AI with embedded traffic control systems is a crucial enabler for smart cities and sustainable urban transportation, while also identifying open research gaps and future directions.