Abstract: Managing traffic efficiently has become one of the most urgent challenges in today’s rapidly growing cities. Traditional fixed-time traffic signals often fall short because they cannot react to changing road conditions, which leads to unnecessary delays, increased fuel consumption, and higher pollution levels. Over the past few years, the combination of Internet of Things (IoT) technologies, edge computing, cloud platforms, and artificial intelligence has opened new possibilities for creating more adaptive and responsive traffic systems. This review brings together recent research from 2020 to 2025 and examines how sensors, embedded devices, and communication networks are being used to monitor real-time traffic flow, prioritize emergency vehicles, and optimize signal timing. The paper also explores advanced methods such as deep reinforcement learning, computer vision–based vehicle detection, blockchain-secured IoT frameworks, federated learning, and digital twin simulations. By comparing these approaches, the review highlights both their strengths and the remaining challenges that need attention. Overall, the study emphasizes that IoT-enabled smart traffic systems especially those combining edge intelligence with cloud analytics offer a practical and scalable pathway toward safer, cleaner, and more efficient urban mobility.

Keywords: Internet of Things (IoT), Smart Traffic Management, Edge Computing, Adaptive Signal Control, Vehicle Detection, Emergency Vehicle Priority, Cloud IoT Platforms, Traffic Flow Prediction, Intelligent Transportation Systems (ITS), Deep Learning, Reinforcement Learning, Urban Mobility.

Downloads: | DOI: 10.17148/IARJSET.2026.13531