International Journal of Multidisciplinary Engineering Sciences
Peer Reviewed Open Access Journal
Peer Reviewed Open Access Journal
Rapid urban growth and the rising number of vehicles on roads have created serious challenges such as heavy traffic congestion, increased road accidents, excessive fuel usage, and higher levels of environmental pollution in urban areas. Conventional traffic management methods mainly depend on fixed-time traffic signals and manual supervision, which are often unable to respond effectively to changing traffic patterns and real-time road conditions. To address these limitations, the adoption of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has enabled the development of smart traffic management systems that support real-time traffic monitoring, predictive analysis, and automated traffic regulation. This paper presents an AI-IoT-based intelligent traffic management framework integrating smart sensors, machine learning algorithms, edge computing, and cloud infrastructure for efficient traffic monitoring and congestion control. The proposed system continuously collects traffic data through IoT-enabled cameras, vehicle sensors, RFID devices, and smart traffic signals. Machine learning algorithms analyze traffic patterns, predict congestion levels, and optimize traffic signal operations dynamically. The framework supports emergency vehicle prioritization, accident detection, and intelligent route management for smart city transportation systems. Experimental analysis demonstrates improved traffic flow efficiency, reduced congestion rates, minimized waiting time, and enhanced traffic prediction accuracy compared with conventional traffic management approaches. The proposed framework provides a scalable and intelligent solution suitable for smart city transportation infrastructures and sustainable urban mobility.
Intelligent Traffic Management, Artificial Intelligence, Internet of Things, Smart Cities, Machine Learning, Traffic Prediction, Smart Transportation, Congestion Control
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