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Title	Developing An Intelligent Traffic Management System For Smart Cities Through The Integration Of Machine Learning And Iot Technologies
ID_Doc	19433
Authors	Al-Jawahry H.M.
Year	2025
Published	Lecture Notes in Networks and Systems, 1306 LNNS
DOI	http://dx.doi.org/10.1007/978-981-96-3728-7_39
Abstract	In response to the rapid urbanization of cities, Intelligent Traffic Management Systems are needed to reduce traffic congestion. As part of this paper, we present the design and implementation of an Intelligent Traffic Management System (ITMS) that improves traffic flow and optimizes urban mobility utilizing machine learning (ML) and Internet of Things (IoT) technologies. By monitoring, analyzing, and predicting traffic congestion points using IoT sensor data in real-time, the proposed system monitors traffic conditions, analyzes patterns, and optimizes traffic flow. A traffic signal control system and route optimization software work together to adapt to changing traffic conditions using advanced machine learning algorithms. Simulated and real-world deployments of the system proved its effectiveness, reducing travel times and reducing emissions and traffic flow significantly. In this study, the researchers highlight the potential of integrating machine learning and the IoT in creating smart cities that improve transportation efficiency and sustainability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Author Keywords	Intelligent system; IoT; Smart cities

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