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Smart city article details

Title	Dynamic Traffic Management Using Ai
ID_Doc	21430
Authors	Jeyakumar L.; Raj K.; Stephen L.S.V.; Gurumoorthy K.; Thulasilingam L.; Manivannan S.A.
Year	2025
Published	AIP Conference Proceedings, 3175, 1
DOI	http://dx.doi.org/10.1063/5.0254506
Abstract	The primary difficulties of urban life are caused by the expanding population and the number of automobiles. Traffic management plays a crucial role in reducing time and fuel usage. Data collection and processing from many sources are now feasible because to the development of contemporary connectivity, computation, and affordable sensors. Each of these components is tested using a specific test approach. Unit testing helps to identify possible errors in individual components. The components with errors can be identified and corrected from errors. Intelligent traffic management is a new discipline that has developed recently thanks to the Internet of Things (IoT) and smart cities. By combining IoT with image and video processing techniques, a novel approach to traffic light control is proposed. The suggested model bases the timing of the traffic lights on the density and the volume of passing vehicles. The You Only Look Once (YOLO) algorithm locates and identifies different things in a photograph (in real-time). Using pre-defined datasets, object detection in YOLO is carried out as a regression problem and offers the class probabilities of the discovered photos. In terms of training the algorithm, the data collected by examining traffic intersections is analyzed using the YOLO module. The densely populated traffic lane is identified and the traffic control signal is switched over to open accordingly. The experimental statistics has proved to be accurate and hence it is proposed to be used in a real-time system. The analytical and practical findings show how effective the suggested models are at intelligent traffic management. © 2025 Author(s).
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