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Title	Real-Time Traffic Management System Using Yolov8 And Cnn: A Deep Learning Approach With Iot Integration
ID_Doc	44470
Authors	Saklani S.; Manchanda M.; Sharma R.; Singh D.
Year	2025
Published	1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025
DOI	http://dx.doi.org/10.1109/CE2CT64011.2025.10939584
Abstract	Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.
Author Keywords	CNN; Edge Devices; IoT; Real-Time Traffic Control; Smart Cities; YOLOv8

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