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Smart traffic control: machine learning for dynamic road traffic management in urban environments

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Abstract

Roadside and outside environmental elements contribute to the road traffic setting's highly dynamic and turbulent nature. The human factor, primarily disregarded in the present research, is an essential element that contributes to the traffic context in addition to infrastructure-related elements like traffic signals, road infrastructure, and other road networks. Timing the green light and tracing the object that makes the incorrect turn using real-time visual information for traffic monitoring are still challenging tasks for the conventional traffic control system. We describe a self-adaptive real-time algorithm based on real-time traffic flow and monitoring. Combining image processing with AI-powered, self-adaptive machine learning for controlling traffic clearance at intersections is a forward-thinking approach with great potential. The suggested system uses the You Only Look Once v3 (YOLOv3) model and single image processing using a neural network to determine traffic clearance at the signal. YOLOv3 method to recognize objects from video frames. Subsequently, the centroid object tracking technique is used to monitor the movement of each vehicle within a proposed framework. We implemented algorithms to identify vehicles traveling in the incorrect direction based on their trajectories. This integrated approach enhances accurate object recognition, real-time vehicle tracking, and the detection of traffic violations, enhancing overall road safety measures. The experimental findings are quite promising, achieving an exclusive comparison between expected and actual vehicle numbers is crucial for any traffic monitoring system. The average object detection accuracy of 88.43% is impressive, and the exceptional 90.45% accuracy in tracking vehicles engaging in wrong turns or reckless driving behaviors is particularly noteworthy—it provides the system's ability to address safety concerns effectively. Integrating a Convolutional Neural Network (CNN) into the algorithm to alleviate traffic congestion at intersections is a smart move. CNNs are known for their effectiveness in image processing tasks, making them well-suited for tasks like object detection and tracking in complex environments like intersections.

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Data availability

The datasets analyzed during the current study are available from https://github.com/hkphd20/Traffic-Data-Set.

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Acknowledgements

The authors gratefully acknowledge the official Incubation Center Smart City of the State Government of Madhya Pradesh, India, a public research and development organization, for providing the dataset in this study.

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  1. Deptt of Computer Science & Engineering, Jabalpur Engineering College, Jabalpur, Madhya Pradesh, India

    Hameed Khan & Jitendra Singh Thakur

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  1. Hameed Khan
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Correspondence to Hameed Khan.

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Khan, H., Thakur, J.S. Smart traffic control: machine learning for dynamic road traffic management in urban environments. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19331-4

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