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An Intelligent Traffic Monitoring System in Congested Regions with Prioritization for Emergency Vehicle Using UAV Networks

Department of Computer Engineering, Mizoram University, Mizoram 796004, India
Karunya Institute of Technology and Sciences, Coimbatore 641114, India
School of Information Communication and Technology, Bahrain Polytechnic, Muharraq 33349, Bahrain
Department of Cyber Security, Air University, Islamabad 44000, Pakistan
Institute of Computer Science and Digital Innovation, University College Sedaya International (UCSI) University, Kuala Lumpur 56000, Malaysia
Faculty of Computers and Information Technology, Computer Science Department, University of Tabuk, Tabuk 71491, Kingdom of Saudi Arabia
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Abstract

Unmanned Aerial Vehicles (UAVs) are enabled to be fast and flexible in managing traffic compared to the conventional methods. However, in emergencies, this system takes more time to identify and clear the traffic because of fixed time control. To overcome this problem, an automated intelligent traffic monitoring and controlling system is designed using YOLO V3 neural architecture and implemented to detect the emergency vehicles from video stream data from UAVs using deep Convolution Neural Network (CNN) along with re-routing algorithm to provide the safest alternate route from current position to destination, in a heavy traffic environment. The real-time visual data collected through UAV video cameras are trained using machine learning algorithms to produce statistical profiles that are used continuously as updated inputs to the existing traffic simulation models for improving predictions. The proposed automated system performs exemplary in recognizing emergency vehicles and diverting them to an alternate route for quick transportation in various scenarios.

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Tsinghua Science and Technology
Pages 1387-1400
Cite this article:
Ambeth Kumar VD, Ramachandran V, Rashid M, et al. An Intelligent Traffic Monitoring System in Congested Regions with Prioritization for Emergency Vehicle Using UAV Networks. Tsinghua Science and Technology, 2025, 30(4): 1387-1400. https://doi.org/10.26599/TST.2023.9010078
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