Traffic congestion has emerged as a pervasive challenge across global urban landscapes, inducing delays, productivity losses, and heightened air pollution. Conventional traffic signal systems often falter in adapting to evolving traffic dynamics, compromising road network efficiency. To address this a novel paradigm—a smart traffic control system leveraging advanced computer technology is proposed. This system employs real-time monitoring and analysis to dynamically adjust traffic signals, optimizing traffic flow, and mitigating congestion-related adversities. The integration of deep learning and computer vision technologies is used to enable a nuanced understanding of visual data and patterns. Remarkably, the YOLO tool is utilized to enhance the system’s capacity to swiftly identify emergency vehicles and give them priority. The proposed system is designed to efficiently handle traffic density and includes features for prioritizing emergency vehicles. Furthermore, it employs a non-uniform allocation of waiting times to lanes, which is contingent upon real-time traffic density and patterns. A working model demo was designed and it was found effective in making intelligent decisions.
An Intelligent Traffic Control System Incorporating Deep Learning and Computer Vision with Prioritized and Dynamic Timing
Lect. Notes in Networks, Syst.
International Conference on Information and Communication Technology for Competitive Strategies ; 2023 ; Jaipur, India December 08, 2023 - December 09, 2023
2024-04-18
11 pages
Article/Chapter (Book)
Electronic Resource
English
Traffic congestion , Intelligent traffic control , Deep learning , Computer vision , YOLO , Sustainable cities , Emergency vehicle detection Engineering , Communications Engineering, Networks , Science and Technology Studies , Systems and Data Security , Computational Intelligence , Artificial Intelligence
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