An Intelligent Traffic Control System Incorporating Deep Learning and Computer Vision with Prioritized and Dynamic Timing

Abstract

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.