Adaptive Traffic Control and Management Using Yolo V8

P. Vinay Kumar; MD. UdayKiran; G. SaiVamshi; A. Vaishnavi; B. Nandhini

doi:10.15680/IJIRCCE.2023.1206053

P. Vinay Kumar Assistant Professor, Department of IT, Malla Reddy Engineering College, Secunderabad, Telangana, India
MD. UdayKiran B.Tech Ⅳ Year, Department of IT, Malla Reddy Engineering College, Secunderabad, Telangana, India
G. SaiVamshi B.Tech Ⅳ Year, Department of IT, Malla Reddy Engineering College, Secunderabad, Telangana, India
A. Vaishnavi B.Tech Ⅳ Year, Department of IT, Malla Reddy Engineering College, Secunderabad, Telangana, India
B. Nandhini B.Tech Ⅳ Year, Department of IT, Malla Reddy Engineering College, Secunderabad, Telangana, India

DOI: https://doi.org/10.15680/IJIRCCE.2023.1206053

Keywords: Traffic control, Traffic light system, Traffic management, Intelligent transport systems, Smart surveillance, Computer Vision, Machine Learning, Object detection, YOLOV8.

Abstract

Traffic congestion is a pressing issue exacerbated by population growth and increased automobile usage in urban areas. Megacities are particularly affected, necessitating real-time traffic density calculations for improved signal control and management. Our proposed solution utilizes live camera feeds for traffic density analysis and employs AI algorithms to dynamically adjust traffic signals based on vehicle density. This approach aims to reduce congestion, decrease travel times, and mitigate environmental impact. By optimizing traffic control, our system offers faster transit and contributes to a more sustainable urban environment.