• Akshata Rathod
• Sudhakar Sinha
• Omkar Dhage
• Isha Patil
• Rajendra Kokare

Urban traffic congestion is a growing problem, particularly in megacities, due to increasing populations and the rising number of vehicles. Traditional traffic control methods rely on fixed-duration traffic light cycles, which fail to adapt to real-time traffic conditions, resulting in unnecessary delays, increased fuel consumption, and elevated pollution levels. These static systems do not account for variations in traffic density, leading to inefficient traffic management, especially during peak hours. Our model introduces a dynamic traffic control system that optimizes traffic light schedules at four-way intersections based on real-time traffic density. Utilizing live images from traffic cameras, our approach uses image processing and artificial intelligence (AI) to calculate vehicle density and adjust traffic light cycles dynamically. This method aims to reduce congestion, minimize wait times, and improve overall traffic flow.

Traffic control, Traffic light system, Traffic management, Intelligent Transport Systems, Smart surveillance, Computer Vision, Machine Learning, Object detection, YOLO, Green-Signal.

"Smart Flow Traffic Management System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.f326-f331, October-2024, Available :http://www.jetir.org/papers/JETIR2410529.pdf

"Smart Flow Traffic Management System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppf326-f331, October-2024, Available at : http://www.jetir.org/papers/JETIR2410529.pdf