REAL-TIME TRAFFIC LIGHT OPTIMIZATION USING AI AND IOT
Aniket Phand1, Shweta Bagade2, Nikhil Bandgar3, Prof. Ganesh Wayal4
1Department of Computer Engineering, Padmabhooshan Vasantdada Institute of Technology, Pune, India
aniketphand21@gmail.com1
2Department of Computer Engineering, Padmabhooshan Vasantdada Institute of Technology, Pune, India
shwetambagade26@gmail.com2
3Department of Computer Engineering, Padmabhooshan Vasantdada Institute of Technology, Pune, India
nikhilbandgar9102@gmail.com3
4Head of Department of Computer Engineering, Padmabhooshan Vasantdada Institute of Technology, Pune, India
hod_comp@pvpittssm.edu.in4
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In modern cities, the escalating number of vehicles has led to significant traffic issues, affecting road capacity and service quality. Traditional traffic control systems, reliant on fixed signal timers, struggle to adapt to changing traffic dynamics, exacerbating congestion. This paper proposes an innovative approach to traffic management utilizing advanced Computer Vision technology, specifically the YOLOv7 object detection algorithm. By analysing live CCTV footage at intersections, the system dynamically assesses traffic density, identifies vehicle types, and adjusts signal timings in real-time. The system architecture ensures swift processing and response times, with YOLOv7 enabling rapid and accurate vehicle detection. This enables the system to make timely decisions, thereby enhancing overall traffic management efficacy. Leveraging Artificial Intelligence (AI) and Machine Learning techniques, the proposed solution addresses the urgent need for innovative traffic management strategies in urban areas, aiming to alleviate congestion, enhance traffic flow, and reduce environmental impact. Hence, the integration of YOLOv7 technology with adaptive traffic signal switching algorithms represents a promising step towards addressing the complex challenges of urban traffic congestion.
Key Words: Computer Vision, Artificial Intelligence, Machine Learning, Traffic Prediction, Adaptive Control, YOLO.
