Paper Contents
Abstract
The traffic build-up in cities affects time taken to travel, fuel expenses, and pollution levels. Standard traffic control systems use fixed timers for traffic lights, so the flow of traffic is not as efficient as it could be. This paper presents the AI Enhanced Adaptive Traffic Flow Management System, which is capable of changing the timings for traffic signals based on the real time traffic situation. The system determines traffic volume by using computer vision and deep learning technologies, specifically YOLO (You Only Look Once) vehicle detection, in live streaming videos from traffic cameras. To enhance the effectiveness of traffic control and minimize delays, the system modifies the green signal time depending on the density of cars in the monitored area. This system prioritizes severe emergencies such as, ambulances and firefighters in real time. Such vehicles are detected by YOLO, which then immediately modifies traffic lights to provide the necessary clearance for them. The anticipated results are better efficiency of road traffic, reduction of traffic jams, and better urban mobility. And faster emergency response
Copyright
Copyright © 2025 Mohanadevi A . This is an open access article distributed under the Creative Commons Attribution License.
