International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Because fixed-time and count-based signal control systems have limits, urban traffic congestion continues to be a major problem. VisionFlow, an AI-based intelligent traffic management system that combines adaptive decision-making and real-time computer vision for effective signal optimization, is presented in this study. Using live camera feeds and upstream photos, the system uses the YOLOv8 deep learning model to identify and categorize automobiles. In order to dynamically distribute green signal durations, VisionFlow presents a dual-algorithm architecture that combines an Adaptive Waiting Time (AWT) algorithm with Vehicle Actuated Control (VAC). In contrast to conventional methods, the AWT algorithm ensures equity and less traffic by prioritizing lanes based on both vehicle count and cumulative waiting time. Additionally, the system includes anti-starvation measures, upstream surge detection, and emergency vehicle management. A. Traffic conditions, signal phases, urgency heatmaps, and performance data are all displayed on a real-time interactive dashboard. When compared to VAC, experimental findings show that the suggested AWT technique greatly lowers average waiting time and increases traffic flow efficiency. For next-generation smart city traffic control systems, VisionFlow provides a workable and scalable option.

Keywords: YOLOv8, computer vision, intelligent traffic management, adaptive waiting time algorithm, Actuated Vehicle Control Systems for Smart Cities Optimizing Traffic Signals AI-Powered Traffic Control and Real-Time Traffic Monitoring.

Downloads: | DOI: 10.17148/IJARCCE.2025.1412119