An Adaptive ROI-Based Framework for Traffic Congestion Detection in Mixed Traffic Environments Using Deep Learning

Traffic congestion is a critical issue in urban areas, leading to increased travel time, fuel consumption, and environmental pollution. This paper proposes a real-time traffic congestion detection system based on deep learning and multi-object tracking. The system utilizes the YOLOv8 model for vehicle detection and ByteTrack for tracking vehicles across video frames. A region of interest (ROI) is defined to focus on relevant traffic areas, and congestion is determined using two key metrics: vehicle density and Motorcycle Equivalent Unit (MEU) - based traffic representation. Experimental results demonstrate that the proposed system achieves competitive detection accuracy, with a mean Average Precision (mAP@0.5) of 0.911 for vehicle detection. The system is able to distinguish between congested and non-congested traffic conditions in real-world scenarios. These results indicate that the proposed approach is suitable for intelligent traffic monitoring and smart city applications.