Real-Time Detection of Vehicle Queue States in Urban Traffic Using Deep Learning

Traffic congestion and vehicle queue formation at signalized intersections represent critical challenges in modern urban transportation systems, requiring accurate real-time detection methods for effective traffic management. This study presents a deep learning-based approach for real-time vehicle queue state classification that integrates You Only Look Once (YOLO) object detection with Simple Online Real-time Tracking (SORT) algorithms using standard traffic camera footage. The proposed system performs multi-class vehicle classification, real-time vehicle tracking with unique ID assignment, and speed estimation through camera calibration techniques, achieving 16.42 FPS average processing speed across diverse video scenarios. A comprehensive queue state detection methodology is developed that categorizes traffic conditions into three categories: Heavy traffic, stable flow, and free flow based on the analysis of average speeds of the detected vehicles, excluding motorcycles and bicycles due to their distinct traffic behavior patterns. Experimental validation across several test datasets encompassing both high and low resolutions demonstrates robust vehicle detection performance across all vehicle classes. Speed estimation accuracy ranges from 89% to 99%, validated against vehicle counting and tracking in designated traffic lanes, providing essential data for queue analysis. The system achieves vehicle counting accuracy ranging from 78.57% to 100% across different scenarios. The system offers a cost-effective alternative to traditional sensor-based methods by utilizing existing traffic-surveillance infrastructure, making it suitable for widespread deployment in intelligent transportation systems. Results indicate the proposed approach successfully detects queue states in real-time conditions across diverse traffic scenarios, from heavy congestion to free flow conditions. This research advances computer vision-based traffic monitoring by demonstrating the practical effectiveness of integrated object detection and tracking algorithms, contributing to improved traffic flow optimization and congestion management.