Intelligent Traffic Analysis: Determination of Macro Traffic Parameters Using Object Detection and Tracking

Abstract

Urbanization and increasing traffic volumes have intensified congestion, highlighting the need for intelligent transportation solutions. Traditional traffic data collection methods are often inaccurate and inefficient. This thesis proposes an advanced traffic analysis system using YOLOv8 for object detection and DeepSORT for tracking to estimate traffic parameters like volume, density, and speed from video inputs. A dataset of 20,000 images across seven vehicle classes (Car, Bike, Bus, Truck, Hiace/Van, Tractor, and Rickshaw) was annotated using Roboflow. The YOLOv8 model demonstrated high precision and recall, with a mAP@0.5 of 0.88 and a mAP@0.5:0.95 of 0.64. DeepSORT effectively tracked moving objects, maintaining unique ID’s across frames for accurate traffic parameter estimation. The developed API integrates detection and tracking data from video streams, providing precise traffic volume, density, and speed estimates. Tests on video clips yielded accurate traffic volume estimates of 992, 6,397, and 2,879 vehicles per hour for the G10-F10 and Srinagar Highway F-9 intersection. It also determined traffic densities of 21.94, 19.81, and 31.25 pcu/mile/lane and speeds of 27.46, 53.81, and 31.82 mph for the same locations. This research showcases the potential of real-time YOLOv8 and DeepSORT for traffic analysis. Future improvements could include expanding the dataset, fine-tuning the model, integrating additional data sources, and applying the system to real-world traffic monitoring. The proposed system offers valuable insights for traffic control and urban planning, providing a foundation for enhancing transportation infrastructure's safety and efficiency.