The Framework of Vehicle Detection and Counting System for Handling of Toll Road Congestion using YOLOv8

Abstract

The Global COVID-19 pandemic and the increasing number of vehicles have exacerbated traffic congestion, particularly in developing countries. In Jakarta, Indonesia, congestion on toll roads is a significant issue that needs to be addressed through an Intelligent Transportation System (ITS). One of the key solutions proposed is vehicle detection and traffic prediction on toll roads. This study introduces a computer vision-based approach utilizing YOLOv8 to detect, track, and count vehicles to predict traffic congestion. The system operates by identifying vehicles (cars and trucks), preprocessing the data, and calculating the total number of vehicles within the camera’s range. If the vehicle count surpasses the threshold set by the toll road provider, the system updates the traffic status (normal or congested) and triggers a warning. The vehicle detection system can identify cars and trucks within a range of up to 150 meters. Experimental results using test videos demonstrate that the YOLOv8-based system achieves an accuracy of 98% with an average detection speed of 83.6 milliseconds, ensuring highly efficient performance. With its high accuracy and speed, this system can be effectively integrated into traffic management solutions to alleviate congestion and enhance transportation efficiency in Jakarta.