Traffic Congestion Detection Using YOLOv8 Algorithm With CCTV Data

Abstract

Community development and growth according to data from the Central Java Statistics Agency regarding the number of vehicles in Central Java Province in 2021 is 20 320 743. The increasing growth of society has caused vehicle density which is a serious problem in urban areas. This study developed a congestion detection system using the YOLOv8 algorithm to analyze traffic density from CCTV footage. Automated detection of traffic congestion is a critical challenge in urban transport management. YOLOv8, a fast and accurate object detection algorithm, is used to identify vehicles and count their number in various areas of the highway. This information is then processed to assess road congestion conditions, with the aim of detecting congestion. The data obtained were tested on two road scenarios and traffic conditions to evaluate the performance of the system. The results showed that the proposed system was able to detect congestion with an accuracy level of 59.2% from several experiments. The use of YOLOv8 enables real-time detection with efficient computing resources, making it a potential solution for large-scale deployment. This research shows that the incorporation of advanced object detection algorithms such as YOLOv8 with CCTV data can provide an effective solution for traffic management in large cities. This system is expected to improve response to congestion, help control traffic, and reduce the negative impact of congestion in urban areas