Intelligent Traffic Analysis System Using Deep Learning

Abstract

Due to the rapid increase in both vehicle traffic and urbanization, effective traffic control systems are now essential. This review paper integrates Intelligent Traffic Analysis Systems (ITAS) with Convolutional Neural Networks (CNNs), a deep learning technology, to provide accurate and real-time data analysis. Advanced technologies used by ITAS monitor, analyze, and reduce traffic flow. Specifically designed deep learning models for object detection and tracking are employed to recognize and monitor cars, trucks, and other relevant entities in the recorded data. Transfer learning from previously trained models is used to train the proposed CNN architecture, which is modified for traffic analysis. This approach enhances efficiency and helps avoid road traffic congestion.