AI-Based Analysis of Road Congestion Causes Using Real-Time Traffic Camera Data

Abstract—This study uses real-time data from traffic cameras and artificial intelligence (AI) algorithms to analyse the reasons for traffic congestion in a novel way. By recognising particular sources of congestion, such as accidents, processions, road construction, or general traffic accumulation, the proposed system seeks to supplement current navigation tools. The system makes very accurate predictions about the reasons for congestion by utilising a convolutional neural network (CNN) model that has been trained on a variety of datasets. The methodology, dataset preparation, model architecture, and interaction with a map-based user interface are all covered in detail in this work. The system's ability to give drivers and city planners useful insights is demonstrated by the results. Keywords—Traffic congestion, artificial intelligence, real-time traffic analysis, convolutional neural network (CNN), deep learning, traffic camera data, road construction, accident detection, processions, traffic management, navigation systems, urban planning, machine learning, data augmentation, web-based platform, intelligent transportation systems.