Innovative Approaches to Traffic Anomaly Detection and Classification Using AI

Video anomaly detection plays a crucial role in intelligent transportation systems by enhancing urban mobility and safety. This review provides a comprehensive analysis of recent advancements in artificial intelligence methods applied to traffic anomaly detection, including convolutional and recurrent neural networks (CNNs and RNNs), autoencoders, Transformers, generative adversarial networks (GANs), and multimodal large language models (MLLMs). We compare their performance across real-world applications, highlighting patterns such as the superiority of Transformer-based models in temporal context understanding and the growing use of multimodal inputs for robust detection. Key challenges identified include dependence on large labeled datasets, high computational costs, and limited model interpretability. The review outlines how recent research is addressing these issues through semi-supervised learning, model compression techniques, and explainable AI. We conclude with future directions focusing on scalable, real-time, and interpretable solutions for practical deployment.