Smart Traffic Management and Accident Detection System: A Review

Abstract

Abstract: This paper shows us how deep learning and computer vision are changing the way we can handle our traffic and prevent accidents. With the rapid growth of A.I. and deep learning becoming faster and more powerful, it helps monitor traffic, detect accidents, and even predict them. This technology can do this in real time with impressive accuracy. In conditions like bad weather or low visibility, older traffic systems that relied on fixed rules or basic sensors often struggled to keep up. To solve the problem, researchers have been exploring models such as YOLO11-AMF, RT-DETR-EVD, and hybrid GAN-based frameworks. These models are now using convolutional and transformer-based networks to identify and classify objects more precisely. In this review paper, we use 10 different recent studies and compare them. The selected models primarily use deep learning for accident and emergency vehicle recognition. Some of these approaches even combine audio and video data to give better results. We can evaluate them based on their datasets, mean Average Precision (mAP), and how quickly they can make predictions. This paper also discusses the challenges in this field, including limited training data, high computational costs, and models that don’t always perform well across different environments. Looking forward, we can clearly highlight trends such as federated learning, self-supervised learning, and model training, which are making it easier to run these systems on IoT and edge devices. Overall, this review shows just how powerful deep learning and AI models are in building safer, smart transportation systems and how close we are to fully automated, intelligent traffic management in the near future.