Accident Analysis

Accident analysis research focuses on understanding and predicting accidents across various domains, primarily aiming to improve safety and prevent future incidents. Current research heavily utilizes machine learning, including graph convolutional networks (GCNs) and recurrent neural networks (RNNs) for spatiotemporal analysis of traffic accidents, and large multi-modal models for comprehensive accident scene understanding and responsibility allocation. These advancements leverage diverse data sources, such as road networks, traffic volume, weather data, and multi-view imagery, to improve prediction accuracy and inform targeted interventions, ultimately contributing to reduced accident rates and improved safety outcomes.