Traffic Accident

Traffic accident research focuses on predicting and preventing accidents through data-driven modeling, aiming to reduce fatalities and injuries. Current research employs various machine learning techniques, including graph neural networks, long short-term memory networks, and vision transformers, often incorporating diverse data sources like dashcam footage, weather data, and road network information to improve prediction accuracy and understand contributing factors. These advancements hold significant potential for improving road safety through targeted interventions, autonomous driving system development, and more effective urban planning strategies. The field is also increasingly focused on model interpretability to facilitate the development of actionable insights for policymakers and safety professionals.