Current road accident research focuses mainly on the key role and importance of Artificial Intelligence (AI) in road accident analysis and prevention. After reviewing the literature, this study found that AI had a wide range of potential applications. It can analyse traffic data more accurately and quickly through advanced machine learning and deep learning technologies, and identify accident risks and dangerous driving behaviours, thereby helping to predict and avoid accidents. Furthermore, the research provides insight into the different types of traffic accidents and the severity of injuries they cause, highlighting the importance of understanding these differences to improve road safety and help inform decision-making. The paper has attempted to develop a comprehensive and diverse road crash impact model by exploring some of the elements that influence road crashes, including human factors, vehicle factors and road environment factors. Finally, this paper identifies some innovations and future research directions for this study, including addressing imbalances and quality issues in collecting and processing data, improving the interpretability and transparency of injury severity expressions, and adopting a more comprehensive approach to analysing road crashes. These innovations will promote greater theoretical and practical progress in road crash research to improve road safety and reduce the damage caused by accidents.
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