Analysis of Two-Wheeler Casualty Traffic Accidents with Improved Apriori Algorithm Based on the XGBoost Model

Accurately identifying the significant factors influencing the severity of two-wheeler casualty accidents and mining association rules between multi-factor combinations and accident outcomes is crucial for implementing targeted prevention strategies. This study integrates machine learning and an improved Apriori algorithm to analyse 5,032 two-wheeler casualty accidents. Initial factors were identified through feature screening, feature fusion and K-means clustering, and then ranked by importance using the XGBoost model, resulting in ten significant factors for association rule mining. Accident severity weights were calculated based on equivalent minor injury values, and an improved Apriori algorithm, incorporating weighted support and constraints on consequent terms, was applied to extract association rules for both major and general accidents. Results show that combinations of factors such as lorries, two-wheeler riders aged 59 years or older, segregated roads, asphalt structures, electric bicycles, summer and lack of helmet use are strongly associated with major accidents. Conversely, combinations involving passenger vehicles, motorcycles, two-wheeler riders aged 30 to 43 years, non-segregated roads, cement structures and spring are associated with general accidents. Among these factors, the collision object has the greatest impact on accident severity. Minimising spatial and temporal conflicts between two-wheelers and other vehicles is essential to reducing two-wheeler casualties.