An Association Rule Mining-Based Modeling Framework for Characterizing Urban Road Traffic Accidents

The World Health Organization has recognized road traffic accidents as a global crisis, particularly in urban environments. Despite extensive research endeavors, significant gaps remain in our understanding of how various factors interact to influence urban road traffic incidents. This study analyzed data from 4285 urban road traffic accidents in Hubei Province, employing a two-step clustering algorithm to classify accidents into distinct groups based on specific conditions. Subsequently, association rule mining was utilized to discern relationships between accident characteristics within each cluster. Additionally, a classification based on the association rule algorithm was implemented to develop a predictive model for analyzing factors contributing to casualties. The data were categorized into clusters based on weather and road conditions, with separate discussions conducted for each scenario. The findings indicated that urban congestion is the most critical factor contributing to accidents. Interestingly, rather than in severe weather, accidents were more prevalent during cloudy, light-rain conditions. Electric vehicles and motorcycles emerged as the most vulnerable groups. Furthermore, a notable interaction was observed between the day of the week, time of day, and weather conditions. The predictive model achieved an impressive average accuracy of 86.9%. This methodology facilitates the identification of contributing factors and mechanisms underlying urban road traffic accidents in China and holds potential for establishing accident analysis models in similar contexts. The interactive visualization of association rules further enhances the applicability of the findings. The findings of this study can provide valuable insights for traffic management authorities to understand the causes of urban road traffic accidents, assisting them in devising effective policy measures and countermeasures to reduce the occurrence of accidents and casualties.