Analysis of factors influencing urban road traffic accidents using weighted association rules

Accurate analysis of the influencing factors of traffic accidents helps identify safety hazards and bottlenecks in the transportation system, and proposes corresponding improvement measures and suggestions based on the analysis results. To address the challenges of limited mining and analysis accuracy, coupled with prolonged analysis durations, inherent in conventional approaches to dissecting the factors influencing urban road traffic accidents, this study introduces a novel analysis methodology grounded in weighted association rules. This innovative approach not only enhances our understanding of accident causation but also contributes to safer road networks and improved traffic management strategies. By leveraging weighted association rules to mine urban road traffic accident data, we initially extract valuable insights. Subsequently, principal component analysis is employed to streamline the data's dimensionality, facilitating a more focused and efficient analysis. Within the framework of Grey correlation analysis, we meticulously establish comparison and reference sequences, calculating both the weighted Grey correlation degree and weight coefficient of these sequences to rigorously screen factors. This meticulous process culminates in a comprehensive analysis of the factors influencing urban road traffic accidents. Experimental results underscore the efficacy of this new method with a peak data mining accuracy of 97.4%, an unparalleled analysis accuracy of 97.8%, and a streamlined analysis time of merely 1.36 seconds.