Comprehensive analysis method of key risk factors in the subway operation accident by complex network and accident data.

With the continuous expansion of subway systems in China, the operational systems of subway have become increasingly complex, leading to a rise in risk factors. In the event of an accident, these risks can significantly impact the safety and health of individuals, and pose a direct threat to the reliability, social stability, and economic sustainability of subway systems. To effectively prevent the occurrence of operational accidents and ensure the sustainable development of subway systems, it is essential to explore the key risk factors of subway accidents through appropriate technological approaches. This study proposes an integrated approach combining the 24Model, association rule mining, and complex network theory to conduct in-depth mining and analysis of textual data from accident reports, thereby identifying risk factors and exploring the coupling relationships and importance among them. First, risk factors were extracted by analyzing 76 reports using the 24Model. Then, the Apriori algorithm was applied to derive association rules among the risk factors, based on which a risk factor network model was constructed. Finally, the robustness analysis and mutual information theory were employed to validate the model and identify the key risk factors. The results show that unclear safety responsibilities of employees, safety responsibility of managers, insufficient safety oversight of subcontractors, and lack of targeted content in safety training are the four most critical risk factors. The findings of this study provide important safety management decision-making support for the development of more sustainable subway operational systems.