An Integrated HFACS-Apriori-SEM Analytical Framework for Human Factor Identification and Causal Mechanism Exploration in Road Transportation Accidents Involving Dangerous Goods

To address the limitations of incomplete factor identification, insufficient cross-level coupling quantification, and inadequate causal path verification in traditional human factor analysis of road transportation of dangerous goods (RTDG) accidents, this study developed an integrated HFACS-Apriori-SEM analytical framework that enables full-process analysis from factor identification to causal mechanism exploration and hierarchical path validation. A five-level industry-specific Human Factors Analysis and Classification System (HFACS) framework with 85 causal indicators was established, and standardized coding was conducted for 58 fatal RTDG accidents in China from 2012 to 2022. Twelve core strong association rules were generated using the Apriori algorithm. Among these, the co-occurrence chain “organizational process failure → inadequate supervision → insufficient personnel readiness → routine violations” had the highest support of 0.621. Structural equation modelling (SEM) provided empirical support for a significant hierarchical chain transmission effect of the accident causation. The findings showed that preconditions for unsafe acts exerted the largest indirect effect on accident severity (total effect = 0.69, p < 0.001). Furthermore, unsafe acts were the only direct influencing factor (total effect = 0.85, p < 0.001). In addition, violations accounted for a significantly higher proportion of unsafe acts than errors. This study provides strong empirical evidence that catastrophic RTDG accidents stem from the chain failure of multi-level system defenses, offering a quantitative and targeted decision basis for hierarchical accident prevention and control in the RTDG industry.