Quantitative Analysis of Risk Coupling Effects in Highway Accidents: A Focus on Primary and Secondary Accidents

Analyzing risk coupling effects in highway accidents provides guidance for preventive decoupling measures. Existing studies rarely explore the differences in risk coupling between primary accidents (PA) and secondary accidents (SA) from a quantitative perspective. This study proposes a method to measure the risk coupling effects of PA and SA on highways and examine their differences. A domain-pretrained named entity recognition (NER) model, TRBERT-BiLSTM-CRF, is proposed to identify risk factors and risk types based on 431 accident investigation reports published by the emergency management departments in China. The N-K model was applied to calculate the risk coupling values for different coupling scenarios in PA and SA, and the Wilcoxon signed-rank test was performed on them. Finally, the differences between PA and SA were compared, and targeted accident prevention recommendations are provided. The results showed that our proposed NER model achieved the best macro-F1 score in traffic risk entity recognition. Most of the risk coupling values increased with the number of risk types, but the coupling value of the five factors in the SA was lower than that of the four factors, indicating that the risk types do not always superimpose each other in complex scenarios. Moreover, there were significant differences in the risk coupling mechanisms between PA and SA. The results suggest that the likelihood of PA and SA occurrences should be reduced through standardized vehicle inspections and flexible control measures, respectively, thereby enhancing highway safety.