## 6 Conclusions  
This study proposes a risk coupling analysis framework for PA and SA in highways, aiming to extract risk factors and risk types from accident reports to measure the risk coupling effects. Specifically, a domain-specific NER model, TRBERT-BiLSTM-CRF, which combines a transformer pretrained on domain-specific corpora (TRBERT) with bidirectional LSTM and CRF layers, was used for risk factor extraction, achieving the best macro-F1 score in traffic risk entity recognition. Subsequently, the N-K model was employed to quantify the interactions between risk factors based on the statistical analysis of risk factors, and the Wilcoxon signed-rank test was applied to compare the differences in risk coupling between PA and SA. Finally, recommendations for the prevention of PA and the blocking of SA were formulated based on the analysis results. The main findings of this study were as follows:  
* The risk coupling values increased with the number of risk coupling types in both ACs. However, this was not absolute, as the coupling values for the five risk coupling types in SA were lower than those for the four risk coupling types, suggesting that the occurrence of SA may be more dependent on specific risk types;
* The risk coupling values in SA were generally higher than in PA, indicating that more complex accident scenarios led to an increase in risk coupling values. This can be attributed to the cumulative coupling of new risk factors with initial risk factors and the complex evolving scenarios in SA;
* There was a significant difference in the risk coupling values between PA and SA. The coupling of objective factors (e.g., adverse environmental conditions) is more likely to lead to PA and is easier to prevent. In contrast, the interaction between subjective (e.g., vehicle) and objective factors (e.g., road conditions) is more likely to result in SA, reflecting greater randomness and being harder to prevent. These coupling results and quantitative evidence indicate that the risk coupling mechanisms between PA and SA differ. We suggest that these differences should be considered in the prevention of PA and the blocking of SA. Specifically, given the practical challenges in addressing environmental factors, this study suggests reducing the risk of PA from the perspectives of management and vehicle factors. Specific measures include standardized vehicle inspections, comprehensive training and education programs, and rigorous hazard inspections. Road traffic enforcement agencies and transportation companies should strengthen dynamic vehicle monitoring and control of overloaded vehicles to minimize vehicle-related risks. Additionally, this study suggests reducing the risk of PA evolving into SA from the perspective of road factors and implementing specific measures to establish a robust risk management system. Comprehensive risk management includes information alerts for hazardous road sections, flexible control measures, and nighttime hazard warnings. More importantly, highway emergency response departments should implement safety barriers after a PA occurs to decouple complex accident scenarios and weaken the evolution of PA. This study provides a semiautomated integrated framework for road traffic accident risk analysis. The research findings help prioritize and focus monitoring on the most impactful risk factors, providing valuable insights for informed decision-making and proactive risk mitigation strategies, ultimately reducing the likelihood of PA and SA occurrence. By integrating the TRBERT-BiLSTM-CRF model into existing traffic incident management systems (TIMSs) to automatically process unstructured accident reports, it can enable dynamic risk assessment dashboards for operators, while the extracted risk entities can be used to construct detailed road traffic accident risk knowledge graphs and risk diffusion networks to enhance proactive safety measures.