## 5 Discussion
### Limitations and Future Work  
The proposed framework can semiautomatically extract the risk factors and risk types that lead to PA or the evolution of PA into SA from accident reports and subsequently measure the risk coupling effects of different risk types in both ACs. The results can provide guidance for the prevention of PA and the blocking of SA to develop control measures, aiming to decouple such effects. However, this study also has certain limitations. **Risk factor identification limitations**  
This study has achieved semiautomated identification and extraction of risk factors and risk types. However, the identification of these entities relied on manual labeling. The challenge of high labeling costs can be overcome by using semi-supervised learning methods, such as a collection of pseudo label sets and manual label sets generated by self-training strategies [34]. Furthermore, at this stage, the mapping of risk factors to risk types still relies on expert experience [57, 62]. Different mappings of risk factors would directly affect the calculation of risk coupling probabilities and risk coupling values in this study, and the risk coupling mechanisms may also vary. This remains a challenge in the field of risk assessment. **Coupling analysis limitations**  
A notable limitation is that all the risk factors were assigned to five risk types for coupling analysis. This simplified the mapping of risk state encoding in the previous section, as well as the calculation of risk coupling probabilities and coupling values in the previous section, because the number of coupling scenarios was reduced. The fact that the N-K model can analyze only a single layer of risk factors is a well-known and  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**AC**} & **Key Risk Types** & **Risk Factors** & **Prevention and Control Measures** & **Investigation Suggestions** \\ \hline PA & Vehicle & Noncompliant vehicle specifications, illegal modifications, overloading or oversize load, missing or unclear reflective markings, etc. & Regularly inspect and maintain the vehicle’s driving system, braking system, lighting system, safety system, and facilities to eliminate safety hazards, Strengthen the work on controlling overloaded vehicles. & After PA, the vehicle specifications should be checked for defects and faults, with particular attention given to any overloading or weight limit violations. & After PA, the vehicle specifications should be checked for defects and faults, with particular attention given to any overloading or weight limit violations. & After PA, the vehicle specifications should be checked for any design defects, with a focus on the road conditions and traffic conditions at the time of SA. & After SA, the accident vehicle specifications should be checked for defects and faults and whether the vehicle is loaded with flammable and explosive goods. reasonable limitation [63; 64]. With each additional risk factor, the computational complexity of the N-K model increases exponentially. For example, with 61 risk factors in PA, there were \(\mathbf{2}^{4}\times C_{61}^{4}=521855\) possible coupling scenarios for four risk factors. Capturing the correlations between microlevel factors in coupling analysis requires resource-intensive accident analysis techniques or considering only high-risk nodes [63; 64], which will be left for future work. **Data-related limitations**  
The data used in this study also had certain limitations. There were only 67 SA samples, which led to some coupling scenarios having a frequency of zero in Figure 7, such as the frequency of single factor scenarios being zero (\(P_{10000}\), \(P_{01000}\), \(P_{00100}\), \(P_{00010}\), and \(P_{00001}\)). This poses a challenge to the reliability of the risk coupling mechanism analysis in SA.