## 5 Discussion
### Risk Coupling Value Ranking in the Two Accident Categories  
To identify the key coupling scenarios and risk types, the risk coupling values in both ACs were displayed in a radar chart and ranked. Figure 13 presents the risk coupling values for the two-factor coupling type. Figure 13 indicates the following:  
* In PA, the top five risk coupling scenarios were \(T_{2,8}(R,W)\), \(T_{2,1}(H,V)\), \(T_{2,10}(W,M)\), \(T_{2,7}(V,M)\), and \(T_{2,9}(R,M)\);
* In SA, the top five risk coupling scenarios were \(T_{2,2}(H,R)\), \(T_{2,5}(V,R)\), \(T_{2,8}(R,W)\), \(T_{2,7}(V,M)\), and \(T_{2,9}(R,M)\).  
Figure 13: Risk coupling values for the two-factor coupling type.  
The importance can be determined by the frequency of each risk type appearing in the coupling scenarios. It can be inferred that the most important factor in PA was management-related factors (appearing three times), while in SA, the most important factor was road-related factors (appearing four times). This suggests that the risks leading to PA are not short-term in nature and that improving daily management measures could potentially eliminate these risks. However, the evolution from PA to SA requires specific road conditions, such as curved roads, slippery surfaces, traffic congestion, or nighttime.  
Figure 14 presents the risk coupling values for the three-factor coupling type, and it can be summarized as follows:  
* In PA, the top five risk coupling scenarios were \(T_{3,10}(R,W,M)\), \(T_{3,7}(V,R,W)\), \(T_{3,A}(H,R,W)\), \(T_{3,3}(H,V,M)\), and \(T_{3,9}(V,W,M)\);
* In SA, the top five risk coupling scenarios were \(T_{3,1}(H,V,R)\), \(T_{3,A}(H,R,W)\), \(T_{3,7}(V,R,W)\), \(T_{3,8}(V,R,M)\), and \(T_{3,5}(H,R,M)\).  
It can be inferred that the most important factor in PA was weather-related factors (appearing four times), while the most important factor in SA was road-related factors (appearing five times). This suggests that adverse weather creates a disaster-prone environment where the coupling effect of risks is stronger, easily surpassing the safety threshold and leading to PA.  
Figure 15 presents the risk coupling values for the four-factor coupling type, and it can be summarized as follows:  
* In PA, the top three risk coupling scenarios were \(T_{4,1}(H,V,R,W)\), \(T_{4,5}(V,R,W,M)\), and \(T_{4,4}(H,R,W,M)\);
* In SA, the top three risk coupling scenarios were \(T_{4,1}(H,V,R,W)\), \(T_{4,5}(V,R,W,M)\), and \(T_{4,2}(H,V,R,M)\).  
It can be inferred that in PA, road-related factors and weather-related factors (appearing three times) were equally important, while in SA, vehicle-related and road-related factors (appearing three times) were equally important. As mentioned in the previous section, objective factors are the dominant factors in PA, with inevitability and ease of intervention. In contrast, most factors related to vehicles are triggered by the subjective intentions of the drivers, such as overloading and illegal modifications. Therefore, both objective and subjective factors can lead to SA, indicating that SA have a certain degree of randomness and are harder to intervene in. Additionally, in PA, complex coupling scenarios are more dependent on weather-related factors. However, regardless of how  
Figure 14: Risk coupling values for the three-factor coupling type.  
coupling scenarios evolve in SA, road-related factors remain the primary source of danger leading to the evolution from PA to SA.