## 5 Discussion
### Risk Coupling Value Comparison in the Two Accident Categories  
To compare the differences in risk coupling values between the two ACs, the risk coupling values were plotted as a line chart, as shown in Figure 12. Overall, the risk coupling values for different coupling scenarios in both ACs showed significant fluctuations that depended on the specific risk types. For example, the road-weather coupling scenario corresponded to a higher risk coupling value in PA, while scenarios involving road-related factors corresponded to higher risk coupling values in SA. This indicates that the involvement of road-related factors is more likely to lead to accidents or exacerbate their evolution. Another noticeable phenomenon was that the coupling values increased with the number of the risk coupling types in both ACs, a trend that has also been confirmed in other accidents [3; 7; 56; 57]. However, in SA, the risk coupling values of several coupling scenarios in the four-factor coupling type were higher than those in the five-factor coupling type, such as \(T_{4,1}(H,V,R,W)>T_{5}(H,V,R,W,M)\). This indicates that in SA, the risk coupling is more concentrated. In addition, the risk coupling values in SA were generally higher than those in PA, a conclusion that was also confirmed by the Wilcoxon signed-rank test results (\(Z=-2.1588\)) in the previous section. This can be attributed to the cumulative coupling of new risk factors with initial risk factors and the complex evolving scenarios in SA. In the two-factor coupling type, the road-weather (\(T_{2,8}(R,W)\)) coupling scenario in PA had the highest risk coupling value, while the human-vehicle (\(T_{2,2}(H,R)\)) and vehicle-road (\(T_{2,5}(V,R)\)) coupling scenarios in SA exhibited the highest risk coupling values. This indicates that the coupling of objective factors (e.g., adverse environmental condition) is more likely to lead to PA, with a certain inevitability, and is relatively easier to prevent compared with the coupling of subjective factors (e.g., human and vehicle). In contrast, the coupling of subjective factors (e.g., human and vehicle) with objective factors (e.g.,  
Figure 12: Risk coupling values for different coupling scenarios in the two accident categories. road) is more likely to result in SA, reflecting a degree of randomness and being harder to intervene in. In the three-factor coupling type, the risk coupling value in the vehicle-road-weather (\(T_{3,7}(V,R,W)\)) coupling scenario was the highest in PA, while the human-vehicle-road coupling value (\(T_{3,1}(H,V,R)\)) was relatively low. This suggests that weather-related factors are more significant in PA, whereas the influence of human-related factors diminishes. In SA, the risk coupling value was highest in the human-vehicle-road (\(T_{3,1}(H,V,R)\)) coupling scenario, while the human-vehicle-weather (\(T_{3,2}(H,V,W)\)) coupling value was the lowest. This indicates that road-related factors play a more critical role in SA and that the influence of weather-related factors diminishes. An interesting phenomenon here is that, as the accident evolves, road-related factors gradually take the dominant role, while the impact of weather-related factors weakens after the accident occurs. One possible explanation for this is that weather-related factors can affect visibility and judgment for drivers during high-speed driving. However, once an accident occurs, the scene transitions from dynamic traffic flow to a relatively static road environment (such as road congestion), and speeds may decrease, allowing drivers more time to react to adverse weather. In the four-factor coupling type, the highest risk coupling value for both ACs occurred in the human-vehicle-road-weather (\(T_{4,1}(H,V,R,W)\)) coupling scenario, while the lowest values corresponded to the human-vehicle-road-management (\(T_{4,2}(H,V,R,M)\)) and human-vehicle-weather-management (\(T_{4,3}(H,V,W,M)\)) coupling scenarios for PA and SA, respectively.