## 5 Results and Discussion
### SHAP Results  
Figure 8 shows the SHAP summary plot, which orders features based on their importance in classifying short, medium, intermediate, and long ICT. Crashes involving  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Average** & **Macro Average** & **Macro Average** & **Macro Average** \\  & **Accuracy** & **Recall** & **Precision** & **F1-Score** \\ \hline XGBoost & 0.8573 & 0.7448 & 0.7539 & 0.7444 \\ CatBoost & 0.8446 & 0.7178 & 0.7252 & 0.7161 \\ LightGBM & 0.8849 & 0.7023 & 0.7120 & 0.6972 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overall performance of XGBoost, CatBoost, and LightGBM.  
Figure 6: Comparison of classification ML models for intermediate durations.  
Figure 7: Comparison of classification ML models for longer durations.  
injuries and fatalities significantly increase the probability of intermediate and longer ICT, as seen in Figure 8c,d. The reason for the increased ICT with crashes involving injuries and fatalities is the need for response agencies and emergency medical services (EMS). For short ICT, as evident from Figure 8a, injuries and fatalities have a very low impact as incidents cleared during this duration are mainly less severe. This finding is consistent with prior research [2; 40]. Additionally, it was found that crashes that involve trucks significantly increase the probability of long ICT. This is because the incident involving trucks resulted in an increased number of injuries, fatalities, and property damage. This finding is consistent with other studies that have found that heavy truck crashes increase incident durations significantly in comparison to other vehicles [51; 52]. For the manner of collision, the result shows various impacts on different duration levels. For example, sideswipe and rear-end crashes slightly increase the short, intermediate, and long ICT probability. However, head-on crashes tend to significantly increase the probability of long ICT. This result is expected, as head-on crashes typically result in a higher number of injuries in comparison to sideswipes and rear-end [48; 49].  
In terms of driver age, the variables found to be significant are young age and old age. Young age increases the probability of intermediate ICT, whereas old age significantly increases long ICT. This is due to older drivers having health complications and slower reaction times, which may lead to severe crashes [80]. The results also show that road departure crashes increase the probability of long ICT. This finding is intuitive as road departure crashes are associated with severe injury crashes and are more devastating when such crashes involve encroachment into roadside obstacles [81; 82]. For driver distraction features, the result in Figure 8d indicates that the use of cell phones and distractions inside the vehicles and outside both tend to increase the probability of long ICT. This happens because distraction diverts the driver's attention from the road and surrounding traffic, which may lead to severe crashes [83]. In addition, incidents during peak hours tend to have an increased probability of long ICT. This is due to peak-hour traffic and increased congestion [45; 84].  
Regarding environmental and temporal characteristics, dark, dusk, and dawn are associated with increased probabilities of intermediate and long ICT. This can be due to the lesser availability of incident response resources and poor visibility during dark, which can make it harder for responders to assess the situation quickly and can lead to increased ICT [47]. Also, incidents that occurred during rain, fog, and snow increased the probability of longer ICT. It was reasonable that adverse weather conditions might cause a delay in incident response. This effect of rainy and snowy conditions on ICT is supported by previous research [37].  
Figure 8: Cont.  
Figure 8: SHAP summary plot: (a) short duration; (b) medium duration; (c) intermediate duration; (d) long duration.