## 4 Result and Discussion
### Examining Predictions through the Machine Learning Method
For instance, if we want to make a decision based on the root node, the result of the classification model is \"no injury\", but the confidence of the tree at this level is 50% (1654/(1654 + 1654)). DTs typically reveal some important information based on a dataset. For the dataset used in this study, a clear description of the relevance of factors was not available, due to the wide range of factors influencing the severity of a collision and complexity of the relations. Therefore, the RF was used to further explain the relative importance of factors in the whole dataset from 1999 to 2017 and the last 5 years of the records (i.e., 2013 to 2017). Figure 4 shows both factor importance and relative importance of the factors based on the RF classifier algorithm, which is slightly different from the DT. Comparing theresults of RF in the two datasets does not indicate a significant difference in weights and relative importance of the factors. This implies that the length and period of the dataset have no considerable effects on the results. However, the results from the last 5 years are preferred, due to the probable changes in conditions during a long period. While the sum of relative importance is 1, attributes such as person's age, hour, vehicle age, month, weekday, and weather conditions can describe almost 70% of the underlying model. If a collision occurs, these factors could indicate the probability of the severity of a collision. For instance, looking at the categorical distribution of each factor (Figures 1 and 2), it can be concluded that accidents that occurred during a weekend day of a high season in sunny and clear weather criteria are more likely to have injured drivers and passengers. This may be expected, but with the results of RF, the weight of importance (rate of contribution) of each factor can be observed. Regarding the first responder safety, some of these factors (i.e., person or vehicle age) cannot be measured in advance of a collision, but some other factors such as time and location of the mission (the work assigned to the first responders) and their pertaining factors (i.e., road alignment, configuration, traffic control) are known, whereas weather condition and road surface category can be sensed or predicted. Based on the category of these known factors, the risk level of a first responder can be estimated before starting a mission or operation, and the priority of using the proper safety regulations and devices can be highlighted. Also, the number of team members can be revised to ensure that the necessary safety procedures are taken into consideration. In other words, safety devices, such as flashlights and warning radars, and safety measures, such as the length of traffic control zones, can be modified when there is a high risk of collision. To define this situation, again, the RF model results appear to be helpful (Figure 5). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **All Records** & **Hit a Parked Vehicle** \\ \hline No Injury & 43.0\% & 25.8\% \\ \hline Injury & 56.4\% & 73.8\% \\ \hline Fatality & 0.6\% & 0.4\% \\ \hline Total number of collisions & 3,817,613 & 6399 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Distribution of type of required medical treatment in collisions in NCDB [29] dataset. Figure 3: Decision tree. The accuracy metrics are 0.63 and 0.64 for the DT and RF, respectively. These rates are in the range of the top rank classifiers offered by the Auto-Sklearn library and similar to those reported in previous studies using the same method [34; 39]. However, the accuracy is not close to 1 due to the weak dependency of the severity of collision on the available factors as well as the absence of other important factors in the dataset (e.g., confusion or alcohol consumption of the driver, road geometry, speed, familiarity with the road, etc.).