## 4 Analysis and Results
### Prediction Models
Here, TP refers to a true positive, which is a case in which the model predicted the victim to be in the target age group while he was actually in that category; TN refers to a true negative, which is a case in which the model predicted the victim to be out of the target age group while he was actually not in the target age group; FN refers to a false negative, which is a case in which the model predicted the victim to be out of the target age group while he was actually in that group; lastly, FP refers to a false positive, which is a case in which the model predicted the victim to be in the target age group when he was actually not in that group. All accuracy measures for the logistic regression model are satisfactory, and they are close to each other for development and validation datasets. This shows that the logistic model is accurate and robust in predicting the age group of the crash victims. The CART model developed for the crash dataset is shown in Figure 7, and its accuracy measures are shown in Table 8. The distribution of development and validation datasets was the same as the logistic regression model for a fair comparison. In Figure 7, each node shows the factor used to divide the node further. In the case of a terminal node (last node in that branch), the limiting value of the variable and the probability of the victim being in the target age group (\(p\)-value) are shown. The sequence of each node shows the conditions that would result in a certain probability of the involvement of the target age group. For example, hitting pedestrians results in a 57% probability that a victim from the target age group would be involved in the crash, irrespective of any other factor (node ID 2). Similarly, if the crash is of any other type and has more than six injuries, then there is a 60% probability of involving the target age group (node ID 4). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Dataset** & **Value** \\ \hline \multirow{2}{*}{Accuracy} & Development & 0.73 \\ \cline{2-3}  & Validation & 0.73 \\ \cline{2-3}  & Development & 0.74 \\ \cline{2-3}  & Validation & 0.74 \\ \hline \multirow{2}{*}{Recall} & Development & 1.00 \\ \cline{2-3}  & Validation & 0.98 \\ \hline \multirow{2}{*}{Error} & Development & 0.27 \\ \cline{2-3}  & Validation & 0.27 \\ \hline \multirow{2}{*}{FDR} & Development & 0.26 \\ \cline{2-3}  & Validation & 0.26 \\ \hline \multirow{2}{*}{FNR} & Development & 0.00 \\ \cline{2-3}  & Validation & 0.02 \\ \hline \multirow{2}{*}{F-1} & Development & 0.85 \\ \cline{2-3}  & Validation & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy measures for the logistic regression model. Figure 7: CART model. low probability of involving people from the target age group as compared with other traffic crashes (ID 2). This could indicate that traffic crashes for the target age group are not due to lost control over the vehicle. Similarly, fatal traffic crashes have a relatively higher probability of a victim from the target age group as compared with traffic crashes with only major injuries. Traffic crash types that are combined under \"other types\", comprising less than 5% of crashes, also have a comparatively lower probability of involving victims from the target age group. Traffic crashes that happen due to not leaving a sufficient gap have a 79% possibility of involving the target age group in all types, except hitting pedestrians, as shown in node ID 12. Findings from other terminal nodes are as follows: Traffic crashes with fewer than six injuries and two deaths, which do not happen due to insufficient gaps and result in a vehicle overturn, have a 100% probability of involving victims from the target age group. Traffic crashes that do not happen due to insufficient gaps and do not result in a vehicle overturn or hitting pedestrians have an 80% chance of involving a victim from the target age group if the number of injuries in the crash is more than two, which is shown in node ID 16.