## 4 Analysis and Results
### Prediction Models
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Classes** & **Description/Code** \\ \hline \multirow{3}{*}{Severity} & Fatal & 1 \\ \cline{2-3}  & Major injury & 2 \\ \hline \multirow{6}{*}{Crash type} & Collision & 1 \\ \cline{2-3}  & Hit motorcycle & 3 \\ \cline{2-3}  & Hit road fence & 6 \\ \cline{2-3}  & Hit pedestrians & 8 \\ \cline{2-3}  & Vehicle overturn & 9 \\ \cline{2-3}  & Other types & 20 \\ \hline \multirow{6}{*}{Crash reason} & Driver distraction & 3 \\ \cline{2-3}  & Speeding & 4 \\ \cline{2-3}  & Not giving way & 9 \\ \cline{2-3}  & Sudden turning & 14 \\ \cline{2-3}  & Not leaving sufficient distance & 15 \\ \cline{2-3}  & Other reasons & 46 \\ \hline Number of deaths & N/A & Number of persons killed in RTC \\ \hline Number of injuries & N/A & Number of persons injured in RTC \\ \hline \multirow{2}{*}{Age} & 15 to 44 (inclusive) & 1 \\ \cline{2-3}  & Less than 15 or more than 44 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of modeling parameters. The \(t\)-statistics and \(p\)-values for the utility Equation (6) are given in Table 6. It can be observed that traffic crashes that result in hitting pedestrians have a higher likelihood of involving people from the target age group. Since no other crash type significantly affected the utility equation, it can be said that all other crash types have similar chances of involving people from or outside of the target age group. Moreover, traffic crashes due to not leaving a sufficient gap and sudden turning are less likely to involve victims from the target age group, while all other reasons had similar chances of their involvement. Another important observation is that the traffic crashes involving the target age group are more severe and have a higher number of victims, which is shown by the positive coefficients of the number of injuries and fatal traffic crashes. Values for accuracy, precision, error, recall, false discovery rate (FDR), false-negative rate (FNR), and F1 score were also calculated for the logistic regression model, as per Equations (7)-(13) [65]. These metrics are shown in Table 7 for development and validation samples. \[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{FN}+\text{FP}+\text{TN}) \tag{7}\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{8}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{9}\]  
\[\text{Error}=1-\text{Accuracy} \tag{10}\]  
\[\text{FDR}=1-\text{Precision} \tag{11}\]  
\[\text{FNR}=1-\text{Recall} \tag{12}\] \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & \(t\)**-Statistic** & \(p\)**-Value** \\ \hline Intercept & \(-\)19.26 & 0.00 \\ \hline Intercept & \(-\)19.26 & 0.00 \\ \hline Hit pedestrians & 2.25 & 0.02 \\ \hline Not leaving sufficient gap & \(-\)2.78 & 0.01 \\ \hline Number of injuries & 0.15 & 0.88 \\ \hline Fatal & 7.46 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The \(t\)-statistics and \(p\)-values for the coefficients of the utility Equation (6). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & \(t\)**-Statistic** & \(p\)**-Value** \\ \hline Intercept & \(-\)19.26 & 0.00 \\ \hline Fatal & 2.25 & 0.02 \\ \hline Collision & \(-\)2.78 & 0.01 \\ \hline Vehicle overturn & 0.15 & 0.88 \\ \hline Hit pedestrians & 7.46 & 0.00 \\ \hline Other types & \(-\)1.08 & 0.28 \\ \hline Hit road fence & \(-\)0.02 & 0.98 \\ \hline Not leaving sufficient gap & \(-\)3.16 & 0.00 \\ \hline Sudden turning & \(-\)3.35 & 0.00 \\ \hline Speeding & 0.57 & 0.57 \\ \hline Not giving way & 3.12 & 0.00 \\ \hline Other reasons & 1.64 & 0.10 \\ \hline Number of injuries & 5.45 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The \(t\)-statistics and \(p\)-values for the coefficients of the utility Equation (1).