## 3 Results
### Model Results
#### 3.2.2 Frequency of Elderly Pedestrian Traffic Accidents  
Figure 7 represents the relative importance and impacts of the independent variables on elderly pedestrian traffic accident frequency for three groups. Across all the groups, the number of rapid accelerations consistently emerges as the most influential feature, demonstrating its universal importance in predicting accident frequency. Other driving-related features, such as the number of rapid decelerations and rapid lane changes, are also signifi \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hyper-Parameter**} & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**Match**} & \multicolumn{2}{c}{**Mismatch**} \\ \cline{2-7}  & **Freq** & **Death** & **Freq** & **Death** & **Freq** & **Death** \\ \hline colsample\_bytree & 1.0 & 0.8 & 1.0 & 0.8 & 1.0 & 0.8 \\ max depth & 5 & 7 & 5 & 7 & 5 & 3 \\ learning rate & 0.1 & 0.1 & 0.05 & 0.05 & 0.05 & 0.05 \\ n\_estimators & 300 & 100 & 300 & 200 & 300 & 200 \\ reg\_alpha & 0 & 0 & 0 & 1 & 0.1 & 0.1 \\ reg\_lambda & 1 & 1 & 1 & 5 & 5 & 5 \\ subsample & 0.8 & 1.0 & 0.8 & 1.0 & 0.8 & 1.0 \\ RMSE & 1.2829 & 0.1765 & 0.8549 & 0.2079 & 1.4223 & 0.2169 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Hyper-parameters used in XGBoost models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hyper-Parameter**} & \multicolumn{2}{c}{**Total**} & \multicolumn{2}{c}{**Match**} & \multicolumn{2}{c}{**Mismatch**} \\ \cline{2-7}  & **Freq** & **Death** & **Freq** & **Death** & **Freq** & **Death** \\ \hline Adjusted \(R^{2}\) & 0.3039 & 0.0133 & 0.6364 & 0.0447 & 0.5212 & 0.0102 \\ RMSE & 1.2829 & 0.1765 & 0.8549 & 0.2079 & 1.4223 & 0.2169 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall accuracy of XGBoost models. cant, indicating that vehicle dynamics are critical predictors regardless of group context. Figure 7: Cont. On the other hand, the features related to the built environment and demographics, such as the number of crosswalks, as well as senior and working populations, show varying levels of importance; they are present in all the groups, reflecting their consistent but secondary influence compared to the other driving behaviors. These similarities suggest that core driving patterns and specific infrastructure or demographic factors play a role across all contexts. However, key differences exist in the prominence of these and other features across the groups. For the 'total' group, the importance of the number of crosswalks as well as the senior and working populations is higher compared to the'match' and'mismatch' groups. The existence of built-up and forest areas also affects the accident occurrence, in an opposite direction. This indicates the balanced influence of driving behaviors and environmental or demographic factors in the overall dataset. For the'match' group, the number of rapid accelerations remains the top contributor, but other aggressive driving behaviors like the number of decelerations, speeding, and rapid lane changes show increased significance. Demographic and infrastructural features such as the working population and the number of crosswalks play less dominant roles in this group. This shift suggests that in the'match' group, driving dynamics are more critical predictors, perhaps due to more controlled or uniform environmental conditions. In contrast, the'mismatch' group exhibits a stronger focus on aggressive driving behavior, with speeding becoming more prominent compared to the other groups. Interestingly, features like the senior population and the number of crosswalks show a reduced influence. This highlights that aggressive driving behaviors are the dominant predictors in mismatched scenarios, possibly due to inconsistent environmental or demographic contexts that amplify the impact of such behaviors. Figure 7: SHAP values of XGBoost model estimating the frequency of elderly pedestrian traffic accidents.