## 4 Results
As far as the RUMC-CART model is concerned, Table 4 reveals a higher precision in predicting the \"slight injury\" class compared to the \"serious injury\" class. In the training dataset, the model accurately predicted 5372 instances (68%) of the \"slight injury\" class and 4566 instances (58%) of the \"serious injury\" class. This precision extends to the validation dataset, where 1099 instances (65%) of the \"slight injury\" class and 1140 instances (67%) were correctly predicted, alongside 997 instances (59%) and 970 instances (58%) for the \"serious injury\" class, respectively. On the other hand, for the training dataset of the RUMC-CART model, 2523 instances (32%) of \"slight injury\" crashes were incorrectly predicted as \"serious injury\" crashes, and 3336 instances (42%) of \"serious injury\" crashes were misclassified as \"slight injury\" crashes. Similar misclassifications occurred in the validation and testing datasets, where instances of \"slight injury\" crashes were predicted as \"serious injury\" crashes, and vice versa. These misclassifications were quantified as false positive (_FP_) and false negative (_FN_) values. For instance, in the validation dataset, 590 instances (35%) of \"slight injury\" crashes were classified as \"serious injury\" crashes (_FP_), while 560 instances (33%) of \"serious injury\" crashes were predicted as \"slight injury\" crashes (_FN_). This pattern persisted in the testing dataset, with 700 instances (41%) classified as _FP_ and 715 instances (42%) as _FN_ for \"serious injury\" crashes. The feature importance analysis of the CART model (see Figure 6) reveals significant contributors to crash severity prediction. Key factors include the \"vehicle_type\" at \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Observed Classes**} & \multicolumn{6}{c}{**CART Predicted Classes**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Training Dataset**} & \multicolumn{2}{c}{**Validation Dataset**} & \multicolumn{2}{c}{**Testing Dataset**} \\ \cline{2-7}  & **0** & **1** & **0** & **1** & **0** & **1** \\ \hline
0 & 28,205 (99\%) * & 339 (1\%) & 6151 (99\%) & 86 (1\%) & 6219 (99\%) & 82 (1\%) \\ \hline
1 & 7462 (94\%) & 470 (6\%) & 1639 (82\%) & 369 (18\%) & 1554 (95\%) & 90 (5\%) \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Observed Classes \\ \end{tabular} } & \multicolumn{6}{c}{RUMC-CART Predicted Classes} \\ \cline{2-7}  & \multicolumn{2}{c}{Training Dataset} & \multicolumn{2}{c}{Validation Dataset} & \multicolumn{2}{c}{Testing Dataset} \\ \cline{2-7}  & 0 & 1 & 0 & 1 & 0 & 1 \\ \hline
0 & 5372 (68\%) & 2523 (32\%) & 1099 (65\%) & 590 (35\%) & 1140 (67\%) & 560 (33\%) \\ \hline
1 & 3336 (42\%) & 4566 (58\%) & 700 (41\%) & 997 (59\%) & 715 (42\%) & 970 (58\%) \\ \hline \hline \end{tabular}  
* The values displayed relate to the percentages of each class from the training, validation, and testing datasets.  
\end{table} 
Table 4: Confusion matrices of training, validation, and testing datasets for the CART and RUMC-CART models. 20.33%, highlighting the impactful role of motorcycle involvement. Variables, such as \"number_of_vehicles\" and \"number_of_casualties\", closely follow, with weights of 20.04% and 19.06%, respectively, emphasizing their substantial roles. \"Vehicle_leaving_carriageway\" and \"speed_limit\" play important roles at 13.15% and 6.94%, respectively, underscoring the importance of road and traffic conditions. \"Urban_or_rural_area\" emerges as an observable factor at 5.88%, indicating distinctions in crash dynamics between urban and rural settings. \"Vehicle_manoeuvre\" and \"skidding_and_overturning\" contribute 3.55% and 2.53%, respectively, shedding light on driver actions and vehicular dynamics. Other factors, like \"road_surface_conditions\", \"light_conditions\", and \"hit_object_in_carriageway\", exhibit moderate importance, while the temporal aspect, specifically the \"timeline\" feature, denotes a minor influence, particularly in the pre-COVID period, at 0.95%.