## 4 Results
The feature importance analysis for the RUMC-CART (see Figure 7) model elucidates critical factors influencing crash severity prediction. \"Number_of_casualties\" stands out as the most impactful feature at 22.02%, emphasizing its significance in determining crash severity. Following closely, \"Number_of_vehicles\" and \"vehicle_type: Passenger_car\" contribute substantially at 19.22% and 18.20%, respectively, underscoring their essential roles in predicting crash outcomes. The assigned \"Speed_limit:20 mph\" and \"vehicle_manoeuvre: Turning\" also emerge as factors with observable impacts at 5.38% and 4.94%, respectively, emphasizing the role of road- and driver-related dynamics. Notably, the specific \"vehicle_type: Motorcycle\" and \"vehicle_manoeuvre: Slowing_or_stopping\" contribute to a lesser extent, with 3.27% and 2.89%, respectively, shedding light on distinct vehicle types and driver actions. \"Junction_detail\" and \"sex_of_driver\" exhibit a lower importance at 2.48% and 2.42%, respectively. Additionally, road and environmental variables, such as \"light_conditions: Darkness_no_street_lighting\" and \"weather_conditions: fine\", influence model predictions, with weights of 4.30% and 1.32%, respectively. Figure 6: Feature importance of explanatory variables (CART). Figure 8 displays the root node (node 0) along with the immediate child nodes (node 1 on the left and node 32 on the right) to provide an overview of the decision tree. The \"number_of_vehicles\" variable at the root node (node 0) is utilized to bifurcate the tree into two child nodes (node 1 and node 32). Implementing the RUMC method on our CART model indicates an equal distribution of data for both slight and serious injury classes in node 0. In the subsequent analysis, we scrutinize the decision tree in four distinct sections, starting from the child nodes of node 1 on the left and right sides, followed by the child nodes of node 32 on the left and right sides. The left segment of the RUMC-CART diagram (see Figure 9) explores crash severity in light of vehicle type, speed limit, and surrounding conditions. When two or more vehicles are involved, the \"vehicle_type\" variable is primarily associated with slight injuries in 53% of cases. However, if the vehicle type is not a passenger car, the speed limit becomes influential for injury severity, with 58% of these crashes resulting in serious injuries, especially when the speed limit is not 20 mph. Within this branch, the type of vehicle continues to have an observable impact; if the vehicle is something other than a motorcycle, crashes in rural areas show a higher likelihood of resulting in serious injuries (58%), while urban crashes are more associated with slight injuries (61%). When motorcycles are involved and the speed limit is 30 mph, serious injuries are more likely to occur in 74% of cases. Additional factors, like skidding and overturning, also contribute to the likelihood of injury severity, with pre- or post-COVID periods showing a higher rate of serious injuries (76%). Figure 7: Feature importance of explanatory variables (RUMC-CART). On the right side of the diagram (see Figure 10), the focus shifts to crashes involving passenger cars, where the number of casualties, lighting conditions, and vehicle maneuvers significantly influence crash outcomes. When there are three or more casualties, 62% of these crashes are linked with serious injuries, especially when the vehicle maneuver does not involve slowing or stopping. Further branching reveals that crashes near junctions have a higher likelihood of resulting in serious injuries (73%), while crashes occurring away from junctions also show a significant proportion of severe injuries. Environmental factors, such as lighting conditions, are also found to serve as injury severity determinants; for instance, darkness without street lighting results in serious injuries in 57% of cases.