## 6 Conclusions  
Despite the existence of numerous research endeavors investigating road safety, the attention given to young drivers remains notably lacking and is often overlooked. This study seeks to address this gap by modeling crash severities and the associated influential factors specifically for young drivers aged 17-24 in England, UK. To address imbalances in injury severity data (especially between slight and serious injury levels), the study introduced the use of the RUMC undersampling method on the decision tree CART model. The significant variables identified in this study include the type of vehicle and vulnerabilities, number of vehicles and casualties, area type (urban vs. rural), vehicle maneuversand dynamic factors, and minor influences and timeline. Motorcyclists face heightened injury risks due to their lack of protective enclosures and the aggressive tendencies of novice drivers, exacerbating their inherent vulnerability. Multi-vehicle crashes further compound injury severity, reflecting young drivers' challenges in managing complex traffic scenarios, especially during the early licensure period. Rural areas present elevated risks, with higher speeds and two-lane highways amplifying crash severity among risk-prone young rural drivers. Vehicle maneuvers, such as skidding and overturning, moderately impact injury outcomes, rooted in novice drivers' difficulty in anticipating hazards and their propensity for risky behaviors. Although minor factors, such as road surface conditions and temporal shifts, play a less significant role, they offer intriguing insights into the evolving crash dynamics among young drivers. The model results showed that the CART model has a superior performance in predicting slight injury cases with high accuracy but exhibits an inferior performance with serious injury cases, showing significant fluctuations and more false negatives. The RUMC-CART model, while less accurate overall, offers improved precision for serious injury cases and lower false negative rates, making it more effective in handling imbalanced data, such as those used in this study. Additionally, the RUMC-CART model incorporates a broader set of features, enhancing not only its predictive capabilities for crash severity, but also its explanatory power. A limitation of this study lies in the generalized nature of the findings from the CART and RUMC-CART models. While these models offer valuable insights into crash severity modeling and prediction, they may have limitations in identifying specific, actionable patterns or risk factors to directly guide interventions [47]--especially in the context of our study, which focuses on young drivers and cases involving slight injuries. Future research should seek to refine these models by integrating variables that are tailored to specific demographic groups and crash scenarios, thereby improving their practical applicability and supporting the development of targeted safety interventions. A.T.: Conceptualization, Methodology, Visualization, Investigation, Formal analysis, Data curation, Writing--original draft; K.S. : Data curation, Methodology, Formal analysis; G.F.: Conceptualization, Methodology, Investigation, Validation, Writing--review and editing; A.S.: Conceptualization, Writing--review and editing; N.D.: Validation, Visualization, Writing--review and editing; S.M. : Supervision. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The crash data used in this research are taken from the UK crash database, STATS19, which offers information on injury crashes reported by the police in Great Britain [48]. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
CART Classification and regression trees  
RUMC Random undersampling of the majority class  
WHO World Health Organization  
UK United Kingdom