## 3 Methodology
### Data  
The crash data used in this research are drawn from the UK crash database, STATS19, which offers information on injury crashes reported by the police in Great Britain. Specifi \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Observed Classes**} & \multicolumn{2}{c}{**Predicted Classes**} \\ \cline{2-3}  & **0** & **1** \\ \hline
0 & True Negative (\(TN\)) & False Positive (\(FP\)) \\
1 & False Negative (\(FN\)) & True Positive (\(TP\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Confusion matrix of a binary classification model. cally, the crash data were collected by the local police forces in the UK following standardized guidelines, as outlined in the STATS20 manual. Crashes resulting only in property damages are not included in the STATS-19 database [35]. This study delves into crash data from England spanning a period from 7 April 2019 to 1 February 2022 (a period covering before, during, and slightly after the outbreak of the COVID-19 pandemic), comprising 52,966 observations specifically pertaining to young drivers. In the UK, the pre-COVID era refers to the time before March 2020, the during-COVID era spans from March 2020 to February 2021, and the post-COVID era refers to February 2021 onwards, marked by the gradual easing of restrictions [39]. The dataset covers crashes in both rural and urban settings, offering a holistic perspective of the diverse landscapes within the UK's road network. The research systematically explores the influence of various factors, such as crash-related, vehicle-related, driver-related, and environmental factors. The crash-related variables included various attributes, such as crash severity, number of casualties, and junction detail, all of which provide context about the circumstances of the incidents. Within the vehicle-related category, variables, such as vehicle type, towing and articulation, and vehicle maneuvers, were incorporated in the analysis to capture the role of vehicle characteristics and movements in influencing crash severity outcomes. The driver-related variables, such as the age of the driver--with a specific focus on young drivers aged 17-24--and gender of the driver, aim to capture the impact of human factors on crash severities. Lastly, environmental factors, such as road surface conditions, weather conditions, light conditions, and road type, were included to account for the external conditions surrounding the incidents. A comprehensive overview of all these variables and their specific characteristics is provided in Table 11 in Appendix A. For the purposes of this study, crashes are classified into two categories based on their observed injury severity, namely \"slight\" and \"serious\" injury crashes. The \"slight\" category encompasses injuries of a minor character, like sprains, bruises, or cuts that are not deemed severe, along with slight shocks requiring roadside attention. It also includes very minor injuries not necessitating medical treatment. Due to the low prevalence of fatal injuries in the dataset, fatal and serious injury crashes were combined into a single category, i.e., the \"serious\" injury category. In addition to fatal accidents, \"serious\" crashes involve injuries for which a person is hospitalized as an \"in-patient\" or a specific injury, whether or not hospitalization occurs. These injuries comprise fractures, concussions, internal injuries, crushing, burns (excluding friction burns), severe cuts, severe general shock requiring medical treatment, and injuries causing death 30 or more days after the crash. Among all age groups totaling 388,157 crashes in our study period, 52,966 (13.6%) fell within the 17 to 24 age range, categorizing them as young drivers in our analysis. Table 2 presents the distribution of crash severity levels, highlighting the imbalanced nature of the dataset. Slight injuries dominate with a share of 78.69%, while serious injuries (i.e., fatal and serious) account for 21.41% of the sample.