## 2 Literature Review  
Crash severity analysis is an important and widely studied area in safety research. Past studies are usually focused on two important topics: (1) identification of risk factors associated with crash injury severity and (2) prediction of crash injury levels from the identified contributing variables. In the first area, researchers in the past have investigated various factors that are contributing to crashes involved with teen drivers, such as speeding [4; 5], distracted driving [6; 7], driver age and gender [8; 9], risky behavior [10], impaired driving [11], night-time driving [12], and peer passengers [13; 14]. In the second field, many researchers have developed prediction models on crash severity, which can be generally divided into two groups: parametric models and nonparametric (or machine learning) methods. In crash severity analysis, the dependent variable is discrete, with multiple categories corresponding to different crash severity levels (for example, in this study, or in the TxDOT Crash Records Information System (CRIS), crash severity is defined with the KBACO scale: K: Killed, A: Incapacitating injury, B: Non-incapacitating injury, C: Possible injury, O: Not injured). Since crash severity levels can be considered as either nominal or ordinal variables, different types of statistical modeling techniques for nominal variables can be applied, such as multinomial logit (MNL) models (for example, investigating the impact of road features on crash injury severity [15], examining the impact of alcohol and drug use cyclist injury severities [16], and analyzing the severity of large-truck-involved crashes [17]) and ordered logit (OL) models (for example, investigating the impact of rural highway geometric features on crash injury severity [18], comparing the severity of -freight-involved crashes and non-freight crashes [19], and finding the impact of contributing variables on the severity for highway-rail crossing crashes [20]). It should be noted that parametric models need some model assumptions and pre-defined relationships between dependent and explanatory variables, which restrict their application when the assumptions are violated [21]. Meanwhile, nonparametric or machine learning methods usually do not require such assumptions and can identify and present underlying knowledge in large datasets with diverse variable categories without priori model assumptions or predefined relationships. This makes machine learning approaches particularly useful for traffic safety data analysis. The machine learning technologies applied in the past research include: Random forest [22, 23], SVM (Support Vector Machine) [24, 25], neural networks [26, 27], and XGBoost [28]. Some scholars also performed comparison studies on these two kinds of approaches. Their studies consistently show the advantages of machine learning approaches over traditional statistical modelling methods. For example, [PERSON] and [PERSON] performed a comprehensive comparison between discrete models and tree-based models [21, 29]. As discussed previously, rural roads are particularly dangerous to teen drivers due to geographically isolated rural areas and longer travel distances within rural communities. Combined with inexperience and risky driving behaviors of teen drivers, the situation becomes even more serious, and a comprehensive analysis of teen driver crashes on rural roads is necessary. Texas has one of the largest rural road networks in the nation (nearly 57,000 centerline miles and over 128,000 lane miles of state highway in rural areas, including 50,000 miles of farm-to-market (FM) roads). Therefore, we perform a case study in West Texas rural areas in this study. The major contribution of this study is to conduct a systematic and panoramic machine learning methodology from collecting, cleaning, and coding crash data, performing a comprehensive exploratory data analysis, identifying the effect of factors contributing to the injury severity, and formulating four machine learning models on crash severity prediction.