## 2 Summary of the Literature Review
For the modeling purposes, each crash record was matched with two non-crash cases. The explanatory variables were derived from the traffic data and weather data obtained in real time and matched to the crash and non-crash cases based on the corresponding time and location. The models were compared based on the performance metrics including accuracy, sensitivity, specificity, and AUC (area under the receiver operating characteristic (ROC)curve). The study found that the DL model outperformed the ML models as it provided a relatively balanced performance among all metrics. Furthermore, [PERSON] et al. applied RF in identifying the most critical variables in crash risk analysis study. A logistic regression model was developed to predict the crash risk on freeways in China and ascertain the relationship between traffic flow and risky driving behavior [11]. [PERSON] et al. provided systematic ML models to predict the driving risk based on the data of drivers involved in crashes in China. Four three-based ensemble learning approaches were implemented: a gradient boosting decision tree (GBDT), an extreme gradient boosting decision tree, and RF models. The results revealed that GBDT outperformed other methods with an acceptable average precision of 0.68 [12]. Regarding the crash severity prediction, [PERSON] and [PERSON] compared the Recurrent Neural Network (RNN), Multilayer Perceptron (MLP), and Bayesian Logistic Regression (BLR) models in prediction of injury severity based on 1130 crash records from the North-South Expressway in Malaysia, occurring over six years. The results suggested that the RNN appeared to have better prediction performance than MLP and BLR [13]. In another study, [PERSON] et al., applied RF and Extreme Gradient Boosting (XGBoost) methods to analyze severity of crashes involving teen drivers on rural roads in West Texas. The RF and XGBoost models were each developed using two coding methods: (a) label encoder and (b) one hot encoder. The label encoder assigns numerical values to the unrepeated categories and treats categories in data as ordered. The potential shortcoming of this encoding method is that it can assume the relationships between the categories due to their ordering. On the other hand, one hot encoder converts the categorical variable into a sparse binary matrix. The results of this study indicate that the combination of label encoder and XGBoost appeared to yield better accuracy and computation time [14]. Along the same line of thought, [PERSON] et al. compared the prediction performance of two discrete choice models and four ML models to predict the injury severity in crashes at freeway diverge areas in Florida. This study applied ordered probit (OP) model and multinomial logit model, in comparison to RF, KNN, SVM, and decision tree models. Higher prediction accuracies were obtained with ML models as compared to discrete choice models, with RF showing the best performance and OP performing the worst [15]. In another study, [PERSON] and [PERSON] investigated the factors of injury severity in motorcycle crashes in Ghana. The authors applied ML methods including RF, J48 Decision Tree, and instance-based learning with parameter k, as well as multinomial logit model (MNLM). A ten-fold cross-validation was used to validate the ML models. This study also demonstrated superior performance of the ML methods as compared to the MNLM, with RF having the highest accuracy and extrapolation ability among the models [16]. With regards to data sampling and handling of imbalanced data in crash analysis, [PERSON] and [PERSON] investigated the prediction performance of the eight ML classifiers with/without data balancing in a case study of crash injury severity in Ohio, U.S. They applied the logistic and ordered logistic regression, RF, and ordered RF models. The results reveal that inclusion of data balancing improves performance of predicting severe crashes, while implementing ordering nature to develop an ordered RF model without balancing seems to yield the highest prediction accuracy [17].