## 2 Summary of the Literature Review  
Numerous studies have been conducted that utilized advanced data analysis methods to assess the crash risk in real time. Statistical analysis and machine learning (ML) models are the most common methods used in real-time traffic crash risk and crash severity prediction research. These models highlight the identification of factors affecting traffic crash risk and severity. In fact, both statistical analysis and ML methods focus on the prediction capabilities and false alarm rate of real-time traffic crash risk prediction models. For instance, [PERSON], [PERSON] [6] developed a three-stage sequential binary logit model to predict crash likelihood along a 29-mile segment of the I-88 freeway in San Francisco. The model considered three severity levels and it was calibrated using 22 input variables derived from the data obtained from roadside detectors in 30 s intervals. The weather condition (clear vs. adverse weather) was considered as an additional explanatory variable. The model evaluation was performed using a 20-fold cross-validation, and the findings showed that the traffic flow characteristics contributing to crash likelihood were substantially different at each severity level. [PERSON] [7] investigated accident likelihood by incorporating real-time traffic and weather data for urban arterials in Athens, Greece. The traffic and weather data were aggregated into 1 h intervals. For every crash case, two non-crash cases were identified for the same location and time interval, one week before and one week after the crash occurrence. The random forest (RF) method was used to select the significant variables, and the Bayesian logistic regression (BLR) was used to model the likelihood of crashes. The results showed that the crash likelihood is most impacted by the standard deviation of occupancy and the coefficient of variation of traffic flow. [PERSON] and [PERSON] [8] developed crash risk assessment models that could be applied in real time crash prediction. The models were tested using the data from a 15-mile freeway section of I-70 in Colorado. The study dataset included crash record data and real-time traffic data collected from roadside radar detectors. For each crash case, four non-crash cases were identified for the same location two weeks before and two weeks after the crash occurrence. The classification and regression tree (CART) method was used to select the significant variables. The modeling methods applied in this study included (i) Bayesian logistic regression with fixed-parameters, (ii) Bayesian random-parameter logistic regression considering the seasonal variation, (iii) Bayesian random-effect logistic regression considering segment level heterogeneity, (iv) Support Vector Machine (SVM) with linear kernel, and (v) SVM with Radial Basis Function (RBF) kernel. The results showed that the Bayesian logistic regression with fixed parameters and the SVM with RBF kernel performed better than the other models. [PERSON] et al. [9] proposed a crash prediction model for crashes occurring at the express-way ramps in Central Florida. The dataset used in model calibration and testing consisted of the crash records collected from the Florida DOT statewide crash database, traffic flow data, roadway geometry data, and weather condition data collected from the National Climate Data Center (NCDC). Two separate BLR models were developed for single-vehicle and multi-vehicle crashes. The study found that four variables were significant in both models, including logarithm of the vehicle count in 5 min intervals, ramp configuration, road surface condition, and visibility. The traffic speed was found to be significant only in the single-vehicle model. [PERSON] et al. [10] conducted a comparative study of crash prediction models using several machine learning (ML) and deep learning (DL) methods, including k-nearest neighbor (KNN), Naive Bayes, decision tree (DT), RF, SVM, and shallow neural network. The models were trained and tested using the crash data for an urban motorway in Greece.