## 2 Related Work  
In terms of methodologies, a considerable number of studies have investigated several factors that contribute to RORC based on the collection of a variety of data and data analysis methods. In the 1980s, a lognormal regression model was developed to examine the relationship between the ROR crash frequency and a number of variables such as AADT, shoulder width, and lane width [14]. Since 1990, to address the problems of the increasing variety of variables and the errors in model accuracy, several logit/probit models and structural equations have been widely employed. The logit/probit model was developed to overcome the serious error that arises when the regression model is used in cases where the dependent variables are not continuous variables (quantitative variables) [15]. In social sciences, there are numerous cases where dependent variables are qualitative and discontinuous variables rather than quantitative variables, and to analyze such variables, binomial logit, ordered probit, and ordered logit models are applied. However, the drawback of the logit/probit model is that it cannot consider the correlation between the variables [16]. In addition, the limitations of the structural equation model is that the regression analysis can only investigate the relationship between the dependent variables and the independent variables, and that it cannot clearly determine the type of relationship [17; 18].  
Subsequently, the logit/probit model and structural equation model used for RORC prediction also have a few limitations in their application to road safety diagnosis. In addition, only a few studies have investigated the quantitative analysis of accident occurrence and severity. RORC often leads to fatal crashes, and identifying the factors contributing to its severity and the application of data mining techniques will enhance the utility of the research results. Furthermore, the model accuracy of existing logit/probit models and structural equations should be improved.  
To enhance the accuracy of the prediction power of the RORC severity with regard to the aforementioned requirements, the adoption of a new methodology is essential. Given a set of discrete random variables that can be observed and measured, a Bayesian network is a tool that allows for the expression of the joint probability distribution between variables, while providing explanations in both quantitative and qualitative terms.  
As traffic accident prediction research changes from single-factor analysis to multi-factor analysis, analysis and prevention studies of road transport [19; 20; 21] and maritime transport accidents [22; 23; 24] applying the Bayesian network are being applied.  
Among all Bayesian network types, the significant advantage of the tree augmented naive Bayes (TAN) is that it involves the most natural expression of the probabilistic causal relationship or interdependence between multiple variables. TAN is expected to improve the predictive power for ROR severity, which has not been accomplished in existingprediction models. To quantify the range of influence of each factor in the field of marine transportation, TAN was applied to suggest a causal relationship for each factor [24].