## 4 Discussion  
This article tried to suggest effective safety measures for vehicle accident prevention and preparation on highways by designing a vehicle accident data analysis process. The process developed by us considers many situations that vehicle accident data can have and applies it to other vehicle accident data to obtain results. Through the development of the above process, we tried to analyze the causes of vehicle accidents within the data to help establish a specific accident prevention plan. In the process, we tried to solve the data imbalance problem of vehicle accident data by applying six resampling methods to the data and comparing them. It was found that four classification metrics (accuracy, recall, precision, and f1-score) were reduced, but the performance of specificity was improved. In the case of accident occurrence data, it is more important to predict the occurrence cases. Therefore, although there may be a decrease in overall accuracy, it can be said that resampling was more suitable for vehicle accident data by increasing the prediction performance of accident occurrence. For the classification model, we applied four machine learning models (Support Vector Machine, Random Forest, Gradient Boost, and XGBoost) and one classic statistical model (Logistic Regression). By applying various models for classification, it is possible to figure out the model with better performance through a comparison of each model. Since we designed the process of applying various models, we were able to find suitable models for each data by applying the process to other vehicle accident data. Each model was optimized through hyperparameter tuning for the classification model. By using Bayesian optimization as a tuning technique, the performance of each model was improved, and the final model was obtained. As a result of applying the above methods, it was confirmed that the case in which ENN was applied as the resampling technique and XGBoost was used as the classification model was the case in which the model showed the best performance for this dataset. After finding the most appropriate model, we applied interpretable machine learning models, such as PDP and SHAP, for model interpretation. In the case of PDP, the correlation between two variables was analyzed and the change in the probability of vehicle accident occurrence according to the value of each variable was observed. The SHAP method interprets the results that evaluate the overall importance of each variable. Moreover, SHAP could be used to figure out the individual effects of each variable, which means that the probability of vehicle accident occurrence can change as the values of each variable change. Also, SHAP can represent a feature importance plot with Shapley value, and the individual interpretation of each variable can be expressed in detail with the figure. The conclusions obtained by interpreting the results of the classification prediction model through PDP and SHAP are as follows:  
First, through the PDP plot of lengths and volumes, it was found that the point with a length of 200 m and volumes with 29,233 veh/day is the point with the highest probability of occurrence of an accident. Additionally, through individual PDP for length and volume, it was confirmed that the probability of accident occurrence sharply increased in the length 160-200 m section and volume 23,980-29,233 veh/day section, and the probability of an accident did not increase after 200 m and 29,233 veh/day, respectively, even if length and traffic volume increased. Through this, it was found that the length and the volume had a positive correlation with the accident occurrence probability up to a certain point, but the accident occurrence probability decreased after such a sudden increase in the probability of an accident.