###### Abstract  
In modern society, vehicle accidents have been a factor that has adversely affected national development for a long time. Many countries have tried to solve this issue, and various solutions have been studied. This study aims to design a process for analyzing vehicle accidents to support safety interventions. In the data preprocessing section, a resampling technique was used to solve the data imbalance problem. Then, we applied five different machine learning models for classification by applying hyperparameter optimization. After classification, model-agnostic interpretation techniques were used to interpret the results of a series of machine learning models. Through the above series of processes, we were able to design a process that analyzes vehicle accident data and derives the factors that affect the accident. The classification model that uses XGBoost with ENN (Edited Nearest Neighbor) shows almost 84.3% accuracy. As a result, for \"Length\" and \"Volume\", we found that certain points (Length: 200 m, 29,233 veh/day) were more likely to have an accident. Moreover, variables, such as volume or the volume of heavy vehicle, the probability of an accident increases as the value increases, but in the case of \"Lane width\" and \"Shoulder width\", it can be confirmed that the probability of occurrence decreases as the value increases. These interpretations have meaningful information that could suggest policy recommendations for reducing traffic accidents and can be helpful in establishing effective traffic accident countermeasures. 2020 IEEE/RSJ International Conference on Intelligent Transportation Systems (IEEE)  
quite serious when compared to other countries in the world, and this adversely affects the quality of life of the people, which has a negative influence on national development. A large body of prior works about traffic accidents has been investigated in various countries around the world. Traditional statistical analysis techniques and machine learning techniques have been used together in recent traffic accident research [2]. It can be seen that the use of analytics in machine learning is increasing, and machine learning models perform better than traditional statistical models. While machine learning (ML) has demonstrated superior performance in crash prediction compared to traditional statistical models [3], challenges remain in addressing data imbalance and interpreting model results. Due to the characteristics of data that contain information on the occurrence of traffic accidents, accidents are inevitably unbalanced [4; 5]. It was confirmed in several studies that the imbalanced data problem gives great difficulty in deriving the results of statistical or machine learning methods [6; 7; 8; 9; 10], and for these reasons, earlier studies utilized different kinds of techniques. Few works used case-control datasets for designing the model [11]. These researchers maintain the crash and non-crash ratio by randomly selecting the non-crash events for each crash event. Although these approaches could help to solve the imbalanced data problem, it is possible to cause the loss of important information in the original dataset. This study aims to bridge these gaps by developing a robust classification framework that integrates resampling techniques, hyperparameter optimization, and interpretable ML methods to identify determinant factors and support traffic safety policies. In the case of oversampling methods, they used the synthetic minority oversampling techniques (SMOTE) method [12; 13] or the adaptive synthetic sampling approach for imbalanced learning (ADASYN). Tomek-Link and edited nearest neighbors (ENN) [14] were well-known undersampling methods that resolve the imbalance problem. In addition, there was hybrid sampling that integrates oversampling and undersampling techniques. These methods are sampling techniques made by combining the characteristics of each technique, and there are SMOTE, SMOTETomek, and SMOTEENN [15].