Institute of Transportation, MOTC

        Reducing traffic accident severity is an effective approach to improve road safety. To decrease traffic severity, there are many passive safety systems like safety belts, airbags, brake assist systems and so on. In recent years, building models to predict traffic accident severity is also the subject that many researchers focus on. There are a lot of machine learning and deep learning approaches instead of statistical methods. They can get higher accuracy and faster calculate speed. It needs large datasets to train the model, but there is usually an imbalanced data problem in the datasets. Therefore, it must
preprocess these sets.
       This study divides the traffic accident severity into three levels: death, injury, and non-injury. It is a multi-class classification problem. We collect data from Tainan open datasets and utilize over-sampling and under-sampling methods to resample the imbalanced data. To implement the resample process, we apply SMOTE and Cluster Centroid algorithms separately. We apply two classification models based on the ensemble learning to train the model. This study uses Random Forest and Catboost to execute the two ensemble learning methods. The research results denote that these two models have more than 97.69% and 86.84% accuracy separately in the under-sampling and
over-sampling datasets. This result can apply in autonomous vehicles in the future or provide related apartments some suggestions for making the decision.