This study intends to present a traffic node-based and link-based accident prediction models using XGBoost which is very excellent in performance among machine learning models, and to develop those models with sustainability and scalability. Also, we intend to present those models which predict the number of annual traffic accidents based on road types, weather conditions, and traffic information using XGBoost. To this end, data sets were constructed by collecting and preprocessing traffic accident information, road information, weather information, and traffic information. The SHAP method was used to identify the variables affecting the number of traffic accidents. The five main variables of the traffic node-based accident prediction model were snow cover, precipitation, the number of entering lanes and connected links, and slow speed. Otherwise, those of the traffic link-based accident prediction model were snow cover, precipitation, the number of lanes, road length, and slow speed. As the evaluation results of those models, the RMSE values of those models were each 0.2035 and 0.2107. In this study, only data from Sejong City were used to our models, but ours can be applied to all regions where traffic nodes and links are constructed. Therefore, our prediction models can be extended to a wider range.

1. 서 론
2. 이론적 배경
    2.1 선행연구 고찰
    2.2 XGBoost(eXtreme Gradient Boosting)
    2.3 SHAP(SHapley Additive exPlanations)
3. 자료 구축
    3.1 자료 수집
    3.2 신규변수 생성
    3.3 교통노드 및 교통링크와 교통사고 매핑
    3.4 교통사고 발생당시 환경정보 생성
    3.5 모델구축용 데이터셋 생성
4. 예측모델 구축 및 모델평가
    4.1 예측모델 구축
    4.2 모델평가
5. 결 론
6. 연구의 한계점 및 향후 연구
Acknowledgement

• Un-Sik Kim(주식회사 바이브컴퍼니) | 김운식
• Young-Gyu Kim(주식회사 바이브컴퍼니) | 김영규
• Joong-Hoon Ko(주식회사 바이브컴퍼니) | 고중훈 Corresponding Author