XGBoost를 이용한 교통노드 및 교통링크 기반의 교통사고 예측모델 개발

• 영문명: Development of Traffic Accident Prediction Model Based on Traffic Node and Link Using XGBoost
• 발행기관: 한국산업경영시스템학회
• 간행물 정보: 『산업경영시스템학회지』제45권 제2호, 20~29쪽, 전체 10쪽
• 주제분류: 공학 > 산업공학
• 파일형태: PDF
• 발행일자: 2022.06.30

영문 초록

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.

목차

1. 서 론

2. 이론적 배경

3. 자료 구축

4. 예측모델 구축 및 모델평가

5. 결 론

6. 연구의 한계점 및 향후 연구

Acknowledgement

References

키워드

Traffic Accident Prediction XGBoost Traffic Links Traffic Node