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.

본 실험은 재식밀도와 재식양식이 Sorghum-Sudangrass hybrid(sordan 79)의 생육특성, 건물수량, 조단백질수량, 기호성 등에 미치는 영향을 알아보고자 실시하였으며 그 결과를 요약하면 다음과 같다.1. 재식밀도가 높아짐에 따라 초장, 엽장, 엽폭(P<0.05), 엽수는 대체적으로 감소하였고, 같은 재식밀도에 있어서는 직사각형구에서 증가하였다.2. 1차 예취시 엽비솔은 밀식구중 직사각형구(30kg/ha, 25cm×4cm)