기계학습 알고리즘을 이용한 교통사고 심각도 예측 모델에 관한 연구

다국어 초록 (Multilingual Abstract)

This study aims to propose a predictive model for the severity of traffic accidents based on external environmental factors, which can perform a role to reduce the number of casualties and accident rates. Data collected from traffic accidents occurring in England from 2021 to 2022 were utilized to provide a traffic accidents severity prediction model. The severity of traffic accidents included in the dataset can lead to a multi-class classification prediction model with the three labels. Moreover, since the severity of most traffic accidents is classified as slight, the dataset exhibits characteristics of imbalanced data. Four artificial intelligence algorithms, such as Adaptive Boosting, Gradient Boosting Tree, K-Nearest Neighbors, and Random Forest, were employed for predicting the severity of traffic accidents. The performance analysis of our prediction models presented that the Random Forest algorithmbased model shows the highest accuracy. However, due to the limitation of imbalanced datasets, the other performance metrics, such as Macro Recall, Macro Precision, and Macro F1-measure, for the Random Forest algorithm-based model showed lower performance compared to accuracy.

목차 (Table of Contents)

• 요약
• Ⅰ. 서론
• Ⅱ. 데이터 전처리 과정 및 분석 데이터셋 설명
• Ⅲ. 교통사고 심각도 예측 모델 분석
• Ⅳ. 결론
• 참고문헌