Using highway accident data, this study predicts the probability of rollover, overturning, and fire accidents and identifies the related risk factors. Whereas existing studies rely primarily on limited explanatory variables and classical statistical models, this study simultaneously enhances predictive performance and interpretability by applying and comparing machine learning-based nonlinear prediction-analysis systems (XGBoost and Shapley additive explanations) with logistic regression, which offers advantages in statistical reasoning. The analysis identifies speeding, segment characteristics (tunnel, ramp, shoulder), and vehicle type (SUV, truck, trailer, and tank lorry) as common key risk factors. These results suggest the necessity of establishing a multilayered management system for speeding, improving facilities centered on high-risk sections (tunnel in/out, ramp, and downhill), performing custom inspections for each vehicle type (load, tire, and brake system), and improving driving behavior (enhancing forward attention, introducing a drowsiness warning system, etc.). This study provides a datadriven empirical basis for identifying the causes of major highway accidents and for designing effective prevention policies.

ABSTRACT
1. 서론
2. 기존 문헌 고찰
3. 방법론
    3.1. XGBoost 모형 구축
    3.2. SHAP 분석을 통한 모형 구축
    3.3. 로지스틱 회귀모형 구축
    3.4. 최종 모형 채택
4. 분석결과
    4.1. 전도사고 예측 모형 결과
    4.2. 전복사고 예측 모형 결과
    4.3. 화재사고 예측 모형 결과
5. 결론
REFERENCES

• 이상민(계명대학교 공과대학 교통공학과) | Lee Sangmin
• 윤호성(계명대학교 공과대학 교통공학과) | Yoon Hoseong
• 홍정열(계명대학교 공과대학 교통공학과 조교수) | Hong Jungyeol Corresponding author