Crash risk in metropolitan areas arises from the interaction among driver behavior, enforcement infrastructure, and urban environmental conditions; however, microspatial evidence remains scarce. This study examines the effects of automated speed-enforcement cameras on the crash risk in Seoul at the legal-dong level using the accident risk index, which accounts for both crash frequency and injury severity. The dataset combines crash records, enforcement infrastructure, school-zone information, demographic indicators, and land-use characteristics. Methodologically, a Bayesian negative binomial regression model was employed to address overdispersed crash data, whereas gradient-boosting machine and eXtreme Gradient Boosting models with SHAP interpretations were applied to capture nonlinear effects, heterogeneity, and variable interactions. The results reveal that the crash risk is spatially concentrated, with a small proportion of districts contributing disproportionately to the overall exposure. Regression analysis highlights enforcement and behavioral factors as significant predictors, whereas machine-learning models emphasize the added importance of structural and demographic characteristics, such as road area and floating population. This divergence reflects the sensitivity of the regression to collinearity and linearity assumptions in contrast to the flexibility of tree-based methods in uncovering nonlinear and context-dependent influences. In general, the findings reflect the value of integrating statistical and machine-learning approaches for a more comprehensive understanding of crash risk at the microspatial scale. This study advances the methodological diversity in traffic-safety research and provides practical evidence that cameradeployment strategies should be context sensitive while targeting areas with concentrated risks and distinct structural and demographic profiles.

ABSTRACT
1. 서론
2. 선행 연구 검토
    2.1. 통계적 접근 기반 사고분석 연구 동향
    2.2. 머신러닝 기반 교통사고 분석 동향
3. 연구 데이터 및 변수 정의
4. 연구 방법론
    4.1. 음이항 회귀분석 모델
    4.2. Gradient Boosting Machine(GBM) 및 XGBoost모델
5. 결과 및 해석
    5.1. 음이항 회귀분석 모델 구축
    5.2. Gradient Boosting Machine(GBM) 및 XGBoost모델 구축
6. 결론 및 향후과제
Acknowledgement
REFERENCES

• 박성준(한국도로교통공단 서울특별시지부 사고조사연구원) | Park Seong-Joon
• 박호철(명지대학교 스마트모빌리티공학과 부교수) | Park Ho-Chul
• 정동훈(한국도로교통공단 서울특별시지부 사고조사연구원) | Jeong Dong-Hun
• 전재원(명지대학교 스마트모빌리티공학과 연구교수) | Jeon Jae-Won Corresponding author