| 논문명 |
머신러닝을 활용한 어린이 보행자 교통사고 위험지역 예측 모델 개발 / Predicting High-Risk Areas for Child Pedestrian Accidents Using Machine Learning |
| 저자명 |
채한희(Chae, Han-Hee) ; 이조은(Lee, Jo-Eun) ; 전은비(Jeon Eun-Bi) ; 이경환(Lee, Kyung-Hwan) |
| 수록사항 |
대한건축학회논문집, Vol.41 No.8 (2025-08) |
| 주제어 |
어린이; 보행자 교통사고; 도시공간; 물리환경; 머신러닝 ; Children; Traffic Accident; Urban Space; Physical Environment; Machine Learning |
| 요약1 |
어린이는 인지 능력이 낮고 반응 속도가 느려 교통사고에 특히 취약하다. 이에 어린이 교통사고 예방을 위한 정책이 꾸준히 추진되고 있지만, 주로 사고가 자주 발생하는 지역에 대한 사후 대책에 집중되어 있어, 선제적인 대응이 부족한 상황이다. 본 연구는 어린이 교통사고에 영향을 미치는 물리적 환경 요인을 바탕으로 어린이 교통사고 위험 지역을 예측하는 모델을 개발하고자 한다. 모델의 개발은 서울시 데이터를 활용하였고 모델의 검증은 부산시 실제 교통사고 데이터와의 비교를 통해 진행하였으며, 최종 검증 결과 유의미한 모델을 도출하였다. |
| 요약2 |
Automobiles play a vital role in urban transportation, but the rapid increase in vehicle numbers has caused several urban problems, including
traffic congestion, accidents, and environmental damage. Among these, the rising number of child-related traffic accidents is a major concern.
Children are especially vulnerable due to limited cognitive development and slower reaction times. Despite ongoing efforts to reduce such
incidents, current policies mostly address responses after accidents occur, particularly in high-risk zones, limiting the effectiveness of proactive
prevention. This study aims to develop a predictive model that identifies high-risk areas for child traffic accidents based on physical
environmental factors. Using 18 indicators related to population, road conditions, land use, and facility accessibility, the model was trained
with data from the Seoul Metropolitan Government. Three machine learning algorithms were tested: Decision Tree, Random Forest, and
XGBoost. Among them, XGBoost delivered the highest performance, achieving an R-squared value of 0.9313. SHAP analysis was used to
interpret the model and identify key contributing factors. The proportion of old buildings ranked as the most influential, followed by
pedestrian crossing density, traffic signal density, and the length of school zones. To assess generalizability, the model was applied to
Busan’s urban and accident data. It showed high predictive accuracy with a score of 0.9327, confirming its effectiveness. This predictive
model offers a practical tool for identifying potential accident-prone areas and supporting proactive child traffic safety strategies, especially in
locations with limited accident data. |
