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KCI등재후보 학술저널

머신러닝을 활용한 어린이 스마트 횡단보도 최적입지 선정

Machine Learning based Optimal Location Modeling for Children's Smart Pedestrian Crosswalk: A Case Study of Changwon-si

Road traffic accidents (RTAs) are the leading cause of accidental death among children. RTA reduction is becoming an increasingly important social issue among children. Municipalities aim to resolve this issue by introducing "Smart Pedestrian Crosswalks" that help prevent traffic accidents near children's facilities. Nonetheless such facilities tend to be installed in relatively limited number of areas, such as the school zone. In order for budget allocation to be efficient and policy effects maximized, optimal location selection based on machine learning is needed. In this paper, we employ machine learning models to select the optimal locations for smart pedestrian crosswalks to reduce the RTAs of children. This study develops an optimal location index using variable importance measures. By using k-means clustering method, the authors classified the crosswalks into three types after the optimal location selection. This study has broadened the scope of research in relation to smart crosswalks and traffic safety. Also, the study serves as a unique contribution by integrating policy design decisions based on public and open data.

1. 서 론

2. 이론적 배경

3. 활용데이터 및 분석방법

4. 머신러닝 변수 중요도를 활용한 입지지수 제안

5. k -means 클러스터링 모델을 활용한 최적 입지의 유형화 결과 및 유형별 시설물 제안

6. 향후 연구 진행 방향 및 결론

감사의 글

References

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