Journal of KIBIM (한국BIM학회 논문집)

Korean Institute of Building Information Modeling (한국BIM학회)
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Machine Learning based Optimal Location Modeling for Children's Smart Pedestrian Crosswalk: A Case Study of Changwon-si

머신러닝을 활용한 어린이 스마트 횡단보도 최적입지 선정 - 창원시 사례를 중심으로 -

  • 이수현 (한국외국어대학교 경영학과) ;
  • 서용원 (한국외국어대학교 경영학과) ;
  • 김세인 (한국외국어대학교 경영학과) ;
  • 이재경 (홍익대학교 도시공학과) ;
  • 윤원주 (한국외국어대학교 경영학과)
  • Received : 2022.02.03
  • Accepted : 2022.05.08
  • Published : 2022.06.30
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Abstract

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.

Keywords

Acknowledgement

이 연구는 한국외국어대학교 교내연구지원사업 지원에 의하여 이루어진 것임; 이 논문은 2020년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2021S1A5A2A03061484)

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