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2022 Vol.12, Issue 2
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 -

Suhyeon Lee1
,
Youngwon Suh2
,
Sein Kim3
,
Jaekyung Lee4
,
Wonjoo Yun5*
이 수현1
,
서 용원2
,
김 세인3
,
이 재경4
,
윤 원주5*
1학생회원, 한국외국어대학교 경영학과
2학생회원, 한국외국어대학교 경영학과
3학생회원, 한국외국어대학교 경영학과
4정회원, 홍익대학교 도시공학과 교수
5정회원, 한국외국어대학교 경영학과 교수
*Corresponding Author
30 June 2022. pp. 1-11
PDF XML Citation
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
Smart Pedestrian Crosswalk
Optimal Location
Big Data
Machine Learning
Geographic Information System(GIS)
키워드
스마트 횡단보도
최적입지
빅데이터
머신러닝
지리정보시스템(GIS)
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Information
  • Publisher :Journal of KIBIM
  • Publisher(Ko) :한국BIM학회
  • Journal Title :Journal of KIBIM
  • Journal Title(Ko) :한국BIM학회논문집
  • Volume : 12
  • No :2
  • Pages :1-11
  • Received Date : 2022-02-03
  • Revised Date : 2022-05-06
  • Accepted Date : 2022-05-08
  • DOI :https://doi.org/10.13161/kibim.2022.12.2.001
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