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2021 Vol.39, Issue 5 Preview Page

Article

A Study on the Classification and Understanding of Travel Boundary by City Population Scale Based on Mobile Travel Data

모바일 생활통행데이터 기반 도시 인구 규모별 생활권 분류 및 특성 파악

Kyuhyuk KIM1
,
Dongyup LEE2
,
Dong-ho KIM3
,
Minsu WON4
,
Sungmin HONG5
,
Tai-jin SONG6*
김 규혁1
,
이 동엽2
,
김 동호3
,
원 민수4
,
홍 성민5
,
송 태진6*
1Master’s Degree, Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Korea
2Researcher, Urban Research Division, Korea Research Institute for Human Settlements, Sejong 30147, Korea
3Research Specialist, Transport Big Data Research Division, The Korea Transport Institute, Sejong 30147, Korea
4Associate Research Fellow, Transport Big Data Research Division, The Korea Transport Institute, Sejong 30147, Korea
5Senior Researcher, Transportation Safety Division, Korea Transportation Safety Authority, Gimcheon 39660, Korea
6Assistant Professor, Department of Urban Engineering, Chungbuk National University, Cheongju 28644, Korea
1충북대학교 도시공학과 석사과정
2국토연구원 도시연구본부 연구원
3한국교통연구원 AI ‧ 빅데이터연구본부 책임전문원
4한국교통연구원 AI ‧ 빅데이터연구본부 부연구위원
5한국교통안전공단 교통안전연구본부 책임연구원
6충북대학교 도시공학과 조교수
*Corresponding Author
31 October 2021. pp. 662-680
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Abstract

Understanding spatio-temporal individual travel patterns is a key for establishing a public transportation system which serves from inter-regional mobility to a first-and-last mile mobility. Travel boundary is a spatial area where people moves from place to place with a specified usual activity. This conveys transportation planners pivotal insights to classify a suitable transport service area with better public transportation system. This study used the community detection method to derive multi-level travel boundary in four study cites selected by population scale using mobile phone signaling data. A conceptual travel boundary (15-minutes neighborhood unit) was considered to identify differences in travel boundary pattern spatially in the study sites. This study also analyzed an identification of temporal characteristics of travel boundary on centrality and modularity. As a result, it is found that is the larger the population when the number of travel boundary is equal, the higher the modularity, and even the higher the clustering reliability. In addition, study site with larger population showed the smaller variance in modularity distribution by time. The authors expects that it can facilitate transportation planners designing an efficient and suitable transportation mode in near future.

Keywords
centrality
community detection
mobile big data
O/D Matrix
travel boundary

광역권 대중교통체계 수립에서부터 근린지구 first-and-last mile까지의 기능별 교통체계 수립은 사람들의 이동 관점에서 생활 혹은 활동 기반의 통행패턴을 정확히 이해하는데서부터 출발한다. 생활권은 사람들이 일상생활을 실질적으로 영위하는 공간적인 범위로, 사람들의 생활패턴을 인지하고 기능별 교통체계를 수립하기에 가장 적합한 공간 단위이므로 도시별 ‧ 시간대별 특성에 따라 생활권 분화 패턴의 차이를 확인하는 것은 각 공간에 최적화된 교통서비스 수립 및 제공을 위해 필수적이다. 본 연구에서는 모바일 기지국 데이터를 가공하여 제작한 모바일 생활통행데이터에 Community Detection 기법을 적용하여 4개 도시(특별시, 대도시, 중도시, 소도시)의 Multi-level 생활권, 15분 생활권 등을 도출하였으며, 각 도시별로 시간대별, 주중 ‧ 주말 생활권 분화 패턴의 차이를 확인하였다. 추가로, 단순 읍면동 군집에서 보이지 않는 시사점 도출을 위해 시간대별, 주중 ‧ 주말별 생활권 면적 분포 패턴과 중심성 패턴을 분석하였다. 도시별 Multi-level 생활권 분석 결과, 각 도시의 생활권 개수가 같을 때 인구 규모가 큰 도시일수록 모듈성이 높아 군집된 생활권의 신뢰도가 높은 것으로 나타났다. 시간대별 도시별 Multi-level 생활권의 면적 분포 분석 결과 인구 규모가 큰 도시일수록 생활권 면적 간의 분산이 작아 교통수단별 계획 수립에 적합한 것으로 나타났으며, 주중 ‧ 주말 중심성 변화 분석 결과 역사문화적 또는 관광적 요소가 도시 내 산재하는 서울시와 제천시는 주중 ‧ 주말 간 차별화된 교통계획이 필요함을 확인하였다. 본 연구는 향후 효율적인 교통수단별 정책 및 서비스 수립 시에 기초 자료로 활용될 것으로 판단된다.

키워드
중심성
커뮤니티 군집
모바일 빅데이터
O/D Matrix
생활권
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Information
  • Publisher :Korean Society of Transportation
  • Publisher(Ko) :대한교통학회
  • Journal Title :Journal of Korean Society of Transportation
  • Journal Title(Ko) :대한교통학회지
  • Volume : 39
  • No :5
  • Pages :662-680
  • Received Date : 2021-05-31
  • Revised Date : 2021-06-09
  • Accepted Date : 2021-08-06
  • DOI :https://doi.org/10.7470/jkst.2021.39.5.662
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