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Classification of the metropolitan subway stations and spheres of influence of main commercial areas in Seoul

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  • Chun, Ki Chan
  • Bahk, Jiwon
  • Kim, Heeju
  • Jeong, Hyeong-Chai
  • Kim, Gunn

Abstract

We classify the subway stations in the Seoul metropolitan area using smart card data. For each station, the numbers of passengers result in a 36-dimensional data set. When the numbers of passengers per hour are expressed as coordinates of perpendicular axes, the stations represented as points in 36-dimensional space are naturally classified into four groups by K-means clustering. Even after projecting the 36-dimensional data to two-dimensional data using principal component analysis, the size and components of the group remain almost the same. By analyzing the two principal axes, we obtain a relation between each classified group and its traffic flow. Then, we determine that the group with a strong commercial pattern can be divided into four main business areas. We analyze the boarding stations of passengers who alight from the stations and determine the extent of the power of the four main business areas.

Suggested Citation

  • Chun, Ki Chan & Bahk, Jiwon & Kim, Heeju & Jeong, Hyeong-Chai & Kim, Gunn, 2023. "Classification of the metropolitan subway stations and spheres of influence of main commercial areas in Seoul," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    • Handle: RePEc:eee:phsmap:v:609:y:2023:i:c:s0378437122009451
    DOI: 10.1016/j.physa.2022.128387
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    References listed on IDEAS

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    1. Lee, Hasik & Park, Ho-Chul & Kho, Seung-Young & Kim, Dong-Kyu, 2019. "Assessing transit competitiveness in Seoul considering actual transit travel times based on smart card data," Journal of Transport Geography, Elsevier, vol. 80(C).
    2. Zuoxian Gan & Min Yang & Tao Feng & Harry Timmermans, 2020. "Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations," Transportation, Springer, vol. 47(1), pages 315-336, February.
    3. Kim, Hyungkyoo & Jung, Yoonhee & Oh, Jae In, 2019. "Transformation of urban heat island in the three-center city of Seoul, South Korea: The role of master plans," Land Use Policy, Elsevier, vol. 86(C), pages 328-338.
    4. Chen, Cynthia & Chen, Jason & Barry, James, 2009. "Diurnal pattern of transit ridership: a case study of the New York City subway system," Journal of Transport Geography, Elsevier, vol. 17(3), pages 176-186.
    5. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
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    1. Wang, Qvshun & Ma, Zhuanglin & Yang, Xing & Chien, Steven I-Jy & Zhang, Shengrui & Yin, Yifan, 2025. "Exploring spatiotemporal dynamic of metro ridership and the influence of built environment factors at the station level: A case study of Nanjing, China," Journal of Transport Geography, Elsevier, vol. 129(C).

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