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

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Listed:
  • 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. 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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    Full references (including those not matched with items on IDEAS)

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