A Clustering Refinement Approach for Revealing Urban Spatial Structure from Smart Card Data

Liyang Tang, Yang Zhao*, Kwok Leung Tsui, Yuxin He, Liwei Pan

*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

9 Citations (Scopus)
86 Downloads (CityUHK Scholars)

Abstract

Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with a case study on Shenzhen metro system in China, we investigate the agglomeration pattern of passenger flow among subway stations. Specifically, leveraging inbound and outbound passenger flows at subway stations, we propose a clustering refinement approach based on cluster member stability among multiple clusterings produced by isomorphic or heterogeneous clusterers. Furthermore, we validate and elaborate five clusters of subway stations in terms of regional functionality and urban planning by comparing station clusters with reference to government planning policies and regulations of Shenzhen city. Additionally, outlier stations with ambiguous functionalities are detected using proposed clustering refinement framework.
Original languageEnglish
Article number5606
JournalApplied Sciences (Switzerland)
Volume10
Issue number16
Online published13 Aug 2020
DOIs
Publication statusPublished - Aug 2020

Research Keywords

  • AFCdata
  • Cluster ensemble
  • Clustering
  • Passenger flow
  • Smart card data
  • Urban spatial structure

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  • SDG 11 - Sustainable Cities and Communities

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