Identifying human mobility patterns using smart card data

ABSTRACTHuman mobility is subject to collective dynamics that are the outcome of numerous individual choices. Smart card data which originated as a means of facilitating automated fare collection has emerged as an invaluable source for analysing mobility patterns. A variety of clustering and segmentation techniques has been adopted and adapted for applications ranging from market segmentation to the analysis of urban activity locations. In this paper we provide a systematic review of the state-of-the-art on clustering public transport users based on their temporal or spatial-temporal characteristics as well as studies that use the latter to characterise individual stations, lines or urban areas. Furthermore, a critical review of the literature reveals an important distinction between studies focusing on the intra-personal variability of travel patterns versus those concerned with the inter-personal variability of travel patterns. We synthesise the key analysis approaches as well as substantive findings and subsequently identify common trends and shortcomings and outline related directions for further research.

• Record URL:
  • https://doi.org/10.1080/01441647.2023.2251688
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/7802200
• Supplemental Notes:
  • © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group 2023. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Cats, Oded
• Publication Date: 2024-1

Language

• English

Media Info

• Media Type: Digital/other
• Features: References; Tables;
• Pagination: pp 213-243
• Serial:
  • Transport Reviews
  • Volume: 44
  • Issue Number: 1
  • Publisher: Routledge
  • ISSN: 0144-1647
  • EISSN: 1464-5327
  • Serial URL: http://www.tandfonline.com/loi/ttrv20

Subject/Index Terms

• TRT Terms: Cluster analysis; Data analysis; Literature reviews; Mobility; Public transit; Smart cards; Travel patterns
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01903711
• Record Type: Publication
• Files: TRIS
• Created Date: Dec 29 2023 9:32AM