A visual segmentation method for temporal smart card data

In many cities, worldwide public transit companies use smart card system to manage fare collection. Analysis of this acquisitive information provides a comprehensive insight of user's influence in the interactive public transit network. In this regard, analysis of temporal data, describing the time of entering to the public transit network is considered as the most substantial component of the data gathered from the smart cards. Classical distance-based techniques are not always suitable to analyze this time series data. A novel projection with intuitive visual map from higher dimension into a three-dimensional clock-like space is suggested to reveal the underlying temporal pattern of public transit users. This projection retains the temporal distance between any arbitrary pair of time-stamped data with meaningful visualization. Consequently, this information is fed into a hierarchical clustering algorithm as a method of data segmentation to discover the pattern of users.

• Record URL:
  https://doi.org/10.1080/23249935.2016.1273273
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23249935
• Supplemental Notes:
  • © 2017 Hong Kong Society for Transportation Studies Limited. Abstract reprinted with permission of Taylor & Francis.
• Authors:
  • Ghaemi, Mohammad Sajjad
  • Agard, Bruno
  • Trépanier, Martin
  • Partovi Nia, Vahid
• Publication Date: 2017-5

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 381-404
• Serial:
  • Transportmetrica A: Transport Science
  • Volume: 13
  • Issue Number: 5
  • Publisher: Taylor & Francis
  • ISSN: 2324-9935
  • EISSN: 2324-9943
  • Serial URL: http://www.tandfonline.com/loi/ttra21

Subject/Index Terms

• TRT Terms: Cluster analysis; Data analysis; Data mining; Public transit; Smart cards; Time series analysis; Transit riders
• Uncontrolled Terms: Data segmentation; Temporal data
• Subject Areas: Data and Information Technology; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01635513
• Record Type: Publication
• Files: TRIS
• Created Date: May 25 2017 1:56PM