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Mining Behavioural Patterns in Urban Mobility Sequences Using Foursquare Check-in Data from Tokyo

  • Galina Deeva
  • Johannes De Smedt
  • Jochen De Weerdt
  • María ÓskarsdóttirEmail author
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 882)

Abstract

In a study of mobility and urban behaviour, we analyse a longitudinal mobility data set from a sequence mining perspective using a technique that discovers behavioural constraints in sequences of movements between venues. Our contribution is two-fold. First, we propose a methodology to convert aggregated mobility data into insightful patterns. Second, we discover distinctive behavioural patterns in the sequences relative to when in the day they were formed. We analyse sequences of venues as well as sequences of subcategories and categories to discover how people move through Tokyo. The results indicate that our methodology is capable of discovering meaningful behavioural patterns, that can be potentially used to improve urban mobility.

Keywords

Mobility Urban analysis Sequence classification Behavioural constraints Supervised learning 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Galina Deeva
    • 1
  • Johannes De Smedt
    • 2
  • Jochen De Weerdt
    • 1
  • María Óskarsdóttir
    • 3
    Email author
  1. 1.Faculty of Economics and Business, Department of Decision Sciences and Information ManagementKU LeuvenLeuvenBelgium
  2. 2.School Management Science and Business Economics GroupUniversity of Edinburgh BusinessEdinburghUK
  3. 3.Department of Computer ScienceReykjavik UniversityReykjavíkIceland

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