Public Transport Smart Card Data Analysis Using Tucker Decomposition

  • Mio HosoeEmail author
  • Masashi KuwanoEmail author
Conference paper
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 12)


With the development of information and communication technology, various kinds of data have been collected and accumulated in large quantities. Along with this, many methods for analyzing large-scale data have been proposed. This study extracts travel characteristics from smart card data of a private rail system using Tucker decomposition, a valid method for analyzing high order data. Applying Tucker decomposition to a 6th-order tensor dataset (weatherday of the weektime of daypassenger typeorigin stationdestination station), the results reveal what kind of users move from which station to which station, in what weather, on which days of the week, and at what time of day. Moreover, the dataset of 6,489,600 elements was compressed to 1,440 elements with the suggested approach, and the main travel patterns of passengers could be confirmed.


Tensor factorization Smart card High order data Travel pattern 



The authors especially thank Takamastu-Kotohira Electric Railroad Co. in Takamatsu City, Kagawa Prefecture, Japan for providing smart card data.


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Management of Social Systems and Civil EngineeringTottori UniversityTottoriJapan

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