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Public Transport Smart Card Data Analysis Using Tucker Decomposition

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Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems (IES 2019)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 12))

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Abstract

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.

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Acknowledgements

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

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Authors and Affiliations

  1. Department of Management of Social Systems and Civil Engineering, Tottori University, 4-101 Koyama-Minami, Tottori, Japan

    Mio Hosoe & Masashi Kuwano

Authors
  1. Mio Hosoe
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  2. Masashi Kuwano
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Corresponding authors

Correspondence to Mio Hosoe or Masashi Kuwano .

Editor information

Editors and Affiliations

  1. Department of Computer Science, National Defense Academy of Japan, Yokosuka-shi, Japan

    Hiroshi Sato

  2. Faculty of Maritime Safety Technology, Japan Coast Guard Academy, Wakabacho, Japan

    Saori Iwanaga

  3. Department of Applied Mathematics and Physics, Tottori University, Tottori, Japan

    Akira Ishii

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Hosoe, M., Kuwano, M. (2020). Public Transport Smart Card Data Analysis Using Tucker Decomposition. In: Sato, H., Iwanaga, S., Ishii, A. (eds) Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems. IES 2019. Proceedings in Adaptation, Learning and Optimization, vol 12. Springer, Cham. https://doi.org/10.1007/978-3-030-37442-6_6

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