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A Framework for Large-Scale Train Trip Record Analysis and Its Application to Passengers’ Flow Prediction after Train Accidents

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Advances in Knowledge Discovery and Data Mining (PAKDD 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8443))

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Abstract

We have constructed a framework for analyzing passenger behaviors in public transportation systems as understanding these variables is a key to improving the efficiency of public transportation. It uses a large-scale dataset of trip records created from smart card data to estimate passenger flows in a complex metro network. Its interactive flow visualization function enables various unusual phenomena to be observed. We propose a predictive model of passenger behavior after a train accident. Evaluation showed that it can accurately predict passenger flows after a major train accident. The proposed framework is the first step towards real-time observation and prediction for public transportation systems.

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References

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Author information

Authors and Affiliations

  1. Institute of Industrial Science, The University of Tokyo, Japan

    Daisaku Yokoyama, Masahiko Itoh, Masashi Toyoda, Satoshi Kawamura & Masaru Kitsuregawa

  2. Tokyo Metro Co. Ltd., Japan

    Yoshimitsu Tomita & Satoshi Kawamura

  3. National Institute of Informatics, Japan

    Masaru Kitsuregawa

Authors
  1. Daisaku Yokoyama
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  2. Masahiko Itoh
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  3. Masashi Toyoda
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  4. Yoshimitsu Tomita
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  5. Satoshi Kawamura
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  6. Masaru Kitsuregawa
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Editor information

Editors and Affiliations

  1. National Cheng Kung University, Tainan, Taiwan, R.O.C.

    Vincent S. Tseng  & Hung-Yu Kao  & 

  2. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Tu Bao Ho

  3. Nanjing University, China

    Zhi-Hua Zhou

  4. National Chengchi University, Taipei, Taiwan, R.O.C.

    Arbee L. P. Chen

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© 2014 Springer International Publishing Switzerland

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Yokoyama, D., Itoh, M., Toyoda, M., Tomita, Y., Kawamura, S., Kitsuregawa, M. (2014). A Framework for Large-Scale Train Trip Record Analysis and Its Application to Passengers’ Flow Prediction after Train Accidents. In: Tseng, V.S., Ho, T.B., Zhou, ZH., Chen, A.L.P., Kao, HY. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2014. Lecture Notes in Computer Science(), vol 8443. Springer, Cham. https://doi.org/10.1007/978-3-319-06608-0_44

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  • DOI: https://doi.org/10.1007/978-3-319-06608-0_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06607-3

  • Online ISBN: 978-3-319-06608-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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