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

  • Daisaku Yokoyama
  • Masahiko Itoh
  • Masashi Toyoda
  • Yoshimitsu Tomita
  • Satoshi Kawamura
  • Masaru Kitsuregawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8443)

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.

Keywords

Smart card data Spatio-Temporal analysis Train transportation Passenger behavior 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daisaku Yokoyama
    • 1
  • Masahiko Itoh
    • 1
  • Masashi Toyoda
    • 1
  • Yoshimitsu Tomita
    • 2
  • Satoshi Kawamura
    • 2
    • 1
  • Masaru Kitsuregawa
    • 3
    • 1
  1. 1.Institute of Industrial ScienceThe University of TokyoJapan
  2. 2.Tokyo Metro Co. Ltd.Japan
  3. 3.National Institute of InformaticsJapan

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