Transportation

, Volume 43, Issue 5, pp 749–769

Precise estimation of connections of metro passengers from Smart Card data

  • Sung-Pil Hong
  • Yun-Hong Min
  • Myoung-Ju Park
  • Kyung Min Kim
  • Suk Mun Oh
Article

DOI: 10.1007/s11116-015-9617-y

Cite this article as:
Hong, SP., Min, YH., Park, MJ. et al. Transportation (2016) 43: 749. doi:10.1007/s11116-015-9617-y

Abstract

The aim of this study is to estimate both the physical and schedule-based connections of metro passengers from their entry and exit times at the gates and the stations, a data set available from Smart Card transactions in a majority of train networks. By examining the Smart Card data, we will observe a set of transit behaviors of metro passengers, which is manifested by the time intervals that identifies the boarding, transferring, or alighting train at a station. The authenticity of the time intervals is ensured by separating a set of passengers whose trip has a unique connection that is predominantly better by all respects than any alternative connection. Since the connections of such passengers, known as reference passengers, can be readily determined and hence their gate times and stations can be used to derive reliable time intervals. To detect an unknown path of a passenger, the proposed method checks, for each alternative connection, if it admits a sequence of boarding, middle train(s), and alighting trains, whose time intervals are all consistent with the gate times and stations of the passenger, a necessary condition of a true connection. Tested on weekly 32 million trips, the proposed method detected unique connections satisfying the necessary condition, which are, therefore, most likely true physical and schedule-based connections in 92.6 and 83.4 %, respectively, of the cases.

Keywords

Physical and schedule-based connection estimation Smart Card data Metro network Passenger’s behaviors 

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Sung-Pil Hong
    • 1
  • Yun-Hong Min
    • 1
    • 3
  • Myoung-Ju Park
    • 1
    • 4
  • Kyung Min Kim
    • 1
    • 2
  • Suk Mun Oh
    • 2
  1. 1.Department of Industrial EngineeringSeoul National UniversitySeoulSouth Korea
  2. 2.Policy-Technology Convergence Research DivisionKorea Railroad Research InstituteUiwang-citySouth Korea
  3. 3.Intelligence Computing LaboratorySamsung Electronics Co. Ltd.Suwon CitySouth Korea
  4. 4.Department of Industrial Engineering and Management Systems EngineeringKyung Hee UniversityYongin CitySouth Korea

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