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Space–time classification of public transit smart card users’ activity locations from smart card data

Author

Listed:
  • Li He

    (Autorité Régionale de Transport Métropolitain)

  • Martin Trépanier

    (Polytechnique Montréal and CIRRELT)

  • Bruno Agard

    (Polytechnique Montréal and CIRRELT)

Abstract

Smart card data from public transit systems has proven to be useful to understand the behaviors of public transit users. Relevant research has been done concerning: (1) the utilization of smart card data, (2) data mining techniques and (3) the utilization of data mining in smart card data. In prior research, the classification of user behavior has been based on trips when temporal and spatial classifications are considered to be separate processes. Therefore, it is of interest to develop a method based on users' daily behaviors that takes into account both spatial and temporal behaviors at the same time. In this work, a methodology is developed to classify smart card users' behaviors based on dynamic time warping (DTW), hierarchical clustering and a sampling method. A three-dimensional space–time prism plot demonstrates the efficiency of the algorithm.

Suggested Citation

  • Li He & Martin Trépanier & Bruno Agard, 2021. "Space–time classification of public transit smart card users’ activity locations from smart card data," Public Transport, Springer, vol. 13(3), pages 579-595, October.
    • Handle: RePEc:spr:pubtra:v:13:y:2021:i:3:d:10.1007_s12469-021-00274-0
    DOI: 10.1007/s12469-021-00274-0
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    References listed on IDEAS

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    Cited by:

    1. Ziqin Lan & Zixuan Zhang & Jiatao Chen & Ming Cai, 2024. "Inferring alighting bus stops from smart card data combined with cellular signaling data," Transportation, Springer, vol. 51(4), pages 1433-1465, August.
    2. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.

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