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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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    1. Jonathan M. Bunker, 2018. "High volume bus stop upstream average waiting time for working capacity and quality of service," Public Transport, Springer, vol. 10(2), pages 311-333, August.
    2. Hiroaki Nishiuchi & Yasuyuki Kobayashi & Tomoyuki Todoroki & Tomoya Kawasaki, 2018. "Impact analysis of reductions in tram services in rural areas in Japan using smart card data," Public Transport, Springer, vol. 10(2), pages 291-309, August.
    3. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
    4. Farber, Steven & O'Kelly, Morton & Miller, Harvey J. & Neutens, Tijs, 2015. "Measuring segregation using patterns of daily travel behavior: A social interaction based model of exposure," Journal of Transport Geography, Elsevier, vol. 49(C), pages 26-38.
    5. Giorgino, Toni, 2009. "Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i07).
    6. Filip Covic & Stefan Voß, 2019. "Interoperable smart card data management in public mass transit," Public Transport, Springer, vol. 11(3), pages 523-548, October.
    7. Yanshuo Sun & Jungang Shi & Paul M. Schonfeld, 2016. "Identifying passenger flow characteristics and evaluating travel time reliability by visualizing AFC data: a case study of Shanghai Metro," Public Transport, Springer, vol. 8(3), pages 341-363, December.
    8. Cecilia Viggiano & Haris N. Koutsopoulos & Nigel H. M. Wilson & John Attanucci, 2017. "Journey-based characterization of multi-modal public transportation networks," Public Transport, Springer, vol. 9(1), pages 437-461, July.
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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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