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

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.

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Acknowledgements

The authors wish to acknowledge the support of the Société de transport de l’Outaouais (STO) for providing data, the Thales group and the Natural Science and Engineering Research Council of Canada (NSERC project RDCPJ 446107-12) for funding.

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Correspondence to Li He.

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He, L., Trépanier, M. & Agard, B. Space–time classification of public transit smart card users’ activity locations from smart card data. Public Transp (2021). https://doi.org/10.1007/s12469-021-00274-0

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Keywords

  • Public transit
  • Smart card data
  • Dynamic time warping
  • Spatiotemporal classification
  • Activity locations