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Probabilistic model for destination inference and travel pattern mining from smart card data

Author

Listed:
  • Zhanhong Cheng

    (McGill University
    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT))

  • Martin Trépanier

    (Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT)
    Department of Mathematics and Industrial Engineering Polytechnique Montréal)

  • Lijun Sun

    (McGill University
    Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT))

Abstract

Inferring trip destination in smart card data with only tap-in control is an important application. Most existing methods estimate trip destinations based on the continuity of trip chains, while the destinations of isolated/unlinked trips cannot be properly handled. We address this problem with a probabilistic topic model. A three-dimensional latent dirichlet allocation model is developed to extract latent topics of departure time, origin, and destination among the population; each passenger’s travel behavior is characterized by a latent topic distribution defined on a three-dimensional simplex. Given the origin station and departure time, the most likely destination can be obtained by statistical inference. Furthermore, we propose to represent stations by their rank of visiting frequency, which transforms divergent spatial patterns into similar behavioral regularities. The proposed destination estimation framework is tested on Guangzhou Metro smart card data, in which the ground-truth is available. Compared with benchmark models, the topic model not only shows increased accuracy but also captures essential latent patterns in passengers’ travel behavior. The proposed topic model can be used to infer the destination of unlinked trips, analyze travel patterns, and passenger clustering.

Suggested Citation

  • Zhanhong Cheng & Martin Trépanier & Lijun Sun, 2021. "Probabilistic model for destination inference and travel pattern mining from smart card data," Transportation, Springer, vol. 48(4), pages 2035-2053, August.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:4:d:10.1007_s11116-020-10120-0
    DOI: 10.1007/s11116-020-10120-0
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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. Cardell-Oliver, Rachel & Olaru, Doina, 2022. "CIAM: A data-driven approach for classifying long-term engagement of public transport riders at multiple temporal scales," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 321-336.
    3. Chen, Ruoyu & Zhou, Jiangping, 2022. "Fare adjustment’s impacts on travel patterns and farebox revenue: An empirical study based on longitudinal smartcard data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 111-133.
    4. Yang, Hongtai & Ping, An & Wei, Hongmin & Zhai, Guocong, 2023. "Unique in the metro system: The likelihood to re-identify a metro user with limited trajectory points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 628(C).

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