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

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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.

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Acknowledgements

An early draft of this paper is presented at the Transitdata2019 workshop. This research is supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada, Mitacs Canada, the Canada Foundation for Innovation (CFI), and exo (https://exo.quebec/en).

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Authors and Affiliations

  1. Department of Civil Engineering, McGill University, Montreal, QC, H3A 0C3, Canada

    Zhanhong Cheng & Lijun Sun

  2. Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT), Montreal, Canada

    Zhanhong Cheng, Martin Trépanier & Lijun Sun

  3. Department of Mathematics and Industrial Engineering Polytechnique Montréal, Montreal, QC, H3T 1J4, Canada

    Martin Trépanier

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  1. Zhanhong Cheng
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  2. Martin Trépanier
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Contributions

The authors confirm contribution to the paper as follows: all authors contributed to the research conception and design; the data analysis was performed by Lijun Sun and Zhanhong Cheng; the first draft of the manuscript was written by Zhanhong Cheng and all authors commented on previous versions of the manuscript; all authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Lijun Sun.

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Cheng, Z., Trépanier, M. & Sun, L. Probabilistic model for destination inference and travel pattern mining from smart card data. Transportation 48, 2035–2053 (2021). https://doi.org/10.1007/s11116-020-10120-0

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