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Transferable supervised learning model for public transport service load estimation

Author

Listed:
  • Tianwei Yin

    (The University of Melbourne)

  • Neema Nassir

    (The University of Melbourne)

  • Joseph Leong

    (The University of Melbourne)

  • Egemen Tanin

    (The University of Melbourne)

  • Majid Sarvi

    (The University of Melbourne)

Abstract

Detailed knowledge of service utilisation and passenger load profiles is the basis for the design, operation, and adjustment of a public transport service. The advancement in sensing technologies enable transit operators to monitor the variabilities in passenger flows continuously and consistently. There is a growing body of literature on using supervised learning models with direct passenger counts from historical observations. However, the incomplete, inaccurate, and biased data from automatic sensors pose challenges in this process. This paper proposes novel supervised learning models to estimate the onboard load profile of public transport services based on two main data sources: (1) limited data collected on a subset of service vehicles by automatic passenger counting (APC) systems, and (2) fare data collected by automatic fare collection (AFC) systems. The specific consideration is given to the fact that the developed models can be transferred across different routes. This is motivated by the commonly “limited coverage” of automated passenger counter devices on service vehicles. We introduce an array of new models, including a superior segment-based model, which demonstrates remarkable improvement in model transferability and accuracy. The proposed methodology utilises separate methods in different segments of a transit line. The proposed models were applied to three tram lines in Melbourne, Australia, where various types of shortcomings exist in the automated data. The test results demonstrate that the proposed models can be transferred and applied to other transit route without relying on historical observations. This would enable transit operators to reduce the number of required devices and monitor service utilisation in a more cost-efficiently manner, particularly in public transport networks where AFC coverage is usually incomplete and negatively skewed. The information on service utilisation will not only help operators to accommodate the variability in passenger demand but also assist passengers in journey planning to avoid overcrowding on services.

Suggested Citation

  • Tianwei Yin & Neema Nassir & Joseph Leong & Egemen Tanin & Majid Sarvi, 2025. "Transferable supervised learning model for public transport service load estimation," Transportation, Springer, vol. 52(1), pages 29-54, February.
    • Handle: RePEc:kap:transp:v:52:y:2025:i:1:d:10.1007_s11116-023-10411-2
    DOI: 10.1007/s11116-023-10411-2
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    References listed on IDEAS

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    1. Cantillo, Angel & Raveau, Sebastián & Muñoz, Juan Carlos, 2022. "Fare evasion on public transport: Who, when, where and how?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 156(C), pages 285-295.
    2. Munizaga, Marcela A. & Gschwender, Antonio & Gallegos, Nestor, 2020. "Fare evasion correction for smartcard-based origin-destination matrices," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 307-322.
    3. Neema Nassir & Mark Hickman & Zhen-Liang Ma, 2015. "Activity detection and transfer identification for public transit fare card data," Transportation, Springer, vol. 42(4), pages 683-705, July.
    4. Michael Siebert & David Ellenberger, 2020. "Validation of automatic passenger counting: introducing the t-test-induced equivalence test," Transportation, Springer, vol. 47(6), pages 3031-3045, December.
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