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Inferring unable-to-board commuters for overcrowded buses using smart card data

Author

Listed:
  • Hong En Tan

    (Institute of High Performance Computing, A*STAR)

  • Muhamad Azfar Ramli

    (Institute of High Performance Computing, A*STAR)

Abstract

As public transportation faces increasing ridership demand, metrics such as the number of passengers denied boarding become important for measuring the service quality of transit systems. Many studies in the past have used automated fare collection (AFC) (also known as smart card data) and automated vehicle location data to infer the probability distributions for commuters that experience unable-to-board (UTB) events in metro systems, but few have studied UTB events for buses. In this paper, we demonstrate that the probability distribution of UTB commuters inferred from AFC data can be modelled by a truncated binomial distribution under certain assumptions. This model is then validated against synthetic UTB events generated using simulations and against actual UTB events recorded from ground surveys. Finally, we apply our model on real AFC data of commuters in the Singapore bus network to serve as a case study. Our method enables transport planners and operators to identify bus stops and time intervals where overcrowding and UTB events is prevalent, so that appropriate measures can be taken to mitigate such occurrences.

Suggested Citation

  • Hong En Tan & Muhamad Azfar Ramli, 2024. "Inferring unable-to-board commuters for overcrowded buses using smart card data," Transportation, Springer, vol. 51(4), pages 1279-1298, August.
    • Handle: RePEc:kap:transp:v:51:y:2024:i:4:d:10.1007_s11116-022-10359-9
    DOI: 10.1007/s11116-022-10359-9
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    References listed on IDEAS

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    1. Yap, Menno & Cats, Oded, 2021. "Taking the path less travelled: Valuation of denied boarding in crowded public transport systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 1-13.
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