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Deep-learning-based model for prediction of crowding in a public transit system

Author

Listed:
  • Arpit Shrivastava

    (Indian Institute of Technology Roorkee)

  • Nishtha Rawat

    (Indian Institute of Technology Roorkee)

  • Amit Agarwal

    (Indian Institute of Technology Roorkee
    Indian Institute of Technology Roorkee)

Abstract

Crowding in public transport is one of the reasons that nudges road users to shift from public transport to private modes of transport. To provide the passengers with a facility to plan their trips as per the dynamic crowding levels, this work proposes a framework for a passenger information system (PIS), in which the transit choices are differentiated with respect to crowding levels on the transit routes at different times of the day. A granular crowding prediction model is developed and integrated with PIS. In this, firstly, the transit segment relation (TSR) is constituted and used to make clusters based on the ridership index. Further, a time-series model is trained for each cluster using boarding TSR. A case study of Bhubaneswar, India, is presented, and three months of ticketing data are used to demonstrate the performance of the proposed prediction model. The prediction model is integrated into the PIS to expedite various route choices.

Suggested Citation

  • Arpit Shrivastava & Nishtha Rawat & Amit Agarwal, 2024. "Deep-learning-based model for prediction of crowding in a public transit system," Public Transport, Springer, vol. 16(2), pages 449-484, June.
  • Handle: RePEc:spr:pubtra:v:16:y:2024:i:2:d:10.1007_s12469-024-00360-z
    DOI: 10.1007/s12469-024-00360-z
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    References listed on IDEAS

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    1. Morency, Catherine & Trépanier, Martin & Agard, Bruno, 2007. "Measuring transit use variability with smart-card data," Transport Policy, Elsevier, vol. 14(3), pages 193-203, May.
    2. Daniel Delling & Thomas Pajor & Renato F. Werneck, 2015. "Round-Based Public Transit Routing," Transportation Science, INFORMS, vol. 49(3), pages 591-604, August.
    3. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    4. W. Klumpenhouwer & S. C. Wirasinghe, 2016. "Cost-of-crowding model for light rail train and platform length," Public Transport, Springer, vol. 8(1), pages 85-101, March.
    5. Yang, Xin & Xue, Qiuchi & Ding, Meiling & Wu, Jianjun & Gao, Ziyou, 2021. "Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data," International Journal of Production Economics, Elsevier, vol. 231(C).
    6. Mo, Baichuan & Koutsopoulos, Haris N. & Zhao, Jinhua, 2022. "Inferring passenger responses to urban rail disruptions using smart card data: A probabilistic framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
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