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.
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Notes
The use of the HERE public transit API is not essential; see Sect. 3.2.
In contrast to the manual work, for a larger scenario this can be scripted; see Choudhary and Agarwal (2021).
Refer to https://dataspace.mobi/dataset/city-bus-bhubaneswar-gtfs-static. It was accessed on Mar. 10, 2021.
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Acknowledgements
The authors wish to thank the National Institute of Urban Affairs (NIUA), Deutsche Gesellschaft für internationale Zusammenarbeit (GiZ) India for facilitating the data and suggestions to make the model realistic. The authors also acknowledge the support of Devesh Pratap Singh, Itisha Jain, undergraduate scholar, and Rupam Fedujwar, Research Scholar at Indian Institute of Technology Roorkee, for their contribution.
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Shrivastava, A., Rawat, N. & Agarwal, A. Deep-learning-based model for prediction of crowding in a public transit system. Public Transp (2024). https://doi.org/10.1007/s12469-024-00360-z
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DOI: https://doi.org/10.1007/s12469-024-00360-z