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

Arpit Shrivastava (), Nishtha Rawat () and Amit Agarwal ()
Additional contact information
Arpit Shrivastava: Indian Institute of Technology Roorkee
Nishtha Rawat: Indian Institute of Technology Roorkee
Amit Agarwal: Indian Institute of Technology Roorkee

Public Transport, 2024, vol. 16, issue 2, No 5, 449-484

Abstract: 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.

Keywords: Public transport; Occupancy; Crowding; Affinity propagation; LSTM (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12469-024-00360-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:pubtra:v:16:y:2024:i:2:d:10.1007_s12469-024-00360-z

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12469

DOI: 10.1007/s12469-024-00360-z

Access Statistics for this article

Public Transport is currently edited by Stefan Voß

More articles in Public Transport from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().