Rail Digital Twin and Deep Learning for Passenger Flow Prediction Using Mobile Data

Ou, Y; Mihăiţă, AS; Ellison, A; Mao, T; Lee, S; Chen, F

Rail Digital Twin and Deep Learning for Passenger Flow Prediction Using Mobile Data

Ou, Y

Mihăiţă, AS Ellison, A Mao, T

Lee, S Chen, F

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Electronics Switzerland, 2025, 14, (12)
Issue Date:: 2025-06-01

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Field	Value	Language
dc.contributor.author	Ou, Y https://orcid.org/0000-0001-5922-9406
dc.contributor.author	Mihăiţă, AS
dc.contributor.author	Ellison, A
dc.contributor.author	Mao, T https://orcid.org/0000-0002-1657-5437
dc.contributor.author	Lee, S
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date.accessioned	2026-01-06T23:56:39Z
dc.date.available	2026-01-06T23:56:39Z
dc.date.issued	2025-06-01
dc.identifier.citation	Electronics Switzerland, 2025, 14, (12)
dc.identifier.issn	2079-9292
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/191371
dc.description.abstract	Predicting passenger flows in rail transport systems plays an important role for traffic management centers to make fast decisions during service disruptions. This paper presents an innovative cross-disciplinary approach based on digital twins, deep learning, and traffic simulation to predict the total number of passengers in each train stations and evaluate the impact of service disruptions across stations. First, we present a four-layer system architecture for building a digital twin which ingests real-time data streams, including train movements and timetable scheduling. Second, we deploy several deep learning models to predict the total number of passengers in each station using mobile data. The results showcase significant accuracy for recurrent versus non-recurrent traffic conditions even under severe large disruptions such as the COVID-19 travel restrictions. Our case study of the Sydney rail network demonstrates that the proposed digital twin powered by deep learning can provide more granular real-time insights into the impact on passengers, allowing rail operation centers to better mitigate service disruptions.
dc.language	English
dc.publisher	MDPI
dc.relation.ispartof	Electronics Switzerland
dc.relation.isbasedon	10.3390/electronics14122359
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Rail Digital Twin and Deep Learning for Passenger Flow Prediction Using Mobile Data
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-01-06T23:56:37Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	12

Abstract:

Predicting passenger flows in rail transport systems plays an important role for traffic management centers to make fast decisions during service disruptions. This paper presents an innovative cross-disciplinary approach based on digital twins, deep learning, and traffic simulation to predict the total number of passengers in each train stations and evaluate the impact of service disruptions across stations. First, we present a four-layer system architecture for building a digital twin which ingests real-time data streams, including train movements and timetable scheduling. Second, we deploy several deep learning models to predict the total number of passengers in each station using mobile data. The results showcase significant accuracy for recurrent versus non-recurrent traffic conditions even under severe large disruptions such as the COVID-19 travel restrictions. Our case study of the Sydney rail network demonstrates that the proposed digital twin powered by deep learning can provide more granular real-time insights into the impact on passengers, allowing rail operation centers to better mitigate service disruptions.

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http://hdl.handle.net/10453/191371