Modelling public transport disruptions and impact by smart-card data

Zhao, D; Mihǎiţǎ, AS; Ou, Y; Grzybowska, H

Modelling public transport disruptions and impact by smart-card data

Zhao, D Mihǎiţǎ, AS Ou, Y

Grzybowska, H

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2024, 00, pp. 2945-2952
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Zhao, D
dc.contributor.author	Mihǎiţǎ, AS
dc.contributor.author	Ou, Y https://orcid.org/0000-0001-5922-9406
dc.contributor.author	Grzybowska, H
dc.date	2023-09-24
dc.date.accessioned	2024-08-07T05:20:18Z
dc.date.available	2024-08-07T05:20:18Z
dc.date.issued	2024-01-01
dc.identifier.citation	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2024, 00, pp. 2945-2952
dc.identifier.isbn	979-8-3503-9946-2
dc.identifier.issn	2153-0009
dc.identifier.issn	2153-0017
dc.identifier.uri	http://hdl.handle.net/10453/180299
dc.description.abstract	Evaluating disruptions in public transport (PT) utilisation is challenging due to often stochastic traveller behaviour and missing data information on affected services. This paper proposes a new approach for modelling PT patronage and disruption impact using integrated data-driven modelling and the Fourier transform technique. Firstly, using tap-on and off information of smart-card data, we estimate in-vehicle passenger numbers to integrate as well as trips passing through the incident area. Secondly, considering the PT patronage pattern as a periodic function, we employ the Fourier transform to convert it into a sum of simpler trigonometric functions to filter out the one representing common data noise successfully and generate an accurate profile for a typical day. Thirdly, we introduce an enhanced sensitivity test to improve the model's ability to identify the impact of the disruption. Finally, multiple impact measurement methods are compared to capture the disruption impact. The findings demonstrate the effectiveness of leveraging in-vehicle count to maximise data volume and enhance impact identification. The PT patronage pattern can be effectively modelled using the Fourier transform. The utilisation of the enhanced sensitivity test can effectively filter out unnecessary trigonometric components, resulting in a refined model capable of accurately identifying the impact of disruption.
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/LP180100114
dc.relation.ispartof	IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
dc.relation.ispartof	International Conference on Intelligent Transportation
dc.relation.ispartofseries	IEEE International Conference on Intelligent Transportation Systems-ITSC
dc.relation.isbasedon	10.1109/ITSC57777.2023.10422238
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Modelling public transport disruptions and impact by smart-card data
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Bilbao, Spain
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2024-08-07T05:20:11Z
pubs.finish-date	2023-09-28
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-09-24
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Evaluating disruptions in public transport (PT) utilisation is challenging due to often stochastic traveller behaviour and missing data information on affected services. This paper proposes a new approach for modelling PT patronage and disruption impact using integrated data-driven modelling and the Fourier transform technique. Firstly, using tap-on and off information of smart-card data, we estimate in-vehicle passenger numbers to integrate as well as trips passing through the incident area. Secondly, considering the PT patronage pattern as a periodic function, we employ the Fourier transform to convert it into a sum of simpler trigonometric functions to filter out the one representing common data noise successfully and generate an accurate profile for a typical day. Thirdly, we introduce an enhanced sensitivity test to improve the model's ability to identify the impact of the disruption. Finally, multiple impact measurement methods are compared to capture the disruption impact. The findings demonstrate the effectiveness of leveraging in-vehicle count to maximise data volume and enhance impact identification. The PT patronage pattern can be effectively modelled using the Fourier transform. The utilisation of the enhanced sensitivity test can effectively filter out unnecessary trigonometric components, resulting in a refined model capable of accurately identifying the impact of disruption.

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http://hdl.handle.net/10453/180299