Origin–destination matrix estimation for public transport: A multi-modal weighted graph approach

Zhao, D; Mihăiţă, AS; Ou, Y; Grzybowska, H; Li, M

Origin–destination matrix estimation for public transport: A multi-modal weighted graph approach

Zhao, D Mihăiţă, AS Ou, Y

Grzybowska, H Li, M

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Transportation Research Part C: Emerging Technologies, 2024, 165
Issue Date:: 2024-08-01

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Field	Value	Language
dc.contributor.author	Zhao, D
dc.contributor.author	Mihăiţă, AS
dc.contributor.author	Ou, Y https://orcid.org/0000-0001-5922-9406
dc.contributor.author	Grzybowska, H
dc.contributor.author	Li, M
dc.date.accessioned	2025-03-14T04:35:13Z
dc.date.available	2025-03-14T04:35:13Z
dc.date.issued	2024-08-01
dc.identifier.citation	Transportation Research Part C: Emerging Technologies, 2024, 165
dc.identifier.issn	0968-090X
dc.identifier.issn	1879-2359
dc.identifier.uri	http://hdl.handle.net/10453/185813
dc.description.abstract	Estimating the large-scale Origin–Destination (OD) matrices for multi-modal public transport (PT) in different cities can vary largely based on the network itself, what modes exist, and what traffic data is available. In this study, to overcome the issue of traffic data unavailability and effectively estimate the demand matrix, we employ several data sets like the total boarding and alighting, smart card as well as the General Transit Feed Specification (GTFS) in order to capture the PT dynamic patronage patterns. First, we propose a new method to model the dynamic large-scale stop-by-stop OD matrix for PT networks by developing a new enhancement of the Gravity Model via graph theory and Shannon's entropy. Second, we introduce a method entitled “Entropy-weighted Ensemble Cost Features” that incorporates diverse sources of costs extracted from traffic states and the topological information in the network, scaled appropriately. Last, we compare the efficiency of a single travel cost versus various combinations of travel costs when using traditional methods like the Traverse Searching and the Hyman's method, alongside our proposed “Entropy-weighted” method; we demonstrate the advantages of using topological features as travel costs and prove that our method, coupled with multi-modal PT OD matrix modelling, is superior to traditional methods in improving estimation accuracy, as evidenced by lower MAE, MAPE and RMSE, and reducing computing time.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation	http://purl.org/au-research/grants/arc/LP180100114
dc.relation.ispartof	Transportation Research Part C: Emerging Technologies
dc.relation.isbasedon	10.1016/j.trc.2024.104694
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 15 Commerce, Management, Tourism and Services
dc.subject.classification	Logistics & Transportation
dc.subject.classification	35 Commerce, management, tourism and services
dc.subject.classification	40 Engineering
dc.title	Origin–destination matrix estimation for public transport: A multi-modal weighted graph approach
dc.type	Journal Article
utslib.citation.volume	165
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	15 Commerce, Management, Tourism and Services
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)
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	2025-03-14T04:35:09Z
pubs.publication-status	Published
pubs.volume	165

Abstract:

Estimating the large-scale Origin–Destination (OD) matrices for multi-modal public transport (PT) in different cities can vary largely based on the network itself, what modes exist, and what traffic data is available. In this study, to overcome the issue of traffic data unavailability and effectively estimate the demand matrix, we employ several data sets like the total boarding and alighting, smart card as well as the General Transit Feed Specification (GTFS) in order to capture the PT dynamic patronage patterns. First, we propose a new method to model the dynamic large-scale stop-by-stop OD matrix for PT networks by developing a new enhancement of the Gravity Model via graph theory and Shannon's entropy. Second, we introduce a method entitled “Entropy-weighted Ensemble Cost Features” that incorporates diverse sources of costs extracted from traffic states and the topological information in the network, scaled appropriately. Last, we compare the efficiency of a single travel cost versus various combinations of travel costs when using traditional methods like the Traverse Searching and the Hyman's method, alongside our proposed “Entropy-weighted” method; we demonstrate the advantages of using topological features as travel costs and prove that our method, coupled with multi-modal PT OD matrix modelling, is superior to traditional methods in improving estimation accuracy, as evidenced by lower MAE, MAPE and RMSE, and reducing computing time.

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http://hdl.handle.net/10453/185813