Delay propagation in large railway networks with data-driven bayesian modeling

Li, B; Guo, T; Li, R; Wang, Y; Ou, Y; Chen, F

Delay propagation in large railway networks with data-driven bayesian modeling

Li, B Guo, T

Li, R Wang, Y

Ou, Y

Chen, F

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Publisher:: SAGE PUBLICATIONS INC
Publication Type:: Journal Article
Citation:: TRANSPORTATION RESEARCH RECORD, 2021, 2675, (11), pp. 472-485
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Li, B
dc.contributor.author	Guo, T https://orcid.org/0000-0001-5130-3237
dc.contributor.author	Li, R
dc.contributor.author	Wang, Y https://orcid.org/0000-0002-6815-0879
dc.contributor.author	Ou, Y https://orcid.org/0000-0001-5922-9406
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date.accessioned	2022-04-12T21:46:50Z
dc.date.available	2022-04-12T21:46:50Z
dc.date.issued	2021-01-01
dc.identifier.citation	TRANSPORTATION RESEARCH RECORD, 2021, 2675, (11), pp. 472-485
dc.identifier.issn	0361-1981
dc.identifier.issn	2169-4052
dc.identifier.uri	http://hdl.handle.net/10453/156140
dc.description.abstract	Reliability and punctuality are the key evaluation criteria in railway service for both passengers and operators. Delays spanning over spatial and temporal dimensions significantly affect the reliability and punctuality level of train operation. The optimization of capacity utilization and timetable design requires the prediction of the reliability and punctuality level of train operations, which is determined by train delays and delay propagation. To predict the punctuality level of train operations, the distributions of arrival and departure delays must be estimated as realistically as possible by taking into account the complex railway network structure and different types of delays caused by route conflict and connected trips. This paper aims to predict the propagation of delays on the railway network in the Greater Sydney area by developing a conditional Bayesian model. In the model, the propagation satisfies the Markov property if one can predict future delay propagation in the network based solely on its present state just as well as one could knowing the process’s full history, so that it is independent of such historical procedures. Meanwhile, we consider the throughput estimation for the cases of delay caused by interchange line conflicts and train connection in this model. To the best of the authors’ knowledge, this is the first work of data-driven delay propagation modeling that examines both spatial and temporal dimensions under four different scenarios for railway networks. Implementation on real-world railway network operation data shows the feasibility and accuracy of the proposed model compared with traditional probability models.
dc.language	English
dc.publisher	SAGE PUBLICATIONS INC
dc.relation.ispartof	TRANSPORTATION RESEARCH RECORD
dc.relation.isbasedon	10.1177/03611981211018471
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0905 Civil Engineering, 1205 Urban and Regional Planning, 1507 Transportation and Freight Services
dc.subject.classification	Logistics & Transportation
dc.title	Delay propagation in large railway networks with data-driven bayesian modeling
dc.type	Journal Article
utslib.citation.volume	2675
utslib.for	0905 Civil Engineering
utslib.for	1205 Urban and Regional Planning
utslib.for	1507 Transportation and Freight 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/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-04-12T21:46:48Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	2675
utslib.citation.issue	11

Abstract:

Reliability and punctuality are the key evaluation criteria in railway service for both passengers and operators. Delays spanning over spatial and temporal dimensions significantly affect the reliability and punctuality level of train operation. The optimization of capacity utilization and timetable design requires the prediction of the reliability and punctuality level of train operations, which is determined by train delays and delay propagation. To predict the punctuality level of train operations, the distributions of arrival and departure delays must be estimated as realistically as possible by taking into account the complex railway network structure and different types of delays caused by route conflict and connected trips. This paper aims to predict the propagation of delays on the railway network in the Greater Sydney area by developing a conditional Bayesian model. In the model, the propagation satisfies the Markov property if one can predict future delay propagation in the network based solely on its present state just as well as one could knowing the process’s full history, so that it is independent of such historical procedures. Meanwhile, we consider the throughput estimation for the cases of delay caused by interchange line conflicts and train connection in this model. To the best of the authors’ knowledge, this is the first work of data-driven delay propagation modeling that examines both spatial and temporal dimensions under four different scenarios for railway networks. Implementation on real-world railway network operation data shows the feasibility and accuracy of the proposed model compared with traditional probability models.

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http://hdl.handle.net/10453/156140