Yin, J, Ren, X, Liu, R orcid.org/0000-0003-0627-3184 et al. (2 more authors) (2022) Quantitative analysis for resilience-based urban rail systems: A hybrid knowledge-based and data-driven approach. Reliability Engineering and System Safety, 219. 108183. ISSN 0951-8320
Abstract
The rapid expansions of urban rail networks are faced with the growing number of disruptions caused by the complex rail signaling systems, incorrect driving behaviors, and extreme weather. Since urban rail systems are inherently complex and many of these disruptions are usually uncertain and inevitable, the rail managers have gradually paid more attention to the ability to withstand and quickly recover. Nevertheless, only a small number of recent developments have tried to address the ability of an urban rail system to recover from disruptions while considering the inherent structures. In this work, we propose a hybrid knowledge-based and data-driven approach for quantitative analysis of resilience. The aim is to model the causal relationships to quantify the importance of different perturbations to the overall resilience criteria. A set of key features related to the risk assessment and system resilience are summarized according to the historical data in Beijing Metro. Then, we develop a training procedure based on the structure of BN and historical data. Finally, we embed this hybrid approach into software that is applied to Beijing Metro. The results demonstrate the quantitative relationships between system resilience and different types of events.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 Elsevier Ltd. This is an author produced version of an article published in Reliability Engineering & System Safety. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Resilience, Urban rail systems, Bayesian network, Quantitative, Transportation |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Spatial Modelling and Dynamics (Leeds) |
Funding Information: | Funder Grant number Royal Academy of Engineering TSPC1025 |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Nov 2021 12:28 |
Last Modified: | 11 Mar 2023 10:59 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.ress.2021.108183 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180768 |
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Filename: Yin et al - RESS2021 - Resilince - WRR.pdf
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