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Quantifying the resilience of rapid transit systems: A composite index using a demand-weighted complex network model
Quantifying the impact of disruptions on rapid transit resilience is crucial in transport planning. We propose a composite resilience score for rapid transit systems comprising four indicators that measure different physical aspects of resilience. These are computed using a weighted network model in...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049543/ https://www.ncbi.nlm.nih.gov/pubmed/35482635 http://dx.doi.org/10.1371/journal.pone.0267222 |
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author | Tan, Hong En Hong Wen Oon, Jeremy Othman, Nasri bin Legara, Erika Fille Monterola, Christopher Ramli, Muhamad Azfar |
author_facet | Tan, Hong En Hong Wen Oon, Jeremy Othman, Nasri bin Legara, Erika Fille Monterola, Christopher Ramli, Muhamad Azfar |
author_sort | Tan, Hong En |
collection | PubMed |
description | Quantifying the impact of disruptions on rapid transit resilience is crucial in transport planning. We propose a composite resilience score for rapid transit systems comprising four indicators that measure different physical aspects of resilience. These are computed using a weighted network model incorporating the network structure of stations, differences in line capacities, and travel demand. Our method provides a holistic assessment of network resilience and allows for straightforward comparisons of different scenarios including rail expansions and changes in demand. Applying our methodology to multiple configurations of Singapore’s rapid transit system, we demonstrate its effectiveness in capturing the impact of planned future lines. We also showcase through simulated studies how tipping points in resilience arise when demand varies. Furthermore, we demonstrate that system resilience could be unintentionally reduced by redistributing commuting demand to peripheral areas. Our methodology is easily applied to other rapid transit systems around the world. |
format | Online Article Text |
id | pubmed-9049543 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-90495432022-04-29 Quantifying the resilience of rapid transit systems: A composite index using a demand-weighted complex network model Tan, Hong En Hong Wen Oon, Jeremy Othman, Nasri bin Legara, Erika Fille Monterola, Christopher Ramli, Muhamad Azfar PLoS One Research Article Quantifying the impact of disruptions on rapid transit resilience is crucial in transport planning. We propose a composite resilience score for rapid transit systems comprising four indicators that measure different physical aspects of resilience. These are computed using a weighted network model incorporating the network structure of stations, differences in line capacities, and travel demand. Our method provides a holistic assessment of network resilience and allows for straightforward comparisons of different scenarios including rail expansions and changes in demand. Applying our methodology to multiple configurations of Singapore’s rapid transit system, we demonstrate its effectiveness in capturing the impact of planned future lines. We also showcase through simulated studies how tipping points in resilience arise when demand varies. Furthermore, we demonstrate that system resilience could be unintentionally reduced by redistributing commuting demand to peripheral areas. Our methodology is easily applied to other rapid transit systems around the world. Public Library of Science 2022-04-28 /pmc/articles/PMC9049543/ /pubmed/35482635 http://dx.doi.org/10.1371/journal.pone.0267222 Text en © 2022 Tan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Tan, Hong En Hong Wen Oon, Jeremy Othman, Nasri bin Legara, Erika Fille Monterola, Christopher Ramli, Muhamad Azfar Quantifying the resilience of rapid transit systems: A composite index using a demand-weighted complex network model |
title | Quantifying the resilience of rapid transit systems: A composite index using a demand-weighted complex network model |
title_full | Quantifying the resilience of rapid transit systems: A composite index using a demand-weighted complex network model |
title_fullStr | Quantifying the resilience of rapid transit systems: A composite index using a demand-weighted complex network model |
title_full_unstemmed | Quantifying the resilience of rapid transit systems: A composite index using a demand-weighted complex network model |
title_short | Quantifying the resilience of rapid transit systems: A composite index using a demand-weighted complex network model |
title_sort | quantifying the resilience of rapid transit systems: a composite index using a demand-weighted complex network model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9049543/ https://www.ncbi.nlm.nih.gov/pubmed/35482635 http://dx.doi.org/10.1371/journal.pone.0267222 |
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