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Dynamic measures for transportation networks

Most complex network analyses of transportation systems use simplified static representations obtained from existing connections in a time horizon. In static representations, travel times, waiting times and compatibility of schedules are neglected, thus losing relevant information. To obtain a more...

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Detalles Bibliográficos
Autores principales: Lordan, Oriol, Sallan, Jose M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714133/
https://www.ncbi.nlm.nih.gov/pubmed/33270699
http://dx.doi.org/10.1371/journal.pone.0242875
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author Lordan, Oriol
Sallan, Jose M.
author_facet Lordan, Oriol
Sallan, Jose M.
author_sort Lordan, Oriol
collection PubMed
description Most complex network analyses of transportation systems use simplified static representations obtained from existing connections in a time horizon. In static representations, travel times, waiting times and compatibility of schedules are neglected, thus losing relevant information. To obtain a more accurate description of transportation networks, we use a dynamic representation that considers synced paths and that includes waiting times to compute shortest paths. We use the shortest paths to define dynamic network, node and edge measures to analyse the topology of transportation networks, comparable with measures obtained from static representations. We illustrate the application of these measures with a toy model and a real transportation network built from schedules of a low-cost carrier. Results show remarkable differences between measures of static and dynamic representations, demonstrating the limitations of the static representation to obtain accurate information of transportation networks.
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spelling pubmed-77141332020-12-09 Dynamic measures for transportation networks Lordan, Oriol Sallan, Jose M. PLoS One Research Article Most complex network analyses of transportation systems use simplified static representations obtained from existing connections in a time horizon. In static representations, travel times, waiting times and compatibility of schedules are neglected, thus losing relevant information. To obtain a more accurate description of transportation networks, we use a dynamic representation that considers synced paths and that includes waiting times to compute shortest paths. We use the shortest paths to define dynamic network, node and edge measures to analyse the topology of transportation networks, comparable with measures obtained from static representations. We illustrate the application of these measures with a toy model and a real transportation network built from schedules of a low-cost carrier. Results show remarkable differences between measures of static and dynamic representations, demonstrating the limitations of the static representation to obtain accurate information of transportation networks. Public Library of Science 2020-12-03 /pmc/articles/PMC7714133/ /pubmed/33270699 http://dx.doi.org/10.1371/journal.pone.0242875 Text en © 2020 Lordan, Sallan http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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
Lordan, Oriol
Sallan, Jose M.
Dynamic measures for transportation networks
title Dynamic measures for transportation networks
title_full Dynamic measures for transportation networks
title_fullStr Dynamic measures for transportation networks
title_full_unstemmed Dynamic measures for transportation networks
title_short Dynamic measures for transportation networks
title_sort dynamic measures for transportation networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7714133/
https://www.ncbi.nlm.nih.gov/pubmed/33270699
http://dx.doi.org/10.1371/journal.pone.0242875
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