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Road networks structure analysis: A preliminary network science-based approach

Road network studies attracted unprecedented and overwhelming interest in recent years due to the clear relationship between human existence and city evolution. Current studies cover many aspects of a road network, for example, road feature extraction from video/image data, road map generalisation,...

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Detalles Bibliográficos
Autores principales: Reza, Selim, Ferreira, Marta Campos, Machado, J.J.M., Tavares, João Manuel R.S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520960/
https://www.ncbi.nlm.nih.gov/pubmed/36193340
http://dx.doi.org/10.1007/s10472-022-09818-x
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author Reza, Selim
Ferreira, Marta Campos
Machado, J.J.M.
Tavares, João Manuel R.S.
author_facet Reza, Selim
Ferreira, Marta Campos
Machado, J.J.M.
Tavares, João Manuel R.S.
author_sort Reza, Selim
collection PubMed
description Road network studies attracted unprecedented and overwhelming interest in recent years due to the clear relationship between human existence and city evolution. Current studies cover many aspects of a road network, for example, road feature extraction from video/image data, road map generalisation, traffic simulation, optimisation of optimal route finding problems, and traffic state prediction. However, analysing road networks as a complex graph is a field to explore. This study presents comparative studies on the Porto, in Portugal, road network sections, mainly of Matosinhos, Paranhos, and Maia municipalities, regarding degree distributions, clustering coefficients, centrality measures, connected components, k-nearest neighbours, and shortest paths. Further insights into the networks took into account the community structures, page rank, and small-world analysis. The results show that the information exchange efficiency of Matosinhos is 0.8, which is 10 and 12.8% more significant than that of the Maia and Paranhos networks, respectively. Other findings stated are: (1) the studied road networks are very accessible and densely linked; (2) they are small-world in nature, with an average length of the shortest pathways between any two roads of 29.17 units, which as found in the scenario of the Maia road network; and (3) the most critical intersections of the studied network are ’Avenida da Boavista, 4100-119 Porto (latitude: 41.157944, longitude: − 8.629105)’, and ’Autoestrada do Norte, Porto (latitude: 41.1687869, longitude: − 8.6400656)’, based on the analysis of centrality measures.
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spelling pubmed-95209602022-09-29 Road networks structure analysis: A preliminary network science-based approach Reza, Selim Ferreira, Marta Campos Machado, J.J.M. Tavares, João Manuel R.S. Ann Math Artif Intell Article Road network studies attracted unprecedented and overwhelming interest in recent years due to the clear relationship between human existence and city evolution. Current studies cover many aspects of a road network, for example, road feature extraction from video/image data, road map generalisation, traffic simulation, optimisation of optimal route finding problems, and traffic state prediction. However, analysing road networks as a complex graph is a field to explore. This study presents comparative studies on the Porto, in Portugal, road network sections, mainly of Matosinhos, Paranhos, and Maia municipalities, regarding degree distributions, clustering coefficients, centrality measures, connected components, k-nearest neighbours, and shortest paths. Further insights into the networks took into account the community structures, page rank, and small-world analysis. The results show that the information exchange efficiency of Matosinhos is 0.8, which is 10 and 12.8% more significant than that of the Maia and Paranhos networks, respectively. Other findings stated are: (1) the studied road networks are very accessible and densely linked; (2) they are small-world in nature, with an average length of the shortest pathways between any two roads of 29.17 units, which as found in the scenario of the Maia road network; and (3) the most critical intersections of the studied network are ’Avenida da Boavista, 4100-119 Porto (latitude: 41.157944, longitude: − 8.629105)’, and ’Autoestrada do Norte, Porto (latitude: 41.1687869, longitude: − 8.6400656)’, based on the analysis of centrality measures. Springer International Publishing 2022-09-29 /pmc/articles/PMC9520960/ /pubmed/36193340 http://dx.doi.org/10.1007/s10472-022-09818-x Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Reza, Selim
Ferreira, Marta Campos
Machado, J.J.M.
Tavares, João Manuel R.S.
Road networks structure analysis: A preliminary network science-based approach
title Road networks structure analysis: A preliminary network science-based approach
title_full Road networks structure analysis: A preliminary network science-based approach
title_fullStr Road networks structure analysis: A preliminary network science-based approach
title_full_unstemmed Road networks structure analysis: A preliminary network science-based approach
title_short Road networks structure analysis: A preliminary network science-based approach
title_sort road networks structure analysis: a preliminary network science-based approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9520960/
https://www.ncbi.nlm.nih.gov/pubmed/36193340
http://dx.doi.org/10.1007/s10472-022-09818-x
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