Análisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, Guanajuato

La infraestructura de los países y las ciudades la forman sistemas de redes; en el caso del transporte terrestre, la infraestructura está formada por redes de carreteras, avenidas y calles. Las medidas de centralidad de las redes complejas permiten cuantificar el desempeño de cada intersección de av...

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Autores:: Hernández Torres, José Eduardo
Hernández-González, Salvador
Jiménez-García, José Alfredo
Figueroa-Fernández, Vicente

Tipo de recurso:: Article of journal

Fecha de publicación:: 2020

Institución:: Universidad EIA .

Repositorio:: Repositorio EIA .

Idioma:: spa

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dc.title.spa.fl_str_mv	Análisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, Guanajuato
dc.title.translated.eng.fl_str_mv	Application of complex networks theory for transportation infrastructure analysis: Celaya’s city avenue network
title	Análisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, Guanajuato
spellingShingle	Análisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, Guanajuato Red de transporte redes complejas intermediación cercanía Transport network complex networks betweenness centrality Closeness centrality
title_short	Análisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, Guanajuato
title_full	Análisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, Guanajuato
title_fullStr	Análisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, Guanajuato
title_full_unstemmed	Análisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, Guanajuato
title_sort	Análisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, Guanajuato
dc.creator.fl_str_mv	Hernández Torres, José Eduardo Hernández-González, Salvador Jiménez-García, José Alfredo Figueroa-Fernández, Vicente
dc.contributor.author.spa.fl_str_mv	Hernández Torres, José Eduardo Hernández-González, Salvador Jiménez-García, José Alfredo Figueroa-Fernández, Vicente
dc.subject.spa.fl_str_mv	Red de transporte redes complejas intermediación cercanía
topic	Red de transporte redes complejas intermediación cercanía Transport network complex networks betweenness centrality Closeness centrality
dc.subject.eng.fl_str_mv	Transport network complex networks betweenness centrality Closeness centrality
description	La infraestructura de los países y las ciudades la forman sistemas de redes; en el caso del transporte terrestre, la infraestructura está formada por redes de carreteras, avenidas y calles. Las medidas de centralidad de las redes complejas permiten cuantificar el desempeño de cada intersección de avenidas o calles en la red. En este artículo, se analizó la red de avenidas principales de la ciudad de Celaya, Guanajuato empleando el enfoque de redes complejas. De la centralidad de intermediación, centralidad de la cercanía, diámetro y el grado promedio, se identificaron las 5 intersecciones con un papel fundamental en la red de vialidades de la ciudad. Los resultados son de interés para profesionales dedicados al diseño de sistemas logísticos y transporte.
publishDate	2020
dc.date.accessioned.none.fl_str_mv	2020-02-03 00:00:00 2022-06-17T20:20:18Z
dc.date.available.none.fl_str_mv	2020-02-03 00:00:00 2022-06-17T20:20:18Z
dc.date.issued.none.fl_str_mv	2020-02-03
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.eng.fl_str_mv	Journal article
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dc.relation.references.spa.fl_str_mv	Barabási, A. L. (2016). Network Science by Albert-László Barabási. https://doi.org/10.2172/881797 Boeing, G. (2018). A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood. Environmental and Planning B: Urban Analytics and City Science, 1-18. https://doi.org/10.31235/osf.io/hmhts Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55-71. https://doi:10.1016/j.socnet.2004.11.008 Cardillo, A., Scellato, S., Latora, V., & Porta, S. (2006). Structural Properties of Planar Graphs of Urban Street Patterns. Physical Review E, 73(6), 1-7. https://doi.org/10.1103/physreve.73.066107 Cats, O. (2017). Topological evolution of a metropolitan rail transport network: The case of Stockholm. Journal of Transport Geography, 62(June), 172-183. https://doi.org/10.1016/j.jtrangeo.2017.06.002 Cheng, Y.-Y., Roy, L., Lim, E.-p., & Zhu, F. (2015). Measuring centralities for transportation networks beyond structures. In P. Kazienko, & N. Chawla , Applications of Social Mediaand Social Network Analysis (pp. 23-39). New York: Springer. https://doi.org/10.1007/978-3-319-19003-7_2 Crucitti, P., Latora, V., & Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3), 1-4. doi:10.1103/PhysRevE.73.036125 https://doi.org/10.1103/physreve.73.036125 de-la-Peña, J. (2012). Sistemas de Transporte en México: un análisis de centralidad en teoría de redes. Revista Internacional de Estadística y Geografía, 3(3), 72-91. https://doi.org/10.5565/rev/redes.438 Derrible, S. (2012). Network Centrality of Metro Systems. PlosOne, 2012, 1-10. https://doi:10.1371/journal.pone.0040575 Derrible, S., & Kennedy, C. (2011). Applications of Graph Theory and Network Science to Transit Network Design. Transport Reviews, 31(4), 495-519. https://doi.org/10.1080/01441647.2010.543709 Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35-41. https://doi.org/10.2307/3033543 Fruchterman, T., & Reingold, E. (1991). Graph drawing by Force-directed Placement. Software—Practice and Experience, 21(11), 1129-1164. https://doi.org/10.1002/spe.4380211102 Gephi.org. (2017). Gephi: The Open Graph Viz Platform. From https://gephi.org/ Huang, L., Zhu, X., Ye, X., Guo, W., & Wang, J. (2015). Characterizing streethierarchies through networkanalysis and large-scale taxitraffic flow: a case studyof Wuhan, China. Environment and Planning B:Planning and Design, 43(2), 276-296. https://doi.org/10.1177/0265813515614456 Jian, D., Zhao, Y., & Lu, Q.-C. (2015). Vulnerability Analysis of Urban Rail Transit Networks: A Case Study of Shanghai, China. Sustainability, 7(6), 6919-6936. https://doi.org/10.3390/su7066919 Lotero, R. G. (2014). Vulnerabilidad de redes complejas y aplicaciones al transporte urbano: una revision a la literatura. Revista EIA, 11(21), 67-78. https://doi:10.14508/reia.2014.11.21.67-78 Newman, M. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27(1), 39-54. https://doi.org/10.1016/j.socnet.2004.11.009 Newman, M. (2010). Networks: An Introduction. New York, USA: Oxford University Press. Rodrigue, J.-P., Slack, B., & Comtois, C. (2017). Green Logistics, in Ann M. Brewer , Kenneth J. Button , David A. Hensher. In Handbook of Logistics and Supply-Chain Management (Vol. 2, pp. 339-350). Handbooks in Transport. https://doi.org/10.1108/9780080435930-021 Rui, J., Ban, Y., Wang, J., & Haas, J. (2013). Exploring the patterns and evolution of self-organized urban street networks through modeling. The European Physical Journal E, 86(3), 86-74. https://doi.org/10.1140/epjb/e2012-30235-7 Saberi, M., Mahmassani, H., Brockmann, D., & Hosseini, A. (2016). A complex network perspective for characterizing urbantravel demand patterns: graph theoretical analysisof large-scale origin–destination demand networks. Transportation, 44(6), 1383-1402. https://doi.org/10.1007/s11116-016-9706-6 Strano, E., Viana, M., da-Fontoura-Costa, L., Cardillo, A., Porta, S., & Latora, V. (2013). Urban street networks, a comparative analysis of ten European cities. Environment and Planning B: Planning and Design, 40, 1071-1086. https://doi.org/10.1068/b38216 Sun, J., & Tang, J. (2011). A Survey of Models and Algorithms for Social Influence Analysis. In C. Agarwal, Social Network Data Analytics (pp. 177-214). Nueva York: Springer Science. https://doi.org/10.1007/978-1-4419-8462-3_7 Wang, H., Martin-Hernandez, J., & van Mieghem, P. (2008). Betweenness centrality in a weighted network. Physical Review E, 77, 1-10. https://doi.org/10.1103/physreve.77.046105
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dc.relation.citationedition.spa.fl_str_mv	Núm. 33 , Año 2020
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spelling	Hernández Torres, José Eduardoa0d3d271a2f5915d1fee02cbcc65afcc300Hernández-González, Salvadoraaa5e7c02f7bb1389e10fc617be37994300Jiménez-García, José Alfredo72ae1493e26737166b233db04e9f6a3c300Figueroa-Fernández, Vicente338a5fff729253793b2a67c5fc78a81a3002020-02-03 00:00:002022-06-17T20:20:18Z2020-02-03 00:00:002022-06-17T20:20:18Z2020-02-031794-1237https://repository.eia.edu.co/handle/11190/507810.24050/reia.v17i33.13052463-0950https://doi.org/10.24050/reia.v17i33.1305La infraestructura de los países y las ciudades la forman sistemas de redes; en el caso del transporte terrestre, la infraestructura está formada por redes de carreteras, avenidas y calles. Las medidas de centralidad de las redes complejas permiten cuantificar el desempeño de cada intersección de avenidas o calles en la red. En este artículo, se analizó la red de avenidas principales de la ciudad de Celaya, Guanajuato empleando el enfoque de redes complejas. De la centralidad de intermediación, centralidad de la cercanía, diámetro y el grado promedio, se identificaron las 5 intersecciones con un papel fundamental en la red de vialidades de la ciudad. Los resultados son de interés para profesionales dedicados al diseño de sistemas logísticos y transporte.The streets and avenues networks of a city form the infrastructure of land transport systems. The measures of centrality of complex networks allow to quantify the performance of each intersection of avenues or streets in the network. In this article, Celaya’s city network avenues, was analyzed using the complex networks approach. From betweenness centrality, closeness centrality, diameter and average degree; we identify 5 intersections which play a fundamental role in the city's avenue network as well as its location within the city. The results are of interest for professionals dedicated to the design of logistics systems and transportation.application/pdfspaFondo Editorial EIA - Universidad EIARevista EIA - 2020https://creativecommons.org/licenses/by-nc-nd/4.0info:eu-repo/semantics/openAccessEsta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.http://purl.org/coar/access_right/c_abf2https://revistas.eia.edu.co/index.php/reveia/article/view/1305Red de transporteredes complejasintermediacióncercaníaTransport networkcomplex networksbetweenness centralityCloseness centralityAnálisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, GuanajuatoApplication of complex networks theory for transportation infrastructure analysis: Celaya’s city avenue networkArtículo de revistaJournal articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/redcol/resource_type/ARTREFhttp://purl.org/coar/version/c_970fb48d4fbd8a85Barabási, A. L. (2016). Network Science by Albert-László Barabási. https://doi.org/10.2172/881797Boeing, G. (2018). A Multi-Scale Analysis of 27,000 Urban Street Networks: Every US City, Town, Urbanized Area, and Zillow Neighborhood. Environmental and Planning B: Urban Analytics and City Science, 1-18. https://doi.org/10.31235/osf.io/hmhtsBorgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55-71. https://doi:10.1016/j.socnet.2004.11.008Cardillo, A., Scellato, S., Latora, V., & Porta, S. (2006). Structural Properties of Planar Graphs of Urban Street Patterns. Physical Review E, 73(6), 1-7. https://doi.org/10.1103/physreve.73.066107Cats, O. (2017). Topological evolution of a metropolitan rail transport network: The case of Stockholm. Journal of Transport Geography, 62(June), 172-183. https://doi.org/10.1016/j.jtrangeo.2017.06.002Cheng, Y.-Y., Roy, L., Lim, E.-p., & Zhu, F. (2015). Measuring centralities for transportation networks beyond structures. In P. Kazienko, & N. Chawla , Applications of Social Mediaand Social Network Analysis (pp. 23-39). New York: Springer. https://doi.org/10.1007/978-3-319-19003-7_2Crucitti, P., Latora, V., & Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3), 1-4. doi:10.1103/PhysRevE.73.036125 https://doi.org/10.1103/physreve.73.036125de-la-Peña, J. (2012). Sistemas de Transporte en México: un análisis de centralidad en teoría de redes. Revista Internacional de Estadística y Geografía, 3(3), 72-91. https://doi.org/10.5565/rev/redes.438Derrible, S. (2012). Network Centrality of Metro Systems. PlosOne, 2012, 1-10. https://doi:10.1371/journal.pone.0040575Derrible, S., & Kennedy, C. (2011). Applications of Graph Theory and Network Science to Transit Network Design. Transport Reviews, 31(4), 495-519. https://doi.org/10.1080/01441647.2010.543709Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35-41. https://doi.org/10.2307/3033543Fruchterman, T., & Reingold, E. (1991). Graph drawing by Force-directed Placement. Software—Practice and Experience, 21(11), 1129-1164. https://doi.org/10.1002/spe.4380211102Gephi.org. (2017). Gephi: The Open Graph Viz Platform. From https://gephi.org/Huang, L., Zhu, X., Ye, X., Guo, W., & Wang, J. (2015). Characterizing streethierarchies through networkanalysis and large-scale taxitraffic flow: a case studyof Wuhan, China. Environment and Planning B:Planning and Design, 43(2), 276-296. https://doi.org/10.1177/0265813515614456Jian, D., Zhao, Y., & Lu, Q.-C. (2015). Vulnerability Analysis of Urban Rail Transit Networks: A Case Study of Shanghai, China. Sustainability, 7(6), 6919-6936. https://doi.org/10.3390/su7066919Lotero, R. G. (2014). Vulnerabilidad de redes complejas y aplicaciones al transporte urbano: una revision a la literatura. Revista EIA, 11(21), 67-78. https://doi:10.14508/reia.2014.11.21.67-78Newman, M. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27(1), 39-54. https://doi.org/10.1016/j.socnet.2004.11.009Newman, M. (2010). Networks: An Introduction. New York, USA: Oxford University Press.Rodrigue, J.-P., Slack, B., & Comtois, C. (2017). Green Logistics, in Ann M. Brewer , Kenneth J. Button , David A. Hensher. In Handbook of Logistics and Supply-Chain Management (Vol. 2, pp. 339-350). Handbooks in Transport. https://doi.org/10.1108/9780080435930-021Rui, J., Ban, Y., Wang, J., & Haas, J. (2013). Exploring the patterns and evolution of self-organized urban street networks through modeling. The European Physical Journal E, 86(3), 86-74. https://doi.org/10.1140/epjb/e2012-30235-7Saberi, M., Mahmassani, H., Brockmann, D., & Hosseini, A. (2016). A complex network perspective for characterizing urbantravel demand patterns: graph theoretical analysisof large-scale origin–destination demand networks. Transportation, 44(6), 1383-1402. https://doi.org/10.1007/s11116-016-9706-6Strano, E., Viana, M., da-Fontoura-Costa, L., Cardillo, A., Porta, S., & Latora, V. (2013). Urban street networks, a comparative analysis of ten European cities. Environment and Planning B: Planning and Design, 40, 1071-1086. https://doi.org/10.1068/b38216Sun, J., & Tang, J. (2011). A Survey of Models and Algorithms for Social Influence Analysis. In C. Agarwal, Social Network Data Analytics (pp. 177-214). Nueva York: Springer Science. https://doi.org/10.1007/978-1-4419-8462-3_7Wang, H., Martin-Hernandez, J., & van Mieghem, P. (2008). Betweenness centrality in a weighted network. Physical Review E, 77, 1-10. https://doi.org/10.1103/physreve.77.046105https://revistas.eia.edu.co/index.php/reveia/article/download/1305/1272Núm. 33 , Año 2020133333004 pp 117Revista EIAPublicationOREORE.xmltext/xml2802https://repository.eia.edu.co/bitstreams/53daf9c9-3438-44c2-9b04-820a1af9beb8/downloada555262d1005cdc8bb0633b30368ed6fMD5111190/5078oai:repository.eia.edu.co:11190/50782023-07-25 17:25:39.315https://creativecommons.org/licenses/by-nc-nd/4.0Revista EIA - 2020metadata.onlyhttps://repository.eia.edu.coRepositorio Institucional Universidad EIAbdigital@metabiblioteca.com

Análisis de la infraestructura de transporte aplicando redes complejas: red de avenidas de la ciudad de Celaya, Guanajuato

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