Managing Traffic Data through Clustering and Radial Basis Functions
dc.contributor.author | Hernández, Heber | |
dc.contributor.author | Alberdi Celaya, Elisabete | |
dc.contributor.author | Pérez Acebo, Heriberto | |
dc.contributor.author | Álvarez González, Irantzu | |
dc.contributor.author | García López, María José | |
dc.contributor.author | Eguía Ribero, María Isabel | |
dc.contributor.author | Fernández, Kevin | |
dc.date.accessioned | 2021-03-17T11:34:00Z | |
dc.date.available | 2021-03-17T11:34:00Z | |
dc.date.issued | 2021-03-05 | |
dc.identifier.citation | Sustainability 13(5) : (2021) // Article ID 2846 | es_ES |
dc.identifier.issn | 2071-1050 | |
dc.identifier.uri | http://hdl.handle.net/10810/50670 | |
dc.description.abstract | Due to the importance of road transport an adequate identification of the various road network levels is necessary for an efficient and sustainable management of the road infrastructure. Additionally, traffic values are key data for any pavement management system. In this work traffic volume data of 2019 in the Basque Autonomous Community (Spain) were analyzed and modeled. Having a multidimensional sample, the average annual daily traffic (AADT) was considered as the main variable of interest, which is used in many areas of the road network management. First, an exploratory analysis was performed, from which descriptive statistical information was obtained continuing with the clustering by various variables in order to standardize its behavior by translation. In a second stage, the variable of interest was estimated in the entire road network of the studied country using linear-based radial basis functions (RBFs). The estimated model was compared with the sample statistically, evaluating the estimation using cross-validation and highest-traffic sectors are defined. From the analysis, it was observed that the clustering analysis is useful for identifying the real importance of each road segment, as a function of the real traffic volume and not based on other criteria. It was also observed that interpolation methods based on linear-type radial basis functions (RBF) can be used as a preliminary method to estimate the AADT. | es_ES |
dc.description.sponsorship | This research was funded by The University of the Basque Country (UPV/EHU), Call for Innovation Projects “IKD i3 Laborategia” (Call 1-2020, 2019/20). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | average annual daily traffic | es_ES |
dc.subject | sustainable management | es_ES |
dc.subject | spatial analysis | es_ES |
dc.subject | RBFs | es_ES |
dc.subject | clustering | es_ES |
dc.subject | road network level | es_ES |
dc.title | Managing Traffic Data through Clustering and Radial Basis Functions | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2021-03-12T14:42:11Z | |
dc.rights.holder | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2071-1050/13/5/2846/htm | es_ES |
dc.identifier.doi | 10.3390/su13052846 | |
dc.departamentoes | Matemática aplicada | |
dc.departamentoes | Ingeniería mecánica | |
dc.departamentoes | Expresión gráfica y proyectos de ingeniería | |
dc.departamentoeu | Matematika aplikatua | |
dc.departamentoeu | Ingeniaritza mekanikoa | |
dc.departamentoeu | Adierazpen grafikoa eta ingeniaritzako proiektuak |
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Except where otherwise noted, this item's license is described as 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).