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A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms

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A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms

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dc.contributor.author Fogue, Manuel es_ES
dc.contributor.author Garrido, Piedad es_ES
dc.contributor.author Martínez, Francisco J. es_ES
dc.contributor.author Cano Escribá, Juan Carlos es_ES
dc.contributor.author Tavares de Araujo Cesariny Calafate, Carlos Miguel es_ES
dc.contributor.author Manzoni, Pietro es_ES
dc.date.accessioned 2014-01-09T10:18:20Z
dc.date.issued 2013-01
dc.identifier.issn 0957-4174
dc.identifier.uri http://hdl.handle.net/10251/34829
dc.description.abstract [EN] The development of communication technologies integrated in vehicles allows creating new protocols and applications to improve assistance in traffic accidents. Combining this technology with intelligent systems will permit to automate most of the decisions needed to generate the appropriate sanitary resource sets, thereby reducing the time from the occurrence of the accident to the stabilization and hospitalization of the injured passengers. However, generating the optimal allocation of sanitary resources is not an easy task, since there are several objectives that are mutually exclusive, such as assistance improvement, cost reduction, and balanced resource usage. In this paper, we propose a novel approach for the sanitary resources allocation in traffic accidents. Our approach is based on the use of multiobjective genetic algorithms, and it is able to generate a list of optimal solutions accounting for the most representative factors. The inputs to our model are: (i) the accident notification, which is obtained through vehicular communication systems, and (ii) the severity estimation for the accident, achieved through data mining. We evaluate our approach under a set of vehicular scenarios, and the results show that a memetic version of the NSGA-II algorithm was the most effective method at locating the optimal resource set, while maintaining enough variability in the solutions to allow applying different resource allocation policies. 2012 Elsevier Ltd. All rights reserved. es_ES
dc.description.sponsorship This work was partially supported by the Ministerio de Ciencia e Innovacion, Spain, under Grant TIN2011-27543-C03-01, and by the Diputacion General de Aragon, under Grant "subvenciones destinadas a la formacion y contratacion de personal investigador". en_EN
dc.format.extent 14 es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Expert Systems with Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Resource allocation es_ES
dc.subject Traffic accidents assistance es_ES
dc.subject Multi-objective genetic algorithms es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms es_ES
dc.type Artículo es_ES
dc.embargo.lift 10000-01-01
dc.embargo.terms forever es_ES
dc.identifier.doi 10.1016/j.eswa.2012.07.056
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//TIN2011-27543-C03-01/ES/WALKIE-TALKIE: SOPORTE A ENTORNOS DE TRANSPORTE SEGURO, INTELIGENTE Y SOSTENIBLE PARA LA FUTURA GENERACION DE COCHES INTELIGENTES/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Fogue, M.; Garrido, P.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). A novel approach for traffic accidents sanitary resource allocation based on multi-objective genetic algorithms. Expert Systems with Applications. 40(1):323-336. https://doi.org/10.1016/j.eswa.2012.07.056 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.eswa.2012.07.056 es_ES
dc.description.upvformatpinicio 323 es_ES
dc.description.upvformatpfin 336 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 40 es_ES
dc.description.issue 1 es_ES
dc.relation.senia 233495
dc.contributor.funder Diputación General de Aragón
dc.contributor.funder Ministerio de Ciencia e Innovación


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