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Using evolution strategies to reduce emergency services arrival time in case of accident

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Using evolution strategies to reduce emergency services arrival time in case of accident

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dc.contributor.author Barrachina Villalba, Javier es_ES
dc.contributor.author Garrido, Piedad es_ES
dc.contributor.author Fogue, Manuel 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 2016-07-13T16:59:47Z
dc.date.available 2016-07-13T16:59:47Z
dc.date.issued 2013-11-04
dc.identifier.isbn 978-1-4799-2971-9
dc.identifier.issn 1082-3409
dc.identifier.uri http://hdl.handle.net/10251/67557
dc.description © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works es_ES
dc.description.abstract [EN] A critical issue, especially in urban areas, is the occurrence of traffic accidents, since it could generate traffic jams. Additionally, these traffic jams will negatively affect to the rescue process, increasing the emergency services arrival time, which can determine the difference between life or death for injured people involved in the accident. In this paper, we propose four different approaches addressing the traffic congestion problem, comparing them to obtain the best solution. Using V2I communications, we are able to accurately estimate the traffic density in a certain area, which represents a key parameter to perform efficient traffic redirection, thereby reducing the emergency services arrival time, and avoiding traffic jams when an accident occurs. Specifically, we propose two approaches based on the Dijkstra algorithm, and two approaches based on Evolution Strategies. Results indicate that the Density-Based Evolution Strategy system is the best one among all the proposed solutions, since it offers the lowest emergency services travel times. es_ES
dc.description.sponsorship This work was partially supported by the Ministerio de Ciencia e Innovacióm , Spain, under Grant TIN2011-27543-C03-01, as well as by the Fundación Universitaria Antonio Gargallo, the Obra Social de Ibercaja, the Government of Aragon, and the European Social Fund (T91 Research Group).
dc.format.extent 8 es_ES
dc.language Inglés es_ES
dc.publisher IEEE es_ES
dc.relation.ispartof 2013 IEEE 25th International Conference on Tools with Artificial Intelligence es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Evolution strategies es_ES
dc.subject Traffic accidents assistance es_ES
dc.subject Vehicular networks es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Using evolution strategies to reduce emergency services arrival time in case of accident es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.1109/ICTAI.2013.127
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 Barrachina Villalba, J.; Garrido, P.; Fogue, M.; Martínez, FJ.; Cano Escribá, JC.; Tavares De Araujo Cesariny Calafate, CM.; Manzoni, P. (2013). Using evolution strategies to reduce emergency services arrival time in case of accident. En 2013 IEEE 25th International Conference on Tools with Artificial Intelligence. IEEE. 833-840. https://doi.org/10.1109/ICTAI.2013.127 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 25th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2013) es_ES
dc.relation.conferencedate November, 4-6, 2013 es_ES
dc.relation.conferenceplace Washington, DC, USA es_ES
dc.relation.publisherversion http://dx.doi.org/10.1109/ICTAI.2013.127 es_ES
dc.description.upvformatpinicio 833 es_ES
dc.description.upvformatpfin 840 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.relation.senia 257485 es_ES
dc.contributor.funder Ministerio de Ciencia e Innovación
dc.contributor.funder Fundación Universitaria Antonio Gargallo
dc.contributor.funder Fundación Bancaria Ibercaja
dc.contributor.funder Gobierno de Aragón
dc.contributor.funder European Social Fund


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