Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/11531/56086
Registro completo de metadatos
Campo DC Valor Lengua/Idioma
dc.contributor.authorSuárez del Fueyo, Rocíoes-ES
dc.contributor.authorJunge, Mirkoes-ES
dc.contributor.authorLópez Valdés, Francisco Josées-ES
dc.contributor.authorGabler, Hampton Clayes-ES
dc.contributor.authorWoerner, Lucases-ES
dc.contributor.authorHiermaier, Stefanes-ES
dc.date.accessioned2021-06-07T11:53:29Z-
dc.date.available2021-06-07T11:53:29Z-
dc.date.issued2020-10-12es_ES
dc.identifier.issn1538-9588es_ES
dc.identifier.urihttps:doi.org10.108015389588.2020.1862805es_ES
dc.descriptionArtículos en revistases_ES
dc.description.abstractes-ES
dc.description.abstract  Objective: Crashworthiness assessments in the United States (U.S.) and the European Union (EU) include a large number of safety regulations and consumer testing programs. However, safety standards and testing procedures differ between the two regions. Not much research has been done in relation to this topic, because it has always been assumed that the accident environments in the U.S. and EU are not comparable. The objective of this study is to compare how vehicle occupants are severely injured in motor vehicle collisions in the U.S. and the EU by applying unsupervised learning to accident data.   Methods: A new methodology to identify clusters of seriously injured occupants in NASS-CDS was proposed by the authors in previous research. The current study goes one step further and uses the clusters to compare the injury patterns at the Maximum Abbreviated Injury Scale (MAIS) 3þ level of passenger vehicle occupants in the U.S. and German accident environments. The clustering model developed with NASS-CDS data is applied in this study to German In-Depth Accident Study (GIDAS) data. A machine learning algorithm automatically assigned each GIDAS case to its most similar NASS-CDS cluster controlling for nine different parameters. Those included the injury severity at the body region level, biomechanical characteristics of the occupants, and technical severity of the crash. Results: Differences and analogies between GIDAS and NASS-CDS data within clusters of seriously injured occupants are highlighted. One of the clusters groups the collisions with the greatest mass incompatibility in NASS-CDS and GIDAS data. The injury patterns in the clusters that include elderly people match significantly between the U.S. and German data sets. The lack of younger population and elevated body mass index (BMI) values in the GIDAS sample make the injury patterns within these population groups less comparable than in the other clusters.   Conclusions: Remarkably similar injury patterns at the MAIS 3þ level have been found in U.S. and German accident data sets after controlling for nine different parameters. This research provides evidence to indicate that how belted vehicle occupants are severely injured in the U.S. and in the EU is not necessarily different.en-GB
dc.format.mimetypeapplication/pdfes_ES
dc.language.isoen-GBes_ES
dc.rightses_ES
dc.rights.uries_ES
dc.sourceRevista: Traffic Injury Prevention, Periodo: 1, Volumen: online, Número: Supl 1, Página inicial: S78, Página final: S83es_ES
dc.subject.otherInstituto de Investigación Tecnológica (IIT)es_ES
dc.titleInjury patterns within clusters of seriously injured occupants comparing real-world crashes in the United States and the European Uniones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.accessRightsinfo:eu-repo/semantics/restrictedAccesses_ES
dc.keywordses-ES
dc.keywordsReal-world crash data; serious injuries; GIDAS; NASS-CDS; cluster analysisen-GB
Aparece en las colecciones: Artículos

Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
IIT-21-021A.pdf1,66 MBAdobe PDFVisualizar/Abrir     Request a copy


Los ítems de DSpace están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.