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dc.contributor.authorGarcía Herrero, Susana 
dc.contributor.authorGutiérrez, José Manuel
dc.contributor.authorHerrera, Sixto
dc.contributor.authorAzimian, Amin
dc.contributor.authorMariscal Saldaña, Miguel Ángel 
dc.date.accessioned2024-04-25T09:33:44Z
dc.date.available2024-04-25T09:33:44Z
dc.date.issued2020-05
dc.identifier.issn0925-7535
dc.identifier.urihttp://hdl.handle.net/10259/9037
dc.description.abstractTo reduce traffic accidents, an accurately estimated model is needed to capture the true relationships between the injury severity and risk factors. This study aims to propose a robust procedure to address the biases in police-reported accident data and subsequently to conduct sensitivity analyzes in order to estimate the variations in injury severity and distraction probability based on drivers’ behaviors/characteristics and psychophysical conditions. The results show that: (i) the excess speed will likely increase the probability of serious/fatal injury for drivers of all age groups by 10%; (ii) distraction and driver’ errors will likely increase the probability of serious/fatal injury in all drivers driving at a proper speed up to 1.5%; (iii) alcohol and drug consumption can significantly increase the probability of being distracted and making errors by 28.5% and 33.5% respectively; (iv) Alcohol consumption reduces the probability of driving at an appropriate speed in drivers under 25 by 40%. However, the results for drugs consumption are not as significant as the ones for alcohol consumption.en
dc.description.sponsorshipThis study was supported by funds from the following projects: “Modelo cuantitativo de Red Bayesiana con capacidad predictiva de la gravedad del accidente en función de los comportamientos y actuaciones de las personas” [Qualitative Bayesian Network Model for Predicting Accident Severity Based on Personal Behaviors and Actions]. Ref. SPIP2015-01852 supported by funds from DGT (Directorate-General for Traffic). “Modelización mediante técnicas de machine learning de la influencia de las distracciones del conductor en la seguridad vial” [Modeling the influence of driver distractions on road safety using machine learning techniques]. Ref. BU300P18 supported by funds from FEDER (Fondo Europeo de Desarrollo Regional, Junta de Castilla y León).en
dc.language.isoenges
dc.publisherElsevieren
dc.relation.ispartofSafety Science. 2020, V. 125, 104586en
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectTraffic accidenten
dc.subjectDrugsen
dc.subjectAlcoholen
dc.subjectSpeeden
dc.subjectDistractionen
dc.subjectHuman erroren
dc.subjectBayesian networken
dc.subjectBias identificationen
dc.subject.otherTransportees
dc.subject.otherTransportationen
dc.subject.otherSaludes
dc.subject.otherHealthen
dc.titleSensitivity analysis of driver's behavior and psychophysical conditionsen
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1016/j.ssci.2019.104586es
dc.identifier.doi10.1016/j.ssci.2019.104586
dc.journal.titleSafety Scienceen
dc.volume.number125es
dc.type.hasVersioninfo:eu-repo/semantics/acceptedVersiones


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