Mostrar el registro sencillo del ítem
dc.contributor.author | García Herrero, Susana | |
dc.contributor.author | Gutiérrez, José Manuel | |
dc.contributor.author | Herrera, Sixto | |
dc.contributor.author | Azimian, Amin | |
dc.contributor.author | Mariscal Saldaña, Miguel Ángel | |
dc.date.accessioned | 2024-04-25T09:33:44Z | |
dc.date.available | 2024-04-25T09:33:44Z | |
dc.date.issued | 2020-05 | |
dc.identifier.issn | 0925-7535 | |
dc.identifier.uri | http://hdl.handle.net/10259/9037 | |
dc.description.abstract | To 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.sponsorship | This 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.iso | eng | es |
dc.publisher | Elsevier | en |
dc.relation.ispartof | Safety Science. 2020, V. 125, 104586 | en |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Traffic accident | en |
dc.subject | Drugs | en |
dc.subject | Alcohol | en |
dc.subject | Speed | en |
dc.subject | Distraction | en |
dc.subject | Human error | en |
dc.subject | Bayesian network | en |
dc.subject | Bias identification | en |
dc.subject.other | Transportes | es |
dc.subject.other | Transportation | en |
dc.subject.other | Salud | es |
dc.subject.other | Health | en |
dc.title | Sensitivity analysis of driver's behavior and psychophysical conditions | en |
dc.type | info:eu-repo/semantics/article | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.relation.publisherversion | https://doi.org/10.1016/j.ssci.2019.104586 | es |
dc.identifier.doi | 10.1016/j.ssci.2019.104586 | |
dc.journal.title | Safety Science | en |
dc.volume.number | 125 | es |
dc.type.hasVersion | info:eu-repo/semantics/acceptedVersion | es |