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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/9037

    Título
    Sensitivity analysis of driver's behavior and psychophysical conditions
    Autor
    García Herrero, SusanaUBU authority Orcid
    Gutiérrez, José Manuel
    Herrera, Sixto
    Azimian, Amin
    Mariscal Saldaña, Miguel ÁngelUBU authority Orcid
    Publicado en
    Safety Science. 2020, V. 125, 104586
    Editorial
    Elsevier
    Fecha de publicación
    2020-05
    ISSN
    0925-7535
    DOI
    10.1016/j.ssci.2019.104586
    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.
    Palabras clave
    Traffic accident
    Drugs
    Alcohol
    Speed
    Distraction
    Human error
    Bayesian network
    Bias identification
    Materia
    Transportes
    Transportation
    Salud
    Health
    URI
    http://hdl.handle.net/10259/9037
    Versión del editor
    https://doi.org/10.1016/j.ssci.2019.104586
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    Documento(s) sujeto(s) a una licencia Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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