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<dc:title>Sensitivity analysis of driver's behavior and psychophysical conditions</dc:title>
<dc:creator>García Herrero, Susana</dc:creator>
<dc:creator>Gutiérrez, José Manuel</dc:creator>
<dc:creator>Herrera, Sixto</dc:creator>
<dc:creator>Azimian, Amin</dc:creator>
<dc:creator>Mariscal Saldaña, Miguel Ángel</dc:creator>
<dc:subject>Traffic accident</dc:subject>
<dc:subject>Drugs</dc:subject>
<dc:subject>Alcohol</dc:subject>
<dc:subject>Speed</dc:subject>
<dc:subject>Distraction</dc:subject>
<dc:subject>Human error</dc:subject>
<dc:subject>Bayesian network</dc:subject>
<dc:subject>Bias identification</dc:subject>
<dc:description>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.</dc:description>
<dc:date>2024-04-25T09:33:44Z</dc:date>
<dc:date>2024-04-25T09:33:44Z</dc:date>
<dc:date>2020-05</dc:date>
<dc:type>info:eu-repo/semantics/article</dc:type>
<dc:identifier>0925-7535</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/9037</dc:identifier>
<dc:identifier>10.1016/j.ssci.2019.104586</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>Safety Science. 2020, V. 125, 104586</dc:relation>
<dc:relation>https://doi.org/10.1016/j.ssci.2019.104586</dc:relation>
<dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 Internacional</dc:rights>
<dc:publisher>Elsevier</dc:publisher>
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