2026-04-17T20:33:41Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/90372024-05-20T09:42:35Zcom_10259_9006com_10259_5086com_10259_2604com_10259_5501col_10259_9007col_10259_5502

García Herrero, Susana Gutiérrez, José Manuel Herrera, Sixto Azimian, Amin Mariscal Saldaña, Miguel Ángel Traffic accident Drugs Alcohol Speed Distraction Human error Bayesian network Bias identification 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. 2024-04-25 2024-04-25 2020-05 info:eu-repo/semantics/article 0925-7535 http://hdl.handle.net/10259/9037 10.1016/j.ssci.2019.104586 eng Safety Science. 2020, V. 125, 104586 https://doi.org/10.1016/j.ssci.2019.104586 http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier