2026-04-17T20:35:18Zhttps://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 2024-04-25T09:33:44Z 2024-04-25T09:33:44Z 2020-05 0925-7535 http://hdl.handle.net/10259/9037 10.1016/j.ssci.2019.104586 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. 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). eng Elsevier Safety Science. 2020, V. 125, 104586 https://doi.org/10.1016/j.ssci.2019.104586 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Traffic accident Drugs Alcohol Speed Distraction Human error Bayesian network Bias identification Transportes Salud Transportation Health Sensitivity analysis of driver's behavior and psychophysical conditions info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Safety Science 125