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

Sensitivity analysis of driver's behavior and psychophysical conditions 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 Transportes Salud Transportation Health 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). 2024-04-25T09:33:44Z 2024-04-25T09:33:44Z 2020-05 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion 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 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess Elsevier https://riubu.ubu.es/bitstream/10259/9037/4/Garcia-ss_2020.pdf.jpg Hispana TEXT http://creativecommons.org/licenses/by-nc-nd/4.0/ RIUBU. Repositorio Institucional de la Universidad de Burgos http://hdl.handle.net/10259/9037