RT info:eu-repo/semantics/article
T1 Sensitivity analysis of driver's behavior and psychophysical conditions
A1 García Herrero, Susana
A1 Gutiérrez, José Manuel
A1 Herrera, Sixto
A1 Azimian, Amin
A1 Mariscal Saldaña, Miguel Ángel
K1 Traffic accident
K1 Drugs
K1 Alcohol
K1 Speed
K1 Distraction
K1 Human error
K1 Bayesian network
K1 Bias identification
K1 Transporte
K1 Transportation
K1 Salud
K1 Health
AB 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.
PB Elsevier
SN 0925-7535
YR 2020
FD 2020-05
LK http://hdl.handle.net/10259/9037
UL http://hdl.handle.net/10259/9037
LA eng
NO 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).
DS Repositorio Institucional de la Universidad de Burgos
RD 18-may-2024