RT info:eu-repo/semantics/article
T1 An Investigation into Unsafe Behaviors and Traffic Accidents Involving Unlicensed Drivers: A Perspective for Alignment Measurement
A1 Boulagouas, Wafa
A1 García Herrero, Susana
A1 Chaib, Rachid
A1 Febres Eguiguren, Juan Diego
A1 Mariscal Saldaña, Miguel Ángel
A1 Djebabra, Mébarek
K1 Alignment
K1 Regulatory
K1 Behaviors
K1 Drivers
K1 Bayesian
K1 Network
K1 Traffic
K1 Accidents
K1 Unlicensed
K1 Unsafe
K1 Transportes
K1 Transportation
K1 Salud
K1 Health
AB Road traffic plays a vital role in countries’ economic growth and future development. However, traffic accidents are considered a major public health issue affecting humankind. Despite efforts by governments to improve traffic safety, the misalignment between the policy efforts and on-ground infringements, distractions and breaches reflect the regulatory failure. This paper uses the Bayesian network method to investigate unsafe behaviors and traffic accidents involving unlicensed drivers as a perspective for the regulatory alignment assessment. The findings suggest that: (1) unlicensed drivers are more likely to have unsafe driving behaviors; (2) the probability of being involved in a severe traffic accident increases when the drivers are unlicensed and decreases in the case of licensed drivers; (3) young drivers are noticeably more likely to engage in unsafe behaviors, usually leading to serious injuries and deaths, when their driving licenses are invalid; (4) women are more likely to engage in right-of-way violations and to have collisions with no serious injuries, contrary to unlicensed men drivers, who are involved in other types of traffic accidents resulting in serious injuries.
PB MDPI
SN 1660-4601
YR 2020
FD 2020-09
LK http://hdl.handle.net/10259/9016
UL http://hdl.handle.net/10259/9016
LA eng
NO This work started with funds from the Dirección General de Tráfico (DGT) for the Project “Modelo Cuantitativo de Red Bayesiana con capacidad predictiva de la gravedad del accidente en función de los comportamientos y actuaciones de las personas”, ref. SPIP2015-1852 and pursued with research project “Modelización mediante técnicas de machine learning de la influencia de las distracciones del conductor en la seguridad vial. Diseño de un sistema integrado: simulador de conducción, eye tracker y dispositivo de distracción. 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 24-nov-2024