Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/5269
Título
Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment
Autor
Publicado en
International Journal of Environmental Research and Public Health. 2020, V. 17, n. 2, 96
Editorial
MDPI
Fecha de publicación
2019-12
DOI
10.3390/ijerph17010096
Resumen
This study analyses factors associated with cyclist injury severity, focusing on vehicle type,
route environment, and interactions between them. Data analysed was collected by Spanish police
during 2016 and includes records relating to 12,318 drivers and cyclist involving in collisions with
at least one injured cyclist, of whom 7230 were injured cyclists. Bayesian methods were used to
model relationships between cyclist injury severity and circumstances related to the crash, with the
outcome variable being whether a cyclist was killed or seriously injured (KSI) rather than slightly
injured. Factors in the model included those relating to the injured cyclist, the route environment,
and involved motorists. Injury severity among cyclists was likely to be higher where an Heavy
Goods Vehicle (HGV) was involved, and certain route conditions (bicycle infrastructure, 30 kph
zones, and urban zones) were associated with lower injury severity. Interactions exist between the
two: collisions involving large vehicles in lower-risk environments are less likely to lead to KSIs
than collisions involving large vehicles in higher-risk environments. Finally, motorists involved in a
collision were more likely than the injured cyclists to have committed an error or infraction. The study
supports the creation of infrastructure that separates cyclists from motor tra c. Also, action needs to
be taken to address motorist behaviour, given the imbalance between responsibility and risk.
Palabras clave
Cycling
Road safety
Injured cyclist
Bayesian network
Data mining
Materia
Transportes
Transportation
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