2026-04-19T06:34:56Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/52692024-05-17T10:09:47Zcom_10259_5501com_10259_5086com_10259_2604col_10259_5502
Repositorio Institucional de la Universidad de Burgos
author
Aldred, Rachel
author
García Herrero, Susana
author
Anaya Boig, Esther
author
Herrera, Sixto
author
Mariscal Saldaña, Miguel Ángel
2020-04-15T16:09:45Z
2020-04-15T16:09:45Z
2019-12
http://hdl.handle.net/10259/5269
10.3390/ijerph17010096
1660-4601
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.
eng
Atribución 4.0 Internacional
Cycling
Road safety
Injured cyclist
Bayesian network
Data mining
Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/5269/1/Aldred-ijerph_2020.pdf
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Aldred-ijerph_2020.pdf
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https://riubu.ubu.es/bitstream/10259/5269/5/Aldred-ijerph_2020.pdf.txt
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Aldred-ijerph_2020.pdf.txt