2026-04-19T06:29:38Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/52692024-05-17T10:09:47Zcom_10259_5501com_10259_5086com_10259_2604col_10259_5502

Aldred, Rachel García Herrero, Susana Anaya Boig, Esther Herrera, Sixto 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 http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess 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