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Open AccessArticle

Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment

1
School of Architecture and Cities, Westminster University, London NW1 5LS, UK
2
Escuela Politécnica Superior, Universidad de Burgos, 09001 Burgos, Spain
3
Center for Environmental Policy, Imperial College London, London SW7 2AZ, UK
4
Meteorology Group, Applied Mathematics and Computer Science, Universidad de Cantabria, 39005 Santander, Spain
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(1), 96; https://doi.org/10.3390/ijerph17010096
Received: 19 November 2019 / Revised: 13 December 2019 / Accepted: 18 December 2019 / Published: 21 December 2019
(This article belongs to the Special Issue Traffic Accident Control and Prevention)
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 traffic. Also, action needs to be taken to address motorist behaviour, given the imbalance between responsibility and risk. View Full-Text
Keywords: cycling; road safety; injured cyclist; Bayesian network; data mining cycling; road safety; injured cyclist; Bayesian network; data mining
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Aldred, R.; García-Herrero, S.; Anaya, E.; Herrera, S.; Mariscal, M.Á. Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment. Int. J. Environ. Res. Public Health 2020, 17, 96.

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