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

Rachel Aldred, Susana García-Herrero, Esther Anaya, Sixto Herrera and Miguel Ángel Mariscal
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Rachel Aldred: School of Architecture and Cities, Westminster University, London NW1 5LS, UK
Susana García-Herrero: Escuela Politécnica Superior, Universidad de Burgos, 09001 Burgos, Spain
Esther Anaya: Center for Environmental Policy, Imperial College London, London SW7 2AZ, UK
Sixto Herrera: Meteorology Group, Applied Mathematics and Computer Science, Universidad de Cantabria, 39005 Santander, Spain
Miguel Ángel Mariscal: Escuela Politécnica Superior, Universidad de Burgos, 09001 Burgos, Spain

IJERPH, 2019, vol. 17, issue 1, 1-16

Abstract: 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.

Keywords: cycling; road safety; injured cyclist; Bayesian network; data mining (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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