Cyclist injury severity in Spain: a Bayesian analysis of police road injury data focusing on involved vehicles and route environment.
File(s)
Published version
OA Location
Author(s)
Aldred, Rachel
García-Herrero, Susana
Anaya, Esther
Herrera, Sixto
Mariscal, Miguel Ángel
Type
Journal Article
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.
Date Issued
2019-12-21
Date Acceptance
2019-12-18
Citation
International Journal of Environmental Research and Public Health, 2019, 17 (1)
ISSN
1660-4601
Publisher
MDPI AG
Journal / Book Title
International Journal of Environmental Research and Public Health
Volume
17
Issue
1
Copyright Statement
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/31877756
PII: ijerph17010096
Subjects
Bayesian network
cycling
data mining
injured cyclist
road safety
Publication Status
Published
Coverage Spatial
Switzerland
Date Publish Online
2019-12-21