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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rachel Aldred & Susana García-Herrero & Esther Anaya & Sixto Herrera & Miguel Ángel Mariscal, 2019. "Cyclist Injury Severity in Spain: A Bayesian Analysis of Police Road Injury Data Focusing on Involved Vehicles and Route Environment," IJERPH, MDPI, vol. 17(1), pages 1-16, December.
    • Handle: RePEc:gam:jijerp:v:17:y:2019:i:1:p:96-:d:300683
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    References listed on IDEAS

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    1. Lluís Sanmiquel & Marc Bascompta & Josep M. Rossell & Hernán Francisco Anticoi & Eduard Guash, 2018. "Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques," IJERPH, MDPI, vol. 15(3), pages 1-11, March.
    2. Fang Zong & Hongguo Xu & Huiyong Zhang, 2013. "Prediction for Traffic Accident Severity: Comparing the Bayesian Network and Regression Models," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-9, October.
    3. Thomas Götschi & Jan Garrard & Billie Giles-Corti, 2016. "Cycling as a Part of Daily Life: A Review of Health Perspectives," Transport Reviews, Taylor & Francis Journals, vol. 36(1), pages 45-71, January.
    4. Tianpei Tang & Senlai Zhu & Yuntao Guo & Xizhao Zhou & Yang Cao, 2019. "Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method," IJERPH, MDPI, vol. 16(7), pages 1-17, April.
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    Cited by:

    1. Fanyu Meng & Pengpeng Xu & Cancan Song & Kun Gao & Zichu Zhou & Lili Yang, 2020. "Influential Factors Associated with Consecutive Crash Severity: A Two-Level Logistic Modeling Approach," IJERPH, MDPI, vol. 17(15), pages 1-16, August.

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