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An Analysis of Factors Affecting the Severity of Cycling Crashes Using Binary Regression Model

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  • Ahmed Jaber

    (Department of Transportation Engineering and Economics, Faculty of Transportation Engineering and Vehicle, Engineering, Budapest University of Technology and Economics, 1111 Budapest, Hungary)

  • János Juhász

    (Department of Transportation Engineering and Economics, Faculty of Transportation Engineering and Vehicle, Engineering, Budapest University of Technology and Economics, 1111 Budapest, Hungary)

  • Bálint Csonka

    (Department of Transportation Engineering and Economics, Faculty of Transportation Engineering and Vehicle, Engineering, Budapest University of Technology and Economics, 1111 Budapest, Hungary)

Abstract

The increasing use of bicycles rises the interest in investigating the safety aspects of daily commuting. In this investigation, more than 14,000 cyclists’ injuries were analyzed to determine the relationship between severity, road infrastructure characteristics, and surface conditions using binary regression. Minor and major severity categories were distinguished. A binary equation consists of 28 factors is extracted. It has been found that each factor related to roadway characteristics has its negative and positive impacts on cyclist severity such as traffic control, location type, topography, and roadway divisions. Regarding the road surface components, good, paved, and marked roads are associated with a higher probability of major injuries due to the expected greater frequencies of cyclists on roads with good conditions. In conclusion, probabilities of major injuries are higher in urban areas, higher speed limits, signalized intersections, inclined topographies, one-way roads, and during the daytime which require more attention and better considerations.

Suggested Citation

  • Ahmed Jaber & János Juhász & Bálint Csonka, 2021. "An Analysis of Factors Affecting the Severity of Cycling Crashes Using Binary Regression Model," Sustainability, MDPI, vol. 13(12), pages 1-12, June.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:12:p:6945-:d:578565
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    References listed on IDEAS

    as
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    Cited by:

    1. Cheng-Yong Huang, 2021. "Risk Factors Analysis of Car Door Crashes Based on Logistic Regression," Sustainability, MDPI, vol. 13(18), pages 1-13, September.
    2. Filip Filipović & Dušan Mladenović & Krsto Lipovac & Dillip Kumar Das & Bojana Todosijević, 2022. "Determining Risk Factors That Influence Cycling Crash Severity, for the Purpose of Setting Sustainable Cycling Mobility," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    3. Mohammadreza Koloushani & Seyed Reza Abazari & Omer Arda Vanli & Eren Erman Ozguven & Ren Moses & Rupert Giroux & Benjamin Jacobs, 2023. "Determination of Optimal Spatial Sample Sizes for Fitting Negative Binomial-Based Crash Prediction Models with Consideration of Statistical Modeling Assumptions," Sustainability, MDPI, vol. 15(20), pages 1-16, October.
    4. Piotr Jaskowski & Piotr Tomczuk & Marcin Chrzanowicz, 2022. "Construction of a Measurement System with GPS RTK for Operational Control of Street Lighting," Energies, MDPI, vol. 15(23), pages 1-22, December.

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