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Number and severity of BI victims, assuming dependence between vehicles involved in the crash

Author

Listed:
  • Miguel Santolino

    (Department of Econometrics, Riskcenter-IREA, University of Barcelona Av. Diagonal 690, 08034 Barcelona. Tel.: +34 93 402 0484)

  • Mercedes Ayuso

    (Department of Econometrics, Riskcenter-IREA, University of Barcelona Av. Diagonal 690, 08034 Barcelona.)

Abstract

The number of victims in vehicles in Spanish motor crashes is analyzed by bodily injury (BI) severity level. Generalized linear mixed models (GLMMs) are applied to model the number of non-serious victims, serious victims and fatalities. Dependence between vehicles involved in the same crash is captured including random effects. After comparing between error distributions, the binomial GLMM is selected. The effect of the driver, vehicle and crash characteristics on the number of BI victims by severity level is analyzed, paying special attention to the influence of the age of the driver and the age of the vehicle. We found a nonlinear relationship between driver’s age and severity, with young and older drivers being the riskiest groups. On the other hand, the expected severity of the crash linearly increased with the vehicle age until the vehicle was 18 years old and then remained constant at the highest severity level from that age. These results are relevant in countries such as Spain with increasing longevity of drivers and aging of the car fleet.

Suggested Citation

  • Miguel Santolino & Mercedes Ayuso, 2020. "Number and severity of BI victims, assuming dependence between vehicles involved in the crash," IREA Working Papers 202018, University of Barcelona, Research Institute of Applied Economics, revised Dec 2020.
    • Handle: RePEc:ira:wpaper:202018
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    File URL: http://www.ub.edu/irea/working_papers/2020/202018.pdf
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    References listed on IDEAS

    as
    1. Mercedes Ayuso & Montserrat Guillen & Jens Perch Nielsen, 2019. "Improving automobile insurance ratemaking using telematics: incorporating mileage and driver behaviour data," Transportation, Springer, vol. 46(3), pages 735-752, June.
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    More about this item

    Keywords

    Motor crashes; Severity; Dependence; Random effects; Driver age; Vehicle age. JEL classification: J11; J14; I10; C5.;
    All these keywords.

    JEL classification:

    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • I10 - Health, Education, and Welfare - - Health - - - General

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