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A Bayesian bivariate hierarchical model with correlated parameters for the analysis of road crashes in Italian tunnels

Author

Listed:
  • Ciro Caliendo

    (University of Salerno)

  • Maurizio Guida

    (University of Salerno)

  • Fabio Postiglione

    (University of Salerno)

  • Isidoro Russo

    (University of Salerno)

Abstract

An analysis of crashes occurring in 252 unidirectional Italian motorway tunnels over a 4-year monitoring period is provided to identify the main causes of crashes in tunnels. In this paper, we propose a full Bayesian bivariate Poisson lognormal hierarchical model with correlated parameters for the joint analysis of crashes of two levels of severity, namely severe (including fatality and injury accidents only) and non-severe (property damage only), providing better insight on the available data with respect to an analysis based on severe and non-severe independent univariate models. In particular, the proposed model shows that for both of severity levels the crash frequency increases with some parameters: the average annual daily traffic per lane, the tunnel length, and the percentage of trucks, while the presence of the sidewalk provides a reduction in severe accidents. Also the presence of the third lane induces a reduction in severe accidents. Moreover, a reduction in the crash frequency of the two crash-types over years is present. The correlation between the parameters might offer additional insights into how some combinations can affect safety in tunnels. The results are critically discussed by highlighting strength and weakness of the proposed methodology.

Suggested Citation

  • Ciro Caliendo & Maurizio Guida & Fabio Postiglione & Isidoro Russo, 2022. "A Bayesian bivariate hierarchical model with correlated parameters for the analysis of road crashes in Italian tunnels," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(1), pages 109-131, March.
    • Handle: RePEc:spr:stmapp:v:31:y:2022:i:1:d:10.1007_s10260-021-00567-5
    DOI: 10.1007/s10260-021-00567-5
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    References listed on IDEAS

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    1. Serena Arima & Lorenza Cretarola & Giovanna Jona Lasinio & Alessio Pollice, 2012. "Bayesian univariate space-time hierarchical model for mapping pollutant concentrations in the municipal area of Taranto," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(1), pages 75-91, March.
    2. Claudia Furlan, 2008. "Hierarchical random effect models for coastal erosion of cliffs in the Holderness coast," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 17(3), pages 335-350, July.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Helai Huang & Hong Chin, 2010. "Modeling road traffic crashes with zero-inflation and site-specific random effects," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(3), pages 445-462, August.
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    Cited by:

    1. Hossein Samadi & Iman Aghayan & Khaled Shaaban & Farhad Hadadi, 2023. "Development of Performance Measurement Models for Two-Lane Roads under Vehicular Platooning Using Conjugate Bayesian Analysis," Sustainability, MDPI, vol. 15(5), pages 1-26, February.
    2. Arsalan Esmaili & Kayvan Aghabayk & Nirajan Shiwakoti, 2022. "Latent Class Cluster Analysis and Mixed Logit Model to Investigate Pedestrian Crash Injury Severity," Sustainability, MDPI, vol. 15(1), pages 1-29, December.

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