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A Bayesian model for ranking hazardous road sites

Author

Listed:
  • Tom Brijs
  • Dimitris Karlis
  • Filip Van den Bossche
  • Geert Wets

Abstract

Summary. Road safety has recently become a major concern in most modern societies. The identification of sites that are more dangerous than others (black spots) can help in better scheduling road safety policies. This paper proposes a methodology for ranking sites according to their level of hazard. The model is innovative in at least two respects. Firstly, it makes use of all relevant information per accident location, including the total number of accidents and the number of fatalities, as well as the number of slight and serious injuries. Secondly, the model includes the use of a cost function to rank the sites with respect to their total expected cost to society. Bayesian estimation for the model via a Markov chain Monte Carlo approach is proposed. Accident data from 519 intersections in Leuven (Belgium) are used to illustrate the methodology proposed. Furthermore, different cost functions are used to show the effect of the proposed method on the use of different costs per type of injury.

Suggested Citation

  • Tom Brijs & Dimitris Karlis & Filip Van den Bossche & Geert Wets, 2007. "A Bayesian model for ranking hazardous road sites," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1001-1017, October.
    • Handle: RePEc:bla:jorssa:v:170:y:2007:i:4:p:1001-1017
    DOI: 10.1111/j.1467-985X.2007.00486.x
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    References listed on IDEAS

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    1. Dimitris Karlis, 2003. "An EM algorithm for multivariate Poisson distribution and related models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(1), pages 63-77.
    2. Trevor C. Bailey & Paul J. Hewson, 2004. "Simultaneous modelling of multiple traffic safety performance indicators by using a multivariate generalized linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 501-517, August.
    3. Harvey Goldstein & David J. Spiegelhalter, 1996. "League Tables and Their Limitations: Statistical Issues in Comparisons of Institutional Performance," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 385-409, May.
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    Cited by:

    1. Ricardo A. Daziano & Luis Miranda-Moreno & Shahram Heydari, 2013. "Computational Bayesian Statistics in Transportation Modeling: From Road Safety Analysis to Discrete Choice," Transport Reviews, Taylor & Francis Journals, vol. 33(5), pages 570-592, September.
    2. Nicholas C. Henderson & Michael A. Newton, 2016. "Making the cut: improved ranking and selection for large-scale inference," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(4), pages 781-804, September.
    3. Areti Boulieri & Silvia Liverani & Kees Hoogh & Marta Blangiardo, 2017. "A space–time multivariate Bayesian model to analyse road traffic accidents by severity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 119-139, January.

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