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Bayesian Analysis of Road Accidents: A General Framework for the Multinomial Case


  • Bolduc, Denis


  • Bonin, Sylvie


The detection of dangerous road sites is usually performed using empirical methods which focus on observed accident frequencies and/or proportions of accidents with a given feature. The most widely used detection tools have an empirical Bayes (EB) background. The EB approaches rely on the comparison of frequencies and/or proportions of accidents at a given site with the amounts that would normally occur at similar sites. Currently, analytical techniques for accident proportions describe the number of accidents with a given feature using a binomial distribution. This paper extends to the multinomial case the general EB technique that we recently suggested to analyze road accident proportions. Our proposed approach is a full-information Bayes method that allows for both deterministic and random heterogeneity as well as spatial-correlation among the sites under investigation. The technique can also be used to analyze accident frequencies. An empirical example based on accident data taken from the Québec city database, will serve to demonstrate its usefulness. Habituellement, la détection des sites d'accidents routiers dangereux est effectuée à partir de méthodes de bayes empiriques appliquées à des taux d'accidents et/ou des proportions d'accidents qui se sont produits dans des conditions données. Ces méthodes comparent les taux et proportions observés avec ceux qui se produisent normalement dans un ensemble de sites routiers considérés comme semblables. Les approches existantes exploitent des lois de distribution binomiales. Dans le présent article, nous décrivons une méthodologie générale à information complète pour analyser le niveau de danger des sites routiers, qui fait appel à des distributions multinomiales. La technique proposée, de type bayésienne, permet de traiter simultanément les problèmes d'hétérogénéité déterministe et aléatoire ainsi que celui de la corrélation spatiale attribuable à la proximité ou l'environnement similaire caractérisant les sites à l'étude. Notre cadre méthodologique englobe des approches bayésiennes de pratique courante qui étudient les proportions d'accidents impliquant une caractéristique donnée. Les propriétés et l'intérêt de la nouvelle méthode sont démontrés à l'aide d'un exemple simple basé sur des données d'accidents de la ville de Québec.

Suggested Citation

  • Bolduc, Denis & Bonin, Sylvie, 1998. "Bayesian Analysis of Road Accidents: A General Framework for the Multinomial Case," Cahiers de recherche 9802, Université Laval - Département d'économique.
  • Handle: RePEc:lvl:laeccr:9802

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    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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