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A Bayesian model for ranking hazardous road sites

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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.

Keywords: Gibbs sampling; Hierarchical Bayes models; Markov chain Monte Carlo methods; Multivariate Poisson distribution; Road accidents

Document Type: Research Article


Affiliations: 1: Hasselt University, Diepenbeek, Belgium 2: Athens University of Economics and Business, Greece

Publication date: October 1, 2007


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