Bayesian model for ranking hazardous road sites

• Published in: Journal of the Royal Statistical Society. Series A, Statistics in society Vol. 170; no. 4; pp. 1001 - 1017
• Main Authors: Brijs, Tom, Karlis, Dimitris, Van den Bossche, Filip, Wets, Geert
• Format: Journal Article
• Language: English
• Published: Oxford, UK Oxford, UK : Blackwell Publishing Ltd 01.10.2007; Blackwell Publishing Ltd; Blackwell Publishers; Blackwell; Royal Statistical Society
• Series: Journal of the Royal Statistical Society Series A
• Subjects: Accidents; Applications; Bayesian method; Cost functions; Data analysis; Data collection; Death; Exact sciences and technology; General topics; Gibbs sampling; Health hazards; Hierarchical Bayes models; Injuries; Markov chain Monte Carlo methods; Markov processes; Markovian processes; Mathematics; Modeling; Monte Carlo simulation; Multivariate Poisson distribution; Parametric inference; Physical trauma; Probability and statistics; Probability theory and stochastic processes; Road accidents; Road intersections; Sciences and techniques of general use; Statistical analysis; Statistical methods; Statistics; Traffic accidents; Traffic estimation; Traffic safety; Belgium; Rank statistic; Statistical distribution; Bayes model; Multivariate analysis; Stochastic method; Markov chain; Parametric method; Distribution function; Cost function; Approximation theory; Multivariate Poisson distribution; Bayes estimation; Monte Carlo method; Multivariate distribution; Scheduling; Markov model; Gibbs sampling; Poisson distribution; Hierarchical Bayes models; Statistical method; Selection problem; Road accidents; Numerical analysis; Hierarchical model; Relevant information; Markov chain Monte Carlo methods; Application
• ISSN: 0964-1998; 1467-985X
• DOI: 10.1111/j.1467-985X.2007.00486.x

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.