A model for identifying and ranking dangerous accident locations: a case study in Flanders

These days, road safety has become a major concern in most modern societies. In this respect, the determination of road locations that are more dangerous than others (black spots or also called sites with promise) can help in better scheduling road safety policies. The present paper proposes a multivariate model to identify and rank sites according to their total expected cost to the society. Bayesian estimation of the model via a Markov Chain Monte Carlo approach is discussed in this paper. To illustrate the proposed model, accident data from 23,184 accident locations in Flanders (Belgium) ar... Mehr ...

Verfasser: BRIJS, Tom
VAN DEN BOSSCHE, Filip
WETS, Geert
KARLIS, Dimitris
Dokumenttyp: Artikel
Erscheinungsdatum: 2006
Verlag/Hrsg.: Blackwell
Schlagwörter: Gibbs sampling / Markov Chain Monte Carlo / empirical Bayes / road / accidents / multivariate Poisson distribution / MULTIVARIATE POISSON-DISTRIBUTION
Sprache: Englisch
Permalink: https://search.fid-benelux.de/Record/base-29058308
Datenquelle: BASE; Originalkatalog
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Link(s) : http://hdl.handle.net/1942/1497