Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/42930
Title: Modelling road accident blackspots data with the discrete generalized Pareto distribution
Authors: Prieto, Faustino
Gómez Déniz, Emilio 
Sarabia, José María
UNESCO Clasification: 1209 Estadística
Keywords: Distribución
Seguros
Issue Date: 2014
Publisher: 0001-4575
Journal: Accident analysis and prevention 
Abstract: This study shows how road traffic networks events, in particular road accidents on blackspots, can be modelled with simple probabilistic distributions. We considered the number of crashes and the number of fatalities on Spanish blackspots in the period 2003-2007, from Spanish General Directorate of Traffic (DGT). We modelled those datasets, respectively, with the discrete generalized Pareto distribution (a discrete parametric model with three parameters) and with the discrete Lomax distribution (a discrete parametric model with two parameters, and particular case of the previous model). For that, we analyzed the basic properties of both parametric models: cumulative distribution, survival, probability mass, quantile and hazard functions, genesis and rth-order moments; applied two estimation methods of their parameters: the μ and (μ + 1) frequency method and the maximum likelihood method; used two goodness-of-fit tests: Chi-square test and discrete Kolmogorov-Smirnov test based on bootstrap resampling; and compared them with the classical negative binomial distribution in terms of absolute probabilities and in models including covariates. We found that those probabilistic models can be useful to describe the road accident blackspots datasets analyzed.
URI: http://hdl.handle.net/10553/42930
ISSN: 0001-4575
DOI: 10.1016/j.aap.2014.05.005
Source: Accident Analysis and Prevention[ISSN 0001-4575],v. 71, p. 38-49
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