@article{ref1,
title="Exploring the application of the negative binomial-generalized exponential model for analyzing traffic crash data with excess zeros",
journal="Analytic methods in accident research",
year="2015",
author="Vangala, Prathyusha and Lord, Dominique and Geedipally, Srinivas Reddy",
volume="7",
number="",
pages="29-36",
abstract="In order to analyze crash data, many new analysis tools are being developed by transportation safety analysts. The Negative Binomial-Generalized Exponential distribution (NB-GE) is such a tool that was recently introduced to handle datasets characterized by a large number of zero counts and is over-dispersed. As the name suggests, this three-parameter distribution is a combination of both Negative binomial and Generalized Exponential distributions. So far, nobody has used this distribution in the context of a regression model for analyzing datasets with excess zeros. This paper therefore describes the application of the NB-GE generalized linear model (GLM). The distribution and GLM were applied to four datasets known to have large dispersion and/or a large number of zeros. The NB-GE was compared to the Poisson, NB as well as the Negative Binomial-Lindley (NB-L) model, another three-parameter recently introduced in the safety literature. The study results show that for datasets characterized by a sizable over-dispersion and contain a large number of zeros, the NB-GE performs similarly as the NB-L, but significantly outclass the traditional NB model. Furthermore, the NB-GE model has a simpler modeling framework than the NB-L, which makes its application relatively straight forward.<p /> <p>Language: en</p>",
language="en",
issn="2213-6657",
doi="10.1016/j.amar.2015.06.001",
url="http://dx.doi.org/10.1016/j.amar.2015.06.001"
}