Citation

Vangala P, Lord D, Geedipally SR. Anal. Meth. Accid. Res. 2015; 7: 29-36.

Copyright

(Copyright © 2015, Elsevier Publishing)

DOI

10.1016/j.amar.2015.06.001

PMID

unavailable

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.

Language: en