Exploring the application of the Negative Binomial–Generalized Exponential model for analyzing traffic crash data with excess zeros Academic Article uri icon

abstract

  • © 2015 Elsevier Ltd. All rights reserved. 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.

author list (cited authors)

  • Vangala, P., Lord, D., & Geedipally, S. R.

citation count

  • 32

publication date

  • July 2015