Modeling young driver motor vehicle crashes : Data with extra zeros

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

49 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)515-521
Journal / PublicationAccident Analysis and Prevention
Volume34
Issue number4
Publication statusPublished - Jul 2002

Abstract

Much of the data collected on motor vehicle crashes is count data. The standard Poisson regression approach used to model this type of data does not take into account the fact there are few crash events and hence, many observed zeros. In this paper, we applied the zero-inflated Poisson (ZIP) model (which adjusts for the many observed zeros) and the negative binomial (NB) model to analyze young driver motor vehicle crashes. The results of the ZIP regression model are comparable to those from fitting a NB regression model for general over-dispersion. The findings highlight that driver confidence/adventurousness and the frequency of driving prior to licensing are significant predictors of crash outcome in the first 12 months of driving. We encourage researchers, when analyzing motor vehicle crash data, to consider the empirical frequency distribution first and to apply the ZIP and NB models in the presence of extra zeros due, for example, to under-reporting. © 2002 Elsevier Science Ltd. All rights reserved.

Research Area(s)

  • Crash frequency, Excess zeros, Negative binomial regression, Poisson regression, Zero-inflated Poisson models

Citation Format(s)

Modeling young driver motor vehicle crashes : Data with extra zeros. / Lee, Andy H.; Stevenson, Mark R; Wang, Kui; Yau, Kelvin K.W.

In: Accident Analysis and Prevention, Vol. 34, No. 4, 07.2002, p. 515-521.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal