Traditional crash count models, such as the Poisson and negative binomial models, do not account for the temporal correlation of crash data. In reality, crashes that occur in the same time frame are likely to share unobserved effects that may have been excluded from the model.
If the temporal correlation of crash data is ignored, the estimated parameters can be biased and less precise. Therefore, there is a need to extend the standard crash count data models by incorporating temporal dependence.
Whereas the literature for modeling
In this paper, the NBINGARCH model is extended to incorporate covariates so that the relationship between a time series of counts and correlated external factors may be properly modeled.
The improved performance of the NBINGARCH model is demonstrated through a simulation study and an application to monthly
In addition, the relationship between monthly vehicle miles traveled (VMT) and gasoline prices in Texas is also examined. Ultimately, gasoline prices had no significant effect on DUI fatal crashes in Texas during that time period, and VMT had a positive effect.
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