Crash frequency modeling using negative binomial models: An application of generalized estimating equation to longitudinal data

The objective of this study is to explore the effects of temporal correlation in crash frequency models at the highway segment level. In this paper, a negative binomial model has been developed using a generalized estimating equation (GEE) procedure that incorporates the temporal correlations amongst yearly crash counts. The longitudinal model employs an autoregressive correlation structure to compare to the more traditional NB model, which uses a Maximum Likelihood Estimation (MLE) method that cannot accommodate temporal correlations. The GEE model with temporal correlation was found to be superior compared to the MLE model, as it does not underestimate the variance in the coefficient estimates, and it provides more accurate and less biased estimates. Furthermore, an autoregressive correlation structure was found to be an appropriate structure for longitudinal type of data used in this study. Ten years (2002–2011) of Missouri Interstate highway crash data have been utilized in this paper. The crash data comprises of traffic characteristics and geometric properties of highway segments.
Source: Analytic Methods in Accident Research - Category: Occupational Health Source Type: research