Extension of the Application of ConwayMaxwell-Poisson Models: Analyzing Traffic Crash Data Exhibiting Under-Dispersion

The objective of this paper is to evaluate the performance of the COM-Poisson GLM for analyzing crash data exhibiting under-dispersion (when conditional on the mean). The COM-Poisson distribution, originally developed in 1962, has recently been re-introduced by statisticians for analyzing count data subjected to either over- or under-dispersion. Over the last year, the COM-Poisson GLM has been evaluated in the context of crash data analysis and it has been shown that the model performs as well as the Poisson-gamma model for crash data exhibiting over-dispersion. To accomplish the objective of this study, several COM-Poisson models were estimated using crash data collected at 162 railway-highway crossings in South Korea between 1998 and 2002. This dataset has been shown to exhibit under-dispersion when models linking crash data to various explanatory variables are estimated. The modeling results were compared to those produced from the Poisson and gamma probability models documented in a previous published study. The results of this research show that the COM-Poisson GLM can handle crash data when the modeling output shows signs of under-dispersion. Finally, they also show that the model proposed in this study provides better statistical performance than th