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This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper\u2010Poisson distribution in analyzing motor vehicle crash count data. The hyper\u2010Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway\u2010highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness\u2010of\u2010fit measures indicated that the hyper\u2010Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper\u2010Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway\u2010Maxwell\u2010Poisson model previously developed for the same data set. The advantages of the hyper\u2010Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper\u2010Poisson model can handle both over\u2010 and underdispersed crash data. Although not a major issue for the Conway\u2010Maxwell\u2010Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model.<\/jats:p>","DOI":"10.1111\/risa.12296","type":"journal-article","created":{"date-parts":[[2014,11,10]],"date-time":"2014-11-10T18:30:11Z","timestamp":1415644211000},"page":"919-930","source":"Crossref","is-referenced-by-count":11,"title":["Application of the Hyper\u2010Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes"],"prefix":"10.1111","volume":"35","author":[{"given":"S. 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