TY  - JOUR
PY  - 2014//
TI  - Application of the hyper-Poisson generalized linear model for analyzing motor vehicle crashes
JO  - Risk analysis
A1  - Khazraee, S. Hadi
A1  - Sáez-Castillo, Antonio Jose
A1  - Geedipally, Srinivas Reddy
A1  - Lord, Dominique
SP  - 919
EP  - 930
VL  - 35
IS  - 5
N2  - The hyper-Poisson distribution can handle both over- and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation-specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper-Poisson distribution in analyzing motor vehicle crash count data. The hyper-Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway-highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness-of-fit measures indicated that the hyper-Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper-Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway-Maxwell-Poisson model previously developed for the same data set. The advantages of the hyper-Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper-Poisson model can handle both over- and underdispersed crash data. Although not a major issue for the Conway-Maxwell-Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model.<p />  <p>Language: en</p>
LA  - en
SN  - 0272-4332
UR  - http://dx.doi.org/10.1111/risa.12296
ID  - ref1
ER  -