Application of finite mixture models for vehicle crash data analysis.
Developing sound or reliable statistical models for analyzing motor vehicle crashes is very important in highway safety studies. However, a significant difficulty associated with the model development is related to the fact that crash data often exhibit over-dispersion. Sources of dispersion can be varied and are usually unknown to the transportation analysts. These sources could potentially affect the development of negative binomial (NB) regression models, which are often the model of choice in highway safety. To help in this endeavor, this paper documents an alternative formulation that could be used for capturing heterogeneity in crash count models through the use of finite mixture regression models. The finite mixtures of Poisson or NB regression models are especially useful where count data were drawn from heterogeneous populations. These models can help determine sub-populations or groups in the data among others. To evaluate these models, Poisson and NB mixture models were estimated using data collected in Toronto, Ontario. These models were compared to standard NB regression model estimated using the same data. The results of this study show that the dataset seemed to be generated from two distinct sub-populations, each having different regression coefficients and degrees of over-dispersion. Although over-dispersion in crash data can be dealt with in a variety of ways, the mixture model can help provide the nature of the over-dispersion in the data. It is therefore recommended that transportation safety analysts use this type of model before the traditional NB model, especially when the data are suspected to belong to different groups.
Park
Byung-Jung
BJ
Zachry Department of Civil Engineering, Texas A&M University, 3136 TAMU, College Station, TX 77843-3136, United States. soldie@tamu.edu
Lord
Dominique
D
D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001336:Automobiles / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D020342:Finite Element Analysis; D006801:Humans; D015233:Models, Statistical; D018986:Motor Vehicles / Q000706:statistics & numerical data; D009864:Ontario; D016012:Poisson Distribution; D012044:Regression Analysis