Examining signalized intersection crash frequency using multivariate zero-inflated Poisson regression

In crash frequency studies, correlated multivariate data are often obtained for each roadway entity longitudinally. The multivariate models would be a potential useful method for analysis, since they can account for the correlation among the specific crash types. However, one issue that arises with this correlated multivariate data is the number of zero counts increases as crash counts have many categories. This paper describes a multivariate zero-inflated Poisson (MZIP) regression model as an alternative methodology for modeling multivariate crash count data by severity. The Bayesian method is employed to estimate the model parameters. Using this Bayesian MZIP model, we can take into account correlations that exist among different severity levels. Our new method also can cope with excess zeros in the data, which is a common phenomenon found in practice. The proposed model is applied to the multivariate crash counts obtained from intersections in Tennessee for five years. The results reveal that, compared to the univariate ZIP models and multivariate Poisson-lognormal (MVPLN) models, the MZIP models provide the best statistic fit and have the smallest estimation bias. Apart from the improvement in goodness of fit, the results of the MZIP models show promise toward the goal of obtaining more accurate estimates by accounting for excess zeros in correlated count data.