Correlated Random-Effects Bivariate Poisson Lognormal Model to Study Single-Vehicle and Multivehicle Crashes

Abstract

AbstractDeveloping crash-prediction models remains one of the primary approaches for studying traffic safety. Most of the current studies on single-vehicle (SV) and multivehicle (MV) crashes have only focused on the effects of exposure and geometric features of roadways and the effects of weather and traffic conditions are rarely incorporated. To provide more insightful observations, detailed weather and traffic data are adopted in this study. As a result of adopting detailed data, multiple daily observations are generated for SV and MV crashes on each roadway segment, forming a multivariate panel data set that poses some methodological challenges. A new approach to analyze SV and MV crashes is proposed by developing a bivariate Poisson lognormal model with correlated segment-specific random effects. The proposed model can characterize both the multivariate and panel nature of the data, and readily address the following three types of serial correlations within the multivariate panel data used in this stu...