Abstract
The multivariate Poisson log normal (MVPLN) Bayesian method has recently been introduced in road safety analysis mainly for network screening and using different crash severity levels. However, there is little or no research applying MVPLN to different crash types. Besides, only one model structure for the expected crashes of a given severity was investigated in previous MVPLN studies. Another knowledge gap is that this method has not yet been evaluated for before–after treatment effect analysis. The objective of this study was to evaluate the application of MVPLN Bayesian method for before–after road safety evaluation studies. Two groups of unsignalized California intersections, for which a naive before–after comparison shows a significant change in the crash frequency after a hypothetical treatment was assigned, were used to conduct the study. It was found that the crash reduction rates are sensitive to the function form of expected crashes. For each model structure, MVPLN, univariate Poisson log normal (PLN) and Poisson-gamma models provided comparable results while PLN was seen to be superior. Finally, models that consider temporal effects of unobserved latent variables were found to be superior to those that don’t.
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