This paper addresses two key issues when applying full Bayesian methods for traffic safety analysis, namely, (a) how to incorporate available information from previous studies (past experiences) for the specification of informative hyper-priors in accident data modeling; and, (b) what are the practical implications of the choice of hyper-priors on the results of a road safety analysis. To address these issues, different simulation scenarios where defined and tested using crash data collected at 3-legged rural intersections in California and crash data collected for rural 4-lane highway segments in Texas. The performance of different hyper-priors was investigated, including Gamma and Uniform probability distributions.

In this paper, we first illustrate how to incorporate an integrated summary of the available evidences from previous studies for the development of informative hyper-priors. By doing so, we show how the accuracy of parameter estimates are considerably improved, in particular when working with limited accident data, i.e., crash datasets with low mean and small sample size. The results however show that the past knowledge incorporated in the hyper-priors can only slightly improve the accuracy of the hotspot identification methods. In addition, when the number of roadway elements or the time period (e.g., years of crash data) is relatively large, the hyper-prior choice does not have a significant impact on the final results of a traffic safety analysis.