Title

Investigating Different Approaches To Develop Informative Priors In Hierarchical Bayesian Safety Performance Functions

Keywords

Bayesian inference; Hierarchical Bayesian model; Informative prior; Safety performance functions; Weather and traffic data

Abstract

The Bayesian inference method has been frequently adopted to develop safety performance functions. One advantage of the Bayesian inference is that prior information for the independent variables can be included in the inference procedures. However, there are few studies that discussed how to formulate informative priors for the independent variables and evaluated the effects of incorporating informative priors in developing safety performance functions. This paper addresses this deficiency by introducing four approaches of developing informative priors for the independent variables based on historical data and expert experience. Merits of these informative priors have been tested along with two types of Bayesian hierarchical models (Poisson-gamma and Poisson-lognormal models). Deviance information criterion (DIC), R-square values, and coefficients of variance for the estimations were utilized as evaluation measures to select the best model(s). Comparison across the models indicated that the Poisson-gamma model is superior with a better model fit and it is much more robust with the informative priors. Moreover, the two-stage Bayesian updating informative priors provided the best goodness-of-fit and coefficient estimation accuracies. Furthermore, informative priors for the inverse dispersion parameter have also been introduced and tested. Different types of informative priors' effects on the model estimations and goodness-of-fit have been compared and concluded. Finally, based on the results, recommendations for future research topics and study applications have been made. © 2013 Elsevier Ltd. All rights reserved.

Publication Date

4-29-2013

Publication Title

Accident Analysis and Prevention

Volume

56

Number of Pages

51-58

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.aap.2013.03.023

Socpus ID

84876580089 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84876580089

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