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Investigating different approaches to develop informative priors in hierarchical Bayesian safety performance
Rongjie Yu1, Mohamed Abdel-Aty
1Department of Civil, Environmental and Construction Engineering, University of Central Florida, Orlando, FL 32816-2450, USA. rongjie.yu@gmail.com
Accident; Analysis and Prevention
|April 25, 2013
Summary
This study introduces four methods for creating informative priors in Bayesian safety performance functions. The Poisson-gamma model with two-stage Bayesian updating priors demonstrated superior model fit and accuracy.
Area of Science:
- Statistical Modeling
- Road Safety Engineering
Background:
- Bayesian inference is common for safety performance functions.
- Formulating informative priors for independent variables is under-explored.
Purpose of the Study:
- To introduce and evaluate methods for developing informative priors.
- To assess the impact of informative priors on Bayesian hierarchical models for safety performance functions.
Main Methods:
- Developed four approaches for informative priors using historical data and expert experience.
- Tested priors with Poisson-gamma and Poisson-lognormal Bayesian hierarchical models.
- Used Deviance Information Criterion (DIC) and R-square for model evaluation.
Main Results:
- The Poisson-gamma model showed superior fit and robustness with informative priors.
- Two-stage Bayesian updating informative priors yielded the best goodness-of-fit and coefficient accuracy.
- Informative priors for the inverse dispersion parameter were also tested and compared.
Conclusions:
- Informative priors significantly enhance Bayesian safety performance function development.
- The Poisson-gamma model and two-stage Bayesian updating priors are recommended for improved accuracy and robustness.
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