Application Of Bayesian Informative Priors To Enhance The Transferability Of Safety Performance Functions
Keywords
Bayesian informative priors; Highway safety Manual; Markov chain Monte Carlo simulations; Negative binomial models; Transferability
Abstract
Introduction Safety performance functions (SPFs) are essential tools for highway agencies to predict crashes, identify hotspots and assess safety countermeasures. In the Highway Safety Manual (HSM), a variety of SPFs are provided for different types of roadway facilities, crash types and severity levels. Agencies, lacking the necessary resources to develop own localized SPFs, may opt to apply the HSM's SPFs for their jurisdictions. Yet, municipalities that want to develop and maintain their regional SPFs might encounter the issue of the small sample bias. Bayesian inference is being conducted to address this issue by combining the current data with prior information to achieve reliable results. It follows that the essence of Bayesian statistics is the application of informative priors, obtained from other SPFs or experts’ experiences. Method In this study, we investigate the applicability of informative priors for Bayesian negative binomial SPFs for rural divided multilane highway segments in Florida and California. An SPF with non-informative priors is developed for each state and its parameters’ distributions are assigned to the other state's SPF as informative priors. The performances of SPFs are evaluated by applying each state's SPFs to the other state. The analysis is conducted for both total (KABCO) and severe (KAB) crashes. Results, conclusions and practical applications As per the results, applying one state's SPF with informative priors, which are the other state's SPF independent variable estimates, to the latter state's conditions yields better goodness of fit (GOF) values than applying the former state's SPF with non-informative priors to the conditions of the latter state. This is for both total and severe crash SPFs. Hence, for localities where it is not preferred to develop own localized SPFs and adopt SPFs from elsewhere to cut down on resources, application of informative priors is shown to facilitate the process.
Publication Date
9-1-2017
Publication Title
Journal of Safety Research
Volume
62
Number of Pages
155-161
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1016/j.jsr.2017.06.005
Copyright Status
Unknown
Socpus ID
85022100358 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85022100358
STARS Citation
Farid, Ahmed; Abdel-Aty, Mohamed; Lee, Jaeyoung; and Eluru, Naveen, "Application Of Bayesian Informative Priors To Enhance The Transferability Of Safety Performance Functions" (2017). Scopus Export 2015-2019. 5306.
https://stars.library.ucf.edu/scopus2015/5306