Study Details

Study Title: A comparative Full Bayesian before-and-after anaylsis and application to urban road safety countermeasures in New Jersey

Authors: Yanmaz-Tuzel and Ozbay

Publication Date:NOV, 2010

Abstract: This paper develops a step-by-step methodology for the application of Full Bayes (FB) approach for before and-after analysis of road safety countermeasures. As part of this methodology, it studies the posterior prediction capability of Bayesian approaches and their use in crash reduction factor (CRF) estimation. A collection of candidate models are developed to investigate the impacts of different countermeasures on road safety when limited data are available. The candidate models include traditional, random effects, non-hierarchical and hierarchical Poisson-Gamma and Poisson-Lognormal (P-LN) distributions. The use of random effects and hierarchical model structures allows treatment of the data in a time-series crosssection panel, and deal with the spatial and temporal effects in the data. Next, the proposed FB estimation methodology is applied to urban roads in New Jersey to investigate the impacts of different treatment measures on the safety of "urban collectors and arterial roads" with speed limits less than 45 mph. The treatment types include (1) increase in lane width, (2) installation of median barriers, (3) vertical and horizontal improvements in the road alignment; and (4) installation of guide rails. The safety performance functions developed via different model structures show that random effects hierarchical P-LN models with informative hyper-priors perform better compared with other model structures for each treatment type. The individual CRF values are also found to be consistent across the road sections, with all showing a decrease in crash rates after the specific treatment except guide rail installation treatment. The highest decrease in the crash rate is observed after the improvement in vertical and horizontal alignment followed by increase in lane width and installation of median barriers. Overall statistical analyses of the results obtained from different candidate models show that when limited data are available, P-LN model structure combined with higher levels of hierarchy and informative priors may reduce the biases in model parameters resulting in more robust estimates.

Study Citation: Yanmaz-Tuzel, O. and Ozbay, K. "A comparative Full Bayesian before-after analysis and application to urban road safety countermeasures in New Jersey." Accident Analysis and Prevention, Vol. 42, No. 6, Oxford, N.Y., Pergamon Press, (2010) pp. 2099-2107.

CMFs Associated With This Study

Category: Access management

Countermeasure: Install raised median

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
0.86142 StarsAllAllPrincipal ArterialUrban


Countermeasure: Install guiderail

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
0.85152 StarsAllAllPrincipal ArterialUrban
1.1-102 StarsAllAllMinor ArterialUrban


Countermeasure: Increase lane width

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
0.72282 StarsAllAllMinor ArterialUrban