Study Details

Study Title: Using a Reliability Process to Reduce Uncertainty in Predicting Crashes at Unsignalized Intersections

Authors: Haleem et al.

Publication Date:MAR, 2010

Abstract: The negative binomial (NB) model has been used extensively by traffic safety analysts as a crash prediction model, because it can accommodate the over-dispersion criterion usually exhibited in crash count data. However, the NB model is still a probabilistic model that may benefit from updating the parameters of the covariates to better predict crash frequencies at intersections. The objective of this paper is to examine the effect of updating the parameters of the covariates in the fitted NB model using a Bayesian updating reliability method to more accurately predict crash frequencies at 3-legged and 4-legged unsignalized intersections. For this purpose, data from 433 unsignalized intersections in Orange County, Florida were collected and used in the analysis. Four Bayesian-structure models were examined: (1) a non-informative prior with a log-gamma likelihood function, (2) a non-informative prior with an NB likelihood function, (3) an informative prior with an NB likelihood function, and (4) an informative prior with a log-gamma likelihood function. Standard measures of model effectiveness, such as the Akaike information criterion (AIC), mean absolute deviance (MAD), mean square prediction error (MSPE) and overall prediction accuracy, were used to compare the NB and Bayesian model predictions. Considering only the best estimates of the model parameters (ignoring uncertainty), both the NB and Bayesian models yielded favorable results. However, when considering the standard errors for the fitted parameters as a surrogate measure for measuring uncertainty, the Bayesian methods yielded more promising results. The full Bayesian updating framework using the log-gamma likelihood function for updating parameter estimates of the NB probabilistic models resulted in the least standard error values.

Study Citation: Haleem, K., Abdel-Aty, M., and Mackie, K. "Using a Reliability Process to Reduce Uncertainty in Predicting Crashes at Unsignalized Intersections." Accident Analysis and Prevention, Vol. 42, No.2, Elsevier, (2010), pp. 654-666.


CMFs Associated With This Study

Category: Access management

Countermeasure: Convert an open median to a TWLTL

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
1.45-452 StarsAllAllNot SpecifiedNot specified

Category:Intersection geometry

Countermeasure: Installation of left-turn lanes on both major road approaches

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
0.9823 StarsAllAllNot SpecifiedNot specified

Countermeasure: Permit through movements from both minor approaches to an intersection instead of from only one minor approach

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
0.31693 StarsAllAllNot SpecifiedNot specified

Countermeasure: Provide a left-turn lane on one major-road approach

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
0.61393 StarsAllAllNot SpecifiedNot specified

Countermeasure: Provide a right-turn lane on one major-road approach

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
0.8203 StarsAllAllNot SpecifiedNot specified
0.75253 StarsAllAllNot SpecifiedNot specified

Category:Intersection traffic control

Countermeasure: Install a stop sign on both minor approaches of an unsignalized intersection

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
1.4-403 StarsAllAllNot SpecifiedNot specified

Countermeasure: Install stop sign on minor approach of an unsignalized intersection

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
1.18-183 StarsAllAllNot specifiedNot specified

Category:Shoulder treatments

Countermeasure: Change left shoulder width from X to Y (feet)

CMF CRF(%)QualityCrash TypeCrash SeverityRoadway TypeArea Type
CMF EquationCRF Equation3 StarsAllAllNot SpecifiedNot specified