Crash prediction modeling for curved segments of rural two-lane two-way highways in Utah.

PDFTech ReportUT-15.1214-8287Rural highwaysTwo lane highwaysHighway curvesAccident risk forecastingRegression analysisCalibrationHighway safetyUtahUtah. Dept. of TransportationSaito, MitsuruKnecht, Casey S.Schultz, Grant G.Cook, Aaron A.Brigham Young University. Dept. of Civil and Environmental EngineeringUnited States. Federal Highway AdministrationUS Transportation CollectionThis report contains the results of the development of crash prediction models for curved segments of ruraltwo-lane two-way highways in the state of Utah. The modeling effort included the calibration of the predictivemodel found in the Highway Safety Manual (HSM) as well as the development of Utah-specific models usingnegative binomial regression. The data for these models came from 1,495 randomly sampled curved segments inUtah, with crash data coming from years 2008-2012. For this research, two sample periods were used: a three-yearperiod from 2010 to 2012 and a five-year period from 2008 to 2012. The calibration factor for the HSM predictivemodel was determined to be 1.50 for the three-year period and 1.60 for the five-year period. A negative binomialmodel was used to develop Utah-specific crash prediction models based on both the three-year and five-yearsample periods. The independent variables used for negative binomial regression included the same set ofvariables used in the HSM predictive model along with other variables such as speed limit and truck traffic thatwere considered to have a significant effect on potential crash occurrence. The significant variables were found tobe average annual daily traffic, segment length, total truck percentage, and curve radius. The main benefit of theUtah-specific crash prediction models is that they provide a reasonable level of accuracy for crash prediction yetonly require four variables, thus requiring much less effort in data collection compared to using the HSMpredictive model