Predicting pavement condition index using international roughness index in Washington DC.

PDFTech ReportDDOT-RDT-14-03Pavement distressPavement management systemsRoughnessMathematical predictionRegression analysisWashington (District of Columbia)District of Columbia. Dept. of TransportationArhin, Stephen A.Noel, Errol C.Howard University. Transportation Research CenterUnited States. Federal Highway AdministrationUS Transportation CollectionA number of pavement condition indices are used to conduct pavement management assessments, two of which are theInternational Roughness Index (IRI) and Pavement Condition Index (PCI). The IRI is typically measured using specializedequipment that calculates the smoothness and ride quality of the roadway segment based on established computeralgorithms, while the PCI is based on subjective rating of the number of pavement distress. The literature suggests thatmost pavement indices are related, as a result of which several jurisdictions have developed models to predict one indexfrom the other(s). This study used three (3) years of IRI-PCI data obtained from the District Department of Transportationto develop models which could potentially predict PCI from IRI by functional classification and by pavement type. Theregression models explored were developed using the ordinary least squares method and were tested on the basis of 5%level of significance. The IRI-PCI models yielded R2and adjusted R2values between 0.008 and 0.0730, indicating that themodels could only explain up to 7.3% of the variations in the data. In addition, the root mean square errors of the modelswere all determined to be greater than 1. Even though the results of the ANOVA tests indicated that the coefficientswere generally statistically significant, the low R2values and high RMSEs indicate that the models do not adequatelypredict PCI from IRI, within the margin of error. A more sophisticated prediction tool, such as artificial neural networks,could be explored to potentially predict PCI from IRI more accurately