The M-E PDG IRI models have been developed by means of Ordinary Least Squares. However, some of the variables that are used in predicting future IRI have to previously be estimated by means of separate models. This might cause bias due to the fact that there is some correlation between the previously estimated distress types and the unobserved components on the IRI model. The bias in this case can be corrected by considering additional variables that are correlated with the distress types that cause the bias, thus eliminating the correlation to the unobserved terms in the model. Therecan also be bias in the IRI model that is generated by unobserved factorsthat are not included in the model. If these factors are pavement sectionspecific, the bias can be removed by taking into consideration the robusttype of data that is available. The authors have used LTPP data that is consistent with the dataset that was originally used to fit the current M-EPDG IRI model for flexible pavements over thick granular bases. The data was then used in modeling IRI by means of Ordinary Least Squares and Instrumental Variable Regressions analyzing the data as pooled, and as a panel dataset (by a fixed effects and a random effects approach) to check for possible bias in the model. The preferred IRI model was determined to be therandom effects approach, and the model parameters were estimated thus correcting for the omitted variable bias and simultaneous equation bias.
Samenvatting