The objective of this paper is to demonstrate the development of pavement performance models by combining experimental and field data. A two step approach was used. In the first step a riding quality model based on serviceability consideration is developed. The data set of the American Association of State Highways Officials (AASHO) Road Test is used to this effect. Due to the experimental nature of the AASHO Road Test data set, some of the estimated parameters of the model may be biased when the model is to be applied to predict performance in the field. In the second step, the original model parameters are reestimated by applying joint estimation allowed for with the incorporation of field data set. This data set was collected through the Minnesota Road Research Project (MnRoad). The final model is referred to as the joint model, and it can be used to predict the performance of in-service pavement sections. Joint estimation allowed for the full potential of both data sources to be exploited. First, the effect of variables not available in the first data source were identified and quantified. Further, the parameter estimates had lower variance because multiple data sources were pooled, and biases in the parameters of the experimental model were corrected. Finally, different measurements of the same property were incorporated by using a measurement error model. Thus, the methodology proposed in this paper makes optimum use of available data and yields models of improved statistical properties compared with techniques such as ordinary least squares
