Three-Stage Least Squares Analysis of Postrehabilitation Pavement Performance

Abstract

Recent studies have provided improvements in the forecasting accuracy of pavement performance modeling, by statistically modeling pavement performance indicators as a system of seemingly unrelated regression equations (SURE). This approach accounts for cross-equation error correlation as a means to control for unobserved factors that lead pavements in poor condition to observe poor performance indicators. In the state of Indiana, the most common pavement performance indicators are the international roughness index (IRI), the rutting depth, and the pavement condition rating (PCR). Even though the first two indicators can be accurately measured, the PCR is based on engineers’ observations of the pavement surface. Therefore, it is possible that the PCR may be measured as a function of the observable IRI and rutting depth. This paper explores this possibility by estimating a three-stage least squares (3SLS) model of IRI, rutting depth, and PCR, using data collected between 1999 and 2011 in Indiana. All three pavement performance indicators are found to be affected by traffic characteristics, previous pavement condition, treatment and surface characteristics, drainage performance, and weather conditions. In addition, the PCR is found to also be significantly affected by the IRI and rutting depth measurements. The results of the 3SLS and SURE models are counterposed, with the 3SLS models providing significant improvements in the forecasting accuracy of the pavement performance. Therefore, pavement performance modeling with 3SLS has the potential to help roadway agencies cut costs through a more effective and efficient allocation of pavement management resources.