Augmented Stochastic Multiple Imputation Model for Airport Pavement Missing Data Imputation

This paper presents a research study to handle the problem of missing data in airport pavement management systems. This study was a continuation of an earlier study addressing the same concern. In the earlier study, a stochastic multiple imputation (SMI) approach was adopted to overcome major limitations associated with conventional data imputation methods. The SMI approach considered the variation of multiple plausible imputations and obtained an unbiased estimate in replacing a missing data value. This approach was found to outperform the three most commonly used imputation methods for missing data handling in pavement management: the linear interpolation method, the substitution by mean method, and the regression method. However, the SMI approach estimated missing data values by using purely statistical techniques, without making use of any unique characteristics of pavement performance data. The present study explored the possibility of further improving the data imputation process by exploiting parallel pavement performance-related data available from pavement condition and performance surveys of airfield pavements. An augmented stochastic multiple imputation (ASMI) approach was proposed to incorporate auxiliary parameters to aid in reducing uncertainty and improving prediction performance of runway pavement condition data. Pavement friction data were used as illustration; the related parameter data included aircraft landing volume, rainfall, and temperature. This study showed that the proposed ASMI approach provided an analytically meaningful method from a pavement engineering point of view that can further improve the quality and reliability of imputing missing data for airport pavement management systems.