Effects of Missing Runway Performance Data on Maintenance Management Decision Making
Publication: Journal of Transportation Engineering
Volume 141, Issue 10
Abstract
A problem commonly encountered in airport pavement management is how to deal with pavement condition and performance databases with various extents of missing data. While robust imputation techniques are now available to handle the problem of missing data, there are little to no guidelines available to assist airport engineers in evaluating the level of impact that data imputation has on the decisions made with respect to airport pavement maintenance. This research attempts to address the aforementioned issue by analyzing how different degrees of missing data would affect the maintenance decision concerning runway pavement sections in need of friction improvement. Using the runway friction data sample for illustration, 200 sets of data with varying degrees of missingness were generated. The generated data sets cover the following 10 levels of missingness: 5.55, 16.66, 25, 36.11, 44.44, 55.55, 66.66, 75, 86.11, and 91.66%. For each level of missingness, a total of 20 data sets with different patterns of missing data were randomly generated, and 20 plans of runway friction maintenance were derived with the help of imputed data. The derived maintenance plans, using data sets containing imputed data, were compared with the actual plan based on the original complete data set, and statistical hypothesis testing was performed to determine the reliability of maintenance plans derived using data sets containing missing data. The results of the analysis indicated a declining trend of reliability with increasing extent of missing data. Subsequent analysis of the results led to the conclusion that if the proportion of missing data remains under 40%, the derived maintenance plan is statistically indifferent from the actual plan at the 95% confidence level.
Get full access to this article
View all available purchase options and get full access to this article.
References
Aiken, R. (1998). Airfield pavement: Keeping nations runways in good condition could require substantially higher spending, Diane, PA.
ASC (Aerodrome Safety Circular). (2004). “Runway friction testing program.”, Canada.
FAA (Federal Aviation Administration). (1997). “Measurement, construction, and maintenance of skid-resistant airport pavement surfaces.”, U.S. Dept. of Transportation, Washington, DC.
Farhan, J., and Fwa, T. F. (2013). “Augmented stochastic multiple imputation model for airport pavement missing data imputation.” Transportation Research Record 2449, Transportation Research Board, Washington, DC, 96–104.
Gentle, J. (2003). Random number generation and Monte Carlo methods, 2nd Ed., Springer, New York, 220.
Hahn, G., and Shapiro, S. (1967). Statistical models in engineering, Wiley, New York.
Little, R. J., and Rubin, D. B. (1987). Statistical analysis with missing data, Wiley, New York.
Rado, Z., and Wambold, J. C. (2005). “Correlation of ground friction measurements to aircraft braking friction calculated from data flight recorders.” Proc., Int. Surface Friction Conf. for Roads and Runways Transit New Zealand, Transit New Zealand, Christchurch, New Zealand.
Yang, J., Lu, J. J., and Gunaratne, M. (2003). “Application of neural network models for forecasting of pavement crack index and pavement condition rating.”, Transportation Research Board, Washington, DC, 3–12.
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 141 • Issue 10 • October 2015
Copyright
© 2015 American Society of Civil Engineers.
History
Received: Oct 14, 2014
Accepted: May 4, 2015
Published online: Jun 19, 2015
Published in print: Oct 1, 2015
Discussion open until: Nov 19, 2015
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.