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Abstract:
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The U.S. National Airspace System (NAS) is inherently highly stochastic. Yet, many existing decision support tools for air traffic flow management take a deterministic approach to problem solving. Modeling the randomness of the delays will provide a more accurate picture of the airspace traffic situation, improve the prediction of the airspace congestion and advance the level of decision making in aviation systems. In this study, we identify the seasonal trend and daily propagation pattern for flight delays and explore and evaluate several approaches to estimate the delays based on their ability to reduce variance and their predictive accuracy. Smoothing splines and varying-coefficient regressions are used in the study. Our models show significant improvement over the current model that the Enhanced Traffic Management System (ETMS) is currently using.
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