Risk-hedged Multistage Stochastic Programming Model for Setting Flow Rates in Collaborative Trajectory Options Programs (CTOP)

摘要

As a new tool in the NextGen portfolio, the Collaborative Trajectory Options Programs (CTOP) combines multiple features from its forerunners including Ground Delay Program (GDP), Airspace Flow Program (AFP) and reroutes, and can manage multiple Flow Constrained Area (FCA) with a single program. A key research question in CTOP is how to traffic flow rates under airspace capacity uncertainties. In this paper, we first investigate existing CTOP optimization models and point out their roles in CTOP and in more general air traffic flow management research, and their advantages and disadvantages in terms of Collaborative Decision Making (CDM) compatibility, model flexibility, performance, and practicality. Having identified the missing piece in the family of CTOP rates optimization models, we proposed a multistage stochastic model to fill the niche, study its connections with other models, and test its performance on a realistic CTOP use case. We discussed in detail the variance and risk issues in air traffic flow management applications, which have thus far received little attention, and showed how to hedge system performance with variance or risk measures in the stochastic models. The models and discussions in this work are not only useful in implementing and analyzing CTOP programs, but are also valuable for the general multiple constrained airspace resources optimization problem.