Saturation Technique for Optimizing Planned Acceptance Rates in Traffic Management Initiatives

Abstract

Traffic Management Initiatives (TMIs), including Ground Delay Programs (GDP) and Collaborative Trajectory Options Programs (CTOP), are tools that air traffic managers use to balance demand and capacity in congested airports and airspace regions. In the current Collaborative Decision Making (CDM) paradigm, the Federal Aviation Administration (FAA) will set the Planned Acceptance Rates (PARs) for the constrained airspace resources, then run resource allocation algorithms to assign ground delays and/or reroutes to affected flights. In this paper, we have addressed a fundamental question in TMI PAR planning: do there exist optimal PARs which only depend on the physical airport or airspace capacity but not the demand? We show that this conjecture holds true in the deterministic capacity case but not in the general stochastic case. Several critical implications of this conclusion are discussed. We propose a new stochastic model and develop a heuristic saturation technique. We demonstrate that this technique can not only reveal the properties and limiting behaviors of GDP models but also could potentially be used as a robust PAR policy when facing demand uncertainty. We then show this ancillary saturation technique in GDP planning becomes an indispensable tool in CTOP optimization. The findings of this paper provide valuable insights in understanding the TMI rate-planning problem and a more robust algorithm for GDP optimization.