An airspace planning and collaborative decision-making model: Part I – probabilistic conflicts, workload, and equity considerations

An airspace planning and collaborative decision-making model: Part I – probabilistic conflicts, workload, and equity considerations

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Article ID: iaor20052682
Country: United States
Volume: 37
Issue: 4
Start Page Number: 434
End Page Number: 456
Publication Date: Nov 2003
Journal: Transportation Science
Authors: , ,
Keywords: programming: mathematical, vehicle routing & scheduling
Abstract:

We present a large-scale, airspace planning and collaborative decision-making model (APCDM) to enhance the management of the U.S. National Airspace System (NAS). Given a set of flights that must be scheduled during some planning horizon, along with alternative surrogate trajectories for each flight as prompted by various airspace restriction scenarios imposed by dynamic severe weather systems or space launch special use airspaces (SUA), we develop a mixed-integer programming model to select a set of flight plans from among these alternatives, subject to flight safety, air traffic control workload, and airline equity constraints. The model includes a three-dimensional probabilistic conflict analysis, the derivation of valid inequalities, the development of air traffic control workload metrics, and the consideration of equity among airline carriers in absorbing costs related to rerouting, delays, and possible cancellations. The resulting APCDM model has potential use for both tactical and strategic applications, such as air traffic control in response to severe weather phenomena or spacecraft launches, FAA policy evaluation (separation standards, workload restrictions, sectorization strategies), Homeland Defense contingency planning, and military air campaign planning. The model can also serve a useful role in augmenting the FAA's National Playbook of standardized flight profiles in different disruption-prone regions of the national airspace. The present paper focuses on the theory and model development; Part II of this paper will address model parameter estimations and implementation test results.

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