Automated sequencing and merging with dynamic aircraft arrival routes and speed management for continuous descent operations

Cita com:
hdl:2117/353630
Document typeArticle
Defense date2021-11
Rights accessOpen Access
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Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
In this paper, we present a novel methodology to manage arrival traffic in terminal airspace. We define two areas around the airport, aiming to efficiently schedule incoming traffic. A four-dimensional (4D) trajectory negotiation/synchronization process between the air traffic control officer (ATCO) and the aircraft is performed in the pre-sequencing area, while the aircraft are still in the en-route phase of flight. On the other hand, in the dynamic-trajectories area, the ATCO, with the help of a ground support tool, generates dynamic arrival routes that automatically adapt to the current traffic demand. These arrival routes allow the aircraft to fly neutral continuous descent operations (CDOs, descents with idle thrust and no speed-brakes usage) and to ensure a separation throughout the arrival procedure. We choose a mixed-integer-programming approach to generate the arrival routes, while we formulate and solve an optimal control problem to generate a set of candidate CDOs per aircraft. Results show that, with a sufficient look-ahead time, it is possible to assign a required time of arrival (RTA) within each aircraft-arrival time window that would allow to efficiently schedule traffic even in the most challenging and dense scenarios. Besides improving efficiency of current operations in terminal airspace, the methodology presented in this paper could become a technical enabler towards an extended arrival manager (E-AMAN) with extended capabilities and, ultimately, to a fully deployed trajectory based operations (TBO) environment.
CitationSaez, R. [et al.]. Automated sequencing and merging with dynamic aircraft arrival routes and speed management for continuous descent operations. "Transportation research. Part C, emerging technologies", Novembre 2021, vol. 132, p. 103402:1-34.
ISSN0968-090X
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S0968090X21003983
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