STAM-based methodology to prevent concurrence events in a Multi-Airport System (MAS)

Nina Schefers, Manuel Angel Amaro Carmona, Juan José Ramos González*, Francisco Saez Nieto, Pau Folch, José Luis Munoz-Gamarra

*Autor corresponent d’aquest treball

Producció científica: Contribució a una revistaArticleRecercaAvaluat per experts

4 Cites (Scopus)

Resum

Pre-tactically managing the imbalances of the network and organizing the resources of the airspace is one of the main objectives of Dynamic Demand & Capacity Balancing (dDCB). Introducing Short-term Air Traffic Flow and Capacity Management (STAM) measures such as minor ground delays or slight speed adjustments applied to a selected number of flights on the day of operations, supports the dDCB process by reducing the traffic complexity and the probability of Air Traffic Controllers interventions. This paper introduces a rolling horizon methodology for detecting concurrence events, analysing their trajectory interdependencies and applying a strategic mitigation measure that shifts the Estimated Take-Off Time (ETOT) within the assigned Calculated Take-Off Time (CTOT) window. The methodology proposes a collaborative decision-making process for a better coordination of departures from airports feeding airspace volumes with high air traffic demand. The experimental part of this paper aims at showing the potential benefits of such a STAM procedure by applying the methodology to distribute the demand of a sector located in the London Terminal Manoeuvring Area (LTMA). Experiments have considered regulated traffic scenarios where ETOT were allocated in order to reduce departure time uncertainties. Lastly, it is analysed the impact of the parameters used by the methodology to calculate the mitigation measures: safe separation criteria, look-ahead time and number of coordinated airports.

Idioma originalAmerican English
Pàgines (de-a)186-208
Nombre de pàgines23
RevistaTransportation Research Part C: Emerging Technologies
Volum110
DOIs
Estat de la publicacióPublicada - 1 de gen. 2020

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