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Aerospace 2018, 5(2), 59; https://doi.org/10.3390/aerospace5020059

Uncertainty Management at the Airport Transit View

1
Department of Aerospace Systems, Air Transport and Airports, Universidad Politécnica de Madrid (UPM), Plaza Cardenal Cisneros N3, 28040 Madrid, Spain
2
CRIDA A.I.E. (Reference Center for Research, Development and Innovation in ATM), Avenida de Aragón N402 Edificio Allende, 28022 Madrid, Spain
3
Department of Telecommunications and Systems Engineering, Universitat Autònoma de Barcelona (UAB), Carrer de Emprius N2, 08202 Sabadell, Spain
*
Author to whom correspondence should be addressed.
Received: 28 January 2018 / Revised: 16 May 2018 / Accepted: 21 May 2018 / Published: 1 June 2018
(This article belongs to the Collection Air Transportation—Operations and Management)

Abstract

Air traffic networks, where airports are the nodes that interconnect the entire system, have a time-varying and stochastic nature. An incident in the airport environment may easily propagate through the network and generate system-level effects. This paper analyses the aircraft flow through the Airport Transit View framework, focusing on the airspace/airside integrated operations. In this analysis, we use a dynamic spatial boundary associated with the Extended Terminal Manoeuvring Area concept. Aircraft operations are characterised by different temporal milestones, which arise from the combination of a Business Process Model for the aircraft flow and the Airport Collaborative Decision-Making methodology. Relationships between factors influencing aircraft processes are evaluated to create a probabilistic graphical model, using a Bayesian network approach. This model manages uncertainty and increases predictability, hence improving the system’s robustness. The methodology is validated through a case study at the Adolfo Suárez Madrid-Barajas Airport, through the collection of nearly 34,000 turnaround operations. We present several lessons learned regarding delay propagation, time saturation, uncertainty precursors and system recovery. The contribution of the paper is two-fold: it presents a novel methodological approach for tackling uncertainty when linking inbound and outbound flights and it also provides insight on the interdependencies among factors driving performance. View Full-Text
Keywords: airport operations; system congestion; delay propagation; Business Process Modelling; Bayesian networks airport operations; system congestion; delay propagation; Business Process Modelling; Bayesian networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Rodríguez-Sanz, Á.; Gómez Comendador, F.; Arnaldo Valdés, R.; Cordero García, J.M.; Bagamanova, M. Uncertainty Management at the Airport Transit View. Aerospace 2018, 5, 59.

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