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International Journal of Aerospace Engineering
Volume 2016 (2016), Article ID 4836260, 11 pages
Research Article

A Data-Driven Air Transportation Delay Propagation Model Using Epidemic Process Models

1Controls and Avionics Research Group, Aerospace Research Center, Istanbul Technical University, Istanbul, Turkey
2Department of Aeronautical Engineering, Istanbul Technical University, Istanbul, Turkey

Received 12 April 2016; Revised 8 July 2016; Accepted 26 July 2016

Academic Editor: Mahmut Reyhanoglu

Copyright © 2016 B. Baspinar and E. Koyuncu. 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.


In air transport network management, in addition to defining the performance behavior of the system’s components, identification of their interaction dynamics is a delicate issue in both strategic and tactical decision-making process so as to decide which elements of the system are “controlled” and how. This paper introduces a novel delay propagation model utilizing epidemic spreading process, which enables the definition of novel performance indicators and interaction rates of the elements of the air transportation network. In order to understand the behavior of the delay propagation over the network at different levels, we have constructed two different data-driven epidemic models approximating the dynamics of the system: (a) flight-based epidemic model and (b) airport-based epidemic model. The flight-based epidemic model utilizing SIS epidemic model focuses on the individual flights where each flight can be in susceptible or infected states. The airport-centric epidemic model, in addition to the flight-to-flight interactions, allows us to define the collective behavior of the airports, which are modeled as metapopulations. In network model construction, we have utilized historical flight-track data of Europe and performed analysis for certain days involving certain disturbances. Through this effort, we have validated the proposed delay propagation models under disruptive events.