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Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks

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Complex Networks and Their Applications VIII (COMPLEX NETWORKS 2019)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 881))

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Abstract

We model airport congestion contagion as an SIS spreading process on an airport transportation network to explain airport vulnerability. The vulnerability of each airport is derived from the US Airport Network data as its congestion probability. We construct three types of airline networks to capture diverse features such as the frequency and duration of flights. The weight of each link augments its infection rate in SIS spreading process. The nodal infection probability in the meta-stable state is used as estimate the vulnerability of the corresponding airport. We illustrate that our model could reasonably capture the distribution of nodal vulnerability and rank airports in vulnerability evidently better than the random ranking, but not significantly better than using nodal network properties. Such congestion contagion model not only allows the identification of vulnerable airports but also opens the possibility to reduce global congestion via congestion reduction in few airports.

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Notes

  1. 1.

    Except that the recognition rate drops dramatically if the infection rate is low when most nodes have an infection probability close to zero.

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Acknowledgement

This work is supported by Netherlands Organisation for Scientific Research NWO (TOP Grant no. 612.001.802).

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Authors and Affiliations

  1. Faculty of Aerospace Engineering, Delft University of Technology, 2628CD, Delft, The Netherlands

    Klemens Köstler

  2. Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, 2628CD, Delft, The Netherlands

    Rommy Gobardhan, Alberto Ceria & Huijuan Wang

Authors
  1. Klemens Köstler
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  2. Rommy Gobardhan
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  3. Alberto Ceria
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  4. Huijuan Wang
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Corresponding author

Correspondence to Huijuan Wang .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

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Köstler, K., Gobardhan, R., Ceria, A., Wang, H. (2020). Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 881. Springer, Cham. https://doi.org/10.1007/978-3-030-36687-2_32

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