Advertisement

Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks

  • Klemens Köstler
  • Rommy Gobardhan
  • Alberto Ceria
  • Huijuan WangEmail author
Conference paper
First Online:
Part of the Studies in Computational Intelligence book series (SCI, volume 881)

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.

Keywords

Airline transportation network Epidemic spreading Network vulnerability 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgement

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

References

  1. 1.
    Kiss, I.Z., Miller, J.C., Simon, P.L.: Mathematics of Epidemics on Networks, vol. 598. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-50806-1CrossRefzbMATHGoogle Scholar
  2. 2.
    Zanin, M., Lillo, F.: Modelling the air transport with complex networks: a short review. Eur. Phys. J. Spec. Top. 215(1), 5–21 (2013).  https://doi.org/10.1140/epjst/e2013-01711-9CrossRefGoogle Scholar
  3. 3.
    Baspinar, B., Koyuncu, E.: A data-driven air transportation delay propagation model using epidemic process models. J. Aero. Eng. (2016).  https://doi.org/10.1155/2016/4836260CrossRefGoogle Scholar
  4. 4.
    Pastor-Satorras, R., Castellano, C., Van Mieghem, P., Vespignani, A.: Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Van Mieghem, P., Omic, J., Kooij, R.: Virus spread in networks. IEEE TNET 17(1), 1–14 (2009).  https://doi.org/10.1109/TNET.2008.925623CrossRefGoogle Scholar
  6. 6.
    Li, D., Qin, P., Wang, H., Liu, C., Jiang, Y.: Epidemics on interconnected lattices. EPL 105(6), 68004 (2014).  https://doi.org/10.1209/0295-5075/105/68004CrossRefGoogle Scholar
  7. 7.
    Qu, B., Wang, H.: SIS epidemic spreading with correlated heterogeneous infection rates. J. Phys. A 472, 13–24 (2017).  https://doi.org/10.1016/j.physa.2016.12.077MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Qu, B., Wang, H.: SIS epidemic spreading with heterogeneous infection rates. IEEE TNSE 4(3), 177–186 (2017).  https://doi.org/10.1109/TNSE.2017.2709786CrossRefzbMATHGoogle Scholar
  9. 9.
    Li, C., van de Bovenkamp, R., Van Mieghem, P.: Susceptible-infected-susceptible model: a comparison of N-intertwined and heterogeneous mean-field approximations. Phys. Rev. E 86(2), 026116 (2012).  https://doi.org/10.1103/PhysRevE.86.026116CrossRefGoogle Scholar
  10. 10.
    Van Mieghem, P.: The N-intertwined SIS epidemic network model. Computing 93(2–4), 147–169 (2011).  https://doi.org/10.1007/s00607-011-0155-yMathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Li, C., Wang, H., Van Mieghem, P.: Epidemic threshold in directed networks. Phys. Rev. E 88(6), 062802 (2013).  https://doi.org/10.1103/PhysRevE.88.062802CrossRefGoogle Scholar
  12. 12.
    Yang, Z., Zhou, T.: Epidemic spreading in weighted networks: an edge-based mean-field solution. Phys. Rev. E 85(5), 056106 (2012).  https://doi.org/10.1103/PhysRevE.85.056106CrossRefGoogle Scholar
  13. 13.
    Lu, D., Yang, S., Zhang, J., Wang, H., Li, D.: Resilience of epidemics for SIS model on networks. Chaos Interdiscip. J. Nonlinear Sci. 27(8), 083105 (2017).  https://doi.org/10.1063/1.4997177MathSciNetCrossRefGoogle Scholar
  14. 14.
    Qu, B., Li, C., Van Mieghem, P., Wang, H.: Ranking of nodal infection probability in susceptible-infected-susceptible epidemic. Sci. Rep. 7(1), 9233 (2017).  https://doi.org/10.1038/s41598-017-08611-9CrossRefGoogle Scholar
  15. 15.
    Barat, A., Barrat, A., Barthélemy, M., Pastor-Satorras, R., Vespignani, A.: The archiecture of complex network weights. Proc. Natl. Acad. Sci. U.S.A. 101(11), 3747–3752 (2014).  https://doi.org/10.1073/pnas.0400087101CrossRefGoogle Scholar
  16. 16.
    Guimerà, R., Mossa, S., Turtschi, A., Amaral, L.: The worldwide air transportation network - anomalous centrality, community structure, and cities’ global roles. Proc. Natl. Acad. Sci. U.S.A. 102(22), 7794–7799 (2005).  https://doi.org/10.1073/pnas.0407994102MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Reggiani, A., Signoretti, S., Nijkamp, P., Cento, A.: Network measures in civil air transport - a case study of Lufthansa. In: Lecture Notes in Economics and Mathematical Systems, vol. 613, pp. 257–282 (2009).  https://doi.org/10.1007/978-3-540-68409-1-14
  18. 18.
    Han, D.D., Qian, J.H., Liu, J.G.: Network topology and correlation features affiliated with European airline companies. Phys. A 388(1), 71–81 (2009).  https://doi.org/10.1016/j.physa.2008.09.021CrossRefGoogle Scholar
  19. 19.
    Fleurquin, P., Ramasco, J.J., Eguiluz, V.M.: Systemic delay propagation in the US airport network. Sci. Rep. 3, 1159 (2013).  https://doi.org/10.1038/srep01159 CrossRefGoogle Scholar
  20. 20.
    Ciruelos, C., Arranz, A., Etxebarria, I., Peces, S.: Modelling delay propagation trees for scheduled flights. In: USA/Europe Air Traffic Management Research and Development Seminar, vol. 11 (2015)Google Scholar
  21. 21.
    Baspinar, B., Koyuncu, E.: A data-driven air transportation delay propagation model using epidemic process models. Int. J. Aerosp. Eng. (2016).  https://doi.org/10.1155/2016/4836260CrossRefGoogle Scholar
  22. 22.
    Chi, L.P., Cai, X.: Structural changes caused by error and attack tolerance in US airport network. Int. J. Mod. Phys. B 18, 2394–2400 (2004).  https://doi.org/10.1142/S0217979204025427CrossRefGoogle Scholar
  23. 23.
    Wilkinson, S.M., Dunn, S., Ma, S.: The vulnerability of the European air traffic network to spatial hazards. Nat. Hazards 60(3), 1027–1036 (2012).  https://doi.org/10.1007/s11069-011-9885-6CrossRefGoogle Scholar
  24. 24.
    Lacasa, L., Cea, M., Zanin, M.: Jamming transition in air transportation networks. J. Phys. A 388(18), 3948–3954 (2009).  https://doi.org/10.1016/j.physa.2009.06.005CrossRefGoogle Scholar
  25. 25.
    United States Bureau of Transportation Statistics. http://www.transtats.bts.gov
  26. 26.
    Wang, H., Li, Q., D’Agostino, G., Havlin, S., Stanley, H.E., Van Mieghem, P.: Effect of the interconnected network structure on the epidemic threshold. Phys. Rev. E 88, 022801 (2013)CrossRefGoogle Scholar
  27. 27.
    Gang, Y., Tao, Z., Jie, W., Zhong-Qian, F., Bing-Hong, W.: Epidemic spread in weighted scale-free networks. Chin. Phys. Lett. 22(2), 510 (2005).  https://doi.org/10.1088/0256-307x/22/2/068 CrossRefGoogle Scholar
  28. 28.
    Odoni, A., De Neufville, R.: Airport Systems: Planning, Design, and Management. McGraw-Hill Professional, New York (2003)Google Scholar
  29. 29.
    Dunn, S., Wilkinson, S.M.: Increasing the resilience of air traffic networks using a network graph theory approach. J. Trans. Res. E 90, 39–50 (2016).  https://doi.org/10.1016/j.tre.2015.09.011CrossRefGoogle Scholar
  30. 30.
    Ciruelos, C., Arranz, A., Etxebarria, I., Peces, S., Campanelli, B., Fleurquin, P., Ramasco, J.J.: Modelling delay propagation trees for scheduled flights. In: Proceedings of the 11th USA/EUROPE Air Traffic Management R&D Seminar, Lisbon, Portugal, pp. 23-26 (2015)Google Scholar
  31. 31.
    Li, C., Li, Q., Van Mieghem, P., Stanley, H.E., Wang, H.: Correlation between centrality metrics and their application to the opinion model. Eur. Phys. J. B 88, 65 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Klemens Köstler
    • 1
  • Rommy Gobardhan
    • 2
  • Alberto Ceria
    • 2
  • Huijuan Wang
    • 2
    Email author
  1. 1.Faculty of Aerospace EngineeringDelft University of TechnologyDelftThe Netherlands
  2. 2.Faculty of Electrical Engineering, Mathematics and Computer ScienceDelft University of TechnologyDelftThe Netherlands

Personalised recommendations

Cite paper

Buy options