Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks

Klemens  Köstler; Rommy  Gobardhan; Alberto Ceria; Huijuan Wang

doi:10.1007/978-3-030-36687-2_32

Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks

Klemens Köstler, Rommy Gobardhan, Alberto Ceria, Huijuan Wang^*

^*Corresponding author for this work

Multimedia Computing

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

1 Citation (Scopus)

102 Downloads (Pure)

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.

Original language	English
Title of host publication	Complex Networks and Their Applications VIII
Subtitle of host publication	Proceedings of the 8th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2019
Editors	Hocine Cherifi, Sabrina Gaito, José Fernendo Mendes, Esteban Moro, Luis Mateus Rocha
Place of Publication	Cham
Publisher	Springer
Pages	385-398
Number of pages	14
Volume	1
ISBN (Electronic)	978-3-030-36687-2
ISBN (Print)	978-3-030-36686-5
DOIs	https://doi.org/10.1007/978-3-030-36687-2_32
Publication status	Published - 2020
Event	COMPLEX NETWORKS 2019: 8th International Conference on Complex Networks and their Applications - Lisbon, Portugal Duration: 10 Dec 2019 → 12 Dec 2019 Conference number: 8th

Publication series

Name	Studies in Computational Intelligence
Volume	881
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	COMPLEX NETWORKS 2019
Country/Territory	Portugal
City	Lisbon
Period	10/12/19 → 12/12/19

Bibliographical note

Accepted author manuscript

Keywords

Airline transportation network
Epidemic spreading
Network vulnerability

Access to Document

10.1007/978-3-030-36687-2_32

mainAccepted author manuscript, 655 KB

Cite this

Köstler, K., Gobardhan, R., Ceria, A., & Wang, H. (2020). Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks. In H. Cherifi, S. Gaito, J. F. Mendes, E. Moro, & L. M. Rocha (Eds.), Complex Networks and Their Applications VIII : Proceedings of the 8th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2019 (Vol. 1, pp. 385-398). (Studies in Computational Intelligence; Vol. 881 ). Springer. https://doi.org/10.1007/978-3-030-36687-2_32

Köstler, Klemens ; Gobardhan, Rommy ; Ceria, Alberto et al. / Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks. Complex Networks and Their Applications VIII : Proceedings of the 8th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2019. editor / Hocine Cherifi ; Sabrina Gaito ; José Fernendo Mendes ; Esteban Moro ; Luis Mateus Rocha. Vol. 1 Cham : Springer, 2020. pp. 385-398 (Studies in Computational Intelligence).

@inproceedings{6952dbce0c1a49e6aea8adfe16315775,

title = "Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks",

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",

author = "Klemens K{\"o}stler and Rommy Gobardhan and Alberto Ceria and Huijuan Wang",

note = "Accepted author manuscript; COMPLEX NETWORKS 2019 : 8th International Conference on Complex Networks and their Applications ; Conference date: 10-12-2019 Through 12-12-2019",

year = "2020",

doi = "10.1007/978-3-030-36687-2_32",

language = "English",

isbn = "978-3-030-36686-5",

volume = "1",

series = "Studies in Computational Intelligence",

publisher = "Springer",

pages = "385--398",

editor = "Hocine Cherifi and Sabrina Gaito and Mendes, {Jos{\'e} Fernendo} and Esteban Moro and Rocha, {Luis Mateus}",

booktitle = "Complex Networks and Their Applications VIII",

}

Köstler, K, Gobardhan, R, Ceria, A & Wang, H 2020, Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks. in H Cherifi, S Gaito, JF Mendes, E Moro & LM Rocha (eds), Complex Networks and Their Applications VIII : Proceedings of the 8th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2019. vol. 1, Studies in Computational Intelligence, vol. 881 , Springer, Cham, pp. 385-398, COMPLEX NETWORKS 2019, Lisbon, Portugal, 10/12/19. https://doi.org/10.1007/978-3-030-36687-2_32

Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks. / Köstler, Klemens ; Gobardhan, Rommy ; Ceria, Alberto et al.
Complex Networks and Their Applications VIII : Proceedings of the 8th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2019. ed. / Hocine Cherifi; Sabrina Gaito; José Fernendo Mendes; Esteban Moro; Luis Mateus Rocha. Vol. 1 Cham: Springer, 2020. p. 385-398 (Studies in Computational Intelligence; Vol. 881 ).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks

AU - Köstler, Klemens

AU - Gobardhan, Rommy

AU - Ceria, Alberto

AU - Wang, Huijuan

N1 - Conference code: 8th

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Airline transportation network

KW - Epidemic spreading

KW - Network vulnerability

UR - http://www.scopus.com/inward/record.url?scp=85076678734&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-36687-2_32

DO - 10.1007/978-3-030-36687-2_32

M3 - Conference contribution

AN - SCOPUS:85076678734

SN - 978-3-030-36686-5

VL - 1

T3 - Studies in Computational Intelligence

SP - 385

EP - 398

BT - Complex Networks and Their Applications VIII

A2 - Cherifi, Hocine

A2 - Gaito, Sabrina

A2 - Mendes, José Fernendo

A2 - Moro, Esteban

A2 - Rocha, Luis Mateus

PB - Springer

CY - Cham

T2 - COMPLEX NETWORKS 2019

Y2 - 10 December 2019 through 12 December 2019

ER -

Köstler K, Gobardhan R, Ceria A , Wang H. Modeling Airport Congestion Contagion by SIS Epidemic Spreading on Airline Networks. In Cherifi H, Gaito S, Mendes JF, Moro E, Rocha LM, editors, Complex Networks and Their Applications VIII : Proceedings of the 8th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2019. Vol. 1. Cham: Springer. 2020. p. 385-398. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-36687-2_32