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On Node Criticality in Air Transportation Networks

Author

Listed:
  • Xiaoqian Sun

    (Beihang University)

  • Sebastian Wandelt

    (Beihang University
    Humboldt-University of Berlin)

  • Xianbin Cao

    (Beihang University)

Abstract

In this study, we analyze the criticality of nodes in air transportation using techniques from three different domains, and thus, three essentially different perspectives of criticality. First, we examine the unweighted structure of air transportation networks, using recent methods from control theory (maximum matching and minimum dominating set). Second, complex network metrics (betweenness and closeness) are used with passenger traffic as weights. Third, ticket data-level analysis (origin-destination betweenness and outbound traffic with transit threshold) is performed. Remarkably, all techniques identify a different set of critical nodes; while, in general, giving preference to the selection of high-degree nodes. Our evaluation on the international air transportation country network suggests that some countries, e.g., United States, France, and Germany, are critical from all three perspectives. Other countries, e.g., United Arab Emirates and Panama, have a very specific influence, by controlling the passenger traffic of their neighborhood countries. Furthermore, we assess the criticality of the country network using Multi-Criteria Decision Analysis (MCDA) techniques. United States, Great Britain, Germany, and United Arab Emirates are identified as non-dominated countries; Sensitivity analysis shows that United Arab Emirates is most sensitive to the preference information on the outbound traffic. Our work gears towards a better understanding of node criticality in air transportation networks. This study also stipulates future research possibilities on criticality in general transportation networks.

Suggested Citation

  • Xiaoqian Sun & Sebastian Wandelt & Xianbin Cao, 2017. "On Node Criticality in Air Transportation Networks," Networks and Spatial Economics, Springer, vol. 17(3), pages 737-761, September.
  • Handle: RePEc:kap:netspa:v:17:y:2017:i:3:d:10.1007_s11067-017-9342-5
    DOI: 10.1007/s11067-017-9342-5
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    References listed on IDEAS

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    Cited by:

    1. Gokhan Karakose & Ronald G. McGarvey, 2019. "Optimal Detection of Critical Nodes: Improvements to Model Structure and Performance," Networks and Spatial Economics, Springer, vol. 19(1), pages 1-26, March.
    2. Wandelt, Sebastian & Shi, Xing & Sun, Xiaoqian, 2021. "Estimation and improvement of transportation network robustness by exploiting communities," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    3. Fernandes, Vicente Aprigliano & Pacheco, Ricardo Rodrigues & Fernandes, Elton & da Silva, William Ribeiro, 2019. "Regional change in the hierarchy of Brazilian airports 2007–2016," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    4. Massimiliano Zanin & Ernestina Menasalvas & Xiaoqian Sun & Sebastian Wandelt, 2018. "From the Difference of Structures to the Structure of the Difference," Complexity, Hindawi, vol. 2018, pages 1-12, December.
    5. Juan Gabriel Brida & Juan Carlos Martín & Concepción Román & Raffaele Scuderi, 2017. "Air and HST Multimodal Products. A Segmentation Analysis for Policy Makers," Networks and Spatial Economics, Springer, vol. 17(3), pages 911-934, September.
    6. Sun, Xiaoqian & Wandelt, Sebastian & Zhang, Anming, 2021. "Comparative accessibility of Chinese airports and high-speed railway stations: A high-resolution, yet scalable framework based on open data," Journal of Air Transport Management, Elsevier, vol. 92(C).
    7. Lin Zhang & Jian Lu & Bai-bai Fu & Shu-bin Li, 2018. "A Review and Prospect for the Complexity and Resilience of Urban Public Transit Network Based on Complex Network Theory," Complexity, Hindawi, vol. 2018, pages 1-36, December.
    8. Huijuan Yang & Meilong Le & Di Wang, 2021. "Airline Network Structure: Motifs and Airports’ Role in Cliques," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
    9. Valdés, Rosa Maria Arnaldo & Comendador, Victor Fernando Gómez & Castán, Javier Alberto Perez & Sanz, Alvaro Rodriguez & Sanz, Luis Perez & Ayra, Eduardo Sanchez & Nieto, Francisco Javier Saez, 2019. "Development of safety performance functions (SPFs) to analyse and predict aircraft loss of separation in accordance with the characteristics of the airspace," Reliability Engineering and System Safety, Elsevier, vol. 186(C), pages 143-161.
    10. Nadia M. Viljoen & Johan W. Joubert, 2018. "The Road most Travelled: The Impact of Urban Road Infrastructure on Supply Chain Network Vulnerability," Networks and Spatial Economics, Springer, vol. 18(1), pages 85-113, March.
    11. Paolo Malighetti & Gianmaria Martini & Renato Redondi & Davide Scotti, 2019. "Integrators’ Air Transport Networks in Europe," Networks and Spatial Economics, Springer, vol. 19(2), pages 557-581, June.
    12. Xiaoqian Sun & Sebastian Wandelt, 2021. "Robustness of Air Transportation as Complex Networks:Systematic Review of 15 Years of Research and Outlook into the Future," Sustainability, MDPI, vol. 13(11), pages 1-19, June.
    13. Owais A. Hussain & Faraz Zaidi & Céline Rozenblat, 2019. "Analyzing Diversity, Strength and Centrality of Cities Using Networks of Multinational Firms," Networks and Spatial Economics, Springer, vol. 19(3), pages 791-817, September.
    14. Mohamad Darayi & Kash Barker & Joost R. Santos, 2017. "Component Importance Measures for Multi-Industry Vulnerability of a Freight Transportation Network," Networks and Spatial Economics, Springer, vol. 17(4), pages 1111-1136, December.

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