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New centrality and causality metrics assessing air traffic network interactions

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  • Mazzarisi, Piero
  • Zaoli, Silvia
  • Lillo, Fabrizio
  • Delgado, Luis
  • Gurtner, Gérald

Abstract

In ATM systems, the massive number of interacting entities makes it difficult to identify critical elements and paths of disturbance propagation, as well as to predict the system-wide effects that innovations might have. To this end, suitable metrics are required to assess the role of the interconnections between the elements and complex network science provides several network metrics to evaluate the network functioning. Here we focus on centrality and causality metrics measuring, respectively, the importance of a node and the propagation of disturbances along links. By investigating a dataset of US flights, we show that existing centrality and causality metrics are not suited to characterise the effect of delays in the system. We then propose generalisations of such metrics that we prove suited to ATM applications. Specifically, the new centrality is able to account for the temporal and multi-layer structure of ATM network, while the new causality metric focuses on the propagation of extreme events along the system.

Suggested Citation

  • Mazzarisi, Piero & Zaoli, Silvia & Lillo, Fabrizio & Delgado, Luis & Gurtner, Gérald, 2020. "New centrality and causality metrics assessing air traffic network interactions," Journal of Air Transport Management, Elsevier, vol. 85(C).
    • Handle: RePEc:eee:jaitra:v:85:y:2020:i:c:s0969699719305307
    DOI: 10.1016/j.jairtraman.2020.101801
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    References listed on IDEAS

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    Cited by:

    1. Lonzius, Christopher & Lange, Anne, 2024. "Aircraft routing clusters and their impact on airline delays," Journal of Air Transport Management, Elsevier, vol. 114(C).
    2. Huang, Wei-Qiang & Liu, Peipei, 2023. "Cross-market risk spillovers among sovereign CDS, stock, foreign exchange and commodity markets: An interacting network perspective," International Review of Financial Analysis, Elsevier, vol. 90(C).
    3. Sismanidou, Athina & Tarradellas, Joan & Suau-Sanchez, Pere, 2022. "The uneven geography of US air traffic delays: Quantifying the impact of connecting passengers on delay propagation," Journal of Transport Geography, Elsevier, vol. 98(C).
    4. Delgado, Luis & Gurtner, Gérald & Cook, Andrew & Martín, Jorge & Cristóbal, Samuel, 2020. "A multi-layer model for long-term KPI alignment forecasts for the air transportation system," Journal of Air Transport Management, Elsevier, vol. 89(C).
    5. Chen, Gong & Fricke, Hartmut & Okhrin, Ostap & Rosenow, Judith, 2024. "Flight delay propagation inference in air transport networks using the multilayer perceptron," Journal of Air Transport Management, Elsevier, vol. 114(C).
    6. Li, Chi & Mao, Jianfeng & Li, Lingyi & Wu, Jingxuan & Zhang, Lianmin & Zhu, Jianyu & Pan, Zibin, 2024. "Flight delay propagation modeling: Data, Methods, and Future opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    7. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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