ARCTIC AIR TRANSPORT AS INFORMATION AND COMPUTATION

  • I.P.: Massimiliano Zanin
  • Coordinador: Massimiliano Zanin
  • Fecha de inicio: 1 de Marzo de 2020
  • Fecha de final: 28 de Febrero de 2025

This project is an ERC Starting Grant of panel SH2, "Institutions, Values, Environment and Space". Air transport has by and large been studied as a transportation process, in which different elements, e.g. aircraft or passengers, move within the system. While intuitive, this approach entails several drawbacks, including the need for large-scale simulations, the reliance on real data, and the difficulty of extracting macro-scale conclusions from large quantities of micro- scale results. The lack of a better approach is in part responsible for our inability to fully understand delay propagation, one of the most important phenomena in air transport.

ARCTIC proposes an ambitious program to change the conceptual framework used to analyse air transport, inspired by the way the brain is studied in neuroscience. It is based on understanding air transport as an information processing system, in which the movement of aircraft is merely a vehicle for information transfer. Airports then become computational units, receiving information from their neighbours through inbound flights under the form of delays; processing it in a potentially non-linear way; and redistributing the result to the system as outbound delays. As already common in neuroscience, such computation can be made explicit by using a combination of information sciences and statistical physics techniques: from the detection of information movements through causality metrics, up to the representation of the resulting transfer structures through complex networks and their topological properties. The approach also entails important challenges, e.g. the definition of appropriate metrics or the translation of the obtained insights into implementable policies.
ARCTIC’s methodology will be used over the next five years to characterize and model delay propagation, as well as to limit its societal and economic impact.

Investigadores

  • Massimiliano Zanin

    Massimiliano Zanin

Publicaciones recientes

Corrupted bifractal features in finite uncorrelated power-law distributed data

Olivares, Felipe; Zanin, Massimiliano
Physica A: Statistical Mechanics and its Applications 603, 1-11 (2022)

Analyzing international events through the lens of statistical physics: The case of Ukraine

Zanin, Massimiliano; Martínez, Johann H.
Chaos: An Interdisciplinary Journal of Nonlinear Science 32, 051103 (2022)

20 years of ordinal patterns: Perspectives and challenges

Leyva Callejas, Inmaculada; Martinez, Johann; Masoller, Cristina; Rosso, Osvaldo A.; Zanin, Massimiliano
EPL , (2022)

Assessing Identifiability in Airport Delay Propagation Roles Through Deep Learning Classification

Ivanoska, Ilinka; Pastorino, Luisina; Zanin, Massimiliano
IEEE Access , 28520 - 28534 (2022)

Telling functional networks apart using ranked network features stability

Zanin, Massimiliano; Güntekin, Bahar; Aktürk, Tuba; Yıldırım, Ebru; Yener, Görsev; Kiyi, Ilayda; Hünerli-Gündüz, Duygu; Sequeira, Henrique; Papo, David
Scientific Reports 12, 2562 (2022)

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