ARCTIC AIR TRANSPORT AS INFORMATION AND COMPUTATION

  • P.I.: Massimiliano Zanin
  • Coordinator: Massimiliano Zanin
  • Start date: March 1, 2020
  • End date: Feb. 28, 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.

Researchers

  • Massimiliano Zanin

    Massimiliano Zanin

Recent Publications

Statistical and Machine Learning Link Selection Methods for Brain Functional Networks: Review and Comparison

Ilinka Ivanoska; Kire Trivodaliev; Slobodan Kalajdziski; Massimiliano Zanin
Brain Sciences 11 (6), 735 (2021)

Assessing Granger Causality on Irregular Missing and Extreme Data

Zanin, Massimiliano
IEEE Access 9, 75362 - 75374 (2021)

Principles and open questions in functional brain network reconstruction

Korhonen, Onerva; Zanin, Massimiliano; Papo, David
Human Brain Mapping , (2021)

A Fast Transform for Brain Connectivity Difference Evaluation

Zanin, Massimiliano; Ivanoska, Ilinka; Güntekin, Bahar; Yener, Görsev; Loncar-Turukalo, Tatjana; Jakovljevic, Niksa; Sveljo, Olivera; Papo, David
Neuroinformatics , (2021)

Uncertainty in Functional Network Representations of Brain Activity of Alcoholic Patients

Zanin, Massimiliano; Belkoura, Seddik; Gomez, Javier; Alfaro, César; Cano, Javier
Brain Topography 34, 6-18 (2021)

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