Summary
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. In this proposal I show how, 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. In the main text of the proposal I present a number of preliminary results that point towards a radically new way of thinking about the dynamics of air transport. 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.
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| Web resources: | https://cordis.europa.eu/project/id/851255 |
| Start date: | 01-03-2020 |
| End date: | 28-02-2025 |
| Total budget - Public funding: | 1 297 024,00 Euro - 1 297 024,00 Euro |
Cordis data
Original description
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. In this proposal I show how, 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. In the main text of the proposal I present a number of preliminary results that point towards a radically new way of thinking about the dynamics of air transport. 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.Status
SIGNEDCall topic
ERC-2019-STGUpdate Date
27-04-2024
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