Periodic Reporting for period 3 - ARCTIC (Air Transport as Information and Computation)
Reporting period: 2023-03-01 to 2024-08-31
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.
Delays are unavoidable consequences of the limitedness of resources in air transport, and also of the need to ensure the highest level of safety and security. To illustrate, a flight landing can be delayed because of saturated airspaces, and of the need of maintaining a safe distance between aircraft. If delays ought to be accepted, a different problem is their propagation, i.e. when the late arrival of one aircraft results in the delay of several other flights. These, also known as secondary or knock-off delays, account for about half of the delays observed in the European system, and have huge social and economic impact.
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 aims at developing the first comprehensive attempt at describing and modelling air transport as an information processing system, by leveraging on existing neuroscience and statistical physics techniques. Objectives include: to understand, from a theoretical perspective, what it means to describe the phenomenon of delay propagation as information processing; to characterise the real delay propagation phenomenon, under both normal and abnormal conditions; and to propose strategies to optimise the dynamics of the system.
Delays are unavoidable consequences of the limitedness of resources in air transport, and also of the need to ensure the highest level of safety and security. To illustrate, a flight landing can be delayed because of saturated airspaces, and of the need of maintaining a safe distance between aircraft. If delays ought to be accepted, a different problem is their propagation, i.e. when the late arrival of one aircraft results in the delay of several other flights. These, also known as secondary or knock-off delays, account for about half of the delays observed in the European system, and have huge social and economic impact.
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 aims at developing the first comprehensive attempt at describing and modelling air transport as an information processing system, by leveraging on existing neuroscience and statistical physics techniques. Objectives include: to understand, from a theoretical perspective, what it means to describe the phenomenon of delay propagation as information processing; to characterise the real delay propagation phenomenon, under both normal and abnormal conditions; and to propose strategies to optimise the dynamics of the system.
During the first half of the project, the team has been focused on the development of the tools, both theoretical and of applied nature, needed to analyse the real system. These include:
- The analysis of the performance of existing causality metrics under real conditions, e.g. when data are incomplete.
- The proposal of new ways of reconstructing functional networks representing the interactions between the elements of the system, here airports, for instance through the use of Deep Learning models.
- The development of software libraries able to integrate information from different sources, e.g. regarding flights and aircraft trajectories; and providing optimised tools for its analysis.
- The development of validation tools, including a software library aimed at generating synthetic flight information under different hypothesis.
In parallel, the team has started analysing the real behaviour of the European system, using historical data gathered during the last years, and providing a first example of the detected propagation maps. A special attention has been devoted to the comparison of the dynamics of the system pre and post COVID-19 pandemics, to understand how the latter has affected European air transport. Finally, some applications to similar problems of social relevance, including the propagation of COVID-19 and the information network underlying the 2022 war in Ukraine, have been explored.
- The analysis of the performance of existing causality metrics under real conditions, e.g. when data are incomplete.
- The proposal of new ways of reconstructing functional networks representing the interactions between the elements of the system, here airports, for instance through the use of Deep Learning models.
- The development of software libraries able to integrate information from different sources, e.g. regarding flights and aircraft trajectories; and providing optimised tools for its analysis.
- The development of validation tools, including a software library aimed at generating synthetic flight information under different hypothesis.
In parallel, the team has started analysing the real behaviour of the European system, using historical data gathered during the last years, and providing a first example of the detected propagation maps. A special attention has been devoted to the comparison of the dynamics of the system pre and post COVID-19 pandemics, to understand how the latter has affected European air transport. Finally, some applications to similar problems of social relevance, including the propagation of COVID-19 and the information network underlying the 2022 war in Ukraine, have been explored.
ARCTIC will provide unique benefits to European science and society. The main one is self-evident: a new way of understanding delay propagation and designing novel mitigation strategies. New paradigms for understanding interactions in air transport can lead to improvements in architectural designs, taking into account the states of the interrelated systems. This will ultimately result in improved competitiveness of European industry, in better policy and decision making processes, and in a reduction of the environmental impact of air transport.
From a more theoretical perspective, ARCTIC will be the first systematic attempt at importing neuroscience and statistical physics techniques into air transport. While some research programmes have stressed the urgency of importing and adapting ideas from other research fields, much progress is still required, due to the complexity of such adaptation. Secondly, the paradigm here presented can easily be exported to many other societal issues, provided the phenomenology results from the interaction of a great number of agents and is characterised by an information processing dynamics. Finally, as by-product of this project, many theoretical tools will have to be developed, as for instance new ways to detect causality relationships between data sets, and ways of validating the resulting information-processing networks.
From a more theoretical perspective, ARCTIC will be the first systematic attempt at importing neuroscience and statistical physics techniques into air transport. While some research programmes have stressed the urgency of importing and adapting ideas from other research fields, much progress is still required, due to the complexity of such adaptation. Secondly, the paradigm here presented can easily be exported to many other societal issues, provided the phenomenology results from the interaction of a great number of agents and is characterised by an information processing dynamics. Finally, as by-product of this project, many theoretical tools will have to be developed, as for instance new ways to detect causality relationships between data sets, and ways of validating the resulting information-processing networks.