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Novel tools to evaluate ATM systems coupling under future deployment scenarios

Periodic Reporting for period 2 - Domino (Novel tools to evaluate ATM systems coupling under future deployment scenarios)

Reporting period: 2019-01-01 to 2019-12-31

Domino, “Novel tools to evaluate ATM systems coupling under future deployment scenarios”, is a SESAR Exploratory Research project funded through Horizon 2020. The project consortium is led by the University of Westminster with partners: EUROCONTROL, University of Trieste, University of Bologna and Innaxis Research Institute.

In air traffic management (ATM) systems, the massive number of interacting entities makes it difficult to predict the system-wide effects that innovations might have. Domino has assessed such effects and to identify the impact that innovations might bring for the different stakeholders, based on agent-based modelling and complex network science. Domino ha modelled scenarios that mirror different system innovations which change the agents’ actions and behaviour. Suitable network metrics were also needed to evaluate the effect of innovations on the network functioning.

The overall objective of Domino was to develop a set of tools, a methodology and a platform to assess the coupling of ATM systems from a flight and a passenger perspective. The platform allows ATM system designers to gain insight on the impact of applying new mechanisms. It provides a view of the impact of deploying solutions in different manners, e.g. harmonised vs. local/independent deployment, and information on the criticality of elements in the system and how this might be different for different stakeholders.
The project activities during the second year of the project focused on the finalisation of the model development, its calibration and the execution and analysis of the different case studies. Highlights include:

* The finalisation of the agent-based model development including its calibration and validation. The model has been developed following the architecture and design performed in the first year.

* The modelling and analysis of the unitary case studies defined as investigative case studies for the three selected mechanism: 4D trajectory adjustment, flight prioritisation and flight arrival coordination. This broad analysis which includes three levels of implementation (Level 0 (current operations), Level 1 (with further capabilities), and Level 2 (more prospective)) allowed us to test the first network metrics and to produce outcomes that could be presented to stakeholders and experts at dedicated workshops.

* The two workshops that were carried out allowed us to mature the model (e.g. modifying the behaviour of some agents) and to identify the steps that were required to advance on the model, metrics and scenarios.

* The deeper analysis of two scenarios considering the input from the consultations activities: ‘Hub delay management’, where ATFM delay is modelled at three hubs in Europe and two mechanisms are tested (4D trajectory adjustment and flight prioritisation), and ‘Effect of E-AMAN scope on arrival management’, where the scope of the E-AMAN is modified evaluating its impact on the flight arrival coordination mechanism. In this final analysis, focus was given to the improvement of the model and its calibration (e.g. including the impact of curfews and re-calibrating the effect of wind), and on the use of advance network metrics (centrality and causality metrics). Some indications on the relationship between these metrics and classical operational indicators were also identified. Some of these findings were reported in a paper presented at the SESAR Innovation Days 2019 conference. At the moment of closing the project a journal paper has been published in a Nature Research Journal and two more are under review for their consideration in other journals.
An agent-based model, which includes flights and passengers whilst considering all operations in the ECAC region, has been implemented and calibrated. Advanced new mechanisms (Level 2) have been modelled and analysed.

Domino has defined and tested new advanced metrics for centrality and causality. These include the definition of ‘trip centrality’, which measures the potential connectivity of an airport and ‘passenger centrality’, which considers planned passengers itineraries and thus measures the actual connectivity of an airport; and ‘causality in tail’, which captures the propagation of extreme events (such as congestion) in the network.
agents sending messages
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summary of 4D Trajectory Adjustment