The overall objective of Domino, is 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 will allow ATM system designers to gain insight on the impact of applying new mechanisms. It will provide a view of the impact of deploying solutions in different manners, e.g., harmonized vs. local/independent deployment, and information on the criticality of elements in the system and how this might be different for different stakeholders.

Domino at a glance

Domino takes as a starting point the stakeholders (passengers, airlines, ANSPs, airports) which interact through the different processes in which they are active. Conversely, these processes can be seen as interacting through the fact that they represent services used by the same stakeholders. In particular, the ATM subsystems can be seen as connected by propagation of delay/cost through flight and passenger flows. This dual view is at the heart of the project.

An agent-based paradigm is particularly well suited to the description of highly coupled systems where a high number of processes take place at the same time at different levels. Domino focus on increasing the understanding of the relationship between the elements in the system
and their connection. Complexity science metrics will be used to describe these interactions. Some of these metrics are based on the use of complex networks as a convenient and powerful tool to visualize and model the interaction between the components.

As described below, different stakeholders interact in an environment defined by the different systems and mechanisms which represent the rules on which these agents operate. These operations are affected by uncertainty and disruptions. Domino will use complexity science tools to extract information on metrics and network interactions.

Development approach considered in Domino

WP3 defines the current system architecture and different case studies which generate changes to the elements in the sytem. Those investigative case studies are executed by WP4, which develops an agent-based model, producing flight and passenger indicators. These will be analysed in WP5 using complexity science to extract knwoledge on the metrics and the coupling of the elements in the system. Close interaction with stakeholders will be required to sensure that the architecture is properly defined, that the model is calibrated and that the results obtained from the analysis of the investigative case studies can be used to modify them (creating the adaptive case studies) to ease some of the issues identified.

As described below, different stakeholders interact in an environment defined by the different systems and mechanisms which represent the rules on which these agents operate. These operations are affected by uncertainty and disruptions. Domino will use complexity science tools to extract information on metrics and network interactions.

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