ELSAEmpirically grounded agent based models for the future ATM scenario
The structure of ATM as it is known today will drastically change in the SESAR scenario. These changes will be hardly understood by relying on the analysis of single elements, i.e. by applying the current state-of-the-art validation approaches. The SESAR airspace structure will emerge out of the different aircraft trajectories, different network constraints, organisational aspects, and initial conditions. There will be a need for disciplined methods to monitor the airspace structure in quasi real time, to identify emerging properties, like high density areas, or areas where disturbances are propagated or amplified.
ELSA will develop the methods and tools of the science of complex systems to analyse, describe and model the dynamics of the new ATM system, especially those concerning complexity, resilience, and safety.
The general objective is to extensively analyse real ATM data and to develop an Agent Based Model able to properly describe the ATM system in the new SESAR scenario. The project is divided in three parts:
- an extensive statistical analysis of data of the ATM system with tools and concepts borrowed from the Complex Systems Theory. This analysis will focus also on rare events and will explore the correlations among different metrics (e.g. losses of separation, STCA, ACAS-RA).
- the development of a hierarchy of an Agent Based Model of increasing complexity and degree of realism, to reproduce the key feature of the actual ATM system and to predict its behavior in the future trajectory-based SESAR scenario;
- the development and consequent validation of a decision support to prototype, monitor, predict (based on the Agent Based Model), and intervene on the airspace.
The project expected outcome is to define a disciplined method of analysis, monitoring and management of the (SESAR) airspace structure. More in details, the project will deliver indicators and metrics to be used in a semi-automated monitoring processes and will enable the characterization of geographical areas in terms of (i) level of risk, (ii) dynamics by which these characteristics are engendered and by which they propagate in space/time. A second outcome relates to the construction, calibration, and validation of an Agent Based Model, that will be used to simulate realistic ATM scenarios and also helps in understanding the the origin and consequences of the statistical regularities. The third outcome will be the design and implementation of the prototype of a decision support tool, to (i) monitor the current complexity status, (ii) make a prediction of the likely development, (iii) simulate the effects of changes to the system.
Deep Blue Role:
Coordinator and responsible for validation