From EngageWiki
Revision as of 12:59, 6 November 2019 by Admin (talk | contribs) (Created page with "ELSA Empirically Grounded Agent-Based Models for the Future ATM scenario == About == The structure of ATM as it is known today will drastically change in the SESAR scenario. T...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

ELSA Empirically 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 characteristics of the future ATM system will instead emerge out of the interactions among all the different changes as delivered by SESAR, and should then be analysed with the methods and tools of the science of complex systems. 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 get propagated or amplified. Specific attention should be paid to boundary areas, that will be drawn by the interaction between aircraft trajectories and the organizational changes brought by the introduction of the Functional Airspace Blocks. All these changes will demand for suitable means to analyse and monitor the emerging characteristics, in particular to monitor them as resulting from the interactions among trajectory-based operations, organizational changes, and the temporary deployment of different arrays of resources/tools to manage specific situations. To address this aspect, the ELSA project will apply methods used for the data analysis in complex system science.


The general objective of the project is to analyse, describe and model the dynamics of the ATM system, especially those concerning complexity, resilience, and safety. The analysis will be carried out in the current scenario (based on real data) and in a SESAR scenario (based on an Agent Based simulation).

Project Workflow

The project is divided in three parts:

  • an extensive statistical analysis of data of the ATM system with Complex Systems theory techniques in order to characterize statistical regularities (e.g. propagation of delays and safety events, correlation between different metrics, seasonal fluctuations, etc.);
  • the development of a hierarchy of an Agent Based Model of increasing complexity and degree of realism, to simulate the trajectory-based SESAR scenario;
  • the design and implementation of a prototype of a decision support tool, to monitor, predict (based on the Agent Based Model) and intervene on the airspace.

The Agent Based Model has been further developed during the ELSA extension and a "portable" version has been made available to the ATM research community (see dedicated page ELSA Agent Based Model)

Work Completed

The work completed so far is presented in the following ELSA project papers that can be downloaded here:

ELSA Project: Toward a complex network approach to ATM delays analysis, SID 2011: THE paper presents the preliminary results delivered by the WPE project ELSA. After some introductory notes about the project aims and structure, the paper shows how tools borrowed by the Complex Network Theory can be used to study the issue of delays in the air traffic system. File:ELSA - SID2011 - Lillo et al.pdf

Statistical Regularities in ATM: network properties, trajectory deviations and delays, SID 2012: The paper is a network study of the air traffic infrastructure starting from the airports and then refined at the level of navigation points in order to understand what are the main features that may help explaining why some nodes of the network happen to be found in the same community, i.e. cluster. A second investigation presetns a study at the level of flight trajectories with the aim of identify statistical regularities in the spatio-temporal deviations of flights between their planned and actual 4D trajectories. File:ELSA - SID2012 - Vitali et al.pdf

Modelling the air transport with complex networks: A short review: This paper reviews several papers investigating the topology of complex networks and their dynamics for time scales ranging from years to intraday intervals, and consider also the resilience properties of air networks to extreme events. Finally it presents the results of some recent papers investigating the dynamics on air transport network, with emphasis on passengers traveling in the network and epidemic spreading. File:Zanin Lillo - Modelling the air transport with complex networks.pdf

How the ATM System modifies in presence of extreme exogenous events: the special case of the Iceland Volcano eruption, ECCS 2012: This paper explores the European air traffic system by considering three different networks: (i) the airports network, (ii) the bipartite flightairway network, with the aim of identifying which are the most used and critical airways and their properties, and (iii) the bipartite flight-navpoint network, which consists in a more fine-grained level of investigation, allowing for a detailed analysis on which are the navpoints playing a strategic role in the flight trajectories and their properties. All the analyses are carried out with a particular focus on seasonal variation and impacts of exceptional events like the volcano ash of April 2010. File:ELSA - ECCS2012 - Vitali et al.pdf

An Agent Based Model of the Air Traffic Management, ECCS 2013: This paper describes the empirically grounded agent based model that has been developed to reproduce the current ATM scenario and simulate the future trajectory-based SESAR scenario. File:ELSA - ECCS2013 - Bongiorno et al.pdf

Exploratory analysis of safety data and their interrelation with flight trajectories and network metrics, ATOS-ISIATM 2013: This paper presents an exploratory analysis of the correlation between different network metrics and safety events. The objective is to develop analysis methods and indicators that relate the network structure with safety events. File:ELSA - ATOS-ISIATM2013 - Monechi et al.pdf


The project has delivered a set of best analyses to characterize geographical areas in terms of (i) level of complexity and/or resilience, (ii) dynamics by which these characteristics propagate in space/time. The analyses include the following:

  1. Community analysis: seasonal variations of the clusters of airports.
  2. Community analysis: analysis of the cluster of airports during extreme events (the volcanic ash cloud)
  3. Analysis of controllers’ management strategies: differences among countries and sectors.
  4. Analysis of the correlations between safety events and traffic metrics
  5. Analysis of the most critical points in the airspace.

Potential future applications of the proposed analyses include the following.

  • Community analysis: inform sectorisation with the analysis of traffic flows, validate the applied sectorisation by post-flight analysis.
  • ATCOs management strategies: develop indicators to monitor ATCOs’ behaviour and detect unexpected deviations, inform definition of business trajectories with post-flight analysis.
  • Correlation between network and safety metrics: identify hotspots to be monitored, identify resilient areas to be analysed and re-inforced, correlate safety KPIs with network characteristics and load.
  • Critical navpoints: support to airspace design activities. This analysis can be done for post- flight analysis (based on actual trajectories), but also to predict the position of critical points (based on the user preferred trajectories). This feature will be useful in a SESAR scenario, with no fixed airway structure.

In general, these analyses may bring benefits to the ATM community by supporting a data-driven approach to monitoring and managing the airspace. NOP and ANSPs may use them to detect hidden problems, or regularities over different time periods (from one single day to months, or a whole year).

A second outcome relates to the construction, calibration, and validation of an Agent Based Model, that will be used to simulate realistic Air Traffic scenarios and also helps in analysing the statistical regularities that could not be thoroughly analysed from the empirical data.

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) receive a prediction of the likely development, (iii) simulate the effects of changes to the system. This part of the work is still ongoing. A prototype of the decision -support tool is shown below.


The main value delivered by ELSA to the SESAR community lies in the coordination of the data analysis part with the simulation of a future SESAR scenario. Three aspects are relevant. First, ELSA developed several methods and tools to numerically describe the current ATM system at the EU level, but the same methods and tools can be used to measure the benefits brought by the implementation of SESAR concepts, e.g. integration of sectors in FABs, user-preferred trajectories. Such an extensive application of different analysis methods, together with rigorous validation, is a distinctive contribution to ATM research. Second, the ELSA simulation was calibrated on real data, both as input parameters (e.g. flight plans, aircraft speeds) and as end results (e.g. overall delay, number and types of re-routings). Third, although during the project the ELSA simulator did not reach a sufficient maturity in the current scenario to be extended to the SESAR one, it can be potentially used to run different scenarios, applying the ELSA analyses to measure with rigorous quantitative metrics the achieved optimisation, compare different solutions, carry out stress tests to see how various configurations cope with shocks like strikes, bad weather, large volcanic ashes, and so on. All these simulations can be run by modifying key ingredients like user trajectories, airspace structure, ATM behaviour, to observe the resulting interactions and system-level dynamics.

Research Team

Coordinator: Deep Blue

Partners: University of Palermo, Scuola Normale Superiore di Pisa

This project has received funding from the SESAR Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 783287.