resilience2050

The Science

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2· The capacity to recover [quickly] from difficulties; toughness

We study ATM resilience, as the capacity of the aviation system to behave as scheduled in spite of incidences, so that flights arrive on time whenever they encounter a difficulty!

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The Science

We have studied how different disturbances (operational, weather, airport related..) affect aviation, analysing the system resilience

The project

Resilience2050 is a European FP7 aviation collaborative research project. Resilience2050 is coordinated by Innaxis, and there are six participants from five different countries.

News

You can read the latest news about Resilience in our blog.

How to measure resilience

Resilience is the property of a system to recover from unexpected disturbances. In aviation, many disturbances can affect air traffic. If the system is not resilient, these "disturbances" may cause what we call "perturbations". For example: weather issues (disturbances) frequently generate delay (perturbations). In order to know how resilient a system we need to be able to measure this property: are disturbances (bad weather, operational issues) generating perturbations (delay, stress)? Is the system able to avoid them? How "much"?

This project defines a resilience metric that quantifies the difference between disturbed states (those affected by disturbances) and reference states (those not affected). Resilient systems easily recover from disturbances, hence showing no deviation if compared with reference-state KPIs. That deviation has been measured in terms of the slope difference between linear regression fits:

  1. Several graphs like the following are generated (one graph per disturbance kind, one per airports/route). Within each graph, each dot represents data of one single flight. The X axe represents the first delay figure (departure delay at airport A). The Y axe measures the delay after its propagation (arrival delay at airport B).
  2. Regression is calculated for the reference flights (those with no disturbances) and for the disturbed ones (those affected by disturbances).
  3. If linear regression is used, the difference between the slope in each group represents the system resilience behaviour, or in other words, how it reacts after a given disturbance. More details available in Project deliverables section.

Data visualization

After the resilience metric was calculated for EU airports and EU routes, we needed to make it more accessible and easy to understand. That’s where data visualization becomes relevant. That is the reason of the following two dynamic graphics depicting: firstly, the EU resilience per routes, and secondly the map on ATM- European resilience.

Resilience between European airports

This graphs plots the resilience of the EU routes. The greener the link, the more resilient the route is against the particular disturbance. The more orange/red, the less resilient that route is. The arriving airports are on the right. The departing ones are on the left, grouped per disturbances affecting those airports.
[icon name="eye" class="" unprefixed_class=""] Missing some of the lines? Note that some of the lines are not displayed. Those are the cases when there was not enough data to enable significant resilience metrics. In other words: t has never snowed in Mallorca in the time frame considered: no way of properly calculating how it would react

ATM Resilience on the map

This graphs plots geographically, which is the resilience level per route. Click on the right to filter per disturbance.