PROJECT ID

START

PROJECT TYPE

Exploratory research

FLAGSHIP

Not applicable

STATUS

Completed

SESAR PROGRAMME

SESAR 2020

PROJECT DURATION

2020-05-01 > 2022-10-31

TOTAL COST

€ 1 999 411,25

EU CONTR.

€ 1 999 411,25

GRANT ID

893204

PARTICIPANTS

Universidad Carlos III de Madrid, Universitat Politecnica De Catalunya, Istanbul Teknik Universitesi, Ecole Nationale De L' Aviation Civile (ENAC), Flightkeys, Deutsches Zentrum für Luft- und Raumfahrt, Boeing Aerospace Spain

Stable and resilient ATM by integrating Robust airline operations into the network

Trajectory-based operations (TBO) share a common plan for a flight’s trajectory in a four-dimensional context (latitude, longitude, altitude and time). One of the key enablers of TBO  is the automated updating of trajectories in reaction to developing uncertainties, including disruptive weather events such as storms. However, a high frequency of updates and modifications leads to the degradation of system stability.

In order to achieve stable and resilient ATM performance even in disturbed scenarios, the  START project set out to develop, implement and validate optimisation algorithms for robust and predictable airline operations. It used a combination of applied mathematics, artificial intelligence (AI), data science, and algorithm design to create more certain and reliable outcomes.

START began by modelling uncertainties at the micro (trajectory) level and used radar/ automatic dependent surveillance-broadcast (ADS-B) to assimilate observations every 15  minutes. The trajectory uncertainties were propagated using assimilated models and a  stochastic trajectory predictor. It also modelled uncertainties at the macro (ATM network)  level, and again assimilated observations every 15 minutes,  relying on satellite data to provide storm and network status, and used the assimilated models to propagate ATM  network uncertainties. START then developed an AI algorithm capable of generating a set of pan-European robust trajectories covering the entirety of traffic over Europe, designed to make the European ATM system more resilient when faced with uncertainties.

The algorithm was demonstrated by modelling its implementation as an advanced flight dispatch functionality for airspace users to obtain robust trajectories.

The concepts were validated using system-wide simulation procedures in order to evaluate their stability and enable the simulations to provide insight into uncertainties that impact  TBO systems.

Artificial Intelligence to increase air safety in the face of storms

 

Benefits

  • Increased airspace resilience and stability
  • Fewer flight cancellations, lower airline costs
  • Fewer conflicts, less weather encounters

Participants

Universidad Carlos III de Madrid
Universitat Politècnica de Catalunya
İstanbul Teknik Üniversitesi
École nationale de l'aviation civile
FLIGHTKEY
Deutsches Zentrum für Luft- und Raumfahrt e.V
Boeing Deutschland GmbH
European Union
START - Stable and resilient ATM by integrating Robust airline operations into the network Thunderstorm