Skip to main content
European Commission logo

Enhance aircraft performance and optimisation through utilisation of artificial intelligence

European Union
Geo-spatial type
Total project cost
€705 125
EU Contribution
€568 550
Project website
Project Acronym
STRIA Roadmaps
Vehicle design and manufacturing (VDM)
Network and traffic management systems (NTM)
Transport mode
Airborne icon
Transport policies
Other specified
Transport sectors
Passenger transport,
Freight transport


Call for proposal
Link to CORDIS

PERF-AI will apply machine learning techniques on flight data (parametric & non-parametric approaches) to accurately measure actual aircraft performance throughout its lifecycle.

Within current airline operations, both at flight preparation (on-ground) & at flight management (in-air) levels, the trajectory is first planned, then managed by the Flight Management System (FMS) using a single manufacturer’s performance model that is the same for every aircraft of the same type, & also on weather forecast that is computed long before the flight. It induces a lack of accuracy during the planning phase with a flight route pre-established at specific altitudes & speeds to optimize fuel burn, from take-off to landing using aircraft performances that are not those of the real aircraft. Also, the actual flight will usually shift from the original plan because of Air Traffic Control (ATC) constraints, adverse weather, wind changes & tactical re-routing, without possibility for the flight crew, either using the FMS or through connected services to tactically recompute the trajectory to continuously optimize the flight path. This is due to the limitations of the performance databases that the current systems are using.

Hence, PERF-AI is focusing on identifying adequate machine learning algorithms, testing their accuracy & capability to perform flight data statistical analysis & developing mathematical models to optimize real flight trajectories with respect to the actual aircraft performance, thus, minimizing fuel consumption throughout the flight.

The consortium consists of Safety-Line (FR) & INRIA (FR), having full expertise at Aircraft Performance & Data Science, hence, able to fully propose, test & validate different statistical models that will allow to accurately solve some optimization challenges & implement them in an operational environment.

PERF-AI total grant request to the CSJU is 568 550€ with total project duration of 24 months.


Parent Programmes
Institution Type
Public institution
Institution Name
European Commission
Type of funding
Public (EU)
Specific funding programme
Other Programme
JTI-CS2-2017-CfP07-SYS-01-08 Application of machine learning techniques to enable and enhance aircraft performances database and facilitate mission optimisation objectives


Lead Organisation
Safety Line
130 RUE DE LOURMEL, 75015 PARIS, France
EU Contribution
€318 675
Partner Organisations
Institut National De Recherche En Informatique Et Automatique
Domaine de Voluceau- Rocquencourt, B.P. 105 LE CHESNAY, France
Organisation website
EU Contribution
€249 875


Technology Theme
Information systems
Machine learning for air traffic management
Development phase

Contribute! Submit your project

Do you wish to submit a project or a programme? Head over to the Contribute page, login and follow the process!