EUROCONTROL Air Transport Innovation Network

This is one of the first projects that are part of our Air Transport Innovation Network to hand out the prototype for industrialisation. Its objective is to improve the information exchanged between different operational partners – airports, airlines, ground handlers and the EUROCONTROL Network Manager. The project is addressing challenges such as multiple point-to-point data interfaces between airlines, airports and ground handlers; airport to airport data exchange; security aspects regarding the exchanged data, etc. The project team has built a data exchange platform covering flight related data relevant to planning and operations with a standard SWIM data interface. EUROCONTROL is now making the documentation publicly available to the market, so that commercial providers can use it to further develop the platform as a product.

This process innovation project delivered a comprehensive approach to quantify the runway collision barrier model. Using the Network Manager Safety Functions Map (SAFMAP) comprehensive barrier model, the project researched and validated an approach to characterise and quantify the individual barriers success and failure rates and to integrate these into an overall risk and resilience quantitate model. The approach allows users to investigate model sensitivities to changes in the traffic conditions and to analyse the effects of addressing specific risk. A great benefit from using the approach is the ability to explicitly predict safety benefits of new investments – new infrastructure, procedures, or training.

The project objective is to visualise predicted knock-on delay and the impact on traffic demand for the next 12h from an FMP, Airport and Airline perspective.

Knock-on delays are mostly caused by aircraft rotations, i.e. a late inbound arrival of an aircraft causing a late departure on the next flight operated by the same aircraft. A smaller share of knock-on delays is caused by connecting passengers, crew, or cargo.

The project targets knock-on delays caused by the aircraft rotations and uses the calculations done by iDEMAND (based on business rules) and the RNN (Night Curfew) models to predict the impact of knock-on delays on the demand picture for FMPs, airports and airlines.

The project aims to create a real-time Decision-Making Support tool:

• For FMPs, the new interface allows to visualise the predicted demand vs. the ETFMS demand (Airspace or Traffic Volume Set) and to identify if/when knock-on delay will dissipate traffic demand peaks rendering the use of ATFM Regulations unnecessary. Or the opposite, when knock-on delays will cause traffic peaks which are not yet visible in ETFMS.
• For airports, the new interface allows to visualise the predicted arrival demand vs. the scheduled demand to identify if/when knock-on delays will shift scheduled peaks with an indication of the predicted arrival delay for each individual flight.
• For airlines, the new interface allows to visualise the predicted departure delay for each flight using a weighing indicator taking the varying degrees of importance (length of delay, aircraft type, seat capacity) into account. The interface also visualises systemic reactionary delays in the airline network caused by suboptimal flight schedules and planning, i.e. identify lack of schedule resilience.

The Flexible Use of Airspace (FUA) concept provides the European ATM system with the potential to increase the efficiency of airspace usage through enhanced civil-military cooperation. The Military Aviation Authorities’ representatives consider that, in addition to their efforts to alleviate the impact of airspace segregations on civil traffic, the Aircraft Operators (AO) could improve their taking upon the airspace usage opportunities made available throughout FUA.

Furthermore, latest performance reports show room for improvement to filing/re-filing the most horizontally efficient route plan given the state of airspace availability for the duration of the flight.

Before addressing improvement solutions, it is essential to assess in depth both FUA and non-FUA barriers to AO in better exploiting airspace availability opportunities. To that end, the factors and criteria driving AO flight planning and execution need to be captured and understood. Only the results of such an exercise could drive the identification and development of a quick-win digital solution(s) for improving CURA as sought-after by the EATIN programme.

A qualitative study report will provide conclusions and recommendations to alleviate the identified CURA barriers in areas such as procedures, system support, and civil-military cooperation.

Proposed by Vueling, the project has emerged due to the need of airspace users and airports to have a better predictability of the periods of diversion due to weather events. Nowadays, the information on the periods of diversion is very limited for the airspace users and airports, raising high uncertainty on the operations management throughout the day when the weather gets worse. Stakeholders do not have any tool to know which is the probability for a given flight to be diverted in terms of weather (METAR, TAFOR and/or WIND). It is proposed to develop and validate an AI model to predict the likelihood and trend of diversion, in order to avoid diverted flights (change scheduled flight or delay the flight), avoid undesirable delays for the next flights and avoid unexpected high workload to the Handling Services at another airport. Particpants: Vueling, Transavia, Swiss, Air Europa, Prague airport, Istanbul airport, EBAA.

Proposed by Istanbul Airport, the objective of this project is to establish a real-time communication of the delay reasons.

If delayed, flights are more likely to have been affected by non-ATFM delays (e.g. knock-on delays, baggage loading, security checks, late fuelling, de-icing, etc.) with little real-time visibility to airport operators, ANSPs and the EUROCONTROL Network Manager on the reasons for these delays. Having a real-time insight into the delay reasons improves the situational awareness across the different actors and allows them to react during the operational day. The real-time sharing of the delay information in a centralised manner would allow for the tactical management of staff and resources adjusted to the actual operational situation. This allows early identification of disruptions at outstations potentially impacting the operation and at the same time centralises the collection of delay reasons to be used for post-ops performance analysis.

The READI project addresses a current need for airport operators and its ecosystem of operational stakeholders: real-time awareness of the delay drivers at the (own) airport and understanding of the delay drivers at outstations (departing airports for inbound flights).
The READI project grouped airports, airlines, ground handlers with IATA endorsing the project and EUROCONTROL acting as “honest data broker” providing the technical platform

There is a lag in the current delay data reporting mechanism with the information becoming available too late to the airport operator to deploy remedial actions to stabilise or improve the local departure punctuality (OTP).

The lack of real-time delay information can be addressed by technology (in the near future) but more importantly it’s the legacy delay code schema that limits the live sharing of delay causes. The legacy delay code schema focus is on the delay reason and stakeholder rather than the turnaround process.
The legacy IATA AHM730 delay code schema was therefore deemed not fit for the live trials which were conducted between May 8-21 2023.The READI project opted to use the new IATA AHM732 delay code schema which has a 3-layer structure:

• Process – which aircraft turnaround process is affected?
• Reason – what is the actual reason causing a flight delay?
• Stakeholder – which stakeholder is responsible? ex. handler, passenger, etc.  

The READI project only used the first Process layer to power a performance dashboard displaying live operational information helping local stakeholders to deploy tactical measures. The second and third layer of the AHM732 was not used in the READI project but in future will allow in-depth post-operational analysis and steer strategic changes.

The results of the limited (both in time and scope) live trials showed great value in a process centric reporting of tactical issues. On the day it is (often) sufficient for operational partners to know which turnaround processes are under pressure with post-ops analysis allowing for more in-depth analysis into the delay reasons and assess which stakeholders caused the delays.

Airport operations currently rely on fixed taxi times (default and variable for CDM airports, static for non-CDM airports), without accounting for factors such as weather conditions or ongoing airport work. Additionally, users often receive late notifications regarding stand allocations and the runways in use for departures and arrivals.

This lack of real-time information and dynamic adjustments to taxi times results in overestimated fuel requirements for airlines and inefficient resource planning at airports, including stand allocations and ground handler availability.

The TITOP project was proposed by Swiss and started in January 2023 to address these challenges by providing dynamic taxi-time values for inbound and outbound flights considering weather situations and works at the airports, several hours before departure.

The project combines three machine learning models trained on historical data, which can be used together or individually:

• DE-ICING PREDICTION: this model provides the probability of de-icing for a given flight and whether the de-icing will be remote or at the stand.
• RUNWAY-IN-USE PREDICTION (PRIU): the model predicts which runway will be used for an inbound or outbound flight.
• TAXI-TIME PREDICTION (TITOP): the model provides the prediction of taxi duration between the landing runway and the stand for an inbound flight and between the stand and the departure runway for an outbound flight.

Benefits of these predictions include:

• Operational: More efficient stand planning for airports, better awareness for ground-handlers planning, and more accurate flight plan information
• Economic and Environmental: Reduced and optimised fuel consumption

The project is running a live trial until end of March 2025 to assess the impacts of the three models on operations.

Stakeholders involved:
Airlines: Austrian, Vueling, SunExpress, Swiss, Air France, Transavia France, Transavia Netherlands
Airports: Brussels, Heathrow, Aéroports de Paris, IGA Istanbul, Prague, Vienna, Stockholm

The OpTT2.0 project was proposed by Swiss Airlines during the 5th EUROCONTROL Air Transport Innovation Network (EATIN) cycle and was kicked-off in September 2023.

Inspired by the OpTT1.0 model, currently deployed and used in operations at Prague airport, a new machine learning model has been developed to predict the Target Off-Block Times and turnaround durations across all European CDM airports and for any airline operating from these airports. Predictions are available at early planning phases (e.g., a few days before the day of operations) and receive updates as turnaround operations approach.

The model could be used to increase the stability of TOBT releases and reduce the number of their updates, thereby helping to improve airport and airline operational planning.

A scoping study reported its findings in December 2023 to stakeholders and the model is expected to be tested in May 2024 and in the following months.

Stakeholders involved:

• Aeroport de Paris, Aeroporti di Roma, Heathrow, Brussels, Prague, Schiphol, Frankfurt, Swedavia, Dusseldorf airports
• Swiss, Austrian, Vueling, Transavia, SunExpress airlines

Airlines, ground handlers and airport operators may have limited automated information available to predict the passenger connectivity on the day of operation for Customer Care and iOCC to make effective decisions. At present these services often rely on public websites (e.g. Flightradar24) to check the arrival time of third-party (long-haul) flights arrivals to assess whether or not connecting passengers will make their transfer in time.

The objective of the PaxCOIN project is to create a multi-tiered indicator which can be easily shared amongst all operational stakeholders at an airport showing the probability and operational impact of passengers not making their connecting flights. The goal of this indicator is to enhance the travel experience for passengers and to boost the operational effectiveness for airlines, airports and ground handlers.

The aim of the project is to conduct live trials during 2024 and assess whether the PaxCOIN indicator provides an operational and financial benefit. This can be achieved through a reduction of missed passenger connections, an earlier detection when a passenger connection will be missed so mitigation measures can be activated sooner, a reduction of EU261 related costs, an optimisation of the passenger connections within and outside the airline concerned or an optimisation of the park and gate procedures.

Initiated in July 2023 with the active participation of four airlines - Vueling, Turkish Airlines, Netjets, and SunExpress - the WIND project emerged as a response to a critical challenge faced by airlines, which is the absence of an official and robust airports weather database that could help airlines improve scheduling and performance analysis.

WIND was proposed by Vueling during the 6th ideation cycle of EATIN. At its heart is a dynamic PowerBI dashboard enabling airlines to easily access historical METAR information for each airport, offering flexibility in filtering based on date, time, and specific weather parameters. The data can be seamlessly visualised and exported in Excel format for post-analysis.

Complementing METAR data, WIND incorporates additional valuable information, encompassing historical regulations and diversions, along with the percentage of runway utilisation per airport. Currently, the first version of WIND is undergoing validation by both airlines and EUROCONTROL, marking a pivotal phase in ensuring the tool's effectiveness and reliability.

The project was pitched by Vueling and aims to deliver more accurate route planning by improving the predictions of the planned SIDs and STARs at the 10 to 3-hour mark prior to take off. Stakeholders who will join the scoping phase are Air France, SunExpress, EUROCONTROL MUAC, Fintraffic ANS and Liverpool airport ATS.