Airport operational staff developing a plan that is later corrected according to deviations on D-day (delays, breakdowns, etc.). Airport plans in strategic and pre-tactical phases face a very large number of errors and approximations. As demand planning is usually driven by flight schedules and passenger forecasts based on static models, there is a need of late adjustments during the tactical phase to make up for what was poorly planned in strategic and pre-tactical phases which in turn negatively impacts the network.

This solution builds upon previous SESAR work about airport operations plan and integration of airport and network, focusing on the strategic and pre-tactical planning phases through enhanced/better data and earlier information exchange. The integration of airlines’ flight schedule vision in the strategic and pre-tactical planning at network level will allow planning closer to reality. The use of additional and better information provided by Airspace Users and Network, supported by artificial intelligence /machine learning will provide better predictions and a smooth transition towards tactical phase (D-Day) with reduced adjustments.

The regulatory contextual information existing at the beginning of the development activities, is notably characterised by the Common Project 1 (CP1) Regulation (Commission implementing regulation (EU) 2021/116 of 1 February 2021) which cover airport integration into the air traffic management (ATM) Network and the staged deployment of the airport operations plan (AOP). FASTNet Solution 0347 will potentially propose new data elements for inclusion within network departure Information messages and investigate earlier sharing of information with the Network will be explored. It is not expected that FASTNet will contribute directly to any modification of the text of the CP1 regulation but those airports deploying CP1 may see proposals for change to the relevant standards applicable to CP1 as a result of the FASTNet project.

BENEFITS

• Operational efficiency: the predictability of airport and network operations will increase as airport and network planning will be closer to reality. Tactical measures on D-day will be reduced as more reliable information on flight intentions is known in advance. Positive contribution to punctuality through reduction of departure delays is expected.
• Passenger experience: improving the predictability of operations, better planning of flights and adequacy between demand and airport resources are expected to produce a positive effect on passenger experience.
• Resilience: increased predictability of operations until D-1, supported by Artificial Intelligence/ Machine Learning forecast models will lead to a more stable capacity at the airport and in the Network and contribute in anticipating disturbances and potential disruptions, allowing pro-active management of operations.