• SJU reference # PJ.02-W2-14.6a /Release 10
  • Stakeholders
  • Benefits Environment
    Operational efficiency
  • Status In the pipeline

Enhanced optimised runway delivery for arrivals (eORD) with machine learning

Airport arrivals can become constrained as a result of wake vortex separations, wind conditions and high runway occupancy. SESAR is developing a more efficient separation delivery tool for arrivals to support complex separation rules at capacity-constrained airports.

The solution proposes a more efficient delivery for arrivals in order to support complex separation rules at capacity-constrained airports. 
Enhanced optimised separation delivery with machine learning uses more accurate predictions of final speed profiles derived from advanced big data/machine learning techniques. 

The more accurate predictions of final approach speed profiles are used to more accurately predict the spacing that needs to be delivered at the deceleration fix (DF) in order to achieve the required spacing/separation at the separation delivery point. This more accurate spacing/separation delivery leads to further reduced spacing between consecutive arrivals for the majority of aircraft pairs.

Overall, the solution brings benefits in terms of increased runway throughput capacity due to the reduced, optimised spacing/separation on the final approach. As the spacing/separation is based on more flight specific behaviour/performance, there is a potential positive impact on safety.


Increased runway throughput

Potential positive impact on safety