currently, at many airports, the runway throughput is the limiting factor for the overall capacity. Among the most important constraining parameters is the separation minima expressed in distance. On the top of these minima, the difference of the leader and follower aircraft speed profiles imposes to consider buffer to cope with compression effect. Currently, Air Traffic Control Officers (ATCO’s) take these buffers on the basis of their training and experience. However, this experience will not be sufficient to safety deploy advanced concepts, like pair-wise separations, that increase variability in the separations to be delivered and therefore in the compression buffer to be considered. Systematic analysis of years of radar tracks has allowed to better predict the buffers to apply by characterising the time to fly (T2F) given a separation distance and True Airspeed (TAS) profile as a function of meteorological parameters.
This paper presents how Machine Learning (ML) techniques may be used for predicting the T2F and TAS profile on final approach. Different ML techniques will be assessed on their forecast performance, computational time and amount of data needed for delivering a reliable prediction. The techniques will be applied on 2 different major European airports traffic and will be benchmarked against Optimized Runway Delivery (ORD) study using a Model Based Approach (MBA) for deriving the T2F and TAS. As a result the most efficient ML techniques will be applied on two case studies for predicting the T2F and TAS.
Original languageEnglish
Title of host publication7th International Conference on Research in Air Transportation
Subtitle of host publicationPhiladelphia, USA
EditorsD. Lovell, H. Fricke
Number of pages8
Publication statusPublished - 2016
Event7th International Conference on Research in Air Transportation - Drexel University, Philadelphia, United States
Duration: 20 Jun 201624 Jun 2016
Conference number: 7


Conference7th International Conference on Research in Air Transportation
Abbreviated titleICRAT'16
CountryUnited States
Internet address

    Research areas

  • T2F, TAS, ML, ensemble

ID: 8959677