Abstract
One of the factors limiting busiest airport’s runway throughput capacity is the spacing to be applied between landing aircraft to ensure that the runway is vacated when the follower aircraft reaches the runway threshold. Today, because the Controller is not able to always anticipate the runway occupancy time (ROT) of the leader aircraft, significant spacing buffers are added to the minimum required spacing in order to cover all possible cases, which negatively affects the resulting arrival throughput. The present paper shows how a Machine Learning (ML) analysis can support the development of accurate, yet operational, models for ROT prediction depending on all impact parameters. Based on Gradient Boosting Regressors, those ML models make use of flight information (such as aircraft type, airline, flight data) and weather information to model the ROT. This paper shows how it can be used operationally to increase runway capacity while maintaining or reducing the risk of delivery of separations below runway occupancy time. The methodology and related benefits are assessed using three years of field measurements gathered at Zurich airport.
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