Machine Learning Supporting Enhanced Optimized Spacing Delivery between Consecutive Departing Aircraft

Abstract The Optimised Spacing Delivery (further referred to as OSD) tool has the objective of calculating necessary time spacing between two consecutive departing aircraft in order fulfil all required and separation constraints. OSD, developed SESAR 2020 Wave 1 [1] is based on analytical models [2] predict trajectory speed profiles. use this by Air Traffic Controller supports safe, consistent efficient delivery or departure pairs providing via an automated count-down timer tower runway controller. In improve paper introduces enhanced eOSD) which builds OSD using Machine Learning techniques make more accurate predictions behaviour (e.g. trajectory/climb profile, profile) wind initial path, so further optimising departures. Zurich airport data were used develop asses performance eOSD compared tool.