DART aims to present to the ATM community an understanding on what can be achieved today in trajectory prediction by using data-driven models, also accounting for network complexity effects. It is expected that data-driven techniques help to improve the performance and accuracy of predictions by complementing classical model-based prediction approaches. These improved predictions will enable advanced collaborative decision making processes, which finally will lead to a more efficient ATM procedures.
DART will deliver understanding on the suitability of applying data-driven models for enhancing our abilities to compute predictions of aircraft trajectories, accounting also for ATM network complexity effects concerning multiple correlated aircraft trajectories.
DART will explore the applicability of a collection of data mining, machine learning and agent-based models and algorithms to derive a data-driven trajectory prediction capability, accounting also for ATM network complexity effects.
DART aims at high-fidelity aircraft trajectory prediction capabilities, supporting the trajectory life-cycle at all stages efficiently.
Call: H2020-SESAR-2015-1 , Type of Action: SESAR-RIA , Duration: 24 months, Start Date: 20 Jun 2016