Reinforcement Learning, and particularly Q-learning has been studied in the context of predicting trajectories, exploiting historical data about trajectories, enhanced with aircraft intent information . This is a recently-proposed approach whose potential and limitations have been explored by the DART project .
datAcron project is funded by the European Union’s Horizon 2020 Programme under grant agreement No. 687591.
datAcron is a research and innovation collaborative project targeting at introducing novel methods to detect threats and abnormal activity of very large numbers of moving entities in large geographic areas.
DART (Data-driven AiRcraft Trajectory prediction research) addresses the topic “ER-02-2015 – Data Science in ATM” exploring the applicability of data science and complexity science techniques to the ATM domain. DART delivers an understanding on the suitability of applying big data and agent –based modelling techniques for predicting aircraft trajectories based on data-driven models and accounting for ATM network complexity effects, considering multiple correlated trajectories.