Lessons Learned in the Application of Machine Learning Techniques to Air Traffic Management

There is an increasing interest in applying methods based on Machine Learning Techniques
(MLT) to problems in Air Traffic Management (ATM). The current interest is based on developments
in Cloud Computing, the availability of open software and the success of MLT in
automation, consumer behavior and finance involving large databases. This paper reviews the
current-state-of-the art in applying MLT to aviation operations, its promises and challenges.
Historically aviation operations have been analyzed using physics-based models and provide
information for making operational decisions. Aviation operations involving many decision
makers, multiple objectives, poor or unavailable physics-based models and a rich historical
database are prime candidates for analysis using data-driven methods. The promises and
challenges in applying MLT to ATM is traced through three examples based on the authors’
experience, each separated by a decade, to show the influence of data and feature selection in
the successful application of MLT to ATM. As always, the best approach depends on the task,
the physical understanding of the problem and the quality and quantity of the available data.