Supervised Machine Learning Model to Help Controllers Solving Aircraft Conflicts

Md Siddiqur Rahman (1, 2)

1 ENAC - Ecole Nationale de l'Aviation Civile
2 IRIT-SIG - Systèmes d’Informations Généralisées

When two or more airplanes find themselves less than a minimum distance apart on their trajectory, it is called a conflict situation. To solve a conflict, air traffic controllers use various types of information and decide on actions pilots have to apply on the fly. With the increase of the air traffic, the controllers’ workload increases; making quick and accurate decisions is more and more complex for humans. Our research work aims at reducing the controllers’ workload and help them in making the most appropriate decisions. More specifically, our PhD goal is to develop a model that learns the best possible action(s) to solve aircraft conflicts based on past decisions or examples. As the first steps in this work, we present a Conflict Resolution Deep Neural Network (CR-DNN) model as well as the evaluation framework we will follow to evaluate our model and a data set we developed for evaluation.

Mots clés: Air traffic control; Aircraft conflict resolution; Machine learning; Deep Neural Network

Domaines: Machine Learning [stat.ML]; Informatique [cs]

Md Siddiqur Rahman. Supervised Machine Learning Model to Help Controllers Solving Aircraft Conflicts. TPDL 2020, ADBIS 2020: ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium International Conference on Theory and Practice of Digital Libraries European Conference on Advances in Databases and Information Systems, Aug 2020, Lyon, France. pp.355-361, ⟨10.1007/978-3-030-55814-7_31⟩. ⟨hal-02921265⟩