Authors:
Md Siddiqur Rahman
1
;
2
;
3
;
Laurent Lapasset
1
;
3
and
Josiane Mothe
4
;
3
Affiliations:
1
DEVI, Ecole Nationale de l’Aviation Civile, Toulouse, France
;
2
IRIT UMR5505 CNRS, Univ.de Toulouse 1 Capitole, Toulouse, France
;
3
Univ.de Toulouse, Toulouse, France
;
4
INSPE, IRIT, UMR5505 CNRS, Toulouse, France
Keyword(s):
Aircraft Conflict Resolution, Machine Learning, Neural Network, Multi-label Classification.
Abstract:
An aircraft conflict occurs when two or more aircraft cross at a certain distance at the same time. Aircraft heading changes are the common resolution at the en-route level (high altitude). One or more alternative heading changes are possible to resolve a single conflict. We consider this problem as a multi-label classification problem. We developed a multi-label classification model which provides multiple heading advisories for a given conflict. This model we named CRMLnet is based on the use of a multi-layer neural network that classifies all possible heading resolution in a multi-label classification manner. When compared to other machine learning models that use multiple single-label classifiers such as SVM, K-nearest, and LR, our CRMLnet achieves the best results with an accuracy of 98.72% and ROC of 0.999. The simulated data set which consists of conflict trajectories and heading resolutions we have developed and used in our experiments is delivered to the research community o
n demand. It is freely accessible online at: https://independent.academia.edu/MDSIDDIQURRAHMAN9.
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