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Multi-label Classification of Aircraft Heading Changes using Neural Network to Resolve Conflicts

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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https://hal.archives-ouvertes.fr/hal-03617636
Contributor : Md Siddiqur Rahman Connect in order to contact the contributor
Submitted on : Wednesday, March 23, 2022 - 3:42:46 PM
Last modification on : Monday, July 4, 2022 - 9:31:47 AM

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Distributed under a Creative Commons Attribution - NonCommercial - NoDerivatives 4.0 International License

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Md Siddiqur Rahman, Laurent Lapasset, Josiane Mothe. Multi-label Classification of Aircraft Heading Changes using Neural Network to Resolve Conflicts. 14th International Conference on Agents and Artificial Intelligence (ICAART 2022), Feb 2022, Online, United States. pp.403-411, ⟨10.5220/0010829500003116⟩. ⟨hal-03617636⟩

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