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

Md Rahman, Md Rahman, Md Rahman, Laurent Lapasset, Laurent Lapasset, Josiane Mothe, Josiane Mothe

2022

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 on demand. It is freely accessible online at: https://independent.academia.edu/MDSIDDIQURRAHMAN9.

Paper Citation

in Harvard Style

Rahman M., Lapasset L. and Mothe J. (2022). Multi-label Classification of Aircraft Heading Changes using Neural Network to Resolve Conflicts. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 403-411. DOI: 10.5220/0010829500003116

in Bibtex Style

@conference{icaart22,
author={Md Rahman and Laurent Lapasset and Josiane Mothe},
title={Multi-label Classification of Aircraft Heading Changes using Neural Network to Resolve Conflicts},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={403-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010829500003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Multi-label Classification of Aircraft Heading Changes using Neural Network to Resolve Conflicts
SN - 978-989-758-547-0
AU - Rahman M.
AU - Lapasset L.
AU - Mothe J.
PY - 2022
SP - 403
EP - 411
DO - 10.5220/0010829500003116