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

TL;DR

This paper introduces CRMLnet, a neural network-based multi-label classification model that predicts multiple aircraft heading advisories to assist air traffic controllers in conflict resolution, achieving high accuracy and ROC scores.

Contribution

The paper presents a novel neural network model for multi-label aircraft conflict resolution, improving decision support for controllers with high accuracy.

Findings

CRMLnet achieved 98.72% accuracy.

The model attained a ROC of 0.999.

Simulated dataset provided for research use.

Abstract

An aircraft conflict occurs when two or more aircraft cross at a certain distance at the same time. Specific air traffic controllers are assigned to solve such conflicts. A controller needs to consider various types of information in order to solve a conflict. The most common and preliminary information is the coordinate position of the involved aircraft. Additionally, a controller has to take into account more information such as flight planning, weather, restricted territory, etc. The most important challenges a controller has to face are: to think about the issues involved and make a decision in a very short time. Due to the increased number of aircraft, it is crucial to reduce the workload of the controllers and help them make quick decisions. A conflict can be solved in many ways, therefore, we consider this problem as a multi-label classification problem. In doing so, we are proposing a multi-label classification model which provides multiple heading advisories for a given conflict. This model we named CRMLnet is based on a novel application of a multi-layer neural network and helps the controllers in their decisions. When compared to other machine learning models, our CRMLnet has achieved the best results with an accuracy of 98.72% and ROC of 0.999. The simulated data set that we have developed and used in our experiments will be delivered to the research community.