Multi-label Classification of Aircraft Heading Changes Using Neural Network to Resolve Conflicts
This work addresses the problem of reducing air traffic controller workload and enabling quick decisions in conflict resolution, representing a domain-specific incremental improvement.
The paper tackles aircraft conflict resolution by framing it as a multi-label classification problem, proposing CRMLnet, a neural network model that provides multiple heading advisories, achieving 98.72% accuracy and 0.999 ROC.
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.