  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.
