An accurate forecast of general aviation traffic is crucial for air navigation service providers, as it affects the overall efficiency of air traffic management and capacity planning. This paper presents a deep learning methodology for predicting general aviation traffic, combining calendar and meteorological sources through a detailed feature-engineering procedure. The approach is rigorously evaluated using historical data from the Nice Cote D’Azur Terminal Control Center sectors, resulting in a significant 32% enhancement in global prediction performance with recurrent neural network models compared to existing operational tools. The paper explores additional analysis techniques to gain a deeper understanding of the predictions that are produced by each model.
Abecassis et al. (Mon,) studied this question.