Predictive Distribution of Mass and Speed Profile to Improve Aircraft Climb Prediction

Abstract

Ground-based aircraft trajectory prediction is a major concern in air traffic management. Focusing on the climb phase, neural networks are trained to predict some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. For each unknown parameter, our model predicts a Gaussian distribution. This predicted distribution is a predictive distribution: it is the distribution of possible unknown parameter values conditional to the observed past trajectory of the considered aircraft. Using this distribution, one can extract a predicted value and the uncertainty related to this specific prediction. This study relies on Automatic Dependent Surveillance-Broadcast data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by the network. The obtained data set contains millions of climbing segments from all over the world. Using this data set, it is shown that despite having an error slightly larger than previously tested methods, the predicted uncertainty allows us to reduce the size of prediction intervals while keeping the same coverage probability. Furthermore, it is shown that the trajectories with a similar predicted uncertainty have an observed error close to the predicted one. The data set and the machine learning code are publicly available.