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

Abstract

Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. Focusing on the climb phase, we predict some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. This speed intent is parameterized by three values (cas1, cas2, $M$). These missing parameters might be useful to predict the future trajectory of a climbing aircraft. In this work, an ensemble of neural networks uses the observed past trajectory of the considered aircraft as input and predicts a Gaussian Mixture Model (GMM) modeling the joint distribution of (mass, cas1, cas2, $M$). Ideally, this predicted distribution will be close to a conditional distribution: the distribution of possible (mass, cas1, cas2, $M$) values given the observed past trajectory of the considered aircraft. This study relies on ADS-B data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by this sensor network. The obtained data set contains millions of climbing segments from all over the world. Using this data, we show that using the proposed predictive model instead of a regression model brings almost as much information as using a regression model instead of a simple mean. The data set and the machine learning code are publicly available.