Conditioning Aircraft Trajectory Prediction on Meteorological Data with a Physics-Informed Machine Learning Approach

TL;DR

This paper introduces a physics-informed machine learning method that improves aircraft trajectory prediction by incorporating meteorological data and physical constraints, leading to more accurate and plausible trajectories.

Contribution

It presents a novel approach that combines data-driven learning of aircraft dynamics with physics-based models to enhance trajectory prediction accuracy.

Findings

20% improvement in model skillfulness over baseline

Effective incorporation of meteorological data improves predictions

Physics-based constraints ensure physically plausible trajectories

Abstract

Accurate aircraft trajectory prediction (TP) in air traffic management systems is confounded by a number of epistemic uncertainties, dominated by uncertain meteorological conditions and operator specific procedures. Handling this uncertainty necessitates the use of probabilistic, machine learned models for generating trajectories. However, the trustworthiness of such models is limited if generated trajectories are not physically plausible. For this reason we propose a physics-informed approach in which aircraft thrust and airspeed are learned from data and are used to condition the existing Base of Aircraft Data (BADA) model, which is physics-based and enforces energy-based constraints on generated trajectories. A set of informative features are identified and used to condition a probabilistic model of aircraft thrust and airspeed, with the proposed scheme demonstrating a 20% improvement in skilfulness across a set of six metrics, compared against a baseline probabilistic model that ignores contextual information such as meteorological conditions.