Long Short-Term Memory Network Based Trajectory Prediction Incorporating Air Traffic Dynamics

Master thesis (2021)

Authors

J. Overkamp Aerospace Engineering

Contributors

Junzi Sun (supervisor 1)
J.M. Hoekstra Control & Operations (supervisor 2)
Faculty
Aerospace Engineering
Copyright: © 2021 Jean-Luc Overkamp
Long Short-Term Memory networks Trajectory prediction 4D trajectories Air traffic dynamics Air traffic complexity 2D Convolutional LSTM
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Published Date

09-09-2021

Language

English

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Faculty

Aerospace Engineering

Abstract

Accurate 4D trajectory predictions are required for the implementation of Trajectory Based Operations. In addition, decentralized, free routing can make medium- to long-term flight trajectories more difficult to predict. Novel trajectory prediction techniques are needed, independent of waypoint-to-waypoint navigation and air traffic control operator behaviour. This research aims to improve the accuracy of medium- to long-term 4D flight trajectory predictions by incorporating a model that encompasses the dynamics of the air traffic situation. Data-driven techniques are well-suited to trajectory prediction purposes as high-fidelity air traffic and environmental data are widely available. A statistical analysis is first conducted to select the most suitable air traffic dynamics features for trajectory prediction purposes. The selected air traffic dynamics features are then translated to a spatiotemporal map. This paper proposes a composite, deep neural network to predict individual trajectories, merging a LSTM network with a 2D Convolutional LSTM based network.