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

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

Overkamp, Jean-Luc (TU Delft Aerospace Engineering)

Sun, J. (mentor)
Hoekstra, J.M. (graduation committee)

Delft University of Technology

Aerospace Engineering

2021-09-09

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.

Trajectory prediction
Air traffic dynamics
air traffic complexity
4D trajectories
Long Short-Term Memory networks
2D Convolutional LSTM

http://resolver.tudelft.nl/uuid:15358485-1caf-48df-84c8-9f4c389a27fa

Student theses

master thesis

© 2021 Jean-Luc Overkamp