A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes

A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes

Sakyi-Gyinae, Master (TU Delft Aerospace Engineering; TU Delft Control & Simulation)

Ellerbroek, J. (mentor)
Hoekstra, J.M. (mentor)
Rudnyk, I. (mentor)

Delft University of Technology

Aerospace Engineering

2019-10-03

With the increasing trend in air traffic demand and evidence of large deviations from filed flight plans, airspace capacity is not being optimally utilized. In order to improve air traffic flow and capacity management systems, so that air traffic control operators can handle more aircraft safely, air traffic predictability needs to be improved. Quantitatively, this means reducing the measure of spread of aircraft deviations from filed flight plans. In this thesis, a long short term memory (LSTM) network is proposed to predict trajectories in Maastricht upper airspace in a data-driven approach, using statistical aircraft deviation related features. The results show that the LSTM model has a lower prediction error at predicting trajectories than the current model used by the network manager. The LSTM model finally demonstrates its application within air traffic demand optimization where the LSTM based sector load predictions provide a more accurate estimation than the filed flight plan based predicted occupancy count.

Machine learning
Trajectory Prediction
Aircraft Deviations
Neural Network
LSTM
Airspace Demand Optimalization
feature design
Feature selection
feature selection method

http://resolver.tudelft.nl/uuid:274b4386-539a-4193-80e9-f120c8d4832e

Student theses

master thesis

© 2019 Master Sakyi-Gyinae