TY - GEN
T1 - Interactive Temporal Convolutional Network for Spatio-temporal flight trajectory prediction and safety assessment in terminal manoeuvring area
AU - Liu, Ye
AU - Ng, Kam K.H.
AU - Chu, Nana
N1 - Publisher Copyright:
© 2024, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2024/7
Y1 - 2024/7
N2 - The NextGen programme has significantly improved the reliability and predictability of air traffic management (ATM) by implementing four-dimensional (4D) trajectory prediction, which comprises spatial-temporal trajectory prediction, considering time, longitude, latitude, and altitude. This paper proposes a novel interactive methodology for real-time interactive 4D trajectory prediction to facilitate ATM. The research utilises image processing and deep learning to improve interaction capabilities with the ATM and ensure the security of the Hong Kong Flight Information Region (HKFIR). The proposed methodology includes establishing data processing and extracting holding patterns utilising image processing. The proposed Interactive Temporal Convolutional Network (ITCN) is compared with several popular time- series prediction models, including the Recurrent Neural Network (RNN), the Long Short- Term Memory (LSTM) neural network, the Gated Recurrent Unit (GRU) neural network, and the Transformer for short-term spatio-temporal flight trajectory prediction.
AB - The NextGen programme has significantly improved the reliability and predictability of air traffic management (ATM) by implementing four-dimensional (4D) trajectory prediction, which comprises spatial-temporal trajectory prediction, considering time, longitude, latitude, and altitude. This paper proposes a novel interactive methodology for real-time interactive 4D trajectory prediction to facilitate ATM. The research utilises image processing and deep learning to improve interaction capabilities with the ATM and ensure the security of the Hong Kong Flight Information Region (HKFIR). The proposed methodology includes establishing data processing and extracting holding patterns utilising image processing. The proposed Interactive Temporal Convolutional Network (ITCN) is compared with several popular time- series prediction models, including the Recurrent Neural Network (RNN), the Long Short- Term Memory (LSTM) neural network, the Gated Recurrent Unit (GRU) neural network, and the Transformer for short-term spatio-temporal flight trajectory prediction.
UR - http://www.scopus.com/inward/record.url?scp=85204227573&partnerID=8YFLogxK
U2 - 10.2514/6.2024-4550
DO - 10.2514/6.2024-4550
M3 - Conference article published in proceeding or book
AN - SCOPUS:85204227573
SN - 9781624107160
T3 - AIAA Aviation Forum and ASCEND, 2024
BT - AIAA Aviation Forum and ASCEND, 2024
PB - American Institute of Aeronautics and Astronautics Inc. (AIAA)
T2 - AIAA Aviation Forum and ASCEND, 2024
Y2 - 29 July 2024 through 2 August 2024
ER -