Chapter
Dec 14, 2021
Aircraft Trajectory Prediction Using Social LSTM Neural Network
Authors: Zhengfeng Xu [email protected], Weili Zeng [email protected], Lijing Chen [email protected], and Xiao Chu [email protected]Author Affiliations
Publication: CICTP 2021
ABSTRACT
In this paper, we propose a Social Long Short-Term Memory (SLSTM) Neural Network for aircraft trajectory prediction. This model builds an LSTM network for each aircraft and uses a merging layer to merge the hidden states of its neighboring LSTMs. Then, it uses the merging results and trajectory information as its input for the next time step. Finally, the model is trained by maximizing the probability of real trajectory and evaluate the prediction effect. The experiment is conducted with the flight trajectory dataset over the San Francisco Bay Area in 2006. The evaluation shows that our model has the smallest error from 17 to 18 o’clock when the airspace’s flight trajectory density is the highest. The average horizontal error per point is about 282 meters, and the average vertical error per point is about 10 meters.
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CICTP 2021
Pages: 87 - 97
History
Published online: Dec 14, 2021
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1College of Civil Aviation, Nanjing Univ. of Aeronautics and Astronautics, Nanjing. Email: [email protected]
2College of Civil Aviation, Nanjing Univ. of Aeronautics and Astronautics, Nanjing. Email: [email protected]
3College of Civil Aviation, Nanjing Univ. of Aeronautics and Astronautics, Nanjing. Email: [email protected]
4College of Civil Aviation, Nanjing Univ. of Aeronautics and Astronautics, Nanjing. Email: [email protected]
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Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.