Aircraft Trajectory Prediction Using Social LSTM Neural Network

In this paper, the authors 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 the 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.

• Record URL:
  • https://doi.org/10.1061/9780784483565.009
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784483565
• Supplemental Notes:
  • © 2021 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Xu, Zhengfeng
  • Zeng, Weili
  • Chen, Lijing
  • Chu, Xiao
• Conference:
  • 21st COTA International Conference of Transportation Professionals
  • Location: Xi'an , China
  • Date: 2021-12-16 to 2021-12-19
• Publication Date: 2021

Language

• English

Media Info

• Media Type: Web
• Pagination: pp 87-97
• Monograph Title: CICTP 2021: Advanced Transportation, Enhanced Connection

Subject/Index Terms

• TRT Terms: Aircraft; Flight plans; Forecasting; Trajectory
• Geographic Terms: San Francisco Bay Area
• Subject Areas: Aviation; Operations and Traffic Management; Planning and Forecasting;

Filing Info

• Accession Number: 01831685
• Record Type: Publication
• ISBN: 9780784483565
• Files: TRIS, ASCE
• Created Date: Dec 28 2021 9:32AM