A Deep Learning Approach to Predict General Aviation Traffic Counts

Amir Abecassis, Daniel Delahaye, Moshe Idan

Open source

DOI: 10.2514/6.2024-2701
Published: 2024-01-04
Container: AIAA SCITECH 2024 Forum
Publisher: American Institute of Aeronautics and Astronautics
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.2514/6.2024-2701,
  title = {A Deep Learning Approach to Predict General Aviation Traffic Counts},
  author = {Amir Abecassis and Daniel Delahaye and Moshe Idan},
  year = {2024},
  journal = {AIAA SCITECH 2024 Forum},
  doi = {10.2514/6.2024-2701},
  url = {https://doi.org/10.2514/6.2024-2701}
}

RIS

TY  - JOUR
TI  - A Deep Learning Approach to Predict General Aviation Traffic Counts
AU  - Amir Abecassis
AU  - Daniel Delahaye
AU  - Moshe Idan
PY  - 2024
JO  - AIAA SCITECH 2024 Forum
DO  - 10.2514/6.2024-2701
UR  - https://doi.org/10.2514/6.2024-2701
ER  -

APA

Abecassis, A., Delahaye, D., & Idan, M. (2024). A Deep Learning Approach to Predict General Aviation Traffic Counts. AIAA SCITECH 2024 Forum. https://doi.org/10.2514/6.2024-2701

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crossref · retrieved 2026-11-10T09:55:42.728Z