The Impact of Covid-19 on Air Traffic: Spatiotemporal Forecasting with Deep Learning and Benchmarks
Data preprocessing
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Technical Details | Jupyter Notebook, Python, Gephi |
Baseline and deep learning modeling
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Technical Details | Jupyter Notebook, TorchTS, PyTorchLightning |
Input data
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Description | Flight and Covid-19 data |
Output data
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Description | All modeling results from baseline and deep learning modeling scripts |
- Collection
- Cite This Work
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Driker, Adelle E.; Hu, Yuan; Yan, Bo; Yu, Rose (2022). The Impact of Covid-19 on Air Traffic: Spatiotemporal Forecasting with Deep Learning and Benchmarks. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J03F4PTD
- Description
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Many global industries have been affected by the COVID 19 pandemic, the airline industry being one of the most heavily hit. As a result, flight trends around the world have drastically deviated from their normal patterns. This presents an interesting scenario to explore, especially with the transience and abnormality of effects of the Covid data. The phenomenon also created uncertainty for both passengers and airline companies, especially due to the multiple waves of virus mutations prompting the following questions: How should airlines plan future flights? When should passengers schedule their travels? In other words, given a country’s COVID situation, how should an airline/passengers plan ahead? To solve this problem, we present a solution in the form of an air traffic forecast using baseline models: AR and ARIMA, and deep learning models: LSTM (Long Short Term Memory), LSTM-mis-regression, LSTM-quantile-regression, and other models that capture the temporal complexity of such data. These algorithms are developed for time series analysis that are available to the open-source community, and can better serve airlines and passengers.
- Creation Date
- 2021 to 2022
- Date Issued
- 2022
- Advisor
- Contributors
- Series
- Topics
Formats
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- Language
- English
- Related Resources
- Johns Hopkins Covid-19 GitHub Repository: https://github.com/CSSEGISandData/COVID-19
- OpenSky Flight Network: https://opensky-network.org
- Time series forecasting with PyTorch (Rose-STL-Lab) GitHub Repository: https://github.com/Rose-STL-Lab/torchTS
Source data
Reference
- License
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Creative Commons Attribution 4.0 International Public License
- Rights Holder
- Driker, Adelle E.; Hu, Yuan; Yan, Bo
- Copyright
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Under copyright (US)
Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.
Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work.
- Digital Object Made Available By
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Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp)
- Last Modified
2022-09-09