Summary of Airport Delay Prediction with Temporal Fusion Transformers, by Ke Liu et al.
Airport Delay Prediction with Temporal Fusion Transformers
by Ke Liu, Kaijing Ding, Xi Cheng, Guanhao Xu, Xin Hu, Tong Liu, Siyuan Feng, Binze Cai, Jianan Chen, Hui Lin, Jilin Song, Chen Zhu
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to predict airport arrival delays at a quarter-hour level using the Temporal Fusion Transformer (TFT) model. The goal is to improve upon previous categorical and aggregated predictions, which are often inaccurate. The TFT model incorporates various inputs, including airport demand and capacity forecasts, historic operation efficiency data, wind and visibility conditions, enroute weather and traffic information. The results show satisfactory performance with small prediction errors on the test set. The study also provides interpretability analysis of the model outputs, highlighting key input factors for delay prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research tries to improve predicting flight delays at airports. Most previous predictions were either yes or no and didn’t provide exact times. This study uses a new way to predict delays called Temporal Fusion Transformer (TFT). The TFT model looks at various things like how busy the airport is, how well it usually runs, weather, and traffic. The results show that this method works pretty well and can help airlines, airports, and passengers make better decisions. |
Keywords
» Artificial intelligence » Transformer
