Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.08293 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913535219466240 |
|---|---|
| author | Liu, Ke Ding, Kaijing Cheng, Xi Xu, Guanhao Hu, Xin Liu, Tong Feng, Siyuan Cai, Binze Chen, Jianan Lin, Hui Song, Jilin Zhu, Chen |
| author_facet | Liu, Ke Ding, Kaijing Cheng, Xi Xu, Guanhao Hu, Xin Liu, Tong Feng, Siyuan Cai, Binze Chen, Jianan Lin, Hui Song, Jilin Zhu, Chen |
| contents | Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the novel Temporal Fusion Transformer model and predict numerical airport arrival delays at quarter hour level for U.S. top 30 airports. Inputs to our model include airport demand and capacity forecasts, historic airport operation efficiency information, airport wind and visibility conditions, as well as enroute weather and traffic conditions. The results show that our model achieves satisfactory performance measured by small prediction errors on the test set. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_08293 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Airport Delay Prediction with Temporal Fusion Transformers Liu, Ke Ding, Kaijing Cheng, Xi Xu, Guanhao Hu, Xin Liu, Tong Feng, Siyuan Cai, Binze Chen, Jianan Lin, Hui Song, Jilin Zhu, Chen Machine Learning Since flight delay hurts passengers, airlines, and airports, its prediction becomes crucial for the decision-making of all stakeholders in the aviation industry and thus has been attempted by various previous research. However, previous delay predictions are often categorical and at a highly aggregated level. To improve that, this study proposes to apply the novel Temporal Fusion Transformer model and predict numerical airport arrival delays at quarter hour level for U.S. top 30 airports. Inputs to our model include airport demand and capacity forecasts, historic airport operation efficiency information, airport wind and visibility conditions, as well as enroute weather and traffic conditions. The results show that our model achieves satisfactory performance measured by small prediction errors on the test set. In addition, the interpretability analysis of the model outputs identifies the important input factors for delay prediction. |
| title | Airport Delay Prediction with Temporal Fusion Transformers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.08293 |