Summary of Aircraft Landing Time Prediction with Deep Learning on Trajectory Images, by Liping Huang et al.
Aircraft Landing Time Prediction with Deep Learning on Trajectory Images
by Liping Huang, Sheng Zhang, Yicheng Zhang, Yi Zhang, Yifang Yin
First submitted to arxiv on: 2 Jan 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 proposed deep learning method predicts aircraft landing time by analyzing trajectory images, which contain information about aircraft position, speed, heading, relative distances, and arrival traffic flows. The approach uses convolutional neural networks to model arrival aircraft sequencing on the runway, incorporating real-time runway usage, external factors like weather conditions, and automatic holding-related featurizing. Experimental results demonstrate a significant reduction in mean absolute error (MAE) from 82.23 seconds to 43.96 seconds and an average accuracy of 96.1% with 79.4% of predictions errors being less than 60 seconds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Aircraft landing time prediction is important for air traffic management. Researchers used pictures of aircraft movements to predict when planes will land safely. They took information like where the plane is, how fast it’s going, and what direction it’s heading and put it into a special kind of computer program. This helped them make better predictions about when planes will arrive. The results showed that this method can be very accurate, with most predictions being correct. |
Keywords
* Artificial intelligence * Deep learning * Mae
