Summary of Amelia: a Large Model and Dataset For Airport Surface Movement Forecasting, by Ingrid Navarro et al.
Amelia: A Large Model and Dataset for Airport Surface Movement Forecasting
by Ingrid Navarro, Pablo Ortega-Kral, Jay Patrikar, Haichuan Wang, Zelin Ye, Jong Hoon Park, Jean Oh, Sebastian Scherer
First submitted to arxiv on: 30 Jul 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 paper addresses the growing demand for air travel by developing predictive models for future movements and traffic flows in terminal airspaces. The challenge lies in accurately predicting these movements considering various factors such as airport topologies, agent interactions, and numerous variables. Data-driven predictive models have shown promise in handling these complexities and enabling tasks like collision risk assessment, taxi-out time prediction, departure metering, and emission estimations. However, prior works lack large-scale curated surface movement datasets within the public domain and generalizable trajectory forecasting models. To address this, the authors propose two contributions: Amelia-48, a large surface movement dataset collected using the System Wide Information Management (SWIM) Surface Movement Event Service (SMES), covering 48 airports in the US National Airspace System; and Amelia-TF model, a transformer-based next-token-prediction large multi-agent multi-airport trajectory forecasting model trained on position data from 10 different airports with varying topology. The paper demonstrates the generalization capabilities of the model by validating it on unseen airports. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper aims to develop better air traffic management systems and models that can predict future movements in airport terminals. This is important because more people are flying, and we need to make sure air travel is safe and efficient. To do this, the authors create a big dataset of information about what’s happening on airport surfaces, like where planes are going and when they’ll take off. They also build a special model that can predict these movements based on the data. The paper shows that this model works well, even when it’s used at airports with different layouts. |
Keywords
» Artificial intelligence » Generalization » Token » Transformer
