  The growing demand for air travel requires technological advancements in air
traffic management as well as mechanisms for monitoring and ensuring safe and
efficient operations. In terminal airspaces, predictive models of future
movements and traffic flows can help with proactive planning and efficient
coordination; however, varying airport topologies, and interactions with other
agents, among other factors, make accurate predictions challenging. Data-driven
predictive models have shown promise for handling numerous variables to enable
various downstream tasks, including collision risk assessment, taxi-out time
prediction, departure metering, and emission estimations. While data-driven
methods have shown improvements in these tasks, prior works lack large-scale
curated surface movement datasets within the public domain and the development
of generalizable trajectory forecasting models. In response to this, we propose
two contributions: (1) Amelia-48, a large surface movement dataset collected
using the System Wide Information Management (SWIM) Surface Movement Event
Service (SMES). With data collection beginning in Dec 2022, the dataset
provides more than a year's worth of SMES data (~30TB) and covers 48 airports
within the US National Airspace System. In addition to releasing this data in
the public domain, we also provide post-processing scripts and associated
airport maps to enable research in the forecasting domain and beyond. (2)
Amelia-TF model, a transformer-based next-token-prediction large multi-agent
multi-airport trajectory forecasting model trained on 292 days or 9.4 billion
tokens of position data encompassing 10 different airports with varying
topology. The open-sourced model is validated on unseen airports with
experiments showcasing the different prediction horizon lengths, ego-agent
selection strategies, and training recipes to demonstrate the generalization
capabilities.
