Amelia: A Large Dataset and Benchmark for Airport Surface Movement Forecasting

TL;DR

This paper introduces Amelia, a comprehensive large-scale dataset and benchmark for airport surface movement forecasting, enabling advancements in predictive models to improve air traffic safety and efficiency.

Contribution

It provides the first large-scale, publicly available dataset and benchmark for airport surface movement prediction, along with processing tools and baseline models.

Findings

Amelia-42 dataset contains over 9 TB of data from 42 airports.

Amelia42-Mini offers a 15-day sample per airport for easy access.

Baseline transformer model demonstrates the feasibility of trajectory forecasting.

Abstract

Demand for air travel is rising, straining existing aviation infrastructure. In the US, more than 90% of airport control towers are understaffed, falling short of FAA and union standards. This, in part, has contributed to an uptick in near-misses and safety-critical events, highlighting the need for advancements in air traffic management technologies to ensure safe and efficient operations. Data-driven predictive models for terminal airspace show potential to address these challenges; however, the lack of large-scale surface movement datasets in the public domain has hindered the development of scalable and generalizable approaches. To address this, we introduce Amelia-42, a first-of-its-kind large collection of raw airport surface movement reports streamed through the FAA's System Wide Information Management (SWIM) Program, comprising over two years of trajectory data (~9.19 TB) across 42 US airports. We open-source tools to process this data into clean tabular position reports. We release Amelia42-Mini, a 15-day sample per airport, fully processed data on HuggingFace for ease of use. We also present a trajectory forecasting benchmark consisting of Amelia10-Bench, an accessible experiment family using 292 days from 10 airports, as well as Amelia-TF, a transformer-based baseline for multi-agent trajectory forecasting. All resources are available at our website: https://ameliacmu.github.io and https://huggingface.co/AmeliaCMU.