A large-scale data set of aircraft interaction networks

Area of Science:

• Complex systems analysis
• Network science
• Air traffic management

Background:

• Complex network concepts are vital for studying real-world phenomena like spreading dynamics and system resilience.
• Availability of real-world datasets is essential for validating complex network models and algorithms.
• Air traffic provides a rich domain for complex network research due to its dynamic and interconnected nature.

Purpose of the Study:

• To present a comprehensive dataset of daily aircraft interactions within European airspace.
• To facilitate research in complex network analysis using real-world, large-scale graph data.
• To support the development of new tools for understanding air traffic dynamics and network resilience.

Main Methods:

• Processing of planned and executed aircraft trajectories.
• Pairwise comparison of aircraft positions to identify interactions.
• Integration of multi-scale metadata on airspace status.

Main Results:

• A dataset comprising 1,708 network representations of daily aircraft interactions.
• Data spans 854 days between 2015 and 2021 across a vast European airspace.
• The dataset includes comprehensive metadata on airspace conditions.

Conclusions:

• The presented dataset is a valuable resource for complex network research.
• It enables the testing and validation of algorithms for air traffic dynamics and network resilience.
• This data can advance the understanding of large-scale real-world network behavior.