A large-scale data set of aircraft interaction networks

Complex network concepts have become the foundation of many real-world studies, encompassing topics like the dynamics of spreading processes or the evaluation of the resilience of complex systems. One of their major enablers is the availability of real data sets, on which to test and validate models and algorithms. We here present a data set containing 1,708 network representations of daily interactions between aircraft over a vast area of the European airspace, for 854 days spanning between 2015 and 2021. It has been obtained by processing trajectories, both planned and executed, and comparing aircraft positions in a pairwise fashion. This is further complemented by metadata about the status of the airspace, in a multi-scale structure. This database may act as the data source of any study willing to use these interactions to develop new tools for understanding air traffic dynamics; and more generally, to test complex networks algorithms and models on large-scale real graphs.

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Publication Details

Journal
PLoS ONE
Published
2025-11-24
DOI
https://doi.org/10.1371/journal.pone.0336909
Primary Topic
Air Traffic Management and Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

A large-scale data set of aircraft interaction networks

Massimiliano Zanin, Raúl López-Martín
PLoS ONE
Air Traffic Management and Optimization
article

A large-scale data set of aircraft interaction networks

Massimiliano Zanin, Raúl López-Martín
article en

Abstract

Complex network concepts have become the foundation of many real-world studies, encompassing topics like the dynamics of spreading processes or the evaluation of the resilience of complex systems. One of their major enablers is the availability of real data sets, on which to test and validate models and algorithms. We here present a data set containing 1,708 network representations of daily interactions between aircraft over a vast area of the European airspace, for 854 days spanning between 2015 and 2021. It has been obtained by processing trajectories, both planned and executed, and comparing aircraft positions in a pairwise fashion. This is further complemented by metadata about the status of the airspace, in a multi-scale structure. This database may act as the data source of any study willing to use these interactions to develop new tools for understanding air traffic dynamics; and more generally, to test complex networks algorithms and models on large-scale real graphs.

PLoS ONEVol. 20(11)
Institute for Cross-Disciplinary Physics and Complex Systems (ES)
Ministerio de Ciencia, Innovación y Universidades, European Commission, Agencia Estatal de Investigación
Openalex Percentile: Top 52%
Air Traffic Management and Optimization
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