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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Synthetic time series of delays in European and US airports

Authors: Zanin, Massimiliano; Esteve, Pau;

Synthetic time series of delays in European and US airports

Abstract

Data are organised in four files: EUArr.npy, EUDep.npy, USArr.npy, and USDep.npy. The file name encodes the region and the type of delays (arrival and departure). Each file is a standard NumPy array, that can be opened with the corresponding Python library. It contains a single four-dimensional tensor of size 30 x 100 x d x 24, the four dimensions respectively represent the 30 airports of the region, the 100 independent realisations of the generation process, the d days available for each region (610 for EU and 1,825 for US), and the 24 hours of the day.

Synthetic time series of hourly delays, in 30 airports in Europe and US.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average