DOI: 10.5281/zenodo.17775432
title: ELFLYSVE: Probability distributions for air traffic at Swedish airports

description: About

This dataset was produced as part of the ELFLYSVE research project, which investigates the potential of electric aviation in Sweden. One of the project’s work packages focuses on techno-economic modeling and optimization of airport energy systems, specifically their design and sizing to meet the requirements for electric aircraft (EA) charging.

Since there is currently no commercial EA traffic in operation, the project developed a methodology to generate synthetic flight timetables. These timetables are based on probability distributions derived from historical air traffic data in Sweden. The historical flight data was provided by Luftfartsverket (LFV), Sweden’s air traffic management service provider, and includes all scheduled flights (anonymized aircraft identifiers) to or from Swedish airports for the years 2019–2023 (approximately 1.2 million flights).

Methodology

The methodology takes the perspective of an individual airport (i.e. incoming and outgoing aircraft), rather than trying to plan for a network of airports and routes for EA which is a highly complex and uncertain task. From the historical flight data, representative distributions were created for



Number of arrivals

Arrival time

Turnaround time


This open data repository contains five types of datasets, provided both for Sweden as a whole and for individual Swedish airports (ten datasets in total):

(1) Weekday arrivals,(2) Monthly arrivals,(3) Arrival minute,(4) Turnaround time (full)(5) Turnaround time (clustered)

The (full) notation in dataset (4) indicates normalization based on the entire turnaround dataset. The (clustered) version in dataset (5) uses normalization within clusters, where a cluster is defined as an hour interval. This clustering accounts for the dependency between turnaround time and arrival time (e.g., arrivals near midnight typically have longer turnaround times than those during morning peak hours). Dynamic programming was applied to the full turnaround dataset to identify clusters with similar turnaround times.

These datasets serve as input for synthetic timetable generation, followed by EA route assignment and charging load simulations. Further details on the methodology and results will be available in a journal paper (submission in January, 2026).

Data description

(1) Weekday arrivals

This dataset contains the normalised probability distributions of number of arrivals per day of the week:



Sweden: norm_distribution_weekday_arrivals_SE.csv

Individual airports: norm_distribution_weekday_arrivals_per_airport.csv





Column
Description
Data type


weekday
Day of the week (0 = Monday, 6 = Sunday)
Integer


probability (or ICAO-code)
Normalised arrival probability (for individual airports, this column contains ICAO codes)
Float




(2) Monthly arrivals

This dataset contains the normalised probability distributions of number of arrivals per month of the year:



Sweden: norm_distribution_monthly_arrivals_SE.csv

Individual airports: norm_distribution_monthly_arrivals_per_airport.csv





Column
Description
Data type


month
Month of the year (1 = January, 12 = December)
Integer


probability (or ICAO-code)
Normalised arrival probability (for individual airports, this column contains ICAO codes)
Float




(3) Arrival minute

This dataset contains the normalised probability distributions for arrival minute of the day:



Sweden: norm_distribution_arrival_minute_SE.csv

Individual airports: norm_distribution_arrival_minute_per_airport.csv





Column
Description
Data type


minute
Minute of the day (0-1439)
Integer


probability (or ICAO-code)
Normalised arrival probability (for individual airports, this column contains ICAO codes)
Float




(4) Turnaround time (full)

This dataset contains the normalised probability distributions of turnaround time:



Sweden: norm_distribution_turnaround_minutes_SE_full.csv

Individual airports: norm_distribution_turnaround_minutes_per_airport_full.csv


See explanation in Methodology section for the "(full)" notation.




Column
Description
Data type


minutes
Turnaround time expressed in number of minutes (0-1439)
Integer


probability (or ICAO-code)
Normalised probability of turnaround time (for the individual airport dataset, this column is expressed as the ICAO-code of each airport)
Float




 (5) Turnaround time (clustered)

This dataset contains the normalised probability distributions of turnaround time within clusters:



Sweden: norm_distribution_turnaround_minutes_SE_clustered.csv

Individual airports: norm_distribution_turnaround_minutes_per_airport_clustered.csv


See explanation in Methodology section for the "(clustered)" notation.




Column
Description
Data type


airport (only in "...per_airport_clustered.csv")
ICAO-code of the airport
String


hour_interval
Hour interval of the cluster (e.g. 5-18)
String


turnaround_minutes
Turnaround time expressed in number of minutes
Integer


probability 
Normalised probability of turnaround time within the specified cluster
Float




 

Additional notes

This research was funded by the Swedish Transport Administration (TRV 2023/34443).