Data Snapshot #49 - Did you know... which were the busiest worldwide city pairs in 2024?
Read the article Published February 14, 2025
One-off publication Latest release February 14, 2025
Data and Resources
-
top5 city pairs per region2024 (API table).csvCSV
Consolidated from the source files in this dataset. Formats are normalised...
-
top100 city pairs worldwide2024 (API table).csvCSV
Consolidated from the source files in this dataset. Formats are normalised...
-
top100 city pairs worldwide2019 (API table).csvCSV
Consolidated from the source files in this dataset. Formats are normalised...
-
eurocontrol-data-snapshot-49-busiest-city-pairs.pdfPDF
-
eurocontrol-data-snapshot-49.xlsxXLSX
Released 2025-02-14.
-
ds49.pngPNG
-
Read the articleHTML
API
top5 city pairs per region2024
API name: data-snapshot-49--top5-city-pairs-per-region2024
First 5 rows · Download all rows: CSV · TSV · JSON · XML
Code examples
curl
curl 'https://polaris.goens.dev/api/3/action/datastore_search?resource_id=data-snapshot-49--top5-city-pairs-per-region2024&limit=5'
Python
import pandas as pd
df = pd.read_csv('https://polaris.goens.dev/datastore/dump/data-snapshot-49--top5-city-pairs-per-region2024?format=csv')
R
df <- read.csv('https://polaris.goens.dev/datastore/dump/data-snapshot-49--top5-city-pairs-per-region2024?format=csv')
top100 city pairs worldwide2024
API name: data-snapshot-49--top100-city-pairs-worldwide2024
First 5 rows · Download all rows: CSV · TSV · JSON · XML
Code examples
curl
curl 'https://polaris.goens.dev/api/3/action/datastore_search?resource_id=data-snapshot-49--top100-city-pairs-worldwide2024&limit=5'
Python
import pandas as pd
df = pd.read_csv('https://polaris.goens.dev/datastore/dump/data-snapshot-49--top100-city-pairs-worldwide2024?format=csv')
R
df <- read.csv('https://polaris.goens.dev/datastore/dump/data-snapshot-49--top100-city-pairs-worldwide2024?format=csv')
top100 city pairs worldwide2019
API name: data-snapshot-49--top100-city-pairs-worldwide2019
First 5 rows · Download all rows: CSV · TSV · JSON · XML
Code examples
curl
curl 'https://polaris.goens.dev/api/3/action/datastore_search?resource_id=data-snapshot-49--top100-city-pairs-worldwide2019&limit=5'
Python
import pandas as pd
df = pd.read_csv('https://polaris.goens.dev/datastore/dump/data-snapshot-49--top100-city-pairs-worldwide2019?format=csv')
R
df <- read.csv('https://polaris.goens.dev/datastore/dump/data-snapshot-49--top100-city-pairs-worldwide2019?format=csv')
API documentation and rate limits
Cite this data
Source: EUROCONTROL Aviation Intelligence Unit. Data Snapshot #49 - Did you know... which were the busiest worldwide city pairs in 2024?. https://polaris.goens.dev/dataset/data-snapshot-49
May be copied in whole or in part, provided that EUROCONTROL is mentioned as the source and it is not used for commercial purposes. Full disclaimer
Data sources
- Published by
- Flightradar24 Historical Global Utilisation
- Contact
- aviation.intelligence@eurocontrol.int
The table view shows where each column comes from, under Data Dictionary.
Notes from the source
- top5 city pairs per region2024 top100 city pairs worldwide2024 top100 city pairs worldwide2019
- *https://www.eurocontrol.int/sites/default/files/2024-10/eurocontrol-seven-year-forecast-2024-2030-region-definition.pdf
Additional Info
| Field | Value |
|---|---|
| Update frequency | One-off |
| Time resolution | |
| Data from | |
| Data to | |
| Data description | https://www.eurocontrol.int/publication/eurocontrol-data-snapshot-49-busiest-city-pairs-2024 |
| Methodology | |
| Source page | https://ansperformance.eu/publications/ectl/datasnap/ |