Global aviation panel (passengers, departures, freight)

Commercial use OKFreshLicense: CC-BY-4.0

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Value-added panel of the World Bank's World Development Indicators (keyless API v2, CC BY 4.0; ICAO Civil Aviation Statistics): air passengers carried, registered carrier departures and air freight for 186 economies, 1970-2023, with a passengers-per-departure utilization proxy, trailing 1-year and 10-year changes (the COVID collapse/recovery lens) and within-year traffic ranks. Carrier-registration methodology: traffic is attributed to the airline's registration country, not the airport. Joins on economy_code with the catalog's other World Bank panels.

Source: World Bank aviation intelligence
Rows: 7,949
Columns: 19
Source cadence: Yearly
Last refreshed: Oct 8, 2026
Theme: transport

Column	Type	Description
economy_code	string	ISO 3166-1 alpha-3 economy code (World Bank API field countryiso3code).
economy_name	string	Economy name as published by the World Bank API.
region	string	World Bank region (API field region.value).
income_group	string	World Bank income group (API field incomeLevel.value).
year	integer	Reference year (1970-2023).
passengers	float	Air transport passengers carried (WDI indicator IS.AIR.PSGR, ICAO Civil Aviation Statistics). Carrier-registration methodology: passengers carried by airlines registered in the country, regardless of origin or destination. (unit: passengers)
departures	float	Air transport registered carrier departures worldwide (WDI indicator IS.AIR.DPRT, ICAO). Domestic take-offs and take-offs abroad of air carriers registered in the country. (unit: departures)
freight_mtkm	float	Air transport freight (WDI indicator IS.AIR.GOOD.MT.K1, ICAO): the volume of freight, express and diplomatic bags carried on each flight stage, measured in ton-km. (unit: million ton-km)
pax_per_departure	float	Passengers per departure = passengers / departures (connector-derived fleet-utilization proxy, informational). Null when either input is null or departures is zero. (unit: passengers per departure)
pax_change_1y_pct	float	Trailing 1-year percent change of passengers (the COVID collapse/recovery lens); null when the base year is absent or zero. (unit: %)
departures_change_1y_pct	float	Trailing 1-year percent change of departures; null when the base year is absent or zero. (unit: %)
freight_change_1y_pct	float	Trailing 1-year percent change of air freight; null when the base year is absent or zero. (unit: %)
pax_change_10y_pct	float	Trailing 10-year percent change of passengers (the structural growth lens); null when the base year is absent or zero. (unit: %)
departures_change_10y_pct	float	Trailing 10-year percent change of departures; null when the base year is absent or zero. (unit: %)
freight_change_10y_pct	float	Trailing 10-year percent change of air freight; null when the base year is absent or zero. (unit: %)
pax_rank	integer	Rank of passengers within the year (1 = most passengers); null where passengers is null. (unit: rank)
departures_rank	integer	Rank of departures within the year (1 = most departures); null where departures is null. (unit: rank)
freight_rank	integer	Rank of freight_mtkm within the year (1 = most freight); null where freight_mtkm is null. (unit: rank)
row_hash	string	Deterministic 12-hex row identity hash (economy_code\|year).

First 10 sample rows — a preview, not the complete dataset.

economy_code	economy_name	region	income_group	year	passengers	departures	freight_mtkm	pax_per_departure	pax_change_1y_pct	departures_change_1y_pct	freight_change_1y_pct	pax_change_10y_pct	departures_change_10y_pct	freight_change_10y_pct	pax_rank	departures_rank	freight_rank	row_hash
ABW	Aruba	Latin America & Caribbean	High income	2,017	223,502	2,132	—	104.832	—	—	—	—	—	—	134	141	—	5bccfd097be4
ABW	Aruba	Latin America & Caribbean	High income	2,018	274,280	2,276	—	120.51	22.719	6.754	—	—	—	—	133	143	—	7eaf77908500
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,970	84,700	4,000	7.6	21.175	—	—	—	—	—	—	94	94	53	e46415eeb918
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,971	97,400	4,700	2.1	20.723	14.994	17.5	-72.368	—	—	—	94	86	90	5f80a37e313f
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,972	104,900	4,800	0.8	21.854	7.7	2.128	-61.905	—	—	—	91	84	101	84935b318a87
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,973	96,600	3,700	1.1	26.108	-7.912	-22.917	37.5	—	—	—	92	98	100	c76222d74502
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,974	96,100	4,300	13.3	22.349	-0.518	16.216	1,109.091	—	—	—	97	91	52	fa8cd9f8ead1
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,975	99,000	4,700	10.5	21.064	3.018	9.302	-21.053	—	—	—	101	91	67	776a6a97716f
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,976	101,700	4,500	12.9	22.6	2.727	-4.255	22.857	—	—	—	102	96	69	d9e71c2bbeed
AFG	Afghanistan	Middle East, North Africa, Afghanistan & Pakistan	Low income	1,977	111,200	4,900	13.9	22.694	9.341	8.889	7.752	—	—	—	102	98	62	13d0a4d19d07

Profiled Oct 9, 2026 from snapshot 20261008T122622Z-7c3e2c59c572

Measured

Completeness: 94%
Rows: 7,949
Columns: 19
Columns with gaps: 13

Rows per yearyear · 1970–2023 · 54 years

19702023

Column	Missing	Distinct	Range	Distribution
economy_codevarchar	0%	232	—	ARG54 AUS54 AUT54
economy_namevarchar	0%	207	—	Algeria54 Argentina54 Australia54
regionvarchar	0%	6	—	Europe & Central Asia2,006 Sub-Saharan Africa 1,930 Latin America & Caribbean 1,298
income_groupvarchar	0%	4	—	High income2,879 Upper middle income2,201 Lower middle income1,939
yearbigint	0%	50	1,970 → 2,023median 1,998
passengersdouble	0.25%	5,863	0 → 941,557,000median 904,242	160 outside 1st–99th percentile
departuresdouble	0.29%	4,524	0 → 10,099,031median 16,200	160 outside 1st–99th percentile
freight_mtkmdouble	5.5%	4,812	0 → 46,005median 21.4	76 outside 1st–99th percentile
pax_per_departuredouble	0.68%	6,293	0 → 591.18median 57.07	158 outside 1st–99th percentile
pax_change_1y_pctdouble	3.8%	7,668	-100 → 112,802median 5.14	154 outside 1st–99th percentile
departures_change_1y_pctdouble	3.6%	5,837	-100 → 18,455median 2.45	154 outside 1st–99th percentile
freight_change_1y_pctdouble	10.7%	5,274	-100 → 1,264,705median 3.79	142 outside 1st–99th percentile
pax_change_10y_pctdouble	25.9%	4,955	-100 → 325,781median 52.61	118 outside 1st–99th percentile
departures_change_10y_pctdouble	25.9%	5,011	-100 → 4,332median 23.68	118 outside 1st–99th percentile
freight_change_10y_pctdouble	31.6%	5,438	-100 → 1,297,290median 40.06	55 outside 1st–99th percentile
pax_rankbigint	0.25%	170	1 → 164median 74	133 outside 1st–99th percentile
departures_rankbigint	0.29%	170	1 → 165median 74	129 outside 1st–99th percentile
freight_rankbigint	5.5%	158	1 → 153median 70	102 outside 1st–99th percentile
row_hashvarchar	0%	7,640	—	000df08048851 00130a437af41 00190f572f921

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/wb_aviation_intel/aviation_panel" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/wb_aviation_intel/aviation_panel").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/wb_aviation_intel/aviation_panel

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261008T122622Z-7c3e2c59c572 and its content hash, so readers get exactly the data you used.

APA

World Bank aviation intelligence. (2026). Global aviation panel (passengers, departures, freight) [Data set, snapshot 20261008T122622Z-7c3e2c59c572, sha256 7c3e2c59c572]. Datazimuts. Retrieved 2026-10-09, from https://datazimuts.com/en/datasets/wb_aviation_intel/aviation_panel?snapshot=20261008T122622Z-7c3e2c59c572

BibTeX

@misc{dz_wb_aviation_intel_aviation_panel_7c3e2c59,
  title        = {{Global aviation panel (passengers, departures, freight)}},
  author       = {{World Bank aviation intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/wb_aviation_intel/aviation_panel?snapshot=20261008T122622Z-7c3e2c59c572}},
  note         = {Snapshot 20261008T122622Z-7c3e2c59c572, sha256 7c3e2c59c572ded06b6d7d42a634515f5d7dca63ac52f66bf51a3ab1a94ae223; accessed 2026-10-09}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=wb_aviation_intel%2Faviation_panel&lang=en&theme=auto&snapshot=20261008T122622Z-7c3e2c59c572&x=year&y=year&agg=avg" title="Global aviation panel (passengers, departures, freight)" width="100%" height="380" style="border:0" loading="lazy"></iframe>