Month x reporting-carrier x origin-airport operational reliability from the official BTS TranStats Airline On-Time Performance files (keyless monthly ZIPs, https://www.transtats.bts.gov/DL_SelectFields.asp?Table_ID=236; the DOT Part-234 reporting carriers (12 in recent months; Hawaiian/Spirit also report in the 2025 window months), top 120 US origin airports by scheduled departures). Each row: scheduled flights, cancelled/diverted/delayed/on-time counts (BTS definition: arrival delay >= 15 min = delayed; identity gate flights == ontime+delayed+cancelled+diverted on every row), punctuality rates, total arrival-delay minutes decomposed into the five BTS delay causes (carrier/weather/NAS/security/late-aircraft; global identity gate: causes sum to total delay minutes within 0.1%, measured residual -0.024% on the live 13-month window), cause shares, average delay per delayed flight, MoM on-time momentum (YoY for the latest month), a documented 0-100 reliability_score ranked within each month, top delay cause, and cancel-wave / improving / record-low flags. Units: counts, minutes, percents, pp changes; shares sum to 1. Caveats: BTS rejects <0.01% of carrier-submitted records with arithmetic errors; the latest 1-2 months can be revised as carriers re-file; delay causes are carrier-reported per BTS definitions. Coverage: United States, 2025-08 -> 2026-07 (trailing 12 months; BTS publishes with ~2 months lag). Provenance: keyless BTS TranStats downloads, U.S. public domain - cite the U.S. Department of Transportation, Bureau of Transportation Statistics. Primary key: (month, carrier_iata, origin). Cadence: refreshed monthly. Nullability: cause shares / avg delay null when a row has no delay minutes; yoy_pp_ontime only on the latest month. Sample use: filter origin='ORD' and month='2026-07-01' to compare carriers' reliability_rank out of Chicago O'Hare.

Source: U.S. Bureau of Transportation Statistics, Airline On-Time Performance
Rows: 12,252
Columns: 38
Source cadence: Monthly
Last refreshed: Sep 28, 2026
Theme: transport

Column	Type	Description
month	string	Year and month of the observation (first day of month); the primary key with carrier_iata and origin. (unit: ISO date)
as_of	string	Data vintage: first day of the latest published month; identical upstream data gives an identical as_of, so re-ingests are no-ops. (unit: ISO date)
country_code	string	ISO alpha-3 country code (USA for every row; foreign origins are out of scope). (unit: ISO 3166-1 alpha-3)
carrier_iata	string	Reporting (operating) airline's IATA code: one of the 12 DOT Part-234 on-time reporters. (unit: IATA)
carrier_name	string	Canonical airline name from the DOT Air Travel Consumer Report carrier list. (unit: text)
origin	string	Origin airport IATA code (top 120 US airports by trailing-13-month departures). (unit: IATA)
origin_city	string	Origin city name as published by BTS. (unit: text)
origin_state	string	Origin US state abbreviation as published by BTS. (unit: text)
flights	integer	Scheduled departures in the month for the (carrier, origin). Identity: flights == ontime + delayed + cancelled + diverted. (unit: count)
cancelled	integer	Flights cancelled by the carrier (BTS Cancelled=1). (unit: count)
diverted	integer	Flights diverted en route (BTS Diverted=1). (unit: count)
delayed	integer	Flights arriving >= 15 min late (BTS ArrDel15=1), excluding cancelled/diverted. (unit: count)
ontime	integer	Flights arriving < 15 min late (BTS ArrDel15=0), excluding cancelled/diverted. (unit: count)
ontime_rate_pct	float	100 * ontime / flights (BTS on-time definition). (unit: percent)
delayed_rate_pct	float	100 * delayed / flights. (unit: percent)
cancelled_rate_pct	float	100 * cancelled / flights. (unit: percent)
diverted_rate_pct	float	100 * diverted / flights. (unit: percent)
total_delay_min	float	Sum of arrival-delay minutes over delayed flights (early arrivals count 0). (unit: minutes)
carrier_delay_min	float	Delay minutes attributed to circumstances within the carrier's control (BTS cause). (unit: minutes)
weather_delay_min	float	Delay minutes attributed to extreme/severe weather (BTS cause). (unit: minutes)
nas_delay_min	float	Delay minutes attributed to National Aviation System causes - ATC, airport operations (BTS cause). (unit: minutes)
security_delay_min	float	Delay minutes attributed to security causes (BTS cause). (unit: minutes)
late_aircraft_delay_min	float	Delay minutes attributed to a previous flight arriving late (BTS cause). (unit: minutes)
carrier_delay_share	float	Share of total_delay_min from the carrier cause (null when no delay minutes). (unit: share)
weather_delay_share	float	Share of total_delay_min from the weather cause (null when no delay minutes). (unit: share)
nas_delay_share	float	Share of total_delay_min from the NAS cause (null when no delay minutes). (unit: share)
security_delay_share	float	Share of total_delay_min from the security cause (null when no delay minutes). (unit: share)
late_aircraft_delay_share	float	Share of total_delay_min from the late-aircraft cause (null when no delay minutes). (unit: share)
avg_delay_min_per_delayed	float	Average arrival-delay minutes per delayed flight (null when none delayed). (unit: minutes)
top_delay_cause	string	The largest of the five delay-cause shares: carrier, weather, nas, security, late_aircraft, or none when there were no delay minutes. (unit: category)
mom_pp_ontime	float	Month-over-month change of ontime_rate_pct in percentage points (null for the panel's first month). (unit: percentage points)
yoy_pp_ontime	string	Year-over-year change of ontime_rate_pct vs 2025-07 in percentage points; populated only for the latest panel month (2026-07). (unit: percentage points)
reliability_score	float	Documented 0-100 composite: 100(0.55min-max(ontime_rate_pct) + 0.30min-max(100-cancelled_rate_pct) + 0.15min-max(100-diverted_rate_pct)), min-maxed within each month across the panel. (unit: 0-100)
reliability_rank	integer	Rank of reliability_score within the month (1 = best; ties broken by flights desc, carrier, origin). (unit: rank)
cancel_wave_flag	integer	1 when cancelled_rate_pct >= 5%. (unit: 0/1)
improving_flag	integer	1 when mom_pp_ontime >= 2.0. (unit: 0/1)
record_low_ontime_flag	integer	1 when the month is the (carrier, origin) combo's minimum ontime_rate_pct over the 12-month window. (unit: 0/1)
row_hash	string	16-char SHA-256 of the canonical row tuple; identical input gives identical hashes. (unit: hash)

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

month	as_of	country_code	carrier_iata	carrier_name	origin	origin_city	origin_state	flights	cancelled	diverted	delayed	ontime	ontime_rate_pct	delayed_rate_pct	cancelled_rate_pct	diverted_rate_pct	total_delay_min	carrier_delay_min	weather_delay_min	nas_delay_min	security_delay_min	late_aircraft_delay_min	carrier_delay_share	weather_delay_share	nas_delay_share	security_delay_share	late_aircraft_delay_share	avg_delay_min_per_delayed	top_delay_cause	mom_pp_ontime	yoy_pp_ontime	reliability_score	reliability_rank	record_low_ontime_flag	row_hash
2025-08-01	2026-07-01	USA	AA	American Airlines Inc.	ABQ	Albuquerque, NM	NM	254	8	2	62	182	71.654	24.409	3.15	0.787	8,680	4,096	45	2,145	0	2,394	0.472	0.005	0.247	0	0.276	140	carrier	—	—	82.402	789	0	f2e62b7bcdbe8e5c
2025-08-01	2026-07-01	USA	AA	American Airlines Inc.	ALB	Albany, NY	NY	124	0	1	20	103	83.065	16.129	0	0.806	4,172	1,445	94	131	0	2,502	0.346	0.023	0.031	0	0.6	208.6	late_aircraft	—	—	89.597	280	0	d0ac9b1eca370636
2025-08-01	2026-07-01	USA	AA	American Airlines Inc.	ANC	Anchorage, AK	AK	93	0	0	30	63	67.742	32.258	0	0	3,393	770	0	86	67	2,470	0.227	0	0.025	0.02	0.728	113.1	late_aircraft	—	—	82.258	798	0	722806a06d410e67
2025-08-01	2026-07-01	USA	AA	American Airlines Inc.	ATL	Atlanta, GA	GA	608	13	2	169	424	69.737	27.796	2.138	0.329	18,436	4,691	1,250	1,913	37	10,545	0.254	0.068	0.104	0.002	0.572	109.089	late_aircraft	—	—	82.27	797	0	ca62e56aac45c2bb
2025-08-01	2026-07-01	USA	AA	American Airlines Inc.	AUS	Austin, TX	TX	903	33	2	229	639	70.764	25.36	3.654	0.221	24,180	5,139	1,255	3,378	7	14,401	0.213	0.052	0.14	0	0.596	105.59	late_aircraft	—	—	82.525	785	0	4edef28dd2e636af
2025-08-01	2026-07-01	USA	AA	American Airlines Inc.	AVL	Asheville, NC	NC	92	2	1	26	63	68.478	28.261	2.174	1.087	2,141	439	169	237	0	1,296	0.205	0.079	0.111	0	0.605	82.346	late_aircraft	—	—	80.543	884	0	34763fe9dbb61f65
2025-08-01	2026-07-01	USA	AA	American Airlines Inc.	BDL	Hartford, CT	CT	409	8	0	90	311	76.039	22.005	1.956	0	6,795	1,461	80	1,580	0	3,674	0.215	0.012	0.233	0	0.541	75.5	late_aircraft	—	—	86.235	563	0	3e8c273f35d52f41
2025-08-01	2026-07-01	USA	AA	American Airlines Inc.	BHM	Birmingham, AL	AL	6	0	0	1	5	83.333	16.667	0	0	16	9	0	7	0	0	0.563	0	0.438	0	0	16	carrier	—	—	90.833	186	0	948489e76d4d9c9d
2025-08-01	2026-07-01	USA	AA	American Airlines Inc.	BNA	Nashville, TN	TN	812	18	5	221	568	69.951	27.217	2.217	0.616	19,980	5,040	157	3,402	0	11,381	0.252	0.008	0.17	0	0.57	90.407	late_aircraft	—	—	81.977	818	1	d40b8436c59d5da8
2025-08-01	2026-07-01	USA	AA	American Airlines Inc.	BOI	Boise, ID	ID	93	2	4	25	62	66.667	26.882	2.151	4.301	3,531	1,421	0	395	0	1,715	0.402	0	0.112	0	0.486	141.24	late_aircraft	—	—	75.215	1,016	1	8bae273e90976b55

Sep 28, 2026Current
20260928T083226Z-975e9f1d2936 · sha256 975e9f1d2936…
12,252 rows · first snapshot

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

cURL

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

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/us_ontime_airline_signals/us_airline_ontime_monthly").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/us_ontime_airline_signals/us_airline_ontime_monthly

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

Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.

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Pinned to snapshot 20260928T083226Z-975e9f1d2936 and its content hash, so readers get exactly the data you used.

APA

U.S. Bureau of Transportation Statistics, Airline On-Time Performance. (2026). US airline on-time & delay-cause intelligence, monthly [Data set, snapshot 20260928T083226Z-975e9f1d2936, sha256 975e9f1d2936]. Datazimuts. Retrieved 2026-09-28, from https://datazimuts.com/en/datasets/us_ontime_airline_signals/us_airline_ontime_monthly?snapshot=20260928T083226Z-975e9f1d2936

BibTeX

@misc{dz_us_ontime_airline_signals_us_airline_ont_975e9f1d,
  title        = {{US airline on-time \& delay-cause intelligence, monthly}},
  author       = {{U.S. Bureau of Transportation Statistics, Airline On-Time Performance}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/us_ontime_airline_signals/us_airline_ontime_monthly?snapshot=20260928T083226Z-975e9f1d2936}},
  note         = {Snapshot 20260928T083226Z-975e9f1d2936, sha256 975e9f1d2936bb2807ca1a66dffb1ccf15c27264083827cb880973343eff64bf; accessed 2026-09-28}
}

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US airline on-time & delay-cause intelligence, monthly

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