US airline on-time & delay-cause intelligence, monthly
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.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 12 252
- Colonnes
- 38
- Cadence de la source
- Mensuelle
- Dernière actualisation
- 28 sept. 2026
- Thème
- transport
| Colonne | 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*(0.55*min-max(ontime_rate_pct) + 0.30*min-max(100-cancelled_rate_pct) + 0.15*min-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) |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| 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 | cancel_wave_flag | improving_flag | 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 | 0 | 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 | 0 | 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 | 0 | 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 | 0 | 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 | 0 | 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 | 0 | 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 | 0 | 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 | 0 | 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 | 0 | 0 | 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 | 0 | 0 | 1 | 8bae273e90976b55 |
- Actuelle
20260928T083226Z-975e9f1d2936 · sha256 975e9f1d2936…
12 252 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/us_ontime_airline_signals/us_airline_ontime_monthly" | jq '{title, rows, columns_count, license}'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)Point d’accès API : https://datazimuts.com/v1/datasets/us_ontime_airline_signals/us_airline_ontime_monthly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20260928T083226Z-975e9f1d2936 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
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/fr/datasets/us_ontime_airline_signals/us_airline_ontime_monthly?snapshot=20260928T083226Z-975e9f1d2936
@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/fr/datasets/us_ontime_airline_signals/us_airline_ontime_monthly?snapshot=20260928T083226Z-975e9f1d2936}},
note = {Snapshot 20260928T083226Z-975e9f1d2936, sha256 975e9f1d2936bb2807ca1a66dffb1ccf15c27264083827cb880973343eff64bf; accessed 2026-09-28}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=us_ontime_airline_signals%2Fus_airline_ontime_monthly&lang=fr&theme=auto&snapshot=20260928T083226Z-975e9f1d2936&x=ontime&y=flights&agg=avg" title="US airline on-time & delay-cause intelligence, monthly" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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