Percentage of flights arriving on-time. A flight is on-time if it arrives within 15 minutes of the schedule arrival time. Data are available for those carriers that had at least 1% of domestic enplanements in the previous year. The last 25 months of data include only carriers that reported in each of the last 25 months to retain comparability. Earlier data includes all reporting carriers. A scheduled operation consists of any nonstop segment of a flight. The Bureau of Transportation Statistics air collects performance data from U.S. air carriers and international carriers operating within the U.S.

The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product.

In 2024, U.S. airlines carried around 852.1 million passengers on domestic flights across the United States. This was an increase from the roughly 819.3 million domestic passengers carried by U.S. airlines in the previous year.

Success.ai’s Aviation Data provides verified access to professionals across the airlines, aviation, and aerospace industries. Leveraging over 700 million LinkedIn profiles, this dataset delivers actionable insights, contact details, and firmographic data for pilots, engineers, airline executives, aerospace manufacturers, and more. Whether your goal is to market aviation technology, recruit aerospace specialists, or analyze industry trends, Success.ai ensures your outreach is powered by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Aviation Data? Comprehensive Professional Profiles

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You can get all global flight information in 1 API call or track flights based on flight number, airline, departure/arrival airport, and more. The data updates frequently, around every 5 minutes. The details of the data include:

Geography: Location information such as latitude, longitude, altitude, and direction. Speed: Vertical and horizontal speed of aircraft. Departure and arrival: IATA codes and ICAO codes of the departure and arrival airport. Aircraft and flight: IATA and ICAO number of flight and registration number, ICAO code, and ICAO24 code of aircraft. Airline: IATA code, and ICAO code of airline. System information: Squawk, status, and last updated in Epoch.

Here's an example response from the API: [ { "geography": { "latitude": 43.5033, "longitude": -79.1297, "altitude": 7833.36, "direction": 70 }, "speed": { "horizontal": 833.4, "isGround": 0, "vertical": 0 }, "departure": { "iataCode": "YHM", "icaoCode": "CYHM" }, "arrival": { "iataCode": "YQM", "icaoCode": "CYQM" }, "aircraft": { "icaoCode": "B763", "regNumber": "CGYAJ", "icao24": "C08412" }, "airline": { "iataCode": "W8", "icaoCode": "CJT" }, "flight": { "iataNumber": "W8620", "icaoNumber": "CJT620", "number": "620" }, "system": { "updated": 1513148168, "squawk": "0000" }, "status": "en-route" } ]

Developer Information:

1) Available Endpoints &depIata= &depIcao= &arrIata= &arrIcao= &aircraftIcao= &regNum= &aircraftIcao24= &airlineIata= &airlineIcao= &flightIata= &flightIcao= &flightNum= &status= &limit= &lat=&lng=&distance=

2) Flights Tracker API Output

Specific flight based on: Flight IATA Number: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&flightIata=W8519

All flights of a specific Airlines: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&airlineIata=W8

Flights from departure location: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&depIata=MAD

Flights from arrival location: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&arrIata=GIG

Flights within a circle area based on lat and lng values and radius as the distance: GET https://aviation-edge.com/v2/public/flights?key=[API_KEY]&lat=51.5074&lng=0.1278&distance=100&arrIata=LHR

Combinations: two airports and a specific airline flying between them: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&depIata=ATL&arrIata=ORD&airlineIata=UA

In 2024, U.S. airlines recorded ****** million passengers on domestic and international flights. The previous year, the number of passengers at U.S. airports officially surpassed the pre-pandemic peak of ***** million passengers recorded in 2019.

Graph and download economic data for Enplanements for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights (ENPLANED11) from Jan 2000 to May 2025 about flight, passenger, air travel, travel, domestic, and USA.

The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached **** million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to **** million in 2020. The flight volume increased again in the following years and was forecasted to reach ** million in 2025.

Data that looks at how market structure affects delays for US domestic flights between the years 2004 - 2017.

Data on airline delays come from the Airline On-Time Performance Data (OTPD) from the US Bureau of Transportation Statistics. The data on tail numbers and seat capacity come from the Federal Aircraft Administration Aircraft Registry. The data on flight-related whether comes from the Local Climatological Data (LCD) provided by the National Center for Environmental Information.

Available only on the web, provides information for airport pair markets rather than city pair markets. This table only lists airport markets where the origin or destination airport is an airport that has other commercial airports in the same city. Midway Airport (MDW) and O'Hare (ORD) are examples of this. All records are aggregated as directionless markets. The combination of Airport_1 and Airport_2 define the airport pair market. All traffic traveling in both directions is added together. https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports

This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. Context on the original work is given in the related article (https://journals.open.tudelft.nl/joas/article/download/7201/5683) and on the associated GitHub page (https://github.com/AeroMAPS/AeroSCOPE/).A simple data exploration interface will be available at www.aeromaps.eu/aeroscope.The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data. 1- DISCLAIMER The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use. As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance. 2- DESCRIPTION Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple. Please refer to the support article for more details (see above). The dataset contains the following columns:

"First column" : index airline_iata : IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found. acft_icao : ICAO code of the aircraft type acft_class : Aircraft class identifier, own classification.

WB: Wide Body NB: Narrow Body RJ: Regional Jet PJ: Private Jet TP: Turbo Propeller PP: Piston Propeller HE: Helicopter OTHER seymour_proxy: Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model. source: Original data source for the record, before compilation and enrichment.

ANAC: Brasilian Civil Aviation Authorities AUS Stats: Australian Civil Aviation Authorities BTS: US Bureau of Transportation Statistics T100 Estimation: Own model, estimation on Wikipedia-parsed route database Eurocontrol: Aggregation and enrichment of R&D database OpenSky World Bank seats: Number of seats available for the data entry, AFTER airport residual scaling n_flights: Number of flights of the data entry, when available iata_departure, iata_arrival : IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal. departure_lon, departure_lat, arrival_lon, arrival_lat : Origin and destination coordinates, could be NaN if the IATA identifier is erroneous departure_country, arrival_country: Origin and destination country ISO2 code. WARNING: disable NA (Namibia) as default NaN at import departure_continent, arrival_continent: Origin and destination continent code. WARNING: disable NA (North America) as default NaN at import seats_no_est_scaling: Number of seats available for the data entry, BEFORE airport residual scaling distance_km: Flight distance (km) ask: Available Seat Kilometres rpk: Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor) fuel_burn_seymour: Fuel burn per flight (kg) when seymour proxy available fuel_burn: Total fuel burn of the data entry (kg) co2: Total CO2 emissions of the data entry (kg) domestic: Domestic/international boolean (Domestic=1, International=0)

3- Citation Please cite the support paper instead of the dataset itself.

Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2023). Compilation of an open-source traffic and CO2 emissions dataset for commercial aviation. Journal of Open Aviation Science. https://doi.org/10.59490/joas.2023.7201

You can connect to the actual flight routes around the world with your API key at any time with very fast response times. It is possible to view all routes at the same time via a single API key. For your convenience, we have also developed many different filters so that you can pull the exact data you are looking for. This way, you may get data of the routes of a specific airline, routes from or to a specific airport (both IATA and ICAO codes work), or may get an individual flight based on its flight number.

A common use of the air routes API is to develop software in the aviation industry. While Aviation Edge’s focus is to collect and maintaining aviation data, you are free to develop countless applications, tools, and platforms by using our data.

The details included in the routes data are: Departure data: IATA code, ICAO code, terminal, and time. Arrival data: IATA code, ICAO code, terminal, and time. Airline: IATA code of airline. Flight: Flight number. Aircraft: Registration number of the aircraft.

Here's an example response from the API: [ { "departureIata": "OTP", "departureIcao": "LROP", "departureTerminal": 2, "departureTime": "09:15:00", "arrivalIata": "TRN", "arrivalIcao": "LIMF", "arrivalTerminal": 1, "arrivalTime": "10:45:00", "airlineIata": "0B", "airlineIcao": "BMS", "flightNumber": "101", "codeshares": null, "regNumber": "YR-BAP" } ]

Developer information: 1) Available Endpoints &departureIata= &departureIcao= &airlineIata= &airlineIcao= &flightNumber=

2) Output Airports, Airlines or Flights routes output: GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&departureIata=OTP GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&departureIcao=LROP GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&airlineIata=0B GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&airlineIcao=BMS GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&flightNumber=101 For information about a specific route (example). GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&departureIata=OTP&airlineIata=0B&flightNumber=101

This dataset contains various metrics about 40 airports of the United State of America captured from realtime data. It includes airport delays and delay reasons as well as information about airport closure and reopening events. In addition, this dataset provides influence factors such as weather conditions, visibilty information, wind speed/direction and temperature. The dataset includes the following airports given by their IATA code: ATL, BNA, BOS, BWI, CLE, CLT, CVG, DCA, DEN, DFW, DTW, EWR, FLL, IAD, IAH, IND, JFK, LAS, LAX, LGA, MCI, MCO, MDW, MEM, MIA, MSP, ORD, PDX, PHL, PHX, PIT, RDU, SAN, SEA, SFO, SJC, SLC, STL, TEB, TPA. The data was collected from 2015-06-03 until 2015-06-10, every 15 minutes and for each of the airport above defined, by using REST API (http://services.faa.gov/airport/status) provided by the Federal Aviation Administration. API Documentation can be found at: http://services.faa.gov/ MD5 Checksum: 9aee3d984bf25f8867db3ac900442126

General Aviation (GA) comprises all civil flights except scheduled passenger airline services. More than 90% of the roughly 220,000 civil aircraft registered in the United States (US) are GA aircraft. In contrast with airline service aircraft which operate with two pilots in a structured higher-altitude operational envelope, GA aircraft are often individually piloted in a more unstructured lower-altitude environment. This low altitude environment is also where a bulk of the next generation of Uncrewed Aerial Vehicles (UAVs) are expected to operate. These UAVs are expected to seamlessly interact with other UAVs and manned air traffic operating in this shared airspace. Nowhere is this manned-manned and potentially unmanned-manned interaction more pronounced than in low-altitude terminal airspace around airports. Low altitudes, multi-agent close-proximity interactions, dynamically changing conditions, and rapid decision making are hallmarks of this type of airspace as compared to en-route airspace where agents are typically well-separated.This dataset contains aircraft trajectories in an untowered terminal airspace collected over 8 months surrounding the Pittsburgh-Butler Regional Airport [ICAO:KBTP], a single runway GA airport, 10 miles North of the city of Pittsburgh, Pennsylvania. The trajectory data is recorded using an on-site setup that includes an ADS-B receiver. The trajectory data provided spans days from 18 Sept 2020 till 23 Apr 2021 and includes a total of 111 days of data discounting downtime, repairs, and bad weather days with no traffic. Data is collected starting at 1:00 AM local time to 11:00 PM local time. The dataset uses an Automatic Dependent Surveillance-Broadcast (ADS-B) receiver placed within the airport premises to capture the trajectory data. The receiver uses both the 1090 MHz and 978 MHz frequencies to listen to these broadcasts. The ADS-B uses satellite navigation to produce accurate location and timestamp for the targets which is recorded on-site using our custom setup. Weather data during the data collection time period is also included for environmental context. The weather data is obtained post-hoc using the METeorological Aerodrome Reports (METAR) strings generated by the Automated Weather Observing System (AWOS) system at KBTP. The raw METAR string is then appended to the raw trajectory data by matching the closest UTC timestamps.We also provide processed data that filters, interpolates and transforms data from a global frame to an airport-centred inertial frame. The inertial frame is centred at one end of the runway with the x-axis along the runway. Trajectories are filtered with aircrafts under 6000 ft MSL and around a 5km radius around the airport origin. We also remove duplicates and interpolate data every second. The proceed files also contain wind-data; a crucial factor in decision-making; separated in components along and perpendicular to the runway direction.More Information and Supplemental ToolsPlease visit http://theairlab.org/trajair/ for more information.

In 2024, Delta Air Lines and United Airlines were the leading airlines in the U.S., with a domestic market share of 21 percent. That year, American Airlines had the second-largest market share of 20 percent. U.S. airlines' domestic market share The passenger air transportation market is a thriving industry, taking individuals to locations around the globe. American Airlines was the third largest airline in the North America based on operating revenue, reaching nearly 40.5 billion U.S. dollars in 2023. Passenger airlines can face much scrutiny for their passenger satisfaction and comfort. A 2025 North American Airline Satisfaction Study by J.D. Power & Associates listed Southwest Airlines as the best long-haul, closely followed by low-cost carrier JetBlue Airways. United Airlines, Delta Air Lines, American Airlines and Southwest Airlines are the top-ranked airlines based on 2024 domestic market share. Delta operates out of Atlanta, and Hartsfield-Jackson Atlanta International Airport, Delta’s hub, sees the most passenger traffic in the United States. Chicago-headquartered United Airlines is a subsidiary of United Continental Holdings. United has flights to 210 domestic destinations and 120 destinations internationally.

COVID-19(+) Interactions Within Air Travel

Modeling potential interactions between healthy individuals and those carrying COVID-19, denoted hereafter as (+), has been identified as a key methodology in the effort to predict, combat, and respond to COVID-19. In order to contribute to this effort within the domain of airline travel, this dataset allows users to see all flights during the time period from 01MAR-14APR where airline passengers may have come in contact with a COVID-19(+) TSA Screening Agent during their presumed incubation period, 7 days, before that agent went in quarantine.

Acknowledgements

• Federal Data Partner: Transportation Security Administration (TSA).
• Industry Data Partner: Airline Data Inc.

Inspiration

The CORD-19 Research Challenge has been a great inspiration for this effort. Its focus on natural language processing has prompted the need for additional efforts in other statistical machine learning methods, such as those used in the UNCOVER COVID-19 Challenge. With COVID-19 research as a global focal point, I hope that this dataset provides researchers with another set of features to help build models towards finding answers.

Methodology

Airline Data Inc. provided airline schedule information for the time period of 01MAR-14APR. This is one of the data products available as a part of their Data Hub. The airline schedule includes information on future and historical airline flights updated in real-time as it is filed by the airlines. This data provides access to origins and destinations, flight times, aircraft types, seats, customized route mapping, and much more. For this work, we focused on getting flight information to include terminals and carriers in order to determine potential contact of passengers and, at the time, unknowingly COVID-19(+) TSA agents. Airline Data Inc. additionally provided the T100 data from March and April of last year. The T100 provides information on particular routes (ORD->JFK) for U.S. domestic and international air service reported by carriers. This dataset includes passenger counts, available seats, load factors, equipment types, cargo, and other operating statistics. These datasets were combined to estimate the number of passengers flying various routes thought the time period in question. Undoubtedly these numbers are much lower than those of the previous year, but we make the assumption that airline travel declined in a relatively equal proportions across the US, making the load factors for last year comparatively accurate. Since the T100 data is only released on a monthly basis, these figures will not be able to be updated until the coming months.

The Transportation Security Administration posted publicly on their website a list of all Screening and Baggage Officers who tested positive for COVID-19. This list included the airport they worked in, their last day of work, and their work location with shift information. This data was taken and used to down-select the data from Airline Data Inc. to only include those flights that met the following criteria: - Origin airport with COVID-19(+) TSA Officer - Flight took off (the flight schedule data will show all potential flights even those that do not take off) - TSA Officer on shift at time of departure - TSA Officer working in terminal from which the flight departed

This dataset contains scheduled and actual departure and arrival times reported by certified US air carriers that account for at least 1% of domestic scheduled passenger revenues. The data was collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS). The dataset contains date, time, origin, destination, airline, distance, and delay status of flights for flights between 2016 and 2018 The report, focusing on data from year 2016-2018, estimated that air transportation delays put a 4 billion dollar dent in the country's gross domestic product that year. Full report can be found here. In order to answer this question, we are going to analyze the provided dataset, containing up to 18 M different internal flights in the US for 2016-2018 and their causes for delay, diversion and cancellation; if any. The data comes from the U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics (BTS).

This dataset is composed by the following variables: Number Column Name Description 1 **Year **2016, 2017, 2018 2 **Month **1-12 3 **DayofMonth **1-31 4 **DayOfWeek **1 (Monday) - 7 (Sunday) 5 DepTime actual departure time (local, hhmm) 6 **CRSDepTime **scheduled departure time (local, hhmm) 7 **ArrTime **actual arrival time (local, hhmm) 8 **CRSArrTime **scheduled arrival time (local, hhmm) 9 **ActualElapsedTime **in minutes 10 **CRSElapsedTime **in minutes 11 **AirTime **in minutes 12 **ArrDelay **arrival delay, in minutes: A flight is counted as "on time" if it operated less than 15 minutes later the scheduled time shown in the carriers' Computerized Reservations Systems (CRS). 13 **DepDelay **departure delay, in minutes 14 **Origin **origin IATA airport code 15 **Dest **destination IATA airport code 16 **Distance **in miles 17 **TaxiIn **taxi in time, in minutes 18 **TaxiOut **taxi out time in minutes 19 **Cancelled ***was the flight cancelled 20 **CancellationCode **reason for cancellation (A = carrier, B = weather, C = NAS, D = security) 21 **Diverted **1 = yes, 0 = no 22 **CarrierDelay **in minutes: Carrier delay is within the control of the air carrier. Examples of occurrences that may determine carrier delay are: aircraft cleaning, aircraft damage, awaiting the arrival of connecting passengers or crew, baggage, bird strike, cargo loading, catering, computer, outage-carrier equipment, crew legality (pilot or attendant rest), damage by hazardous goods, engineering inspection, fuelling, handling disabled passengers, late crew, lavatory servicing, maintenance, oversales, potable water servicing, removal of unruly passenger, slow boarding or seating, stowing carry-on baggage, weight and balance delays. 23 **WeatherDelay **in minutes: Weather delay is caused by extreme or hazardous weather conditions that are forecasted or manifest themselves on point of departure, enrouted, or on point of arrival. 24 **NASDelay **in minutes: Delay that is within the control of the National Airspace System (NAS) may include: non-extreme weather conditions, airport operations, heavy traffic volume, air traffic control, etc. 25 **SecurityDelay **in minutes: Security delay is caused by evacuation of a terminal or concourse, re-boarding of aircraft because of security breach, inoperative screening equipment and/or long lines in excess of 29 minutes at screening areas. 26 **LateAircraftDelay **in minutes: Arrival delay at an airport due to the late arrival of the same aircraft at a previous airport. The ripple effect of an earlier delay at downstream airports is referred to as delay propagation.

Graph and download economic data for Load Factor for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights (LOADFACTOR) from Jan 2000 to May 2025 about flight, passenger, air travel, travel, domestic, and USA.

Graph and download economic data for Available Seat Miles for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights (ASM) from Jan 2000 to May 2025 about flight, miles, passenger, air travel, travel, domestic, and USA.

Context

it's a dataset about the American airline traffic delays and their causes of delays.

Content

this dataset has 22 columns and 292669 rows .

the columns consist of: * year ​ * month ​ * carrier : Abbreviation of carrier ​ * carrier_name : the actual carrier name ​ * airport : Abbreviation of airbort ​ * airport_name : the actual airport name ​ * arr_flights: Number of flights arrived the airport. ​ * arr_del15 : Number of flights delayed.

• carrier_ct: Number of flights delayed due to air carrier ​
• weather_ct: Number of flights delayed due to weather. ​
• nas_ct: Number of flights delayed due to National Aviation System ( non-extreme weather conditions, airport operations, heavy traffic volume, and air traffic control ) check more in here ​
• security_ct: Number of flights delayed due to security ​
• late_aircraft_ct: Number of flights delayed due to a previous flight. ​
• arr_cancelled: Number of flight that has been cancelled. ​
• arr_diverted: Number of flight that has been diverted. ​
• arr_delay: time of delayed flights. ​
• carrier_delay:time of delayed flights due to air carrier. ​
• weather_delay: time of delayed flights due to weather. ​
• nas_delay: time of delayed flights due to National Aviation System. ​
• security_delay: time of delayed flights due to security. ​
• late_aircraft_delay: time of delayed flights due to a previous flight. ​ ​