Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

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.

Motivation

The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data will be periodically included in the dataset until the end of the COVID-19 pandemic.

License

See LICENSE.txt

Disclaimer

The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.

• Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.
  Fields origin or destination are empty when no airport could be found.
• Aircraft information come from the OpenSky aircraft database. Fields typecode and registration are empty when the aircraft is not present in the database.

Description of the dataset

One file per month is provided as a csv file with the following features:

• callsign: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)
• number: the commercial number of the flight, when available (the matching with the callsign comes from public open API)
• icao24: the transponder unique identification number;
• registration: the aircraft tail number (when available);
• typecode: the aircraft model type (when available);
• origin: a four letter code for the origin airport of the flight (when available);
• destination: a four letter code for the destination airport of the flight (when available);
• firstseen: the UTC timestamp of the first message received by the OpenSky Network;
• lastseen: the UTC timestamp of the last message received by the OpenSky Network;
• day: the UTC day of the last message received by the OpenSky Network;
• latitude_1, longitude_1, altitude_1: the first detected position of the aircraft;
• latitude_2, longitude_2, altitude_2: the last detected position of the aircraft.

Examples

Possible visualisations and a more detailed description of the data are available at the following page:
<https://traffic-viz.github.io/scenarios/covid19.html>

Credit

If you use this dataset, please cite:

Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders
"Crowdsourced air traffic data from the OpenSky Network 2019–2020"
Earth System Science Data 13(2), 2021
https://doi.org/10.5194/essd-13-357-2021

In 2023, the estimated number of scheduled passengers boarded by the global airline industry amounted to approximately *** billion people. This represents a significant increase compared to the previous year since the pandemic started and the positive trend was forecast to continue in 2024, with the scheduled passenger volume reaching just below **** billion travelers. Airline passenger traffic The number of scheduled passengers handled by the global airline industry has increased in all but one of the last decade. Scheduled passengers refer to the number of passengers who have booked a flight with a commercial airline. Excluded are passengers on charter flights, whereby an entire plane is booked by a private group. In 2023, the Asia Pacific region had the highest share of airline passenger traffic, accounting for ********* of the global total.

Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Airline Ticket Price dataset concerns the prediction of airline ticket prices. The rows are a sequence of time-ordered observations over several days. Each sample in this dataset represents a set of observations from a specific observation date and departure date pair. The input variables for each sample are values that may be useful for prediction of the airline ticket prices for a specific departure date. The target variables in these datasets are the next day (ATP1D) price or minimum price observed over the next 7 days (ATP7D) for 6 target flight preferences: (1) any airline with any number of stops, (2) any airline non-stop only, (3) Delta Airlines, (4) Continental Airlines, (5) Airtrain Airlines, and (6) United Airlines. The input variables include the following types: the number of days between the observation date and the departure date (1 feature), the boolean variables for day-of-the-week of the observation date (7 features), the complete enumeration of the following 4 values: (1) the minimum price, mean price, and number of quotes from (2) all airlines and from each airline quoting more than 50 % of the observation days (3) for non-stop, one-stop, and two-stop flights, (4) for the current day, previous day, and two days previous. The result is a feature set of 411 variables. For specific details on how these datasets are constructed please consult Groves and Gini (2015). The nature of these datasets is heterogeneous with a mixture of several types of variables including boolean variables, prices, and counts.

Passengers enplaned and deplaned at Canadian airports, annual.

A. SUMMARY This dataset contains the aircraft tail and model information recorded by the Aircraft Parking System at SFO. The columns in the dataset include details such as tail number, model, airline, status, as well as the Creation and Modification Dates of the aircraft record. On the 28th day of each month, this dataset is exported from the Aircraft Parking System at SFO. B. HOW THE DATASET IS CREATED When airline requests for reservation of aircraft parking stand at SFO, a new aircraft parking reservation is entered to the Aircraft Parking System. If the requested Aircraft is not found in the Aircraft Parking System, then the requested Aircraft information is added to the aircraft table of the Aircraft Parking System. On the 28th day of each month, this dataset is replaced with all the records retrieved from the aircraft table of the Aircraft Parking System at SFO. C. UPDATE PROCESS When airline companies undergo a merger or acquisition, the transfer of aircraft ownership is recorded by marking the aircraft previously owned by the originating airline as inactive and registering the aircraft now owned by the new airline as active in the aircraft table. On the 28th day of each month, this dataset is replaced with all the records retrieved from the aircraft table of the Aircraft Parking System at SFO. E. RELATED DATASETS Aircraft Parking Activity Records at SFO Aircraft Parking Location Inventory at SFO Utilize the Tail Number column of the "Aircraft Parking Activity Records at SFO" dataset to connect with the Tail Number column of the "Aircraft Tail Numbers and Models at SFO" dataset, for referencing the corresponding aircraft record.

Airline Industry VQA Dataset

⚠️ Note: This dataset currently contains only the text data (questions). The images are being processed and will be added in a future update. This dataset contains a comprehensive collection of visual question-answering (VQA) pairs generated from official documentation of 18 major airline companies.

  About the Creator

I'm David Soeiro-Vuong, an engineering student specializing in Computer Science, Big Data, and AI, currently working as an… See the full description on the dataset page: https://huggingface.co/datasets/Davidsv/airline-vision-dataset.

A. SUMMARY This dataset stores the parking reservation information recorded by the Aircraft Parking System at SFO. The columns in this dataset include details such as the Source ID, Source Type, Start Datetime , End Datetime, Reserving Company, Operator Company, Model, Tail Number and Spot of the aircraft parking activity. On the 28th day of each month, this dataset is exported from the Aircraft Parking System at SFO. B. HOW THE DATASET IS CREATED When airline requests for reservation of aircraft parking stand at SFO, a new aircraft parking reservation is entered to the Aircraft Parking System. On the 28th day of each month, this dataset is replaced with all the aircraft parking activity records retrieved from the aircraft parking reservation table with reservation Start Date between August 2008 and the previous month of the export run date. C. UPDATE PROCESS Airside Operations Supervisor updates the aircraft parking activity record as requested by the airline for making changes and cancellations. When airline requests for changes or cancellation to the previous made aircraft parking reservation, the aircraft parking activity record is updated with the changes in the Aircraft Parking System. On the 28th day of each month, this dataset is replaced with all the aircraft parking activity records retrieved from the aircraft parking reservation table with reservation Start Date between August 2008 and the previous month of the export run rate. D. HOW TO USE THIS DATASET Utilize this dataset to determine the parking duration of a reservation by calculating the difference between the reservation's end date and start date. E. RELATED DATASETS Aircraft Tail Numbers and Models at SFO Utilize the Tail Number column of the "Aircraft Parking Activity Records at SFO" dataset to connect with the Tail Number column of the "Aircraft Tail Numbers and Models at SFO" dataset, for referencing the corresponding aircraft record. Aircraft Parking Location Inventory at SFO Utilize the Spot column from the "Aircraft Parking Activity Records at SFO" dataset to connect with the Spot Name column in the "Aircraft Parking Locations at SFO" dataset, for referencing the corresponding parking location record.

India All Scheduled Airlines: International: Number of Flight data was reported at 18,502.000 Unit in Mar 2025. This records an increase from the previous number of 16,668.000 Unit for Feb 2025. India All Scheduled Airlines: International: Number of Flight data is updated monthly, averaging 7,797.000 Unit from Apr 2001 (Median) to Mar 2025, with 283 observations. The data reached an all-time high of 18,574.000 Unit in Jan 2025 and a record low of 273.000 Unit in May 2020. India All Scheduled Airlines: International: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

This dataset contains data related to Air Traffic Management hotspots. Hotspots are created in the European airspaces when capacity for some pieces of airspace are foreseen to be infringed due to weather, congestion, strikes, etc. This anonymised dataset records around 5900 hotspots happening at 22 major European airports. These hotspots are generated through a simulator called Mercury that is fed with real data (in particular, real capacity reduction that happened in Europe for over a year, schedules etc) and simulates a day of operation, randomising events like delays, cancellation etc. More details on mercury can be found here [1] and [2].

The data, anonymised in terms of airports and airlines, is a dictionary which is structured as follows:

- the top level key is the id of the airport, the value is list a of all regulations available for this airport.

- each item of the list is a dictionary, with keys:

-- 'slot_times': list of all slots available to flights for this hotspot/regulation, in minutes since midnight.

-- 'etas': list of initial estimated arrival times of flights involved in the regulation, in minutes since midnight.

-- 'flight_ids': list of flight ids (in the same order than etas)

-- 'cost_vectors': list of cost vectors. Each item is a list itself, of length equal to the slot_times list. Each element of that list is the estimated cost that the airline owning the flight would incur, were the flight be assigned to this slot, in terms of: maintenance, crew, rebooking fees, market value loss, and curfew infringement, in 2014 euros. This cost is computed within the Mercury model and is based on [3].

-- 'airlines_flights': dictionary whose keys are airline ids and values are lists of ids of flights owned by the airline.

[1] https://www.sciencedirect.com/science/article/abs/pii/S0968090X21003600

[2] G. Gurtner, L. Delgado, and D.Valput, “An agent-based model for air transportation to capture network effects in assessing delay management mechanisms”, Transportation Research Part C: emerging Technologies, 2021.

Pre-print available here: https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms

[3] A. J. Cook and G. Tanner, “European airline delay cost reference values - updated and extended values (Version 4.1),” University of Westminster, London, 2015a

India All Scheduled Airlines: Domestic: Number of Flight data was reported at 102,319.000 Unit in Mar 2025. This records an increase from the previous number of 92,291.000 Unit for Feb 2025. India All Scheduled Airlines: Domestic: Number of Flight data is updated monthly, averaging 48,100.000 Unit from Apr 2001 (Median) to Mar 2025, with 288 observations. The data reached an all-time high of 102,319.000 Unit in Mar 2025 and a record low of 188.000 Unit in Apr 2020. India All Scheduled Airlines: Domestic: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

Our Flight Events data feed combines Spire Global satellite/terrestrial ADS-B flight event data with ch-aviation’s fleet, operator, and airport data providing an overview of all flights operated by airlines, business and general aviation players on a daily basis.

The value of our Flight Events data feed lies in its high-resolution integration of ADS-B flight tracking with ch-aviation’s comprehensive aircraft and operator data, delivering unmatched visibility into global aircraft movements. By identifying the aircraft type and registration for approximately 98% of all ADS-B-tracked flights, we offer an industry-leading solution for lessors, insurers, airports, OEMs, and analysts seeking precise, reliable, and actionable aviation intelligence.

• High-Resolution ADS-B Integration - Satellite and terrestrial ADS-B flight tracking combined with enriched aircraft and operator data for maximum accuracy and visibility • Comprehensive Aircraft Identification - Aircraft type and registration identified for approximately 98% of all ADS-B-tracked flights, using proprietary matching with ch-aviation data and supplementary publicly available authority data sources. • Global Flight Coverage - Tracks approximately 160,000–190,000 flights per day across commercial aviation, business jet, and general aviation sectors worldwide. • ACMI (Wet-Lease) and Cargo Customer Tracking - Detailed monitoring of ACMI operations, including identification of wet-lease activity between different operators as well as cargo customers identifying flights operated for integrators like DHL Express or FedEx as well as cargo customers such as Amazon. • Aircraft Utilisation Tracking - Tracking of flight hours and cycles at both the operator and individual tail number (aircraft) level • Matched Operator and Aircraft Data - Every flight is linked to comprehensive ch-aviation datasets, including aircraft ID, history, operator, variant, callsign, and airport details allowing customers to leverage the industry’s most comprehensive integration between ADS-B flight event and fleet/operator/airport data. • Fallback Data Enrichment - Where ch-aviation data is unavailable, civil aviation authority and ANSP sources are used to ensure continuity in aircraft identification and data accuracy. • Use Case-Driven Insights - Tailored for industry stakeholders like lessors, insurers, OEMs, airports, and analysts seeking operational, commercial, and technical flight data intelligence.

ch-aviation integrates its Commercial Aviation Aircraft Data and Business Jet Aircraft Data with Spire Global’s satellite-based ADS-B data that is fused by Spire with terrestrial feeds from AirNav and Wingbits.

This data is enriched with mapped callsigns, corrected hexcodes, regional partnership decoding, and identification of wet-leases and cargo customers, enabling detailed insight into each individual flight.

Where ch-aviation data is unavailable, public data from civil aviation authorities and ANSPs is used to ensure broad and reliable aircraft identification and coverage.

The data set is available historically going back to January 1, 2018.

The data set is updated daily.

The sample data shows flights on 2025-03-30, with Swiss, Alaska Airlines, Horizon Air, Jet Aviation Business Jets, and RVR Aviation as operators or wet lease customers.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=flights/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/flights-2/

In 2022, the number of total flights of Vietnam Airlines amounted to approximately *** thousand, indicating a significant increase compared to previous year. Vietnam Airlines is a state-owned enterprise and is among Southeast Asia's leading airline groups.

In financial year 2024, the total air passenger traffic in India reached more than *** million passengers. It was a huge increase compared to the previous year. The domestic passenger traffic saw a compound annual growth rate (CAGR) of *** percent from 2014 to 2024, while the international passenger traffic saw a *** percent CAGR during the same period of time.

We propose to form a network and a set of tools that will create a shared situation awareness with Unmanned Aircraft Systems (UAS) Ground Control Stations (GCSs) and airline dispatchers at Airline Operations Centers (AOCs). Our solution is motivated by the Collaborative Decision Making (CDM) community in commercial aviation, where the CDMnet was created back in 1997 to facilitate collaboration between airlines and the Federal Aviation Administration (FAA). The CDMnet continues to exist today to allow airlines to collaborate on Traffic Flow Management (TFM) decisions that are made by airlines and FAA Air Traffic Service Providers (ATSPs) every day. Today, with the introduction of Unmanned Aircraft Systems (UAS) flying in the National Airspace System (NAS), there is a need for collaboration between UAS, ATSP, and AOCs in UAS Traffic Management (UTM) in order to share airspace resources. Thus, the focus of this SBIR effort is to build a network that allows UAS GCSs to share information and collaborate with airline AOCs in order to create a shared situation awareness and to share and coordinate NAS airspace resources.

India’s leading low-cost air carrier, IndiGo, carried around ***** million domestic and international passengers in the financial year 2024. This was an increasing in comparison to the previous year.   The no-frills airline Established in 2006 and headquartered in Gurgaon, IndiGo climbed the airline ladder to become the largest passenger carrier with a market share of about ** percent. The company’s focus was threefold – offering low fares mainly in the domestic market, being on-time and providing a smooth flying experience. IndiGo was the preferred airline among Indians and was known for its punctuality. Leading the domestic market IndiGo had *** aircraft as part of its fleet and over a thousand daily flights to *** destinations. As a low-cost carrier, it offers only economy seating and no complimentary meals on any flights. It was one of the leading budget airlines in terms of net profit in 2019. As an airline that operates mainly within the South Asian country, it has become a major player in the market since its establishment in 2015. It found a stronger foothold when its competitor Jet Airways suspended operation between early 2019 and mid-2022.

During the ACLOUD (Arctic CLoud Observations Using airborne measurements during polar Day ) campaign in May / June 2017 a downward-looking commercial digital camera equipped with a 180° - fisheye lens was installed on the aircraft Polar 5. Images of the Arctic surface and clouds were taken every 6 seconds. The data set provides rectified fields of calibrated radiances along the flight track for the three spectral bands (red, green, and blue).

In 2023, the U.S. airline industry generated 179.2 billion U.S. dollars in revenue from passenger fares. This represented an increase of approximately 16 percent compared to the fare revenue reported a year earlier. The 2023 passenger revenue was also the new peak registered in the given period.

Early spring sampling was performed in the eastern area of the Shelf-Basin Interactions Project using aircraft. Flights began on 1 April 2004 and finished on 16 April. During this time, we sampled 32 sites on a series of 5 transect lines (Fig. 1). Stations were about 10 km apart along each transect line. Transect lines B, C, and D were at the same spacing; while, lines A and E were 20 km from the nearest transect line. Typically, 4 stations were sampled on each flying day. At each site, a Seabird SBE-19 CTD and a water sampling bottle was deployed through an 25 cm hole augered through the pack-ice. Continuous profiles of pressure, temperature, and salinity were made from the ice hole to either the sediment surface or to about 500 m. CTD data were processed with Seabird software (Seasoft) and the data binned into 0.5 dbar layers. Data herein are the vertical profiles of temperature, salinity, and density. About 30 ml of seawater collected from the Niskin bottle were poured into a 50 ml clean dry polyethylene sample bottle. The nutrient sample bottles quickly froze and were kept that way until measurement of nitrate, nitrite, ammonium, phosphate and silicate by the Nutrient Chemistry Laboratory of the Scripps Institute of Oceanography.