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.

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.

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.

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.

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.

Passengers enplaned and deplaned at Canadian airports, annual.

The average for 2021 based on 152 countries was 15 million passengers. The highest value was in the USA: 666.15 million passengers and the lowest value was in Guatemala: 0 million passengers. The indicator is available from 1970 to 2021. Below is a chart for all countries where data are available.

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.

Russia Number of Flights data was reported at 106,742.000 Number in Feb 2022. This records a decrease from the previous number of 119,379.000 Number for Jan 2022. Russia Number of Flights data is updated monthly, averaging 117,995.000 Number from Jan 2010 (Median) to Feb 2022, with 146 observations. The data reached an all-time high of 189,980.000 Number in Aug 2019 and a record low of 53,348.000 Number in Apr 2020. Russia Number of Flights data remains active status in CEIC and is reported by Federal Agency for Air Transport. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TE003: Airlines Statistics: Number of Airlines, Aircrafts, Airports and Flights. [COVID-19-IMPACT]

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 has been periodically included in the dataset until the end of the COVID-19 pandemic.

We stopped updating the dataset after December 2022. Previous files have been fixed after a thorough sanity check.

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); this field may not be very reliable;

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:

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

Russia Number of Flights: Domestic data was reported at 67,658.000 Number in Feb 2022. This records a decrease from the previous number of 71,658.000 Number for Jan 2022. Russia Number of Flights: Domestic data is updated monthly, averaging 55,400.000 Number from Jan 2010 (Median) to Feb 2022, with 146 observations. The data reached an all-time high of 127,409.000 Number in Jul 2021 and a record low of 27,413.000 Number in Feb 2010. Russia Number of Flights: Domestic data remains active status in CEIC and is reported by Federal Agency for Air Transport. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TE003: Airlines Statistics: Number of Airlines, Aircrafts, Airports and Flights. [COVID-19-IMPACT]

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.

Domestic air carriers boarded more than *** million passengers in Russia in 2024. Overall in that year, the passenger traffic increased by around six percent from 2023. In 2025, the passenger count was expected to decline.

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high-volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.

In 2024, more than *********** passengers traveled by air in China, an increase of **** percent compared to the previous year. This indicated a significant recovery in the aviation industry since the end COVID-19 pandemic control measures. Air travel in China As a significant international hub for both business and leisure travel, China’s airports are sites of bustling activity with cargo and passengers being transported in and out of the nation every day. Air travel is a very time-efficient mode of transportation. More and more people in China's socio-economic strata can afford air travel, which has led to the expansion of the nation’s air fleet. Prior to the COVID-19 outbreak, the number of flight hours surged by a significant degree with China Southern which is owned by China Southern Air Holding Group, reaching nearly ************* flight hours in 2019. The aviation industry shows recovery The outbreak of the COVID-19 pandemic in 2020 led to massive losses for the Chinese aviation industry, with the cancellation of flights to and from China. For the first time in the last decade or so, China experienced a decline in the volume of air passengers and air cargo. The industry recovered in 2021 with a *** percent year-over-year increase in air passenger numbers and an *** percent year-over-year increase in air cargo. Furthermore, the industry was predicted to have enhanced employment opportunities between 2021 and 2040, with a growing demand for cabin crews, technicians, and pilots.

Context

Have you taken a flight in the U.S. in the past 15 years? If so, then you are a part of monthly data that the U.S. Department of Transportation's TranStats service makes available on various metrics for 15 U.S. airlines and 30 major U.S airports. Their website unfortunately does not include a method for easily downloading and sharing files. Furthermore, the source is built in ASP.NET, so extracting the data is rather cumbersome. To allow easier community access to this rich source of information, I scraped the metrics for every airline / airport combination and stored them in separate CSV files.

Occasionally, an airline doesn't serve a certain airport, or it didn't serve it for the entire duration that the data collection period covers*. In those cases, the data either doesn't exist or is typically too sparse to be of much use. As such, I've only uploaded complete files for airports that an airline served for the entire uninterrupted duration of the collection period. For these files, there should be 174 time series points for one or more of the nine columns below. I recommend any of the files for American, Delta, or United Airlines for outstanding examples of complete and robust airline data.

* No data for Atlas Air exists, and Virgin America commenced service in 2007, so no folders for either airline are included.

Content

There are 13 airlines that have at least one complete dataset. Each airline's folder includes CSV file(s) for each airport that are complete as defined by the above criteria. I've double-checked the files, but if you find one that violates the criteria, please point it out. The file names have the format "AIRLINE-AIRPORT.csv", where both AIRLINE and AIRPORT are IATA codes. For a full listing of the airlines and airports that the codes correspond to, check out the airline_codes.csv or airport_codes.csv files that are included, or perform a lookup here. Note that the data in each airport file represents metrics for flights that originated at the airport.

Among the 13 airlines in data.zip, there are a total of 161 individual datasets. There are also two special folders included - airlines_all_airports.csv and airports_all_airlines.csv. The first contains datasets for each airline aggregated over all airports, while the second contains datasets for each airport aggregated over all airlines. To preview a sample dataset, check out all_airlines_all_airports.csv, which contains industry-wide data.

Each file includes the following metrics for each month from October 2002 to March 2017:

1. Date (YYYY-MM-DD): All dates are set to the first of the month. The day value is just a placeholder and has no significance.
2. ASM_Domestic: Available Seat-Miles in thousands (000s). Number of domestic flights * Number of seats on each flight
3. ASM_International*: Available Seat-Miles in thousands (000s). Number of international flights * Number of seats on each flight
4. Flights_Domestic
5. Flights_International*
6. Passengers_Domestic
7. Passengers_International*
8. RPM_Domestic: Revenue Passenger-Miles in thousands (000s). Number of domestic flights * Number of paying passengers
9. RPM_International*: Revenue Passenger-Miles in thousands (000s). Number of international flights * Number of paying passengers

* Frequently contains missing values

Acknowledgements

Thanks to the U.S. Department of Transportation for collecting this data every month and making it publicly available to us all.

Source: https://www.transtats.bts.gov/Data_Elements.aspx

Inspiration

The airline / airport datasets are perfect for practicing and/or testing time series forecasting with classic statistical models such as autoregressive integrated moving average (ARIMA), or modern deep learning techniques such as long short-term memory (LSTM) networks. The datasets typically show evidence of trends, seasonality, and noise, so modeling and accurate forecasting can be challenging, but still more tractable than time series problems possessing more stochastic elements, e.g. stocks, currencies, commodities, etc. The source releases new data each month, so feel free to check your models' performances against new data as it comes out. I will update the files here every 3 to 6 months depending on how things go.

A future plan is to build a SQLite database so a vast array of queries can be run against the data. The data in it its current time series format is not conducive for this, so coming up with a workable structure for the tables is the first step towards this goal. If you have any suggestions for how I can improve the data presentation, or anything that you would like me to add, please let me know. Looking forward to seeing the questions that we can answer together!

Number of flight passengers according to day of the week. Map types: Symbols, Charts. Spatial extent: Switzerland. Times: 2000, 2005, 2010, 2015, 2020. Distinction: Total, Scheduled flight, Charter/Taxi flight

This dataset contains the records of all the flights in the Northern California TRACON. The data was provided by the aircraft noise abatement office (http://www.flyquietsfo.com/) of San Francisco International Airport. The data cover Jan-Mar 2006. It is organized by day and flight. Each record contains some information about the flight and a sequence of 3D position and estimated speed. This data contains thousands of trajectories that can be used for trajectory clustering. The data is used by the Aircraft Noise Abatement Office to analyze the trajectories of aircraft flying in and out SFO. The objective is to minimize the noise pollution due to aircraft in the San Francisco Bay Area The files have the extension "lt6" and are organized as follow, one file per day. line number & explaination 1 TRACK OPNUM (TRACK header word and operation number) 2 eventid (Corralation number) 3 trackstart date (in time since 1900, A8 version four year digit) 4 trackstart time HH:MM:SS 5 trackend time HH:MM:SS 6 airportid 7 ACID (FLIGHTNUM/TAILNUMBER) 8 owner name 9 aircrafttype 10 aircraft category 11 beacon 12 adflag 13 waypoint 14 other_port (dest/origin) 15 runwayname 16 min alt 17 max alt 18 min range 19 max range 20 Count of trackpoints (to follow) 21 x,y,z,v,t (all points is meters relative to MRP, velocity and time from start of track)

Data on flight movements, passengers, cargo and mail at Dutch airports.

Summary of the contents of the EU figure in this publication: The composition of the European Union (EU-15) until 2004: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, The Netherlands, Portugal, Spain, Sweden and United Kingdom. In 2005 the European Union (EU-25) expanded with: Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia and Slovenia. In 2007 Bulgaria and Romania have been added (EU-27). In 2014 Croatia was added (EU-28). As of February 2020 the United Kingdom has left the European Union.

Data available from: Annual figures available from 1997; monthly figures available from January 1999.

Status of the figures: The figures are final up to and including 2023, 2024 and for the current year are provisional.

Up to and including December 2020 the figures of EU countries include data for the UK. This to guarantee the comparability of the data. As of January 2021 data for the UK are included in the figures of "other Europe".

As of 8 November 2022 the figures for Eindhoven airport for the reporting period April, May and June 2022 have been adjusted as a result of additional information. As a result, the marginal totals for the months of April, May and June 2022 have also been adjusted.

Due to renovation work on the runway at Maastricht Aachen Airport, there was no air traffic at this airport from 8 May 2023 to 30 June 2023.

Changes as of 4 August 2025: The figures for June 2025 and for the 2nd quarter 2025 have been added.

When will figures become available? The monthly figures are published as a rule 1 month after the end of the reporting month.

In 2024, the United Kingdom was the leading country in terms of air traffic in Europe, with a total of ***** daily arrivals and departures. Spain ranked second, with ***** daily flights, while Germany rounded out the top three with 4,711.