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.

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.

Have you ever been stuck in an airport because your flight was delayed or cancelled and wondered if you could have predicted it if you'd had more data? This is your chance to find out.

The 2009 ASA Statistical Computing and Graphics Data Expo consisted of flight arrival and departure details for all commercial flights on major carriers within the USA, from October 1987 to April 2008. This is a large dataset containing nearly 120 million records in total.

The aim of the data expo is to provide a graphical summary of important features of the data set. This is intentionally vague in order to allow different entries to focus on different aspects of the data, but here are a few ideas to get you started: •When is the best time of day, day of the week, and time of year to fly to minimise delays? •Do older planes suffer more delays? •How well does weather predict plane delays? •How does the number of people flying between different locations change over time? •Can you detect cascading failures as delays in one airport create delays in others? Are there critical links in the system? •Use the available variables to construct a model that predicts delays.

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.

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 between 1 January 2020 and 1 May 2020. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. 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. 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 the original OpenSky paper: Matthias Schäfer, Martin Strohmeier, Vincent Lenders, Ivan Martinovic and Matthias Wilhelm. "Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research". In Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN), pages 83-94, April 2014. and the traffic library used to derive the data: Xavier Olive. "traffic, a toolbox for processing and analysing air traffic data." Journal of Open Source Software 4(39), July 2019.

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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!

Passengers enplaned and deplaned at Canadian airports, annual.

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.

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)

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.

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 2020. More data will be periodically included in the dataset until the end of the COVID-19 pandemic.

License

Creative Commons CC-BY

The only difference with the original dataset comes from anonymised aircraft information.

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)

aircraft_uid: a unique anonymised identifier for aircraft;

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 the original OpenSky paper:

Matthias Schäfer, Martin Strohmeier, Vincent Lenders, Ivan Martinovic and Matthias Wilhelm. "Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research". In Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN), pages 83-94, April 2014.

and the traffic library used to derive the data:

Xavier Olive. "traffic, a toolbox for processing and analysing air traffic data." Journal of Open Source Software 4(39), July 2019.

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In prospective human exploration of outer space the need to maintain a species over several generations under changed gravity conditions may arise. This paper reports the analysis of the third generation of fruit fly Drosophila melanogaster obtained during the 44.5-day space flight (Foton-M4 satellite 2014 Russia) followed by the fourth generation on Earth and the fifth generation under conditions of a 12-day space flight (2014 in the Russian Segment of the ISS). The obtained results show that it is possible to obtain the third-fifth generations of a complex multicellular Earth organism under changed gravity conditions (in the cycle weightlessness - Earth - weightlessness) which preserves fertility and normal development. However there were a number of changes in the expression levels and content of cytoskeletal proteins that are the key components of the spindle apparatus and the contractile ring of cells.

In financial year 2024, the total air passenger traffic in India reached more than 220 million passengers. It was a huge increase compared to the previous year. The domestic passenger traffic saw a compound annual growth rate (CAGR) of 9.7 percent from 2014 to 2024, while the international passenger traffic saw a 4.5 percent CAGR during the same period of time.

Analysis of ‘New York City Airport Activity’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sveneschlbeck/new-york-city-airport-activity on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains randomly compiled data with detailed information about flights from New York City Airports in 2013 with parameters concerning the airports, the flights and the airlines involved.

Content

The table contains the following parameters:

- year: Year.
- month: Month.
- day: Day.
- dep_time: Departure time, in Eastern time zone.
- dep_delay: Departure delay, in minutes.
- arr_time: Arrival time, in the local time zone.
- arr_delay: Arrival delay, in minutes.
- carrier: Carrier, abbreviated.
- tailnum: Tail number of the airplane.
- flight: Flight number.
- origin: Flight origin, airport code.
- dest: Flight destination, airport code.
- air_time: Time in the air, in minutes.
- distance: Distance between the departure and arrival airports, in miles.
- hour: Scheduled departure hour.
- minute: Scheduled departure minute.

Analysis

Take a look at the notebook "nyc-flights" to get started on how to transform, analyse or visualize the data.

Data Source

Wickham H. 2014. nycflights13: Data about flights departing NYC in 2013. R package version 0.1.

--- Original source retains full ownership of the source dataset ---

The National Security UAS Flight Restrictions in this dataset are currently pending and will become effective on November 07, 2019. The FAA, pursuant to Title 14 of the Code of Federal Regulations (CFR) § 99.7, Special security instructions (SSI), has prohibited all UAS flight operations within the airspace defined under NOTAM FDC 7/7282 . Specific locations are described in the table and on the interactive map provided on this website. The TFRs extend from the surface up to 400 feet Above Ground Level (AGL), apply to all types and purposes of UAS flight operations, and remain in effect 24 hours a day, 7 days a week.

WHAT UAS FLIGHT RESTRICTIONS HAVE BEEN PUT INTO PLACE?

At the request of and pursuant to agreements with the Department of Defense and U.S. Federal security and intelligence agencies (“sponsoring Federal agencies”), the Federal Aviation Administration (FAA) has implemented Special Security Instructions for Unmanned Aircraft System (UAS), issued as temporary flight restrictions (TFR) over select national security sensitive facilities located throughout the U.S. These TFRs are established within the lateral boundaries of these facilities and extend from surface to 400 feet Above Ground Level (AGL). These TFRs apply to all UAS operations specifically including:

· Public aircraft operations conducted in accordance with a Certificate of Authorization or Waiver (COA).

· Civil aircraft operations (other than model aircraft), including those conducted in accordance with a COA and those conducted in accordance with the FAA’s small UAS Rule, 14 CFR Part 107.

· Model Aircraft operations conducted in accordance with 14 CFR Part 101, Subpart E.

UAS operators must comply with these flight restrictions in addition to all other applicable Federal Aviation Regulations, including but not limited to, requirements to secure an FAA airspace authorization and/or waiver prior to flying in the airspace where a TFR is in effect.

The information on this website complements Notice to Airmen (NOTAM) NOTAM FDC 7/7282, which generally notifies the public about these temporary flight restrictions (TFR). This website provides UAS operators with more detailed information about these TFRs, including:

· An explanation of what is restricted

· A table listing the selected facilities over which a TFR has been established

· An interactive map providing visual depictions and information about specific TFRs and geospatial (GIS) data that can be downloaded

· An explanation of which UAS operations may be able to access the airspace within a TFR, including instructions for submitting a request

· Reminders on other requirements for UAS operations

WHAT HAPPENS IF I VIOLATE A TEMPORARY FLIGHT RESTRICTION (TFR)?

The FAA classifies the airspace encompassed by these temporary flight restrictions (TFRs) as “national defense airspace” in accordance with Title 49 of the United States Code (USC) § 40103(b)(3). Violations of these TFRs may prompt the following enforcement actions:

A. The U.S. Government may pursue criminal charges, including charges under Title 49 U.S.C § 46307.

B. The FAA may take administrative action, including imposing civil penalties and the revoking FAA certificates and authorizations to operate UAS under Title 49 U.S.C. §§ 44709 and 46301.

WHAT ARE THE BASIC FLIGHT RESTRICTIONS?

The FAA, pursuant to Title 14 of the Code of Federal Regulations (CFR) § 99.7, Special security instructions (SSI), has prohibited all UAS flight operations within the airspace defined under NOTAM FDC 7/7282. Specific locations are described in the table and on the interactive map provided on this website. The TFRs extend from the surface up to 400 feet Above Ground Level (AGL), apply to all types and purposes of UAS flight operations, and remain in effect 24 hours a day, 7 days a week.

See the full text of NOTAM FDC 7/7282 here.

ARE THERE EXCEPTIONS FOR UAS OPERATIONS TO ACCESS A TFR?

The FAA has authorized UAS operations within the TFRs if those flights are in compliance with the applicable requirements listed below:

1) The UAS flight operation has been pre-approved by the designated facility contact based on criteria established by the sponsoring federal agency in coordination with the FAA. Note: UAS operators seeking approval to operate in one of the TFRs defined in this website under this provision must contact the facility’s designated point of contact identified in the table or interactive map, and secure permission to operate within the airspace prior to entry. Pre-approval from the facility or sponsoring agency does not substitute for compliance with FAA requirements. Depending on the nature of the proposed operation and Class of airspace, waiver or authorization may be needed from the FAA before flight. For more information visit our website at www.faa.gov/uas

2). The UAS flight operation is conducted in direct support of an active national defense, homeland security, law enforcement, firefighting, search and rescue, or disaster response mission, and prior notification has been provided to the designated facility contact. Note: UAS operators seeking approval to operate in one of the TFRs defined in this website under this provision must contact the facility’s designated point of contact identified in the table or interactive map, and provide notification prior to entering the airspace. These operators must make every effort to coordinate with the designated facility to deconflict the UAS flight operation with any safety or security concerns stated by the facility and/or sponsoring Federal agency.

3). The UAS flight operation is conducted in direct support of a significant and urgent governmental interest and is approved by the FAA’s System Operations Support Center (SOSC) in advance of entering the TFR. Note: UAS operators, that meet the criteria for thisprovision , may also qualify for access under provision 2 outlined above and are encouraged to coordinate directly with the facility’s designated point of contact identified in the table or interactive map, by providing notification prior to entering the airspace and taking into consideration any safety or security concerns stated by the facility and/or sponsoring Federal agency.

For urgent and time sensitive requests, contact the FAA’s SOSC at (202) 267-8276 for expedited assistance. The FAA’s SOSC will coordinate with the facility and/or sponsoring Federal agency as appropriate.

ARE THERE OTHER REQUIREMENTS TO OPERATE IN A TFR IN ADDITION TO THE EXCEPTIONS?

Separate and distinct from any of the conditions cited above used to gain access to a TFR defined by NOTAM FDC 7/7282 and described in this website, UAS operators must comply with all applicable Federal Aviation Regulations. For example:

For Model Aircraft:

· Comply with 14 CFR Part 101, Subpart E

NOTE: These provisions require model aircraft operators to notify any airport operator and air traffic control tower within 5 miles of the intended area of flight.

For All Other UAS Operators:

· Comply with a Public Aircraft Certificate of Authorization or Waiver (COA), or

· Comply with 14 CFR Part107, Small Unmanned Aircraft Systems, or

· Comply with Section 333 Exemption and a Certificate of Authorization or Waiver (COA)

NOTE: Public and civil UAS operators flying under the provisions of a COA or 14 CFR Part 107 may need to secure further airspace authorizations or waivers in order to conduct the proposed flight operation in controlled airspace, which may overlap with one of the TFRs defined by NOTAM FDC 7/7282 and this website. In those cases, these operators should follow the pre-existing procedures outlined below.

A. Non-emergency requests for UAS airspace authorizations and waivers must be submitted using the regular process as follows:

· 14 CFR Part 107 requests for airspace authorizations and waivers must be submitted to the FAA athttps://www.faa.gov/uas/request_waiver/

·
Section 333 Exemption holders may request a site specific COA at https://oeaaa.faa.gov/oeaaa/external/uas/portal.jsp

· Public aircraft operators without an existing authorization to operate must secure a public COA athttps://ioeaaa.faa.gov/oeaaa/Welcome.jsp

B. Emergency requests for UAS authorizations/waivers for missions that directly support significant and urgent governmental interests (e.g., active national defense, homeland security, law enforcement, and emergency operations missions), which cannot be supported by the FAA’s routine authorization/waiver processes should be referred to the SOSC at (202) 267-8276

ADDITIONAL QUESTIONS?

If you have any general questions regarding UAS operations, please refer to the following FAA webpage: https://www.faa.gov/uas/ , or contact the FAA by email at uashelp@faa.gov or by phone at (844) FLY-MY-UA.

If you have any additional questions regarding the TFRs defined by NOTAM FDC 7/7282 and this website, please contact the FAA SOSC at (202) 267-8276.

Disclaimers

The restrictions depicted on this site reflect temporary flight restrictions issued for national security reasons at select U.S. Federal facilities. There may be additional temporary flight restrictions that prohibit UAS and manned flight in effect in your area. Seehttp://tfr.faa.gov/tfr2/list.html for additional information on flight restrictions that may be in effect in your area before operating your UAS.