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 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.

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.

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.

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.

Passengers enplaned and deplaned at Canadian airports, annual.

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!

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: 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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A. SUMMARY

This dataset contains the aircraft tail and model information recorded by the Aircraft Parking System at SFO.

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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.

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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.

BACKGROUND The data contained in the compressed file has been extracted from the Marketing Carrier On-Time Performance (Beginning January 2018) data table of the "On-Time" database from the TranStats data library. The time period is indicated in the name of the compressed file; for example, XXX_XXXXX_2001_1 contains data of the first month of the year 2001.

RECORD LAYOUT Below are fields in the order that they appear on the records: Year Year Quarter Quarter (1-4) Month Month DayofMonth Day of Month DayOfWeek Day of Week FlightDate Flight Date (yyyymmdd) Marketing_Airline_Network Unique Marketing Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users, for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. Operated_or_Branded_Code_Share_Partners Reporting Carrier Operated or Branded Code Share Partners DOT_ID_Marketing_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Marketing_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Flight_Number_Marketing_Airline Flight Number Originally_Scheduled_Code_Share_Airline Unique Scheduled Operating Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users,for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. DOT_ID_Originally_Scheduled_Code_Share_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Originally_Scheduled_Code_Share_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Flight_Num_Originally_Scheduled_Code_Share_Airline Flight Number Operating_Airline Unique Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users, for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. DOT_ID_Operating_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Operating_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Tail_Number Tail Number Flight_Number_Operating_Airline Flight Number OriginAirportID Origin Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused. OriginAirportSeqID Origin Airport, Airport Sequence ID. An identification number assigned by US DOT to identify a unique airport at a given point of time. Airport attributes, such as airport name or coordinates, may change over time. OriginCityMarketID Origin Airport, City Market ID. City Market ID is an identification number assigned by US DOT to identify a city market. Use this field to consolidate airports serving the same city market. Origin Origin Airport OriginCityName Origin Airport, City Name OriginState Origin Airport, State Code OriginStateFips Origin Airport, State Fips OriginStateName Origin Airport, State Name OriginWac Origin Airport, World Area Code DestAirportID Destination Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused. DestAirportSeqID Destination Airport, Airport Sequence ID. An identification number assigned by US DOT to identify a unique airport at a given point of time. Airport attributes, such as airport name or coordinates, may change over time. DestCityMarketID Destination Airport, City Market ID. City Market ID is an identification number assigned by US DOT to identify a city market. Use this field to consolidate airports serving the same city market. Dest Destination Airport DestCityName Destination Airport, City Name DestState Destination Airport, State Code DestStateFips De...

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