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.

Since 2006, the total number of air traffic passengers increased steadily. In 2021, due to the coronavirus pandemic, it is estimated that the number of air passengers traveling to and from the U.S. was only 99 million. Passenger aviation in the U.S. After the 2008 financial crisis, the U.S. passenger aviation industry experienced a profound evolution. Between 2009 and 2019, the aviation industry in the U.S. experienced an increasing concentration amongst the leading airline operators. In 2021, there were 18 major air carriers in the U.S. Moreover, between 2009 and 2019, passenger revenue generated by the U.S. airline industry increased exponentially, and then dropped sharply in 2020, due to the pandemic, reaching 49 billion U.S. dollars. Size of U.S. aviation industry Thanks to the efficiency-enhancing measures, major competing aviation firms provided a steady improvement in the aviation industry. Five out of the ten most profitable airlines worldwide are U.S.-based, indicating how large the aviation market in the U.S. is compared to the rest of the world. Besides this, the U.S. had the highest airport connectivity in 2019, reaching a score of 7.29 million.

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.

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.

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.

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!

Daily aircraft utilisation is available for all commercial aviation and business jet aircraft showing the number of flight hours and cycles every day (in UTC) time based on a combination of Spire Global satellite/terrestrial ADS-B data and ch-aviation fleet data.

The data set includes hours, cycles, average stage length as well as data quality indicators for each record.

The data set is updated daily.

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=aircraft_utilisation_daily/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/aircraft-utilisation-daily-ads-b-based-2/

The U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT's monthly Air Travel Consumer Report, published about 30 days after the month's end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released.

• Official source of data - only data from 2003 now
• Packaged, compressed version of the data for years from 1987 to present

United States No. of Flights: JFK Terminal: 1 data was reported at 35.000 Unit in 16 May 2025. This records a decrease from the previous number of 36.000 Unit for 15 May 2025. United States No. of Flights: JFK Terminal: 1 data is updated daily, averaging 0.000 Unit from May 2008 (Median) to 16 May 2025, with 6215 observations. The data reached an all-time high of 0.000 Unit in 16 May 2025 and a record low of 0.000 Unit in 16 May 2025. United States No. of Flights: JFK Terminal: 1 data remains active status in CEIC and is reported by U.S. Customs and Border Protection. The data is categorized under Global Database’s United States – Table US.TA: Airport Statistics: Number of Flights: by Airport. [COVID-19-IMPACT]

United States No. of Flights: SEA Terminal: South Satellite data was reported at 38.000 Unit in 16 May 2025. This records a decrease from the previous number of 43.000 Unit for 15 May 2025. United States No. of Flights: SEA Terminal: South Satellite data is updated daily, averaging 31.000 Unit from May 2008 (Median) to 16 May 2025, with 6219 observations. The data reached an all-time high of 48.000 Unit in 19 Apr 2025 and a record low of 0.000 Unit in 08 May 2018. United States No. of Flights: SEA Terminal: South Satellite data remains active status in CEIC and is reported by U.S. Customs and Border Protection. The data is categorized under Global Database’s United States – Table US.TA: Airport Statistics: Number of Flights: by Airport. [COVID-19-IMPACT]

No. of Flights: FAT Terminal: Main data was reported at 2.000 Unit in 16 May 2025. This records a decrease from the previous number of 4.000 Unit for 15 May 2025. No. of Flights: FAT Terminal: Main data is updated daily, averaging 2.000 Unit from May 2008 (Median) to 16 May 2025, with 6150 observations. The data reached an all-time high of 10.000 Unit in 09 Dec 2024 and a record low of 0.000 Unit in 05 Sep 2020. No. of Flights: FAT Terminal: Main data remains active status in CEIC and is reported by U.S. Customs and Border Protection. The data is categorized under Global Database’s United States – Table US.TA: Airport Statistics: Number of Flights: by Airport. [COVID-19-IMPACT]

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight


    registration
    string
    Aircraft registration


    typecode
    string
    Aircraft ICAO typecode


    icaoaircrafttype
    string
    ICAO aircraft type


    wtc
    string
    ICAO wake turbulence category


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection

string

    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometre


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)


    operator_country
    string
    ISO Alpha-3 country code of the operator


    operator_region
    string
    Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)


    wind_speed_knts
    integer
    METAR, surface wind speed in knots


    wind_dir_deg
    integer
    METAR, surface wind direction in degrees


    wind_gust_knts
    integer
    METAR, surface wind gust speed in knots


    visibility_m
    float
    METAR, visibility in m


    temperature_deg
    integer
    METAR, temperature in degrees Celsius


    press_sea_level_p
    float
    METAR, sea level pressure in hPa


    press_p
    float
    METAR, QNH in hPA


    weather_intensity
    list
    METAR, list of present weather codes: qualifier - intensity


    weather_precipitation
    list
    METAR, list of present weather codes: weather phenomena - precipitation


    weather_desc
    list
    METAR, list of present weather codes: qualifier - descriptor


    weather_obscuration
    list
    METAR, list of present weather codes: weather phenomena - obscuration


    weather_other
    list
    METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

    Column name
    Type
    Description




    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    n_landings
    integer
    Total number of landings observed on this runway in 2019


    ga_rate
    float
    Go-around rate, per 1000 landings


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection
    string
    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometres


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic

load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc )

df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)")

iterate over flights and pull the data from OpenSky Network

flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time

# fetch the data from OpenSky Network
flights.append(
  opensky.history(
    start=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"),
    callsign=row["callsign"],
    return_flight=True,
  )
)

The flights can be converted into a Traffic object

Traffic.from_flights(flights)

Additional files

Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:

validation_table.xlsx: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.

validation_sample.zip: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.

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...

United States No. of Flights: SNA Terminal: C data was reported at 2.000 Unit in 16 May 2025. This stayed constant from the previous number of 2.000 Unit for 15 May 2025. United States No. of Flights: SNA Terminal: C data is updated daily, averaging 2.000 Unit from May 2008 (Median) to 16 May 2025, with 5846 observations. The data reached an all-time high of 6.000 Unit in 05 Nov 2016 and a record low of 0.000 Unit in 11 Sep 2020. United States No. of Flights: SNA Terminal: C data remains active status in CEIC and is reported by U.S. Customs and Border Protection. The data is categorized under Global Database’s United States – Table US.TA: Airport Statistics: Number of Flights: by Airport. [COVID-19-IMPACT]

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

Table 1 of this report covers the 1,000 largest city-pair markets in the 48 contiguous states. For each of the 1,000 largest city-pair markets, Table 1 lists the number of one-way passenger trips per day, the nonstop distance, the average market fare, and identifies the airlines with the largest market share and the lowest average fare; market share and average fares are provided for both airlines. Average fares are average prices paid by all fare paying passengers. They therefore cover first class fares paid to carriers offering such service but do not cover free tickets, such as those awarded by carriers offering frequent flyer programs.

https://www.transportation.gov/policy/aviation-policy/competition-data-analysis/research-reports

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