100+ datasets found
  1. Global air traffic - scheduled passengers 2004-2024

    • statista.com
    • ai-chatbox.pro
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    Statista, Global air traffic - scheduled passengers 2004-2024 [Dataset]. https://www.statista.com/statistics/564717/airline-industry-passenger-traffic-globally/
    Explore at:
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    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.

  2. U.S. Commercial Aviation Industry Metrics

    • kaggle.com
    zip
    Updated Jul 13, 2017
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    Franklin Bradfield (2017). U.S. Commercial Aviation Industry Metrics [Dataset]. https://www.kaggle.com/shellshock1911/us-commercial-aviation-industry-metrics
    Explore at:
    zip(1573798 bytes)Available download formats
    Dataset updated
    Jul 13, 2017
    Authors
    Franklin Bradfield
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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!

  3. Air passenger traffic at Canadian airports, annual

    • www150.statcan.gc.ca
    • open.canada.ca
    • +3more
    Updated Jul 4, 2024
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    Government of Canada, Statistics Canada (2024). Air passenger traffic at Canadian airports, annual [Dataset]. http://doi.org/10.25318/2310025301-eng
    Explore at:
    Dataset updated
    Jul 4, 2024
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    Passengers enplaned and deplaned at Canadian airports, annual.

  4. Global air traffic - number of flights 2004-2025

    • statista.com
    • ai-chatbox.pro
    Updated Jun 27, 2025
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    Statista (2025). Global air traffic - number of flights 2004-2025 [Dataset]. https://www.statista.com/statistics/564769/airline-industry-number-of-flights/
    Explore at:
    Dataset updated
    Jun 27, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    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.

  5. Data Expo 2009: Airline On Time Data

    • kaggle.com
    Updated Mar 20, 2022
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    果丹皮 (2022). Data Expo 2009: Airline On Time Data [Dataset]. https://www.kaggle.com/datasets/wenxingdi/data-expo-2009-airline-on-time-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Mar 20, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    果丹皮
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    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.

  6. P

    @##Can You Change Flights on American Without Fees? Dataset

    • paperswithcode.com
    Updated Jun 28, 2025
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    (2025). @##Can You Change Flights on American Without Fees? Dataset [Dataset]. https://paperswithcode.com/dataset/can-you-change-flights-on-american-without
    Explore at:
    Dataset updated
    Jun 28, 2025
    Description

    Roughly 45 million American Airlines passengers fly annually, and many want to modify their flights at least once. Luckily, American has made fee-free flight changes more accessible. ☎️+1 (855) 217-1878 As of 2021, they eliminated change fees for most Main Cabin and premium-class tickets. ☎️+1 (855) 217-1878 This gives travelers increased flexibility without extra cost.

    The no-fee change policy applies to domestic flights, short-haul international flights to Canada, Mexico, the Caribbean, and most long-haul international flights departing from North or South America. ☎️+1 (855) 217-1878 This does not apply to Basic Economy tickets unless a special waiver is in effect. ☎️+1 (855) 217-1878 Always review your fare rules at the time of booking.

    When changing a flight, you may not pay a change fee, but you must still pay any fare difference. If the new flight costs more, you pay. ☎️+1 (855) 217-1878 If the new flight is cheaper, American Airlines may issue a flight credit for the difference. ☎️+1 (855) 217-1878 This credit is generally valid for 12 months.

    American also offers a same-day confirmed change option, which is different from the regular change policy. For $75, you can switch to an earlier or later flight. ☎️+1 (855) 217-1878 This is not covered under the free change rule unless you're an AAdvantage elite member. ☎️+1 (855) 217-1878 In that case, same-day fees may be waived.

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  7. Daily UK flights

    • ons.gov.uk
    • cy.ons.gov.uk
    xlsx
    Updated Jul 10, 2025
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    Office for National Statistics (2025). Daily UK flights [Dataset]. https://www.ons.gov.uk/economy/economicoutputandproductivity/output/datasets/dailyukflights
    Explore at:
    xlsxAvailable download formats
    Dataset updated
    Jul 10, 2025
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    United Kingdom
    Description

    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.

  8. P

    ###How much does American charge to change a flight date? Dataset

    • paperswithcode.com
    Updated Jun 28, 2025
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    (2025). ###How much does American charge to change a flight date? Dataset [Dataset]. https://paperswithcode.com/dataset/how-much-does-american-charge-to-change-a-2
    Explore at:
    Dataset updated
    Jun 28, 2025
    Description

    As of 2024, American Airlines no longer charges a change fee for most domestic, short-haul international, and select long-haul flights. ☎️+1 (855) 217-1878 This policy primarily applies to Main Cabin, Premium Economy, Business, and First Class fares. ☎️+1 (855) 217-1878 However, Basic Economy remains excluded from this flexible option.

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  9. Airplane Crash Data Since 1908

    • kaggle.com
    zip
    Updated Aug 20, 2019
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    Cem (2019). Airplane Crash Data Since 1908 [Dataset]. https://www.kaggle.com/datasets/cgurkan/airplane-crash-data-since-1908
    Explore at:
    zip(635504 bytes)Available download formats
    Dataset updated
    Aug 20, 2019
    Authors
    Cem
    License

    Open Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
    License information was derived automatically

    Description

    Context

    The aviation accident database throughout the world, from 1908-2019.

    • All civil and commercial aviation accidents of scheduled and non-scheduled passenger airliners worldwide, which resulted in a fatality (including all U.S. Part 121 and Part 135 fatal accidents)
    • All cargo, positioning, ferry and test flight fatal accidents.
    • All military transport accidents with 10 or more fatalities.
    • All commercial and military helicopter accidents with greater than 10 fatalities.
    • All civil and military airship accidents involving fatalities.
    • Aviation accidents involving the death of famous people.
    • Aviation accidents or incidents of noteworthy interest.

    There are similar dataset available on Kaggle. This dataset is cleaned versioned and source code is available on github.

    Content

    Data is scraped from planecrashinfo.com. Below you can find the dataset column descriptions:

    • Date: Date of accident, in the format - January 01, 2001
    • Time: Local time, in 24 hr. format unless otherwise specified
    • Airline/Op: Airline or operator of the aircraft
    • Flight #: Flight number assigned by the aircraft operator
    • Route: Complete or partial route flown prior to the accident
    • AC Type: Aircraft type
    • Reg: ICAO registration of the aircraft
    • cn / ln: Construction or serial number / Line or fuselage number
    • Aboard: Total aboard (passengers / crew)
    • Fatalities: Total fatalities aboard (passengers / crew)
    • Ground: Total killed on the ground
    • Summary: Brief description of the accident and cause if known

    Acknowledgements

    The original data is from the Plane Crash info website (http://www.planecrashinfo.com/database.htm). Dataset is scraped with Python. Source code is also public on Github

    Inspiration

    Find the root cause of plane crashes. Find any insights from dataset such as - Which operators are the worst - Which aircrafts are the worst

  10. Air passenger traffic in India FY 2010-2024

    • statista.com
    Updated Dec 4, 2024
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    Statista (2024). Air passenger traffic in India FY 2010-2024 [Dataset]. https://www.statista.com/statistics/1252947/india-air-passenger-traffic/
    Explore at:
    Dataset updated
    Dec 4, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    India
    Description

    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.

  11. s

    Air passenger origin and destination, transborder journeys, detailed...

    • www150.statcan.gc.ca
    • open.canada.ca
    • +2more
    Updated Jan 17, 2020
    + more versions
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    Government of Canada, Statistics Canada (2020). Air passenger origin and destination, transborder journeys, detailed presentation of outbound and inbound passengers exceeding 400, annual [Dataset]. http://doi.org/10.25318/2310024901-eng
    Explore at:
    Dataset updated
    Jan 17, 2020
    Dataset provided by
    Government of Canada, Statistics Canada
    Area covered
    Canada
    Description

    Air passenger origin and destination data, for transborder journeys, by outbound and inbound passengers exceeding 400, by city and city-pair, annual.

  12. FiveThirtyEight Male Flight Attendants Dataset

    • kaggle.com
    Updated Apr 26, 2019
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    FiveThirtyEight (2019). FiveThirtyEight Male Flight Attendants Dataset [Dataset]. https://www.kaggle.com/fivethirtyeight/fivethirtyeight-male-flight-attendants-dataset/discussion
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Apr 26, 2019
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    FiveThirtyEight
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Content

    Male Flight Attendants

    This folder contains the data behind the story Dear Mona, How Many Flight Attendants Are Men?

    male-flight-attendants.tsv contains the percentage of U.S. employees that are male in 320 different job categories.

    Source: IPUMS, 2012

    Context

    This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

    • Update Frequency: This dataset is updated daily.

    Acknowledgements

    This dataset is maintained using GitHub's API and Kaggle's API.

    This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

  13. Trips by Distance

    • catalog.data.gov
    • s.cnmilf.com
    Updated Feb 1, 2023
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    Bureau of Transportation Statistics (2023). Trips by Distance [Dataset]. https://catalog.data.gov/dataset/trips-by-distance
    Explore at:
    Dataset updated
    Feb 1, 2023
    Dataset provided by
    Bureau of Transportation Statisticshttp://www.rita.dot.gov/bts
    Description

    Updates are delayed due to technical difficulties. How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

  14. P

    @##How Long Before an American Flight Can You Change It? Dataset

    • paperswithcode.com
    Updated Jun 28, 2025
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    (2025). @##How Long Before an American Flight Can You Change It? Dataset [Dataset]. https://paperswithcode.com/dataset/how-long-before-an-american-flight-can-you
    Explore at:
    Dataset updated
    Jun 28, 2025
    Description

    Approximately 83% of American Airlines passengers fly with non-refundable tickets, which makes the question of change deadlines particularly important. ☎️+1 (855) 217-1878 For most ticket types, changes must be made at least 15 minutes before departure. ☎️+1 (855) 217-1878 Waiting longer may result in a complete forfeiture of the fare.

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  15. w

    Immigration system statistics data tables

    • gov.uk
    Updated May 22, 2025
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    Home Office (2025). Immigration system statistics data tables [Dataset]. https://www.gov.uk/government/statistical-data-sets/immigration-system-statistics-data-tables
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    Dataset updated
    May 22, 2025
    Dataset provided by
    GOV.UK
    Authors
    Home Office
    Description

    List of the data tables as part of the Immigration System Statistics Home Office release. Summary and detailed data tables covering the immigration system, including out-of-country and in-country visas, asylum, detention, and returns.

    If you have any feedback, please email MigrationStatsEnquiries@homeoffice.gov.uk.

    Accessible file formats

    The Microsoft Excel .xlsx files may not be suitable for users of assistive technology.
    If you use assistive technology (such as a screen reader) and need a version of these documents in a more accessible format, please email MigrationStatsEnquiries@homeoffice.gov.uk
    Please tell us what format you need. It will help us if you say what assistive technology you use.

    Related content

    Immigration system statistics, year ending March 2025
    Immigration system statistics quarterly release
    Immigration system statistics user guide
    Publishing detailed data tables in migration statistics
    Policy and legislative changes affecting migration to the UK: timeline
    Immigration statistics data archives

    Passenger arrivals

    https://assets.publishing.service.gov.uk/media/68258d71aa3556876875ec80/passenger-arrivals-summary-mar-2025-tables.xlsx">Passenger arrivals summary tables, year ending March 2025 (MS Excel Spreadsheet, 66.5 KB)

    ‘Passengers refused entry at the border summary tables’ and ‘Passengers refused entry at the border detailed datasets’ have been discontinued. The latest published versions of these tables are from February 2025 and are available in the ‘Passenger refusals – release discontinued’ section. A similar data series, ‘Refused entry at port and subsequently departed’, is available within the Returns detailed and summary tables.

    Electronic travel authorisation

    https://assets.publishing.service.gov.uk/media/681e406753add7d476d8187f/electronic-travel-authorisation-datasets-mar-2025.xlsx">Electronic travel authorisation detailed datasets, year ending March 2025 (MS Excel Spreadsheet, 56.7 KB)
    ETA_D01: Applications for electronic travel authorisations, by nationality ETA_D02: Outcomes of applications for electronic travel authorisations, by nationality

    Entry clearance visas granted outside the UK

    https://assets.publishing.service.gov.uk/media/68247953b296b83ad5262ed7/visas-summary-mar-2025-tables.xlsx">Entry clearance visas summary tables, year ending March 2025 (MS Excel Spreadsheet, 113 KB)

    https://assets.publishing.service.gov.uk/media/682c4241010c5c28d1c7e820/entry-clearance-visa-outcomes-datasets-mar-2025.xlsx">Entry clearance visa applications and outcomes detailed datasets, year ending March 2025 (MS Excel Spreadsheet, 29.1 MB)
    Vis_D01: Entry clearance visa applications, by nationality and visa type
    Vis_D02: Outcomes of entry clearance visa applications, by nationality, visa type, and outcome

    Additional dat

  16. Airline Loyalty Program (Canada)

    • kaggle.com
    Updated May 28, 2025
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    Siddharth Vora (2025). Airline Loyalty Program (Canada) [Dataset]. https://www.kaggle.com/datasets/siddharth0935/airline-loyalty-program
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 28, 2025
    Dataset provided by
    Kaggle
    Authors
    Siddharth Vora
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Area covered
    Canada
    Description

    Airline Loyalty Program Promotion Dataset

    This dataset contains information about customer activity and demographics related to an airline's loyalty program, including a promotional campaign aimed at enhancing program enrollment.

    Files

    1. Customer Flight Activity.csv

    FieldDescription
    Loyalty NumberCustomer's unique loyalty number
    YearYear of the period
    MonthMonth of the period
    Flights BookedNumber of flights booked for member only in the period
    Flights with CompanionsNumber of flights booked with additional passengers in the period
    Total FlightsSum of Flights Booked and Flights with Companions
    DistanceFlight distance traveled in the period (km)
    Points AccumulatedLoyalty points accumulated in the period
    Points RedeemedLoyalty points redeemed in the period
    Dollar Cost Points RedeemedDollar equivalent for points redeemed in the period in CDN

    2. Customer Loyalty History.csv

    FieldDescription
    Loyalty NumberCustomer's unique loyalty number
    CountryCountry of residence
    ProvinceProvince of residence
    CityCity of residence
    Postal CodePostal code of residence
    GenderGender
    EducationHighest education level (High school or lower > College > Bachelor > Master > Doctor)
    SalaryAnnual income
    Marital StatusMarital status (Single, Married, Divorced)
    Loyalty CardLoyalty card status (Star > Nova > Aurora)
    CLVCustomer lifetime value - total invoice value for all flights ever booked by member
    Enrollment TypeEnrollment type (Standard / 2018 Promotion)
    Enrollment YearYear Member enrolled in membership program
    Enrollment MonthMonth Member enrolled in membership program
    Cancellation YearYear Member cancelled their membership
    Cancellation MonthMonth Member cancelled their membership

    Context

    The airline implemented a promotional campaign (2018 Promotion) aimed at enhancing program enrollment. The dataset encompasses information regarding: - Customer flight activity and loyalty points - Program signups and enrollment details - Cancellations within the loyalty program - Comprehensive customer demographics

    Potential Use Cases

    • Analyze the effectiveness of the promotional campaign
    • Predict customer churn/cancellations
    • Identify high-value customer segments
    • Understand factors influencing loyalty program engagement
    • Optimize loyalty point redemption strategies
  17. P

    ++Can I Book a Frontier Airlines Flight? Dataset

    • paperswithcode.com
    Updated Jun 23, 2025
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    (2025). ++Can I Book a Frontier Airlines Flight? Dataset [Dataset]. https://paperswithcode.com/dataset/can-i-book-a-frontier-airlines-flight
    Explore at:
    Dataset updated
    Jun 23, 2025
    Description

    Yes—you can book a Frontier Airlines flight easily, and the fastest way is by calling +1 (877) 443‑8285 ☎️ to speak directly with a live Frontier representative. Whether you want help with flight options, seat selection, or special requests, the agent at +1 (877) 443‑8285 ☎️ can assist you in real time.

    Booking through Frontier’s website can be convenient, but it sometimes hides add‑ons or seat fees until the very end. By calling +1 (877) 443‑8285 ☎️, you’ll get transparent pricing and personalized assistance. The agent will walk you through all fare options and fees step by step—no surprises.

    Need help using Frontier Miles or adding a discount code? Just let the rep know during your call to +1 (877) 443‑8285 ☎️. They can apply your miles, loyalty perks, or promo codes as they book the flight, ensuring nothing is missed or misentered.

    Want to reserve a particular seat—such as exit row or extra legroom? Mention your preference to the agent at +1 (877) 443‑8285 ☎️. They can display available seat maps and select the best spot based on your needs and budget while you're on the call.

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  18. Recreational Flyer Fixed Sites

    • udds-faa.opendata.arcgis.com
    • azgeo-data-hub-agic.hub.arcgis.com
    • +4more
    Updated May 16, 2019
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    Federal Aviation Administration - AIS (2019). Recreational Flyer Fixed Sites [Dataset]. https://udds-faa.opendata.arcgis.com/datasets/faa::recreational-flyer-fixed-sites/about
    Explore at:
    Dataset updated
    May 16, 2019
    Dataset provided by
    Federal Aviation Administrationhttp://www.faa.gov/
    Authors
    Federal Aviation Administration - AIS
    Area covered
    Description

    This dataset represents active recreational flyer fixed sites (commonly referred to as flying fields) that are established by an agreement with the FAA. The fixed sites depicted here are located in controlled airspace two or more miles from an airport. At these sites, recreational UAS operations are authorized up to the unmanned aircraft system (UAS) facility map (UASFM) altitudes. If you fly at the fixed sites depicted in this dataset within controlled airspace, you must adhere to the operating limitations of the agreement, which is available from the fixed site sponsor.The FAA currently is upgrading LAANC (Low Altitude Authorization and Notification Capability) to enable recreational flyers to obtain automated authorization to controlled airspace. The FAA is committed to quickly implementing LAANC for recreational flyers. The FAA also is exploring upgrades to DroneZone to enable access for recreational flyers. Until LAANC is available for recreational operations, the FAA is granting temporary airspace authorizations to operate at certain fixed sites (commonly referred to as flying fields) that are established by an agreement with the FAA. For fixed sites that are located in controlled airspace two or more miles from an airport, operations are authorized up to the unmanned aircraft system (UAS) facility map (UASFM) altitudes. The FAA is reviewing fixed sites located within two miles of an airport and will make individualized determinations of what airspace authorization is appropriate. Aeromodelling organizations that sponsor fixed sites, regardless of their location within controlled airspace, can obtain additional information about requesting airspace authorization by email at UAShelp@faa.gov. During this interim period, you may fly in controlled airspace only at authorized fixed sites. The list of authorized fixed sites is available on the FAA’s website at www.faa.gov/uas and will be depicted on the maps on the FAA’s UAS Data Delivery System, which is available at https://udds-faa.opendata.arcgis.com. Agreements establishing fixed sites may contain additional operating limitations. If you fly at a fixed site in controlled airspace, you must adhere to the operating limitations of the agreement, which is available from the fixed site sponsor.As a reminder, existing FAA rules provide that you may not operate in any designated restricted or prohibited airspace. This includes airspace restricted for national security reasons or to safeguard emergency operations, including law enforcement activities. The easiest way to determine whether any restrictions or special requirements are in effect as well as the authorized altitudes where you want to fly is to use the maps on the FAA’s UAS Data Delivery System, which is available at https://udds-faa.opendata.arcgis.com, and to check for the latest FAA Notices to Airmen (NOTAMs). This information may also be available from third-party applications.The FAA will provide notice when LAANC is available for use by recreational flyers.Alternatively, during this interim period, the FAA directs recreational flyers to existing basic safety guidelines, which are based on industry best practices, on its website (faa.gov/uas): • Fly only for recreational purposes • Keep your unmanned aircraft within your visual line-of-sight or within the visual line of sight of a visual observer who is co-located and in direct communication with you • Do not fly above 400 feet in uncontrolled (Class G) airspace • Do not fly in controlled airspace without an FAA authorization • Follow all FAA airspace restrictions, including special security instructions and temporary flight restrictions • Never fly near other aircraft • Always give way to all other aircraft • Never fly over groups of people, public events, or stadiums full of people • Never fly near emergency response activities • Never fly under the influence of drugs or alcoholYou also should be able to explain to an FAA inspector or law enforcement official which safety guidelines you are following if you are flying under the exception for limited recreational unmanned aircraft operations.Please do not contact FAA Air Traffic facilities for airspace authorization because these facilities will no longer accept requests to operate recreational unmanned aircraft in controlled airspace.Please continue to check faa.gov/uas on a regular basis for the most current directions and guidance.

  19. Flight Crew Physiological Data for Crew State Monitoring - Dataset - NASA...

    • data.nasa.gov
    • data.staging.idas-ds1.appdat.jsc.nasa.gov
    Updated Feb 24, 2019
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    nasa.gov (2019). Flight Crew Physiological Data for Crew State Monitoring - Dataset - NASA Open Data Portal [Dataset]. https://data.nasa.gov/dataset/flight-crew-physiological-data-for-crew-state-monitoring
    Explore at:
    Dataset updated
    Feb 24, 2019
    Dataset provided by
    NASAhttp://nasa.gov/
    Description

    This physiological data was collected from pilot/copilot pairs in and out of a flight simulator. It was collected to train machine-learning models to aid in the detection of pilot attentive states. The benchmark training set is comprised of a set of controlled experiments collected in a non-flight environment, outside of a flight simulator. The test set (abbreviated LOFT = Line Oriented Flight Training) consists of a full flight (take off, flight, and landing) in a flight simulator. The pilots experienced distractions intended to induce one of the following three cognitive states: Channelized Attention (CA) is the state of being focused on one task to the exclusion of all others. This is induced in benchmarking by having the subjects play an engaging puzzle-based video game. Diverted Attention (DA) is the state of having one’s attention diverted by actions or thought processes associated with a decision. This is induced by having the subjects perform a display monitoring task. Periodically, a math problem showed up which had to be solved before returning to the monitoring task. Startle/Surprise (SS) is induced by having the subjects watch movie clips with jump scares. For each experiment, a pair of pilots (each with its own crew ID) was recorded over time and subjected to the CA, DA, or SS cognitive states. The training set contains three experiments (one for each state) in which the pilots experienced just one of the states. For example, in the experiment labelled CA, the pilots were either in a baseline state (no event) or the CA state. The test set contains a full flight simulation during which the pilots could experience any of the states (but never more than one at a time). Each sensor operated at a sample rate of 256 Hz. Please note that since this is physiological data from real people, there will be noise and artifacts in the data.

  20. Mode of travel

    • gov.uk
    Updated Apr 16, 2025
    + more versions
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    Department for Transport (2025). Mode of travel [Dataset]. https://www.gov.uk/government/statistical-data-sets/nts03-modal-comparisons
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    Dataset updated
    Apr 16, 2025
    Dataset provided by
    GOV.UKhttp://gov.uk/
    Authors
    Department for Transport
    Description

    Accessible Tables and Improved Quality

    As part of the Analysis Function Reproducible Analytical Pipeline Strategy, processes to create all National Travel Survey (NTS) statistics tables have been improved to follow the principles of Reproducible Analytical Pipelines (RAP). This has resulted in improved efficiency and quality of NTS tables and therefore some historical estimates have seen very minor change, at least the fifth decimal place.

    All NTS tables have also been redesigned in an accessible format where they can be used by as many people as possible, including people with an impaired vision, motor difficulties, cognitive impairments or learning disabilities and deafness or impaired hearing.

    If you wish to provide feedback on these changes then please email national.travelsurvey@dft.gov.uk.

    Revision to table NTS9919

    On the 16th April 2025, the figures in table NTS9919 have been revised and recalculated to include only day 1 of the travel diary where short walks of less than a mile are recorded (from 2017 onwards), whereas previous versions included all days. This is to more accurately capture the proportion of trips which include short walks before a surface rail stage. This revision has resulted in fewer available breakdowns than previously published due to the smaller sample sizes.

    Trips, stages, distance and time spent travelling

    NTS0303: https://assets.publishing.service.gov.uk/media/66ce0f118e33f28aae7e1f75/nts0303.ods">Average number of trips, stages, miles and time spent travelling by mode: England, 2002 onwards (ODS, 53.9 KB)

    NTS0308: https://assets.publishing.service.gov.uk/media/66ce0f128e33f28aae7e1f76/nts0308.ods">Average number of trips and distance travelled by trip length and main mode; England, 2002 onwards (ODS, 191 KB)

    NTS0312: https://assets.publishing.service.gov.uk/media/66ce0f12bc00d93a0c7e1f71/nts0312.ods">Walks of 20 minutes or more by age and frequency: England, 2002 onwards (ODS, 35.1 KB)

    NTS0313: https://assets.publishing.service.gov.uk/media/66ce0f12bc00d93a0c7e1f72/nts0313.ods">Frequency of use of different transport modes: England, 2003 onwards (ODS, 27.1 KB)

    NTS0412: https://assets.publishing.service.gov.uk/media/66ce0f1325c035a11941f653/nts0412.ods">Commuter trips and distance by employment status and main mode: England, 2002 onwards (ODS, 53.8 KB)

    NTS0504: https://assets.publishing.service.gov.uk/media/66ce0f141aaf41b21139cf7d/nts0504.ods">Average number of trips by day of the week or month and purpose or main mode: England, 2002 onwards (ODS, 141 KB)

    <h2 id=

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Statista, Global air traffic - scheduled passengers 2004-2024 [Dataset]. https://www.statista.com/statistics/564717/airline-industry-passenger-traffic-globally/
Organization logo

Global air traffic - scheduled passengers 2004-2024

Explore at:
154 scholarly articles cite this dataset (View in Google Scholar)
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
Worldwide
Description

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.

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