14 datasets found
  1. 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/
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    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.

  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
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    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. 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/
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    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.

  4. 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
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    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.

  5. 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
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    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.

  6. a

    Global Airline Routes

    • hub.arcgis.com
    Updated May 30, 2018
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    Global Airline Routes [Dataset]. https://hub.arcgis.com/datasets/Story::global-airline-routes/about
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    Dataset updated
    May 30, 2018
    Dataset authored and provided by
    ArcGIS StoryMaps
    Area covered
    Description

    This layer visualizes over 60,000 commercial flight paths. The data was obtained from openflights.org, and was last updated in June 2014. The site states, "The third-party that OpenFlights uses for route data ceased providing updates in June 2014. The current data is of historical value only. As of June 2014, the OpenFlights/Airline Route Mapper Route Database contains 67,663 routes between 3,321 airports on 548 airlines spanning the globe. Creating and maintaining this database has required and continues to require an immense amount of work. We need your support to keep this database up-to-date."To donate, visit the site and click the PayPal link.Routes were created using the XY-to-line tool in ArcGIS Pro, inspired by Kenneth Field's work, and following a modified methodology from Michael Markieta (www.spatialanalysis.ca/2011/global-connectivity-mapping-out-flight-routes).Some cleanup was required in the original data, including adding missing location data for several airports and some missing IATA codes. Before performing the point to line conversion, the key to preserving attributes in the original data is a combination of the INDEX and MATCH functions in Microsoft Excel. Example function: =INDEX(Airlines!$B$2:$B$6200,MATCH(Routes!$A2,Airlines!$D$2:Airlines!$D$6200,0))                                                

  7. 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
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    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.

  8. g

    Passenger Numbers, Commercial Flights and Freight at Irish Airports

    • rdm.geohive.ie
    Updated May 24, 2022
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    rdm_curator (2022). Passenger Numbers, Commercial Flights and Freight at Irish Airports [Dataset]. https://rdm.geohive.ie/datasets/0065aa8664044d9696c278f753f0c658
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    Dataset updated
    May 24, 2022
    Dataset authored and provided by
    rdm_curator
    Description

    Description: Aviation statistics are compiled from data supplied by all Irish airports. The following Irish airports provide data to the Central Statistics Office: Dublin, Cork, Shannon, Kerry, Knock, Waterford, Connemara, Donegal and Inishmore. Galway and Sligo airports ceased operations in 2011. There have been no commercial flights in Waterford Airport since June 2016. Data for the five main airports is supplied on a monthly basis. Data for regional airports is supplied annually to the Central Statistics Office.This dataset provides a time-series view of international passenger numbers, commercial flights and total freight (tonnes) for the five main airports of Dublin Shannon, Cork, Knock and Kerry Geography available in RDM: Five main airportsSource: CSO Air and Sea Travel Statistics Weblink: https://www.cso.ie/en/statistics/tourismandtravel/airandseatravelstatistics/Date of last source data update: August 2023Update Schedule: Quarterly Update

  9. R

    Dataset Dataset

    • universe.roboflow.com
    zip
    Updated Apr 28, 2023
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    new-workspace-tmery (2023). Dataset Dataset [Dataset]. https://universe.roboflow.com/new-workspace-tmery/dataset-hwdhe/dataset/1
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    zipAvailable download formats
    Dataset updated
    Apr 28, 2023
    Dataset authored and provided by
    new-workspace-tmery
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Plane Bounding Boxes
    Description

    Here are a few use cases for this project:

    1. Aviation Surveillance: This model could be utilized for real-time monitoring activities on large airfields, helping to identify various aircraft types including commercial planes and military planes.

    2. Military Intelligence: Security forces could use it to analyze satellite or drone footage, distinguishing between military and civilian aircrafts. This might be useful in areas of conflict for monitoring enemy activities.

    3. Aircraft Research: Aviation research organizations can use this model for developing and testing new types of aircraft. They can compare the identified class with the expected one to verify if the new plane corresponds to the desired design.

    4. Air Traffic Control Training: The model could be used in simulation training programs for air traffic controllers, assisting in teaching trainees how to distinguish between different classes of planes.

    5. Drone Safety: Drone operators can use this model to help automatic drone collision avoidance systems distinguish between plane types, improving safety by providing appropriate distances to maintain depending on the category of aircraft detected.

  10. 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
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    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

  11. USA Airports

    • hub.arcgis.com
    • prep-response-portal.napsgfoundation.org
    • +3more
    Updated Dec 9, 2014
    + more versions
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    Esri (2014). USA Airports [Dataset]. https://hub.arcgis.com/maps/5d93352406744d658d9c1f43f12b560c
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    Dataset updated
    Dec 9, 2014
    Dataset authored and provided by
    Esrihttp://esri.com/
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    Description

    The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product.

  12. Unmanned Aerial Vehicle Image Dataset of the Built Environment for 3D...

    • zenodo.org
    • explore.openaire.eu
    zip
    Updated Jun 2, 2023
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    Samuel Fernandes; Anand Prakash; Jessica Granderson; Samuel Fernandes; Anand Prakash; Jessica Granderson (2023). Unmanned Aerial Vehicle Image Dataset of the Built Environment for 3D reconstruction (UAVID3D) [Dataset]. http://doi.org/10.5281/zenodo.7968619
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 2, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Samuel Fernandes; Anand Prakash; Jessica Granderson; Samuel Fernandes; Anand Prakash; Jessica Granderson
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Unmanned Aerial Vehicles (UAV) provide increased access to unique types of urban imagery traditionally not available. Advanced machine learning and computer vision techniques when applied to UAV RGB image data can be used for automated extraction of building asset information and if applied to UAV thermal imagery data can detect potential thermal anomalies. However, these UAV datasets are not easily available to researchers, thereby creating a barrier to accelerating research in this area.

    To assist researchers with added data to develop machine learning algorithms, we present UAVID3D (Unmanned Aerial Vehicle (UAV) Image Dataset of the Built Environment for 3D reconstruction). The raw images for our dataset were recorded with a Zenmuse XT2 visual (RGB) and a FLIR Tau 2 (thermal, https://flir.netx.net/file/asset/15598/original/) camera on a DJI Mavic 2 pro drone (https://www.dji.com/matrice-200-series). The thermal camera is factory calibrated. All data is organized and structured to comply with FAIR principles, i.e. being findable, accessible, interoperable, and reusable. It is publicly available and can be downloaded from the Zenodo data repository.

    RGB images were recorded during UAV fly-overs of two different commercial buildings in Northern California. In addition, thermographic images were recorded during 2 subsequent UAV fly-overs of the same two buildings. UAV flights were recorded at flight heights between 60–80 m above ground with a flight speed of 1 m s and contain GPS information. All images were recorded during drone flights on May 10, 2021 between 8:45 am and 10:30 am and on May 19, 2021 between 2:15 pm and 4:30 pm. Outdoor air temperatures on these two days during the flights were between 78 and 83 degree fahrenheit and between 58 and 65 degree fahrenheit respectively.

    For the RGB flights, UAV path was planned and captured using an orbital flight plan in PIX4D capture at normal flight speed and overlap angle of 10 degree. Thermal images were captured by manual flights approximately 5 m away from each building facade. Due to the high overlap of images, similarities from feature points identified in each image can be extracted to conduct photogrammetry. Photogrammetry allows estimation of the three-dimensional coordinates of points on an object in a generated 3D space involving measurements made on images taken with a high overlap rate. Photogrammetry can be used to create a 3D point cloud model of the recorded region. UAVID3D dataset is a series of compressed archive files totaling 21GB. Useful pipelines to process these images can be found at these two repositories https://github.com/LBNL-ETA/a3dbr, and https://github.com/LBNL-ETA/AutoBFE

    This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Program, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

  13. Z

    DUCC - Dataset for UAS Cellular Communications

    • data.niaid.nih.gov
    Updated Jan 18, 2024
    + more versions
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    Purucker, Patrick (2024). DUCC - Dataset for UAS Cellular Communications [Dataset]. https://data.niaid.nih.gov/resources?id=zenodo_10148421
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    Dataset updated
    Jan 18, 2024
    Dataset provided by
    Reil, Christian
    Purucker, Patrick
    Hoess, Alfred
    License

    Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
    License information was derived automatically

    Description

    MotivationThe Dataset for Unmanned Aircraft System (UAS) Cellular Communications, short DUCC, was created with the aim of advancing communications for Beyond Visual Line of Sight (BVLOS) operations. With this objective in mind, datasets were generated to analyse the behaviour of cellular communications for UAS operations.

    MeasurementA measurement setup was implemented to execute the measurements. Two Sierra Wireless EM9191 modems possessing both LTE and 5G capabilities were utilized in order to establish a connection to the cellular network and measure the physical parameters of the air-link. Every modem was equipped with four Taoglas antennas, two of type TG 35.8113 and two of type TG 45.8113. To capture the measurements a Raspberry Pi 4B is used. All hardware components were integrated into a box and attached to a DJI Matrice 300 RTK. A connection to the drone controller has been established to obtain location, speed and attitude. To measure end-to-end network parameters, dummy data was exchanged bidirectionally between the Raspberry Pi and a server. Both the server as well as the Raspberry Pi are synchronized with the GPS time in order to measure the one-way packet delay. For this purpose, we utilised Iperf3 and customised it to suit our requirements. To ensure precise positioning of the drone a Real Time Kinematik (RTK) station was placed on the ground during the measurements.

    The measurements were performed at three distinct rural locations. Waypoint flights were undertaken with the points arranged in a cuboid formation maximizing the coverage of the air volume. Thereby, the campaigns were conducted with varying drone speeds. Moreover, for location A, different flight routes with rotated grids were implemented to reduce bias. Finally, a validation dataset is provided for location A, where the waypoints were calculated according to Quality of Service (QoS) based path-planning.

    Dataset Structure and UsageThe dataset's structure consists of:-- Dataset |-- LocationX |-- RouteX (in case different routes at LocationX were created) |-- LocXRouteX.kml (file containing the waypoints in the kml format) |-- SpeedXMeterPerSecond (folder containing the datasets recorded with a specific drone speed) |-- YYYY-MM-DD hh_mm_ss.s.pkl.gz (Dataset file) |-- RouteY |-- ... |-- ...

    The dataset files can be loaded using the pandas module in python3. The file "load.py" provides a sample script for loading a dataset as well as the corresponding .kml file which contains the predefined waypoints. In the file "Parameter_Description.csv" each parameter measured is further explained.

    LicenseAll datasets are copyright by us and published under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. This dataset is made available for academic use only. However, we take your privacy seriously! If you find yourself or personal belongings in this dataset and feel unwell about it, please contact us at automotive@oth-aw.de and we will immediately remove the respective data from our server.

    AchnowledgementThe authors gratefully acknowledge the following European Union H2020 -- ECSEL Joint Undertaking project for financial support including funding by the German Federal Ministry for Education and Research (BMBF): ADACORSA (Grant Agreement No. 876019, funding code 16MEE0039).

  14. Fatal civil airliner accidents by country and region 1945-2022

    • statista.com
    • ai-chatbox.pro
    Updated Apr 16, 2024
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    Statista (2024). Fatal civil airliner accidents by country and region 1945-2022 [Dataset]. https://www.statista.com/statistics/262867/fatal-civil-airliner-accidents-since-1945-by-country-and-region/
    Explore at:
    Dataset updated
    Apr 16, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    Worldwide
    Description

    As a result of the continued annual growth in global air traffic passenger demand, the number of airplanes that were involved in accidents is on the increase. Although the United States is ranked among the 20 countries with the highest quality of air infrastructure, the U.S. reports the highest number of civil airliner accidents worldwide. 2020 was the year with more plane crashes victims, despite fewer flights The number of people killed in accidents involving large commercial aircraft has risen globally in 2020, even though the number of commercial flights performed last year dropped by 57 percent to 16.4 million. More than half of the total number of deaths were recorded in January 2020, when an Ukrainian plane was shot down in Iranian airspace, a tragedy that killed 176 people. The second fatal incident took place in May, when a Pakistani airliner crashed, killing 97 people. Changes in aviation safety In terms of fatal accidents, it seems that aviation safety experienced some decline on a couple of parameters. For example, there were 0.37 jet hull losses per one million flights in 2016. In 2017, passenger flights recorded the safest year in world history, with only 0.11 jet hull losses per one million flights. In 2020, the region with the highest hull loss rate was the Commonwealth of Independent States. These figures do not take into account accidents involving military, training, private, cargo and helicopter flights.

  15. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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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/
Organization logo

Global air traffic - number of flights 2004-2025

Explore at:
100 scholarly articles cite this dataset (View in Google Scholar)
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.

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