This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. Context on the original work is given in the related article (https://journals.open.tudelft.nl/joas/article/download/7201/5683) and on the associated GitHub page (https://github.com/AeroMAPS/AeroSCOPE/).A simple data exploration interface will be available at www.aeromaps.eu/aeroscope.The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data. 1- DISCLAIMER The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use. As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance. 2- DESCRIPTION Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple. Please refer to the support article for more details (see above). The dataset contains the following columns:

"First column" : index airline_iata : IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found. acft_icao : ICAO code of the aircraft type acft_class : Aircraft class identifier, own classification.

WB: Wide Body NB: Narrow Body RJ: Regional Jet PJ: Private Jet TP: Turbo Propeller PP: Piston Propeller HE: Helicopter OTHER seymour_proxy: Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model. source: Original data source for the record, before compilation and enrichment.

ANAC: Brasilian Civil Aviation Authorities AUS Stats: Australian Civil Aviation Authorities BTS: US Bureau of Transportation Statistics T100 Estimation: Own model, estimation on Wikipedia-parsed route database Eurocontrol: Aggregation and enrichment of R&D database OpenSky World Bank seats: Number of seats available for the data entry, AFTER airport residual scaling n_flights: Number of flights of the data entry, when available iata_departure, iata_arrival : IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal. departure_lon, departure_lat, arrival_lon, arrival_lat : Origin and destination coordinates, could be NaN if the IATA identifier is erroneous departure_country, arrival_country: Origin and destination country ISO2 code. WARNING: disable NA (Namibia) as default NaN at import departure_continent, arrival_continent: Origin and destination continent code. WARNING: disable NA (North America) as default NaN at import seats_no_est_scaling: Number of seats available for the data entry, BEFORE airport residual scaling distance_km: Flight distance (km) ask: Available Seat Kilometres rpk: Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor) fuel_burn_seymour: Fuel burn per flight (kg) when seymour proxy available fuel_burn: Total fuel burn of the data entry (kg) co2: Total CO2 emissions of the data entry (kg) domestic: Domestic/international boolean (Domestic=1, International=0)

3- Citation Please cite the support paper instead of the dataset itself.

Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2023). Compilation of an open-source traffic and CO2 emissions dataset for commercial aviation. Journal of Open Aviation Science. https://doi.org/10.59490/joas.2023.7201

The Aviation Facilities dataset is updated every 28 days from the Federal Aviation Administration (FAA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The Aviation Facilities dataset is a geographic point database of all official and operational aerodromes in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the aerodrome, 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. For more information about these data, please visit: https://www.faa.gov/air_traffic/flight_info/aeronav/Aero_Data/NASR_Subscription. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529011

Quarterly civil aviation operating statistics (passengers, goods carried (kilograms), passenger-kilometres, passenger tonne-kilometres, goods tonne-kilometres, total tonne-kilometres, hours flown) by sector (total domestic and international, domestic, international, transborder (Canada - United States) and other international). Data are for Canadian air carriers, Levels I and II combined. All data are expressed in thousands.

Airport defines area on land or water intended to be used either wholly or in part for the arrival; departure and surface movement of aircraft/helicopters. This airport data is provided as a vector geospatial-enabled file format and depicted on Enroute charts.Airport information is published every eight weeks by the U.S. Department of Transportation, Federal Aviation Administration-Aeronautical Information Services.Current Effective Date: 0901Z 12 Jun 2025 to 0901Z 07 Aug 2025

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.

This dataset provides detailed statistics on passenger, cargo, and aircraft traffic flows across airports and airlines in Pakistan. It includes annual volumes, domestic and international route data, and trends in air transport activity. The information supports analysis related to travel demand, infrastructure planning, and performance monitoring within the aviation sector. The data is sourced from the Pakistan Civil Aviation Authority (PCAA).

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.

Quarterly civil aviation operating statistics (passengers, goods carried (kilograms), passenger-kilometres, passenger tonne-kilometres, goods tonne-kilometres, total tonne-kilometres, hours flown) for scheduled and charter services both separately and combined, for Canadian air carriers, Levels I and II combined. For scheduled services only, operating statistics also include available seat-kilometres, passenger load factor (percent), available tonne-kilometres and weight load factor (percent). Data are expressed in thousands, except for the number of carriers included, which is expressed in full.

Open Data Portal for Airports Market Outlook

According to our latest research, the Open Data Portal for Airports market size reached USD 1.62 billion in 2024 globally. The market is experiencing robust growth, supported by a CAGR of 12.7% from 2025 to 2033. By the end of 2033, the market is projected to attain a value of approximately USD 4.74 billion. This growth is primarily driven by increasing digitization initiatives at airports, the rising need for real-time data sharing, and the global push for enhanced passenger experiences and operational efficiency.

One of the key growth factors for the Open Data Portal for Airports market is the increasing emphasis on digital transformation across the aviation industry. Airports worldwide are adopting advanced technologies to streamline operations, improve passenger services, and ensure regulatory compliance. Open data portals enable seamless data integration and sharing among various stakeholders, including airlines, ground handlers, regulatory bodies, and passengers. This results in improved operational efficiency, reduced delays, and optimized resource allocation. As airports strive to become smart and connected hubs, the demand for robust open data solutions is expected to surge, further propelling market growth.

Another significant driver is the growing need for transparency and collaboration in airport operations. With the aviation industry facing mounting pressure to enhance safety, security, and customer satisfaction, open data portals provide a unified platform for real-time information exchange. This facilitates better coordination between airport authorities, service providers, and passengers, leading to improved decision-making and responsiveness. Moreover, regulatory mandates in several regions require airports to make certain operational data publicly accessible, further accelerating the adoption of open data portals. The integration of artificial intelligence, machine learning, and analytics into these platforms is also enabling predictive insights and automation, which are highly valued in modern airport management.

The proliferation of smart devices and the Internet of Things (IoT) is another factor fueling the growth of the Open Data Portal for Airports market. IoT-enabled sensors and devices generate vast amounts of data related to passenger flow, baggage handling, security checks, and facility management. Open data portals serve as centralized repositories for this information, making it accessible to authorized stakeholders in real time. This not only enhances operational visibility but also supports the development of innovative applications and services, such as personalized passenger notifications, automated asset tracking, and predictive maintenance. As airports continue to invest in IoT and digital infrastructure, the role of open data portals in harnessing and leveraging this data will become increasingly critical.

From a regional perspective, North America currently dominates the Open Data Portal for Airports market, owing to the presence of major international airports, advanced IT infrastructure, and proactive regulatory frameworks. Europe follows closely, driven by stringent data transparency requirements and a strong focus on passenger experience. The Asia Pacific region is emerging as a high-growth market, supported by rapid airport expansion, increasing air travel demand, and government initiatives to modernize aviation infrastructure. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a relatively slower pace, as airports in these regions gradually embrace digital transformation. Overall, the global outlook for the Open Data Portal for Airports market remains highly positive, with significant growth opportunities across all major regions.

Component Analysis

The Open Data Portal for Airports market is segmented by component into Software and Services. The software segment comprises platforms and applications designed to aggregate, manage, and disseminate airport data across vari

Success.ai’s Aviation Data provides verified access to professionals across the airlines, aviation, and aerospace industries. Leveraging over 700 million LinkedIn profiles, this dataset delivers actionable insights, contact details, and firmographic data for pilots, engineers, airline executives, aerospace manufacturers, and more. Whether your goal is to market aviation technology, recruit aerospace specialists, or analyze industry trends, Success.ai ensures your outreach is powered by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Aviation Data? Comprehensive Professional Profiles

Access verified LinkedIn profiles of pilots, engineers, flight operations managers, safety specialists, and aviation executives. AI-driven validation ensures 99% accuracy, reducing bounce rates and enhancing communication efficiency. Global Coverage Across Aviation and Aerospace Sectors

Includes professionals from airlines, airport authorities, aerospace manufacturers, and aviation technology providers. Covers key regions such as North America, Europe, APAC, South America, and the Middle East. Continuously Updated Dataset

Real-time updates reflect changes in roles, organizational affiliations, and professional achievements, ensuring relevant targeting. Tailored for Aviation and Aerospace Insights

Enriched profiles include work histories, areas of specialization, professional certifications, and firmographic data. Data Highlights: 700M+ Verified LinkedIn Profiles: Access a vast network of aviation and aerospace professionals worldwide. 100M+ Work Emails: Communicate directly with pilots, engineers, and airline executives. Enriched Professional Histories: Gain insights into career paths, certifications, and organizational roles. Industry-Specific Segmentation: Target professionals in commercial aviation, aerospace R&D, airport management, and more with precision filters. Key Features of the Dataset: Aviation and Aerospace Professional Profiles

Identify and connect with airline CEOs, aerospace engineers, maintenance technicians, flight safety experts, and other key professionals. Engage with individuals responsible for operational decisions, technology adoption, and aviation safety protocols. Detailed Firmographic Data

Leverage insights into company sizes, fleet compositions, geographic operations, and market focus. Align outreach to match specific industry needs and organizational scales. Advanced Filters for Precision Targeting

Refine searches by region, job role, certifications (e.g., FAA, EASA), or years of experience for tailored outreach. Customize campaigns to address unique aviation challenges such as sustainability, fleet modernization, or safety compliance. AI-Driven Enrichment

Enhanced datasets provide actionable insights for personalized campaigns, highlighting certifications, achievements, and career milestones. Strategic Use Cases: Marketing Aviation Products and Services

Promote aviation technology, flight operations software, or aerospace equipment to airline operators and engineers. Engage with professionals responsible for procurement, fleet management, and airport operations. Recruitment and Talent Acquisition

Target HR professionals and aerospace manufacturers seeking pilots, engineers, and aviation specialists. Simplify hiring for roles requiring advanced technical expertise or certifications. Collaboration and Partnerships

Identify aerospace manufacturers, airlines, or airport authorities for joint ventures, technology development, or service agreements. Build partnerships with key players driving innovation and safety in aviation. Market Research and Industry Analysis

Analyze trends in airline operations, aerospace manufacturing, and aviation technology to inform strategy. Use insights to refine product development and marketing efforts tailored to the aviation industry. Why Choose Success.ai? Best Price Guarantee

Access high-quality Aviation Data at unmatched pricing, ensuring cost-effective campaigns and strategies. Seamless Integration

Easily integrate verified aviation data into CRMs, recruitment platforms, or marketing systems using APIs or downloadable formats. AI-Validated Accuracy

Depend on 99% accurate data to minimize wasted efforts and maximize engagement with aviation professionals. Customizable Solutions

Tailor datasets to specific aviation sectors, geographic regions, or professional roles to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API

Enhance existing records with verified aviation profiles to refine targeting and engagement. Lead Generation API

Automate lead generation for a consistent pipeline of qualified professionals in the aviation sector, scaling your outreach efficiently. Success.ai’s Aviation Data empowers you to connect with the leaders and innovators shaping the aviation and aerospace industries. With verified conta...

Annual civil aviation financial statistics (total, operating revenue for scheduled and charter services both separately and combined, passenger revenue and goods revenue for both scheduled and charter services, all other flight - related revenue, all other revenue, total, operating expenses, maintenance, ground property and equipment, aircraft operations, maintenance, flight equipment, depreciation, all other expenses, net income (loss), operating income (loss), net, non-operating income (loss), net, interest and discount income, interest expenses, all other non-operating income (loss), net, provision for income taxes, and income (loss) before provision for income taxes). Data are for Canadian air carriers, Levels I and II combined, Level III, and Levels I to III combined. All data are expressed in thousands of dollars.

A sampling of reports involving operations at non-tower airports.

Motivation

The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data has been periodically included in the dataset until the end of the COVID-19 pandemic.

We stopped updating the dataset after December 2022. Previous files have been fixed after a thorough sanity check.

License

See LICENSE.txt

Disclaimer

The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.

Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted. Fields origin or destination are empty when no airport could be found.

Aircraft information come from the OpenSky aircraft database. Fields typecode and registration are empty when the aircraft is not present in the database.

Description of the dataset

One file per month is provided as a csv file with the following features:

callsign: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)

number: the commercial number of the flight, when available (the matching with the callsign comes from public open API); this field may not be very reliable;

icao24: the transponder unique identification number;

registration: the aircraft tail number (when available);

typecode: the aircraft model type (when available);

origin: a four letter code for the origin airport of the flight (when available);

destination: a four letter code for the destination airport of the flight (when available);

firstseen: the UTC timestamp of the first message received by the OpenSky Network;

lastseen: the UTC timestamp of the last message received by the OpenSky Network;

day: the UTC day of the last message received by the OpenSky Network;

latitude_1, longitude_1, altitude_1: the first detected position of the aircraft;

latitude_2, longitude_2, altitude_2: the last detected position of the aircraft.

Examples

Possible visualisations and a more detailed description of the data are available at the following page:

Credit

If you use this dataset, please cite:

Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders "Crowdsourced air traffic data from the OpenSky Network 2019–2020" Earth System Science Data 13(2), 2021 https://doi.org/10.5194/essd-13-357-2021

A variety of reports from ATC Controllers.

Annual civil aviation operating statistics (passengers, goods carried (kilograms), passenger-kilometres, passenger tonne-kilometres, goods tonne-kilometres, total tonne-kilometres, hours flown) for scheduled and charter services both separately and combined. Data are for Canadian air carriers, Levels I and II combined, Level III, and Levels I to III combined. Data are expressed in thousands, except for the number of carriers included, which is expressed in full.

Motivation

The data in this dataset is derived and cleaned from the full OpenSky dataset in order to illustrate in-flight emergency situations triggering the 7700 transponder code. It spans flights seen by the network's more than 2500 members between 1 January 2018 and 29 January 2020.

The dataset complements the following publication:

Xavier Olive, Axel Tanner, Martin Strohmeier, Matthias Schäfer, Metin Feridun, Allan Tart, Ivan Martinovic and Vincent Lenders. "OpenSky Report 2020: Analysing in-flight emergencies using big data". In 2020 IEEE/AIAA 39th Digital Avionics Systems Conference (DASC), October 2020

License

See LICENSE.txt

Disclaimer

The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.

Most aircraft information come from the OpenSky aircraft database and have been filled with manual research from various sources on the Internet. Most information about flight plans has been automatically fetched and processed using open APIs; some manual processing was required to cross-check, correct erroneous and fill missing information.

Description of the dataset

Two files are provided in the dataset:

one compressed parquet file with trajectory information;

one metadata CSV file with the following features:

flight_id: a unique identifier for each trajectory;

callsign: ICAO flight callsign information;

number: IATA flight number, when available;

icao24, registration, typecode: information about the aircraft;

origin: the origin airport for the aircraft, when available;

landing: the airport where the aircraft actually landed, when available;

destination: the intended destination airport, when available;

diverted: the diversion airport, if applicable, when available;

tweet_problem, tweet_result, tweet_fueldump: information extracted from Twitter accounts, about the nature of the issue, the consequence of the emergency and whether the aircraft is known to have dumped fuel;

avh_id, avh_problem, avh_result, avh_fueldump: information extracted from The Aviation Herald, about the nature of the issue, the consequence of the emergency and whether the aircraft is known to have dumped fuel. The complete URL for each event is https://avherald.com/h?article={avh_id}&opt=1 (replace avh_id by the actual value)

Examples

Additional analyses and visualisations of the data are available at the following page:

Credit

If you use this dataset, please cite the original OpenSky paper:

Xavier Olive, Axel Tanner, Martin Strohmeier, Matthias Schäfer, Metin Feridun, Allan Tart, Ivan Martinovic and Vincent Lenders. "OpenSky Report 2020: Analysing in-flight emergencies using big data". In 2020 IEEE/AIAA 39th Digital Avionics Systems Conference (DASC), October 2020

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

and the traffic library used to derive the data:

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

The Unmanned Aircraft System (UAS) program is intended to provide an enhanced level of operational capability, safety, and situational awareness and reduce the risk of injury. The UAS program will be utilized in a responsible, legal, and transparent manner with monthly usage data available on the Open Data Portal.

Cross-Border Flight Data Sharing Market Outlook

According to our latest research, the global Cross-Border Flight Data Sharing market size reached USD 1.89 billion in 2024, reflecting a robust upward trajectory. The market is set to expand at a CAGR of 13.2% during the forecast period, reaching an estimated USD 5.33 billion by 2033. This growth is primarily driven by the increasing demand for real-time data exchange among airlines, airports, and regulatory bodies to enhance operational efficiency and safety standards internationally. As aviation stakeholders intensify efforts to modernize air traffic management and comply with evolving regulatory frameworks, the adoption of advanced data sharing solutions is accelerating globally.

One of the primary growth factors for the Cross-Border Flight Data Sharing market is the growing emphasis on aviation safety and security. The aviation industry is under constant pressure to minimize risks and respond to incidents swiftly. Enhanced sharing of flight data across borders enables stakeholders to analyze trends, detect anomalies, and implement preventive measures more effectively. Regulatory authorities such as the International Civil Aviation Organization (ICAO) and regional aviation bodies are mandating stricter compliance with data transparency and reporting standards, further propelling the adoption of integrated data sharing platforms. This regulatory push, coupled with the rising sophistication of cyber threats, is compelling airlines and airports to invest in secure and interoperable data sharing infrastructures.

Another significant driver is the proliferation of digital transformation initiatives within the aviation sector. Modern airlines and airports are increasingly leveraging cloud-based solutions, IoT devices, and big data analytics to optimize flight operations, passenger experiences, and maintenance processes. Cross-border data sharing plays a pivotal role in enabling predictive maintenance, seamless passenger processing, and efficient cargo management across international routes. The integration of advanced technologies such as artificial intelligence and blockchain is further enhancing the reliability, traceability, and security of shared flight data, fostering greater collaboration among global aviation stakeholders.

The rapid expansion of international air travel and cargo movement is also fueling market growth. As global trade and tourism continue to rebound, especially post-pandemic, there is a heightened need for real-time access to comprehensive flight, passenger, and cargo data. This ensures timely decision-making, enhances situational awareness for air navigation service providers, and supports regulatory compliance across jurisdictions. The trend toward open data ecosystems and collaborative frameworks among airlines, airports, and regulatory authorities is expected to intensify, driving sustained investment in cross-border flight data sharing solutions throughout the forecast period.

From a regional perspective, North America currently dominates the Cross-Border Flight Data Sharing market, accounting for over 38% of global revenue in 2024. This is attributed to the region's advanced aviation infrastructure, stringent regulatory environment, and early adoption of digital technologies. Europe follows closely, driven by the Single European Sky initiative and robust collaboration among member states. Meanwhile, the Asia Pacific region is projected to witness the fastest growth, with a CAGR exceeding 15%, fueled by booming air traffic, expanding airport networks, and increasing investments in aviation modernization across China, India, and Southeast Asia.

Component Analysis

The Component segment of the Cross-Border Flight Data Sharing market is categorized into Software, Hardware, and Services. Software solutions currently constitute the largest share, as they form the backbone of data integration, analytics, and interoperability across disparate aviation systems. These platfor

Air passenger origin and destination data (passenger numbers, city rank), for transborder journeys, by total outbound and inbound passengers exceeding 4000, by city-pair, annual.

Number of commercial aviation departures, domestic and international, by day. Also the number of people screened by TSA in total and at large hub airports by day.