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.

Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

In 2023, the estimated number of scheduled passengers boarded by the global airline industry amounted to approximately *** billion people. This represents a significant increase compared to the previous year since the pandemic started and the positive trend was forecast to continue in 2024, with the scheduled passenger volume reaching just below **** billion travelers. Airline passenger traffic The number of scheduled passengers handled by the global airline industry has increased in all but one of the last decade. Scheduled passengers refer to the number of passengers who have booked a flight with a commercial airline. Excluded are passengers on charter flights, whereby an entire plane is booked by a private group. In 2023, the Asia Pacific region had the highest share of airline passenger traffic, accounting for ********* of the global total.

Using a combination of OAG flight schedule and ch-aviation fleet data, Capacities - Scheduled provides an overview of future flights scheduled per calendar day with a breakdown of seat capacity for five cabin classes (Economy, Economy Plus/Comfort, Premium Economy, Business, First) by operator and route (Continent, Country, Subdivision, Metro Group, Airport).

The data set is updated weekly.

The sample data shows capacity figures for Alaska Airlines, Swiss, and Horizon Air for one week.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=capacities_scheduled/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/capacities-scheduled/

Russia Number of Flights: Domestic data was reported at 67,658.000 Number in Feb 2022. This records a decrease from the previous number of 71,658.000 Number for Jan 2022. Russia Number of Flights: Domestic data is updated monthly, averaging 55,400.000 Number from Jan 2010 (Median) to Feb 2022, with 146 observations. The data reached an all-time high of 127,409.000 Number in Jul 2021 and a record low of 27,413.000 Number in Feb 2010. Russia Number of Flights: Domestic data remains active status in CEIC and is reported by Federal Agency for Air Transport. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TE003: Airlines Statistics: Number of Airlines, Aircrafts, Airports and Flights. [COVID-19-IMPACT]

Our Flight Events data feed combines Spire Global satellite/terrestrial ADS-B flight event data with ch-aviation’s fleet, operator, and airport data providing an overview of all flights operated by airlines, business and general aviation players on a daily basis.

The value of our Flight Events data feed lies in its high-resolution integration of ADS-B flight tracking with ch-aviation’s comprehensive aircraft and operator data, delivering unmatched visibility into global aircraft movements. By identifying the aircraft type and registration for approximately 98% of all ADS-B-tracked flights, we offer an industry-leading solution for lessors, insurers, airports, OEMs, and analysts seeking precise, reliable, and actionable aviation intelligence.

• High-Resolution ADS-B Integration - Satellite and terrestrial ADS-B flight tracking combined with enriched aircraft and operator data for maximum accuracy and visibility • Comprehensive Aircraft Identification - Aircraft type and registration identified for approximately 98% of all ADS-B-tracked flights, using proprietary matching with ch-aviation data and supplementary publicly available authority data sources. • Global Flight Coverage - Tracks approximately 160,000–190,000 flights per day across commercial aviation, business jet, and general aviation sectors worldwide. • ACMI (Wet-Lease) and Cargo Customer Tracking - Detailed monitoring of ACMI operations, including identification of wet-lease activity between different operators as well as cargo customers identifying flights operated for integrators like DHL Express or FedEx as well as cargo customers such as Amazon. • Aircraft Utilisation Tracking - Tracking of flight hours and cycles at both the operator and individual tail number (aircraft) level • Matched Operator and Aircraft Data - Every flight is linked to comprehensive ch-aviation datasets, including aircraft ID, history, operator, variant, callsign, and airport details allowing customers to leverage the industry’s most comprehensive integration between ADS-B flight event and fleet/operator/airport data. • Fallback Data Enrichment - Where ch-aviation data is unavailable, civil aviation authority and ANSP sources are used to ensure continuity in aircraft identification and data accuracy. • Use Case-Driven Insights - Tailored for industry stakeholders like lessors, insurers, OEMs, airports, and analysts seeking operational, commercial, and technical flight data intelligence.

ch-aviation integrates its Commercial Aviation Aircraft Data and Business Jet Aircraft Data with Spire Global’s satellite-based ADS-B data that is fused by Spire with terrestrial feeds from two terrestrial ADS-B data providers.

This data is enriched with mapped callsigns, corrected hexcodes, regional partnership decoding, and identification of wet-leases and cargo customers, enabling detailed insight into each individual flight.

Where ch-aviation data is unavailable, public data from civil aviation authorities and ANSPs is used to ensure broad and reliable aircraft identification and coverage.

The data set is available historically going back to January 1, 2018.

The data set is updated daily.

The sample data shows flights on 2025-03-30, with Swiss, Alaska Airlines, Horizon Air, Jet Aviation Business Jets, and RVR Aviation as operators or wet lease customers.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=flights/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/flights-2/

This dataset is sourced from the Airline Data Project established by the MIT Global Airline Industry Program. It describes financial metrics for Individual airlines, airline sectors and the industry as a whole for the American commercial airline industry. The Original data from the source is collected in the zip file "Original MIT data" and the data relating to Airline finances and the main industry metrics has been cleaned and written into csv files for ease of use.

MIT ADP Description The U.S. commercial airline industry is one of the most diverse, dynamic and perplexing in the world. It is fast-evolving, labor intensive, capital intensive, hyper-competitive and highly susceptible to the ebb and flow of business cycles as well as being among the most regulated of deregulated businesses.

The Airline Data Project (ADP) was established by the MIT Global Airline Industry Program to better understand the opportunities, risks and challenges facing this vital industry. The ADP presents the most important airline industry data in one location in an easy-to-understand, user-friendly format.

The data on this website is sourced from the U.S. Department of Transportation's Form 41 data product. It has been selected and analyzed to present a view of the industry and its important trends, as well as to identify fundamental drivers of success - and in some cases, the early signs of potential failure.

The ADP is designed to support the goals of the MIT Airline Industry Consortium. It is a unique repository of data and analysis that will allow individuals – from academia to the financial community to the news media – to monitor the evolution of the U.S. commercial airline industry.

The ADP is updated in June of each year pending the release of Form 41 data files by the U.S. Bureau of Transportation Statistics. The last update of the ADP was in June 2020 for calendar year 2019 data. If you have questions about what items are included in various Form 41 data categories, you can refer to the U.S. DOT's Form 41 Financial Reporting Categories Item List Guide.

You are invited to review the data on this site and share your feedback on the wealth of information that is available about this highly visible industry.

Glossary: Aircraft Utilization Measure of aircraft productivity, calculated by dividing aircraft block hours by the number of aircraft days assigned to service on air carrier routes. Typically presented in block hours per day.

Available Seat Miles (ASMs) A common industry measurement of airline output that refers to one aircraft seat flown one mile, whether occupied or not. An aircraft with 100 passenger seats, flown a distance of 100 miles, generates 10,000 available seat miles.

Average Aircraft Capacity Average seating configuration of an airline’s operating fleet. The measure is derived by dividing total available seat miles flown by the number of aircraft miles flown. It is important to understand the average aircraft size as it is an important determinant of employees needed to service the operation of a particular airline.

Block Hour Time from the moment the aircraft door closes at departure of a revenue flight until the moment the aircraft door opens at the arrival gate following its landing. Block hours are the industry standard measure of aircraft utilization (see above).

Cost per Available Seat Mile (CASM) Measure of unit cost in the airline industry. CASM is calculated by taking all of an airline’s operating expenses and dividing it by the total number of available seat miles produced. Sometimes, fuel or transport-related expenses are withheld from CASM calculations to better isolate and directly compare operating expenses.

Unit Cost per Unit of Output A measurement that gauges total operating costs in relation to output.

Form 41 Data Information derived from airline filings with the Bureau of Transportation Statistics. Airline financial data is filed with the BTS quarterly; traffic and employment numbers are filed monthly.

Load Factor The number of Revenue Passenger Miles (RPMs) expressed as a percentage of ASMs, either on a particular flight or for the entire system. Load factor represents the proportion of airline output that is actually consumed. To calculate this figure, divide RPMs by ASMs. Load factor for a single flight can also be calculated by dividing the number of passengers by the number of seats.

Operating Revenue ...

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

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

The data set is updated daily.

The sample data shows aircraft flown on 2025-03-30 by Swiss, Alaska Airlines, Horizon Air, Jet Aviation Business Jets, and RVR Aviation, with utilization metrics

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=aircraft_utilisation_daily/&showversions=false

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

India All Scheduled Airlines: Domestic: Number of Flight data was reported at 102,319.000 Unit in Mar 2025. This records an increase from the previous number of 92,291.000 Unit for Feb 2025. India All Scheduled Airlines: Domestic: Number of Flight data is updated monthly, averaging 48,100.000 Unit from Apr 2001 (Median) to Mar 2025, with 288 observations. The data reached an all-time high of 102,319.000 Unit in Mar 2025 and a record low of 188.000 Unit in Apr 2020. India All Scheduled Airlines: Domestic: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

Geospatial Dataset of GNSS Anomalies and Political Violence Events (2023)

Overview

The Geospatial Dataset of GNSS Anomalies and Political Violence Events (2023) is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database.

Dataset Files

The dataset consists of two CSV files:

Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv

Description: Contains all grids and dates that had aircraft traffic during 2023.

Number of Records: 6,777,228

Purpose: Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.

Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv

Description: A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.

Number of Records: 718,237

Purpose: Focuses on areas and times with GNSS anomalies for targeted analysis.

Data Fields

Both files share the same set of fields, which are detailed below:

grid_id

Description: Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.

Format: String combining latitude and longitude (e.g., -10.0_-36.0).

day

Description: Date of the recorded data.

Format: YYYY-MM-DD (e.g., 2023-03-28).

geometry

Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.

Format: POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).

flights

Description: Number of aircraft flights that passed through the grid on that day.

Format: Integer (e.g., 28).

GPS_jumps

Description: Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.

Format: Integer (e.g., 1).

GPS_gaps

Description: Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.

Format: Integer (e.g., 0).

gaps_density

Description: Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.

Format: Decimal (e.g., 0).

jumps_density

Description: Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.

Format: Decimal (e.g., 0.035714286).

event_id_cnty

Description: ACLED event ID corresponding to political violence events in the grid on that day.

Format: String (e.g., BRA69267).

disorder_type

Description: Type of disorder as classified by ACLED (e.g., "Political violence").

Format: String.

event_type

Description: General category of the event according to ACLED (e.g., "Violence against civilians").

Format: String.

sub_event_type

Description: Specific subtype of the event as per ACLED classification (e.g., "Attack").

Format: String.

acled_count

Description: Number of ACLED events in the grid on that day.

Format: Integer (e.g., 1).

acled_flag

Description: Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).

Format: Integer (0 or 1).

Data Sources

GNSS Anomalies Data:

Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.

GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).

Political Violence Events Data:

Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.

Temporal and Spatial Coverage

Temporal Coverage:

From January 1, 2023, to December 31, 2023.

Daily records provide temporal granularity for time-series analysis.

Spatial Coverage:

Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.

Each grid cell represents an area on Earth's surface, facilitating spatial analysis.

Usage and Applications

Security Analysis:

Assess potential correlations between GNSS anomalies and political violence events.

Identify regions with increased risk of GNSS spoofing or signal disruption.

Research and Development:

Develop models to predict socio-political events based on GNSS anomalies.

Study the impact of political instability on aviation safety.

Policy and Decision Making:

Inform aviation authorities and policymakers about regions requiring enhanced navigation security measures.

Support conflict analysis and monitoring efforts.

The volume of air-freight transport in the United Arab Emirates was forecast to decrease between 2024 and 2029 by in total 0.02 billion ton-kilometers. This overall decrease does not happen continuously, notably not in 2026 and 2027. The volume of air-freight transport is estimated to amount to 14 billion ton-kilometers in 2029. As defined by Worldbank, air freight refers to the summated volume of freight, express and diplomatic bags carried across the various flight stages (from takeoff to the next landing). The forecast has been adjusted for the expected impact of COVID-19.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).Find more key insights for the volume of air-freight transport in countries like Oman and Israel.

Context

Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010–2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.

Content

Geography: Global

Time period: 2010 - 2019

Unit of analysis: Global Holidays and Travel Data

Variables

monthly_passengers.csv

global_holidays.csv

Acknowledgements

Thank you to Jon Harmon for curating this dataset.

Dataset Repository

China Air: Passenger Traffic: Domestic data was reported at 664.657 Person mn in 2024. This records an increase from the previous number of 590.516 Person mn for 2023. China Air: Passenger Traffic: Domestic data is updated yearly, averaging 95.618 Person mn from Dec 1970 (Median) to 2024, with 42 observations. The data reached an all-time high of 664.657 Person mn in 2024 and a record low of 0.210 Person mn in 1970. China Air: Passenger Traffic: Domestic data remains active status in CEIC and is reported by Civil Aviation Administration of China. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TI: Air: Passenger Traffic.