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.

A. SUMMARY San Francisco International Airport Report on Monthly Passenger Traffic Statistics by Airline.

B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level

C. UPDATE PROCESS Data updated quarterly

D. HOW TO USE THIS DATASET Airport data is seasonal in nature, therefore any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Passenger Counts belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Passenger Counts as desired.

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.

Passengers enplaned and deplaned at Canadian airports, annual.

This Dataset contains year and airline-wise total number of Passengers who are placed in the 'No Fly list'

Note: Data is as per the recommendations of airline's internal committee, constituted in accordance with Civil Aviation Requirements (CAR), Section 3- Air Transport, Series M, and Part VI titled "Handling of unruly / disruptive passengers"

This dataset provides monthly passenger statistics for air travel covering the years 1958, 1959, and 1960. It includes data for each month, detailing the number of passengers in each year.

Context

Have you taken a flight in the U.S. in the past 15 years? If so, then you are a part of monthly data that the U.S. Department of Transportation's TranStats service makes available on various metrics for 15 U.S. airlines and 30 major U.S airports. Their website unfortunately does not include a method for easily downloading and sharing files. Furthermore, the source is built in ASP.NET, so extracting the data is rather cumbersome. To allow easier community access to this rich source of information, I scraped the metrics for every airline / airport combination and stored them in separate CSV files.

Occasionally, an airline doesn't serve a certain airport, or it didn't serve it for the entire duration that the data collection period covers*. In those cases, the data either doesn't exist or is typically too sparse to be of much use. As such, I've only uploaded complete files for airports that an airline served for the entire uninterrupted duration of the collection period. For these files, there should be 174 time series points for one or more of the nine columns below. I recommend any of the files for American, Delta, or United Airlines for outstanding examples of complete and robust airline data.

* No data for Atlas Air exists, and Virgin America commenced service in 2007, so no folders for either airline are included.

Content

There are 13 airlines that have at least one complete dataset. Each airline's folder includes CSV file(s) for each airport that are complete as defined by the above criteria. I've double-checked the files, but if you find one that violates the criteria, please point it out. The file names have the format "AIRLINE-AIRPORT.csv", where both AIRLINE and AIRPORT are IATA codes. For a full listing of the airlines and airports that the codes correspond to, check out the airline_codes.csv or airport_codes.csv files that are included, or perform a lookup here. Note that the data in each airport file represents metrics for flights that originated at the airport.

Among the 13 airlines in data.zip, there are a total of 161 individual datasets. There are also two special folders included - airlines_all_airports.csv and airports_all_airlines.csv. The first contains datasets for each airline aggregated over all airports, while the second contains datasets for each airport aggregated over all airlines. To preview a sample dataset, check out all_airlines_all_airports.csv, which contains industry-wide data.

Each file includes the following metrics for each month from October 2002 to March 2017:

1. Date (YYYY-MM-DD): All dates are set to the first of the month. The day value is just a placeholder and has no significance.
2. ASM_Domestic: Available Seat-Miles in thousands (000s). Number of domestic flights * Number of seats on each flight
3. ASM_International*: Available Seat-Miles in thousands (000s). Number of international flights * Number of seats on each flight
4. Flights_Domestic
5. Flights_International*
6. Passengers_Domestic
7. Passengers_International*
8. RPM_Domestic: Revenue Passenger-Miles in thousands (000s). Number of domestic flights * Number of paying passengers
9. RPM_International*: Revenue Passenger-Miles in thousands (000s). Number of international flights * Number of paying passengers

* Frequently contains missing values

Acknowledgements

Thanks to the U.S. Department of Transportation for collecting this data every month and making it publicly available to us all.

Source: https://www.transtats.bts.gov/Data_Elements.aspx

Inspiration

The airline / airport datasets are perfect for practicing and/or testing time series forecasting with classic statistical models such as autoregressive integrated moving average (ARIMA), or modern deep learning techniques such as long short-term memory (LSTM) networks. The datasets typically show evidence of trends, seasonality, and noise, so modeling and accurate forecasting can be challenging, but still more tractable than time series problems possessing more stochastic elements, e.g. stocks, currencies, commodities, etc. The source releases new data each month, so feel free to check your models' performances against new data as it comes out. I will update the files here every 3 to 6 months depending on how things go.

A future plan is to build a SQLite database so a vast array of queries can be run against the data. The data in it its current time series format is not conducive for this, so coming up with a workable structure for the tables is the first step towards this goal. If you have any suggestions for how I can improve the data presentation, or anything that you would like me to add, please let me know. Looking forward to seeing the questions that we can answer together!

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.

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.

This dataset contains monthly summaries of air traffic across region's three major airports: Ronald Reagan Washington National Airport (DCA), Washington Dulles International Airport (IAD), and Baltimore/Washington International Thurgood Marshall Airport (BWI), by year (currently available for 2000-2024). The data represent passenger activities broken into the following categories: Domestic Enplanement, International Enplanement, Total Enplanement, Total Deplanement, and Total Enplanement and Deplanement.TPB staff obtain the data regularly from the following locations:DCA and IAD - https://www.mwaa.com/dulles-air-traffic-statisticsBWI - https://www.bwiairport.com/flying-with-us/about-bwi/statisticsThe data are combined into an annual table for all airports. A new tab is added to the dataset for each year. The table is updated annually when the full year's passenger report is posted on the original websites. For more information about TPB's Continuous Airport System Planning (CASP) program, please visit the CASP website.

The average for 2021 based on 152 countries was 15 million passengers. The highest value was in the USA: 666.15 million passengers and the lowest value was in Guatemala: 0 million passengers. The indicator is available from 1970 to 2021. Below is a chart for all countries where data are available.

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

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India Passenger Traffic: All Airports data was reported at 36,106,614.000 Person in Mar 2025. This records an increase from the previous number of 34,911,116.000 Person for Feb 2025. India Passenger Traffic: All Airports data is updated monthly, averaging 18,787,598.000 Person from Nov 2009 (Median) to Mar 2025, with 185 observations. The data reached an all-time high of 37,541,465.000 Person in Dec 2024 and a record low of 61,861.000 Person in Apr 2020. India Passenger Traffic: All Airports data remains active status in CEIC and is reported by Airports Authority of India. The data is categorized under Global Database’s India – Table IN.TA007: Airport Authority of India: Passenger Traffic. [COVID-19-IMPACT]

This dataset contains monthly air travel statistics, detailing the number of passengers in thousands for the years 1958 to 1960. The data is organized by month and includes figures for each year.