Context

Airline data holds immense importance as it offers insights into the functioning and efficiency of the aviation industry. It provides valuable information about flight routes, schedules, passenger demographics, and preferences, which airlines can leverage to optimize their operations and enhance customer experiences. By analyzing data on delays, cancellations, and on-time performance, airlines can identify trends and implement strategies to improve punctuality and mitigate disruptions. Moreover, regulatory bodies and policymakers rely on this data to ensure safety standards, enforce regulations, and make informed decisions regarding aviation policies. Researchers and analysts use airline data to study market trends, assess environmental impacts, and develop strategies for sustainable growth within the industry. In essence, airline data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the aviation sector.

Content

This dataset comprises diverse parameters relating to airline operations on a global scale. The dataset prominently incorporates fields such as Passenger ID, First Name, Last Name, Gender, Age, Nationality, Airport Name, Airport Country Code, Country Name, Airport Continent, Continents, Departure Date, Arrival Airport, Pilot Name, and Flight Status. These columns collectively provide comprehensive insights into passenger demographics, travel details, flight routes, crew information, and flight statuses. Researchers and industry experts can leverage this dataset to analyze trends in passenger behavior, optimize travel experiences, evaluate pilot performance, and enhance overall flight operations.

Dataset Glossary (Column-wise)

• Passenger ID - Unique identifier for each passenger
• First Name - First name of the passenger
• Last Name - Last name of the passenger
• Gender - Gender of the passenger
• Age - Age of the passenger
• Nationality - Nationality of the passenger
• Airport Name - Name of the airport where the passenger boarded
• Airport Country Code - Country code of the airport's location
• Country Name - Name of the country the airport is located in
• Airport Continent - Continent where the airport is situated
• Continents - Continents involved in the flight route
• Departure Date - Date when the flight departed
• Arrival Airport - Destination airport of the flight
• Pilot Name - Name of the pilot operating the flight
• Flight Status - Current status of the flight (e.g., on-time, delayed, canceled)

Structure of the Dataset

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Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable Synthetic datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Kevin Woblick on Unsplash

Thumbnail by: Airplane icons created by Freepik - Flaticon

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 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

This dataset provides detailed information on airline flight routes, fares, and passenger volumes within the United States from 1993 to 2024. The data includes metrics such as the origin and destination cities, distances between airports, the number of passengers, and fare information segmented by different airline carriers. It serves as a comprehensive resource for analyzing trends in air travel, pricing, and carrier competition over a span of three decades.

Data Features:

• tbl: Table identifier
• Year: Year of the data record
• quarter: Quarter of the year (1-4)
• citymarketid_1: Origin city market ID
• citymarketid_2: Destination city market ID
• city1: Origin city name
• city2: Destination city name
• airportid_1: Origin airport ID
• airportid_2: Destination airport ID
• airport_1: Origin airport code
• airport_2: Destination airport code
• nsmiles: Distance between airports in miles
• passengers: Number of passengers
• fare: Average fare
• carrier_lg: Code for the largest carrier by passengers
• large_ms: Market share of the largest carrier
• fare_lg: Average fare of the largest carrier
• carrier_low: Code for the lowest fare carrier
• lf_ms: Market share of the lowest fare carrier
• fare_low: Lowest fare
• Geocoded_City1: Geocoded coordinates for the origin city
• Geocoded_City2: Geocoded coordinates for the destination city
• tbl1apk: Unique identifier for the route

Potential Uses:

• Market Analysis: Assess trends in air travel demand, fare changes, and market share of airlines over time.
• Price Optimization: Develop models to predict optimal pricing strategies for airlines.
• Route Planning: Identify profitable routes and underserved markets for new route planning.
• Economic Studies: Analyze the economic impact of air travel on different cities and regions.
• Travel Behavior Research: Study changes in passenger preferences and travel behavior over the years.
• Competitor Analysis: Evaluate the performance of different airlines on various routes.

Monthly number of passengers carried (arrivals plus departures), broken down by airport and by type of transport (national, international, intra- and extra-EU). Passengers carried are (1) all passengers on a particular flight (with one flight number) counted once only and not repeatedly on each individual stage of that flight, (2) all revenue and non-revenue passengers whose journey begins or terminates at the reporting airport and transfer passengers joining or leaving the flight at the reporting airport. Excludes direct transit passengers.

Passengers enplaned and deplaned at Canadian airports, annual.

This datasets contains information about number of flight, passengers, and cargo in Saudi Arabia's Domestic airports, for 2016- 2019. Data from General Authority for Statistics . Export API data for more datasets to advance energy economics research.Source : Saudi Arabian Airlines Organization.

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.

The data set indicates the maximum number of seats available for passengers to fly. These are seats scheduled, but not necessarily filled. The success of AUS and all airports is driven by passenger demand, government restrictions, and airline business models. Data on available passenger seats in the Official Airline Guide is collected and distributed by the Campbell-Hill Aviation Schedule Report. The report data is then combined to create the total annual passenger seats for the year. This dataset supports measure M.A.7 of SD23.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/Number-of-AUS-passenger-seats-available-for-purcha/26rp-vy2b/

COVID-19(+) Interactions Within Air Travel

Modeling potential interactions between healthy individuals and those carrying COVID-19, denoted hereafter as (+), has been identified as a key methodology in the effort to predict, combat, and respond to COVID-19. In order to contribute to this effort within the domain of airline travel, this dataset allows users to see all flights during the time period from 01MAR-14APR where airline passengers may have come in contact with a COVID-19(+) TSA Screening Agent during their presumed incubation period, 7 days, before that agent went in quarantine.

Acknowledgements

• Federal Data Partner: Transportation Security Administration (TSA).
• Industry Data Partner: Airline Data Inc.

Inspiration

The CORD-19 Research Challenge has been a great inspiration for this effort. Its focus on natural language processing has prompted the need for additional efforts in other statistical machine learning methods, such as those used in the UNCOVER COVID-19 Challenge. With COVID-19 research as a global focal point, I hope that this dataset provides researchers with another set of features to help build models towards finding answers.

Methodology

Airline Data Inc. provided airline schedule information for the time period of 01MAR-14APR. This is one of the data products available as a part of their Data Hub. The airline schedule includes information on future and historical airline flights updated in real-time as it is filed by the airlines. This data provides access to origins and destinations, flight times, aircraft types, seats, customized route mapping, and much more. For this work, we focused on getting flight information to include terminals and carriers in order to determine potential contact of passengers and, at the time, unknowingly COVID-19(+) TSA agents. Airline Data Inc. additionally provided the T100 data from March and April of last year. The T100 provides information on particular routes (ORD->JFK) for U.S. domestic and international air service reported by carriers. This dataset includes passenger counts, available seats, load factors, equipment types, cargo, and other operating statistics. These datasets were combined to estimate the number of passengers flying various routes thought the time period in question. Undoubtedly these numbers are much lower than those of the previous year, but we make the assumption that airline travel declined in a relatively equal proportions across the US, making the load factors for last year comparatively accurate. Since the T100 data is only released on a monthly basis, these figures will not be able to be updated until the coming months.

The Transportation Security Administration posted publicly on their website a list of all Screening and Baggage Officers who tested positive for COVID-19. This list included the airport they worked in, their last day of work, and their work location with shift information. This data was taken and used to down-select the data from Airline Data Inc. to only include those flights that met the following criteria: - Origin airport with COVID-19(+) TSA Officer - Flight took off (the flight schedule data will show all potential flights even those that do not take off) - TSA Officer on shift at time of departure - TSA Officer working in terminal from which the flight departed

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 measure, represented in a line graph, indicates the maximum number of seats available for passengers to fly. These are seats scheduled, but not necessarily filled. The success of AUS and all airports is driven by passenger demand, government restrictions, and airline business models. Data on available passenger seats in the Official Airline Guide is collected and distributed by the Campbell-Hill Aviation Schedule Report. The report data is then combined to create the total annual passenger seats for the year. This dataset supports measure M.A.7 of SD23.

View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/Number-of-AUS-passenger-seats-available-for-purcha/26rp-vy2b/

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!

This Dataset contains year, month and Airlines-wise passengers affected and compensation/facilitation paid due to denied boarding, flight cancellations and delays beyond 2 Hrs of scheduled domestic airlines.

Note: Data is in accordance with the Civil Aviation Requirement Section 3, Series M, Part IV.

In financial year 2024, the total air passenger traffic in India reached more than *** million passengers. It was a huge increase compared to the previous year. The domestic passenger traffic saw a compound annual growth rate (CAGR) of *** percent from 2014 to 2024, while the international passenger traffic saw a *** percent CAGR during the same period of time.

Aviation movements, passengers on board, cargo and mail on board (flight stage): Germany, years, reporting airport, type of flight movement, take-off weight classes

The data set indicates the maximum number of seats available for passengers to fly. These are seats scheduled, but not necessarily filled. The success of AUS and all airports is driven by passenger demand, government restrictions, and airline business models. Data on available passenger seats in the Official Airline Guide is collected and distributed by the Campbell-Hill Aviation Schedule Report. The report data is then combined to create the total annual passenger seats for the year. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/Number-of-AUS-passenger-seats-available-for-purcha/26rp-vy2b/

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.

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

Total monthly number of passengers arriving to and departing from Heathrow Airport, including both international and domestic flights.