Airlines Traffic Passenger Statistics

A New Look at an Old Problem

About this dataset

This dataset contains information on air traffic passenger statistics by the airline. It includes information on the airlines, airports, and regions that the flights departed from and arrived at. It also includes information on the type of activity, price category, terminal, boarding area, and number of passengers

How to use the dataset

Air traffic passenger statistics can be a useful tool for understanding the airline industry and for making travel plans. This dataset from Open Flights contains information on air traffic passenger statistics by airline for 2017. The data includes the number of passengers, the operating airline, the published airline, the geographic region, the activity type code, the price category code, the terminal, the boarding area, and the year and month of the flight

Research Ideas

• Air traffic passenger statistics could be used to predict future trends in air travel.
• The data could be used to generate heat maps of airline traffic patterns.
• The data could be used to study the effects of different factors on air traffic passenger numbers, such as the time of year or day, the price of airfare, or the number of flights offered by an airline

License

License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) - You are free to: - Share - copy and redistribute the material in any medium or format for non-commercial purposes only. - Adapt - remix, transform, and build upon the material for non-commercial purposes only. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - You may not: - Use the material for commercial purposes.

Columns

File: Air_Traffic_Passenger_Statistics.csv | Column name | Description | |:--------------------------------|:------------------------------------------------------------------------------| | Activity Period | The date of the activity. (Date) | | Operating Airline | The airline that operated the flight. (String) | | Operating Airline IATA Code | The IATA code of the airline that operated the flight. (String) | | Published Airline | The airline that published the fare for the flight. (String) | | Published Airline IATA Code | The IATA code of the airline that published the fare for the flight. (String) | | GEO Summary | A summary of the geographic region. (String) | | GEO Region | The geographic region. (String) | | Activity Type Code | The type of activity. (String) | | Price Category Code | The price category of the fare. (String) | | Terminal | The terminal of the flight. (String) | | Boarding Area | The boarding area of the flight. (String) | | Passenger Count | The number of passengers on the flight. (Integer) | | Adjusted Activity Type Code | The type of activity, adjusted for missing data. (String) | | Adjusted Passenger Count | The number of passengers on the flight, adjusted for missing data. (Integer) | | Year | The year of the activity. (Integer) | | Month | The month of the activity. (Integer) |

This dataset provides valuable insights into the aviation industry's trends and growth patterns, making it a valuable resource for analysts, researchers, and aviation enthusiasts.

1.**Country:** This column represents the name of the country where the airline is based. It provides valuable geographical context to the dataset, allowing users to explore passenger trends on a country-by-country basis.

2.**Airline Passengers Carried:** This column contains the number of passengers carried by each airline. It is a critical metric for evaluating an airline's performance and market share.

3.**Year:** The year column indicates the year to which the data corresponds. It allows users to track changes in passenger numbers over time and observe trends and fluctuations in the airline industry.

This dataset, enables data scientists, analysts, and researchers to conduct a wide range of analyses and create data-driven visualizations to answer various questions related to air travel, such as:

• Which countries have the highest and lowest airline passenger numbers?
• How has the global airline industry evolved over the years in terms of passenger volume?
• Are there any seasonal patterns or trends in airline passenger data?
• What are the top airlines in terms of passenger volume for a specific year or country?
• How does economic or political stability in a country correlate with airline passenger numbers?

By making this dataset available on Kaggle, we aim to foster collaboration and innovation within the data science community, allowing users to extract valuable insights from the world of aviation and contribute to a better understanding of global travel patterns. Researchers, analysts, and data enthusiasts can leverage this dataset to gain a deeper understanding of the dynamics of the airline industry and make informed decisions based on the trends observed in the data.

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.

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.

This dataset provides a comprehensive overview of domestic airline routes within the United States. It includes valuable information for analyzing passenger travel patterns, market trends, and airline pricing strategies.

Data Features:

• Year
• Quarter
• City Market IDs
• Departure City
• Arrival City:
• Miles: The distance between the origin and arrival cities in miles.
• Average Daily Passengers: The average number of passengers flying this route per day.
• Average Fare: The average fare paid by passengers for this route (consider including currency information).

Potential Uses:

• Travel Demand Analysis: Identify popular routes, and understand seasonal variations in passenger traffic.
• Market Research: Analyze airline competition on specific routes and assess pricing strategies.
• Route Optimization: Airlines can use this data to evaluate existing routes and identify potential new routes with high passenger demand.
• Business Intelligence: Businesses can use this data to understand travel patterns relevant to their industry and make informed decisions.

Data Cleaning and Transformation Considerations:

• Ensure consistency in city names (consider using the city market ID to group nearby airports).
• Handle missing values appropriately.
• Consider converting categorical features to numerical representations for analysis.

Context

The European Flights Dataset from 2016 to 2022 provides an extensive record of air traffic activities across various European airports. The data includes essential metrics related to IFR (Instrument Flight Rules) movements, covering both departures and arrivals as reported by the Network Manager and Airport Operator. The dataset is comprehensive, with 688,099 entries and 14 columns, detailing flights over a span of seven years.

Content

Geography: Europe

Time period: Jan 2016- May 2022

Unit of analysis: European Flights Dataset

Variables

Acknowledgements

Datasource: Aviation Intelligence Unit Portal

Inspiration: Commercial air transport in August 2021: in recovery

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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

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.

Content

The data comes from the U.S. International Air Passenger and Freight Statistics Report. As part of the T-100 program, USDOT receives traffic reports of US and international airlines operating to and from US airports. There are two datasets available:

• Departures: Data on all flights between US gateways and non-US gateways, irrespective of origin and destination.

Each observation provides information on a specific airline for a pair of airports, one in the US and the other outside. Three main columns record the number of flights: Scheduled, Charter, and Total.

• Passengers: Data on the total number of passengers for each month and year between a pair of airports, as serviced by a particular airline.

U.S. International Air Passenger and Freight data are confidential for a period of 6 months, after which it can be released. As a result, quarterly reports and the year to date/calendar year raw data files available here will always lag by two quarters.

Questions that can be answered with data

• Top 10 busiest airports
• Monthly total of flights

Acknowledgements

Data Provided by the Department of Transportation Office of the Assistant Secretary for Aviation and International Affairs. Updated: December 16, 2020 Dataset Owner: Randall_Keizer

The Air transport domain contains national and international intra and extra-EU data. This provides air transport data for passengers (in number of passengers) and for freight and mail (in 1 000 tonnes) as well as air traffic data by airports, airlines and aircraft. Data are transmitted to Eurostat by EU Member States, EFTA countries and some other reporting countries. Data are compiled following the provisions of the Regulation (EC) N°1358/2003, implementing Regulation N°437/2003 of the European Parliament and of the Council on statistical returns in respect of the carriage of passengers, freight and mail by air. The air transport data are collected at airport level. As from 2003 reference year the data are provided according to the legal act (some countries were given derogation until 2005). Until 2002 partial information (passenger transport only) are available for some countries and airports.

Airports handling less than 15 000 passenger units annually are excluded from the scope of the Regulation. Datasets A1 and B1 are provided on monthly basis, while dataset C1 can be provided either on monthly or annual basis. For some countries optional variable - total number of transfer passengers - is provided as well.

The data are disseminated by Eurostat in on-line database in four sub-domains:

• Air Transport measurement - Passengers
• Air Transport measurement - Freight and mail
• Air Transport measurement - Traffic data by airports, aircraft and airlines
• Air Transport measurement - Data aggregated at standard regional levels (NUTS).

The two first domains contain several data collections:

• Overview of the air transport by country and airport,
• National air transport by country and airport,
• International intra-EU air transport by country and airport,
• International extra-EU air transport by country and airport,
• Detailed air transport by reporting country and routes.

In the tables of the sub-domain "Transport measurement - Passengers", data are broken down by passengers on board (arrivals, departures and total), passengers carried (arrivals, departures and total) and passenger commercial air flights (arrival, departures and total). Additionally, the tables of collection "Detailed air transport by reporting country and routes" provide data on seats available (arrival, departures and total). The data is presented at monthly, quarterly and annual level.

In the tables of the sub-domain "Transport measurement - Freight and mail", data are broken down by freight and mail on board (arrival, departures and total), freight and mail loaded/unloaded (loaded, unloaded and total) and all-freight and mail commercial air flights (arrival, departures and total). The data is presented at monthly, quarterly and annual level.

In the tables of the sub-domain "Transport measurement - Traffic by airports, aircraft and airlines":

• Data by type of aircraft are broken down by total passengers on board, total freight and mail on board in tonnes, total passengers seats available, total commercial air flights (passengers + all-freight and mail), passenger commercial air flights, all-freight and mail commercial air flights. The data is presented at annual level since 2003.
• Data by type of airline are broken down by total passengers on board, total passengers carried, total freight and mail on board, total freight and mail loaded/unloaded, total passengers seats available, total commercial air flights (passengers + all-freight and mail), passenger commercial air flights, all-freight and mail commercial air flights. The data is presented at annual level since 2003.
• Data by airport are broken down by total passengers carried, total transit passengers, total transfer passengers, total freight and mail loaded/unloaded, total commercial aircraft movements, total aircrafts movements. The data is presented at monthly, quarterly and annual level.

The sub-domain "Transport measurement - Data aggregated at standard regional levels (NUTS)", contains two tables:

• Air transport of passengers at regional level
• Air transport of freight at regional level

The tables present the evolution of the number of passengers carried (if not available passengers on board) and the volume of freight and mail loaded or unloaded (if not available freight and mail on board) to/from the NUTS regions (level 2, 1 and 0) since 1999. The data is presented at annual level. The air transport regional data have been calculated using data collected at the airport level in the frame of the regulatory data collection on air transport.

For more details on datasets, data validation and dissemination refer also to Reference Manual on Air Transport Statistics available in the Annex part of the metadata.

A. SUMMARY This dataset consists of San Francisco International Airport (SFO) air traffic cargo dataset contains data about cargo volume into and out of SFO, in both metric tons and pounds, with monthly totals by airline, region and aircraft type. B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level. C. UPDATE PROCESS Data is available starting in July 1999 and will be updated monthly. 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, Cargo Statistics belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Cargo Statistics as desired. E. RELATED DATASETS A summary of monthly comparative air-traffic statistics is also available on SFO’s internet site at https://www.flysfo.com/about/media/facts-statistics/air-traffic-statistics

A. SUMMARY This dataset consists of San Francisco International Airport (SFO) The aircraft landing dataset contains data about aircraft landings at SFO with monthly landing counts and landed weight by airline, region and aircraft model and type. B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level. C. UPDATE PROCESS Data is available starting in July 1999 and will be updated monthly. 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, Cargo Statistics belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Cargo Statistics as desired. E. RELATED DATASETS A summary of monthly comparative air-traffic statistics is also available on SFO’s internet site at https://www.flysfo.com/about/media/facts-statistics/air-traffic-statistics

Data for the market share and on-time performance sections of this project come from publicly available U.S. Department of Transportation airline data which can be found here. Every single domestic flight that is taken in the United States is recorded and made available to the public. While available to the public this data is very large and it is not easy to download.

The first step in downloading the data was to individually download it based on the individual year and month. With this manually intensive process and large datafiles a specific time frame was set from January 2018 to July 2022 (the most recent data available at the time of publication) which is close to five years. This timeframe was chosen because it includes data before the Covid-19 pandemic, during the Covid-19 pandemic, and the vaccine era of the Covid-19 pandemic.

After each of the individual files were downloaded it required manually filtering the top ten selected airlines using Excel. Once this process was completed the files were combined into one large dataset using R.

The Air transport domain contains national and international intra and extra-EU data. This provides air transport data for passengers (in number of passengers) and for freight and mail (in 1 000 tonnes) as well as air traffic data by airports, airlines and aircraft. Data are transmitted to Eurostat by EU Member States, EFTA countries and some other reporting countries. Data are compiled following the provisions of the Regulation (EC) N°1358/2003, implementing Regulation N°437/2003 of the European Parliament and of the Council on statistical returns in respect of the carriage of passengers, freight and mail by air. The air transport data are collected at airport level. As from 2003 reference year the data are provided according to the legal act (some countries were given derogation until 2005). Until 2002 partial information (passenger transport only) are available for some countries and airports.

Airports handling less than 15 000 passenger units annually are excluded from the scope of the Regulation. Datasets A1 and B1 are provided on monthly basis, while dataset C1 can be provided either on monthly or annual basis. For some countries optional variable - total number of transfer passengers - is provided as well.

The data are disseminated by Eurostat in on-line database in four sub-domains:

• Air Transport measurement - Passengers
• Air Transport measurement - Freight and mail
• Air Transport measurement - Traffic data by airports, aircraft and airlines
• Air Transport measurement - Data aggregated at standard regional levels (NUTS).

The two first domains contain several data collections:

• Overview of the air transport by country and airport,
• National air transport by country and airport,
• International intra-EU air transport by country and airport,
• International extra-EU air transport by country and airport,
• Detailed air transport by reporting country and routes.

In the tables of the sub-domain "Transport measurement - Passengers", data are broken down by passengers on board (arrivals, departures and total), passengers carried (arrivals, departures and total) and passenger commercial air flights (arrival, departures and total). Additionally, the tables of collection "Detailed air transport by reporting country and routes" provide data on seats available (arrival, departures and total). The data is presented at monthly, quarterly and annual level.

In the tables of the sub-domain "Transport measurement - Freight and mail", data are broken down by freight and mail on board (arrival, departures and total), freight and mail loaded/unloaded (loaded, unloaded and total) and all-freight and mail commercial air flights (arrival, departures and total). The data is presented at monthly, quarterly and annual level.

In the tables of the sub-domain "Transport measurement - Traffic by airports, aircraft and airlines":

• Data by type of aircraft are broken down by total passengers on board, total freight and mail on board in tonnes, total passengers seats available, total commercial air flights (passengers + all-freight and mail), passenger commercial air flights, all-freight and mail commercial air flights. The data is presented at annual level since 2003.
• Data by type of airline are broken down by total passengers on board, total passengers carried, total freight and mail on board, total freight and mail loaded/unloaded, total passengers seats available, total commercial air flights (passengers + all-freight and mail), passenger commercial air flights, all-freight and mail commercial air flights. The data is presented at annual level since 2003.
• Data by airport are broken down by total passengers carried, total transit passengers, total transfer passengers, total freight and mail loaded/unloaded, total commercial aircraft movements, total aircrafts movements. The data is presented at monthly, quarterly and annual level.

The sub-domain "Transport measurement - Data aggregated at standard regional levels (NUTS)", contains two tables:

• Air transport of passengers at regional level
• Air transport of freight at regional level

The tables present the evolution of the number of passengers carried (if not available passengers on board) and the volume of freight and mail loaded or unloaded (if not available freight and mail on board) to/from the NUTS regions (level 2, 1 and 0) since 1999. The data is presented at annual level. The air transport regional data have been calculated using data collected at the airport level in the frame of the regulatory data collection on air transport.

For more details on datasets, data validation and dissemination refer also to Reference Manual on Air Transport Statistics available in the Annex part of the metadata.

Passengers enplaned and deplaned at Canadian airports, annual.

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.

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"

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

The environmental impact of aviation is enormous given the fact that in the US alone there are nearly 6 million flights per year of commercial aircraft. This situation has driven numerous policy and procedural measures to help develop environmentally friendly technologies which are safe and affordable and reduce the environmental impact of aviation. However, many of these technologies require significant initial investment in newer aircraft fleets and modifications to existing regulations which are both long and costly enterprises. We propose to use an anomaly detection method based on Virtual Sensors to help detect overconsumption of fuel in aircraft which relies only on the data recorded during flight of most existing commercial aircraft, thus significantly reducing the cost and complexity of implementing this method. The Virtual Sensors developed here are ensemble-learning regression models for detecting the overconsumption of fuel based on instantaneous measurements of the aircraft state. This approach requires no additional information about standard operating procedures or other encoded domain knowledge. We present experimental results on three data sets and compare five different Virtual Sensors algorithms. The first two data sets are publicly available and consist of a simulated data set from a flight simulator and a real-world turbine disk.We show the ability to detect anomalies with high accuracy on these data sets. These sets contain seeded faults, meaning that they have been deliberately injected into the system. The second data set is from realworld fleet of 84 jet aircraft where we show the ability to detect fuel overconsumption which can have a significant environmental and economic impact. To the best of our knowledge, this is the first study of its kind in the aviation domain.

This dataset is about airline operations and performance. The data is quantitative and numerical in nature. It can be analyzed and used to derive insights on the airline's performance, capacity utilization, revenue generation, and efficiency. This type of data is commonly used in the airline industry for performance analysis, benchmarking, and decision-making purposes.

1. Month: This column refers to the month in which the data was recorded.

2. DEPARTURES: The number of flights that departed during the month in question.

3. HOURS: Hours flown by the airline during the month in question. This can be used to track the airline's utilization of its fleet.

4. KILOMETRE(TH): Kilometers flown by the airline during the month, measured in thousands. This can be used to track the airline's overall operational performance.

5. PASSENGERS CARRIED: Number of passengers carried by the airline during a given month.

6. PASSENGER KMS.PERFORMED(TH): Passenger kilometers performed by the airline during the month, measured in thousands. This can be used to track the airline's revenue performance.

7. AVAILABLE SEAT KILOMETRE(TH): Seat kilometers available on the airline's flights during the month, measured in thousands. This can be used to track the airline's capacity utilization.

8. PAX.LOAD FACTOR (IN %): Percentage of available seats that were actually occupied by passengers during the month in question. This is a key metric for airlines, as it indicates how effectively they are filling their planes.