1) Data Introduction • The European Flights Dataset is a tabulated dataset of more than 680,000 air traffic records, including instrument flight (IFR) arrivals and operations at major European airports from January 2016 to May 2022.

2) Data Utilization (1) European Flights Dataset has characteristics that: • Each row contains 14 key items, including year, month, flight date, airport code and name, country name, and number of departures, arrivals, and total flights based on IFR. • The data are segmented by airport, country, and month, so they are well structured to analyze time series and spatial changes in European air traffic. (2) European Flights Dataset can be used to: • Analysis of Air Traffic Trends and Recovery: Using IFR operational performance by year, month, and airport, you can analyze changes in air traffic before and after the pandemic, seasonal trends, and speed of recovery. • Airport and Country Comparison Study: National/Airport performance data can be used to compare and evaluate major hub airports, cross-country aviation network structure, policy effectiveness, and more.

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high-volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.

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

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.

The National Airspace System (NAS) is an ever changing and complex engineering system. As the Next Generation Air Transportation System (NextGen) is developed, there will be an increased emphasis on safety and operational and environmental efficiency. Current operations in the NAS are monitored using a variety of data sources, including data from flight recorders, radar track data, weather data, and other massive data collection systems. Although numerous technologies exist to monitor the frequency of known but undesirable behaviors in the NAS, there are currently few methods that can analyze the large repositories to discover new and previously unknown events in the NAS. Having a tool to discover events that have implications for safety or incidents of operational importance, increases the awareness of such scenarios in the community and helps to broaden the overall safety of the NAS, whereas only monitoring the frequency of known events can only provide mitigations for already established problems. This paper discusses a novel approach for discovering operationally significant events in the NAS that are currently not monitored and have potential safety and/or efficiency implications using radar-track data. This paper will discuss the discovery algorithm and describe in detail some flights of interest with comments from subject matter experts who are familiar with the operations in the airspace that was studied.

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!

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

Original data:https://doi.org/10.7910/DVN/HG7NV7This data has been rearranged and converted in parquet.

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

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.

Context

Using this data you can find what caused the delay for flight whether it's Security delay, NAS delay or Carrier delay, etc.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Data Dictionary

1. DayOfWeek → 1 (Monday) - 7 (Sunday)
2. Date → Scheduled date
3. DepTime → Actual departure time (local, hhmm)
4. ArrTime → Actual arrival time (local, hhmm)
5. CRSArrTime → Scheduled arrival time (local, hhmm)
6. UniqueCarrier → Unique carrier code
7. Airline → Airline company
8. FlightNum → flight number
9. TailNum → plane tail number
10. ActualElapsedTime → Actual time an airplane spends in the air(in minutes) with TaxiIn/Out
11. CRSElapsedTime → CRS Elapsed Time of Flight (estimated elapse time), in minutes
12. AirTime → Flight Time (in minutes)
13. ArrDelay → Difference in minutes between scheduled and actual arrival time
14. Origin → Origin IATA(International Air Transport Association) airport code
15. Org_Airport → Origin Airport Name
16. Dest → Destination IATA code
17. Dest_Airport → Destination Airport Name
18. Distance → Distance between airports (miles)
19. TaxiIn → Wheels down and arrival at the destination airport gate, in minutes
20. TaxiOut → The time elapsed between departure from the origin airport gate and wheels off, in minutes
21. Cancelled → Was the flight canceled?
22. CancellationCode → Reason for cancellation
23. Diverted → 1 = yes, 0 = no
24. CarrierDelay → Flight delay due to carrier(e.g. maintenance or crew problems, aircraft cleaning, fueling, etc), 0 = No, yes = (in minutes)
25. WeatherDelay → Flight delay due to weather, 0 = No, yes = (in minutes)
26. NASDelay → Flight delay by NSA(National Aviation System), 0 = No, yes = (in minutes)
27. SecurityDelay → Flight delay by this reason, 0 = No, yes = (in minutes)
28. LateAircraftDelay → Flight delay by this reason, 0 = No, yes = (in minutes)

Acknowledgements

Inspired from others DataSet in the same domain. So, tried to create one balanced dataset ready to use for beginners. This is a public dataset so it's not Licensed by anyone.

The GOES-R PLT ER-2 Flight Navigation Data dataset consists of multiple altitude, pressure, temperature parameters, airspeed, and ground speed measurements collected by the NASA ER-2 high-altitude aircraft for flights that occurred during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT airborne science field campaign took place between March 21 and May 17, 2017 in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). ER-2 navigation data files in ASCII-IWG1 format are available for March 21, 2017 through May 17, 2017.

Data that looks at how market structure affects delays for US domestic flights between the years 2004 - 2017.

Data on airline delays come from the Airline On-Time Performance Data (OTPD) from the US Bureau of Transportation Statistics. The data on tail numbers and seat capacity come from the Federal Aircraft Administration Aircraft Registry. The data on flight-related whether comes from the Local Climatological Data (LCD) provided by the National Center for Environmental Information.

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.

This physiological data was collected from pilot/copilot pairs in and out of a flight simulator. It was collected to train machine-learning models to aid in the detection of pilot attentive states. The benchmark training set is comprised of a set of controlled experiments collected in a non-flight environment, outside of a flight simulator. The test set (abbreviated LOFT = Line Oriented Flight Training) consists of a full flight (take off, flight, and landing) in a flight simulator. The pilots experienced distractions intended to induce one of the following three cognitive states: Channelized Attention (CA) is the state of being focused on one task to the exclusion of all others. This is induced in benchmarking by having the subjects play an engaging puzzle-based video game. Diverted Attention (DA) is the state of having one’s attention diverted by actions or thought processes associated with a decision. This is induced by having the subjects perform a display monitoring task. Periodically, a math problem showed up which had to be solved before returning to the monitoring task. Startle/Surprise (SS) is induced by having the subjects watch movie clips with jump scares. For each experiment, a pair of pilots (each with its own crew ID) was recorded over time and subjected to the CA, DA, or SS cognitive states. The training set contains three experiments (one for each state) in which the pilots experienced just one of the states. For example, in the experiment labelled CA, the pilots were either in a baseline state (no event) or the CA state. The test set contains a full flight simulation during which the pilots could experience any of the states (but never more than one at a time). Each sensor operated at a sample rate of 256 Hz. Please note that since this is physiological data from real people, there will be noise and artifacts in the data.

A. SUMMARY San Francisco International Airport (SFO) keeps track of historical flight operations, also known as aircraft RADAR data for analysis and reporting.

B. HOW THE DATASET IS CREATED Details of flights from the Federal Aviation Administration’s National Offload Program are processed into SFO’s Airport Noise and Operations Management System (ANOMS) where it is correlated with noise reports from the communities and to noise levels collected from noise monitor sites on the San Francisco Peninsula. In ANOMS, various analysis gates (imaginary vertical curtain in space) are used to identify which route flights flew departing and arriving SFO. It serves to quantify, analyze, respond to noise concerns, and report on Runway Use and various programs to reduce aircraft noise in communities surrounding SFO.

C. UPDATE PROCESS Data is available starting in August 2019 and will be updated monthly.

D. HOW TO USE THIS DATASET It is important to note, that this dataset is of flights departing and landing at SFO only and not flight activities associated with other airports in the Bay Area region. This information is the data source used to produce the Flight Operations sections (pages 3-5) of the Airport Director’s Report. These reports are presented at the SFO Airport Community Roundtable Meetings and available online at https://noise.flysfo.com/reports/?category=airport-directors-report

E. RELATED DATASETS Unique Flight Operations - This filtered view contains unique records of flight operations. For example, one record for a flight that departed SFO or one record for a flight that landed at SFO.

Arrival and Departure Routes - This filtered view contains records of flights with details of analysis gate(s) the aircraft flight track penetrates, to derive which route was used to depart and land at SFO.

This dataset contains Operations and Arrival and Departure Routes joined on operation_number. The field gate_penetration is derived by ordering the arrival and departure routes for each operation over gate_penetration_time. Unique_identifier is then created by joining operation_number and gate_penetration.

Other provided datasets are Aircraft Noise Reports, Late Night Aircraft Departures, Air Carrier Runway Use, and Late Night Preferential Runway Use, Aircraft Noise Climates, and Noise Exceedance Rating.

Please contact the Noise Abatement Office at NoiseAbatementOffice@flysfo.com for any questions regarding this data.

Date created: November 17, 2023

This dataset contains reviews of the top 10 rated airlines in 2023 sourced from the Airline Quality (https://www.airlinequality.com) website. The reviews cover various aspects of the flight experience, including seat comfort, staff service, food and beverages, inflight entertainment, value for money, and overall rating. The dataset is suitable for sentiment analysis, customer satisfaction analysis, and other similar tasks.

Usage - Download the dataset file airlines_reviews.csv. - Use the dataset for analysis, visualization, and machine learning tasks.

List of Airlines 1. Singapore Airlines 2. Qatar Airways 3. All Nippon Airways 4. Emirates 5. Japan Airlines 6. Turkish Airlines 7. Air France 8. Cathay Pacific Airways 9. EVA Air 10.Korean Air

This dataset is provided under the MIT License.

Bio-logging is central to study of wildlife, but questions remain about minimization of effects from bio-logging devices. Rarely considered are changes bio-logging devices induce on an animal’s center of mass (COM) and resulting losses of flight control and stability. In a related paper (Katzner and Young 2024), we applied established aeronautical principles to estimate how the COM of a flying bird or bat may be affected by typical positioning of a bio-logging device on the neck, back, hips, or tail. We then adopted modified thresholds from aerospace engineering to estimate limits beyond which changes to COM result in fitness-relevant alterations to flight control and stability. Here we provide measurements for other species, beyond the seven reported in Katzner and Young (2024).

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