The dataset presented in this forum is monthly data. The Port Authority collects monthly data for domestic and international cargo, flights, passengers and aircraft equipment type from each carrier at PANYNJ-operated airports. The data is aggregated and forms the basis for estimating flight fees, parking, concession, and PFC revenues at the Port Authority Airports.

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.

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

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.

This dataset provides detailed information on flight arrivals and delays for U.S. airports, categorized by carriers. The data includes metrics such as the number of arriving flights, delays over 15 minutes, cancellation and diversion counts, and the breakdown of delays attributed to carriers, weather, NAS (National Airspace System), security, and late aircraft arrivals. Explore and analyze the performance of different carriers at various airports during this period. Use this dataset to gain insights into the factors contributing to delays in the aviation industry.

Purpose: The purpose of this dataset is to offer insights into the performance of U.S. carriers at various airports during August 2013 - August 2023, focusing on flight arrivals and delays. By providing detailed information on key metrics such as the number of arriving flights, delays over 15 minutes, cancellations, and diversions, the dataset aims to facilitate analyses of factors contributing to delays, including those attributed to carriers, weather, the National Airspace System (NAS), security, and late aircraft arrivals. Researchers, data scientists, and aviation enthusiasts can leverage this dataset to explore patterns, identify trends, and draw conclusions that contribute to a better understanding of the aviation industry's operational challenges.

Structure: The dataset is structured as a tabular format with rows representing unique combinations of year, month, carrier, and airport. Each row contains information on various metrics, including flight counts, delay counts, cancellation and diversion counts, and delay breakdowns by different factors. The columns provide specific details such as carrier codes and names, airport codes and names, and counts of delays attributed to carrier, weather, NAS, security, and late aircraft arrivals. The structured format ensures that users can easily query, analyze, and visualize the data to derive meaningful insights.

• year: The year of the data.
• month: The month of the data.
• carrier: Carrier code.
• carrier_name: Carrier name.
• airport: Airport code.
• airport_name: Airport name.
• arr_flights: Number of arriving flights.
• arr_del15: Number of flights delayed by 15 minutes or more.
• carrier_ct: Carrier count (delay due to the carrier).
• weather_ct: Weather count (delay due to weather).
• nas_ct: NAS (National Airspace System) count (delay due to the NAS).
• security_ct: Security count (delay due to security).
• late_aircraft_ct: Late aircraft count (delay due to late aircraft arrival).
• arr_cancelled: Number of flights canceled.
• arr_diverted: Number of flights diverted.
• arr_delay: Total arrival delay.
• carrier_delay: Delay attributed to the carrier.
• weather_delay: Delay attributed to weather.
• nas_delay: Delay attributed to the NAS.
• security_delay: Delay attributed to security.
• late_aircraft_delay: Delay attributed to late aircraft arrival.

Usage: Researchers, analysts, and data enthusiasts can utilize this dataset for a variety of purposes, including but not limited to:

Performance Analysis: Assess the on-time performance of different carriers at specific airports and identify potential areas for improvement.

Trend Identification: Analyze temporal trends in delays, cancellations, and diversions to understand whether certain months or periods exhibit higher operational challenges.

Root Cause Analysis: Investigate the primary contributors to delays, such as carrier-related issues, weather conditions, NAS inefficiencies, security concerns, or late aircraft arrivals.

Benchmarking: Compare the performance of various carriers across different airports to identify industry leaders and areas requiring attention.

Predictive Modeling: Use historical data to develop predictive models for flight delays, aiding in the development of strategies to mitigate disruptions.

Industry Insights: Contribute to a broader understanding of the factors influencing operational efficiency within the U.S. aviation sector.

As users explore and analyze the dataset, they can gain valuable insights that may inform decision-making processes, improve operational strategies, and contribute to a more efficient and reliable air travel experience.

Number of Flights That Occurred at the Airport

Analysis of ‘Commercial and Non Commercial Flights per Month, Port Authority of NY NJ: Beginning 1977’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fb0fb76b-30d1-4595-b8bb-051f37586f77 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

The dataset presented in this forum is monthly data. The Port Authority collects monthly data for domestic and international cargo, flights, passengers and aircraft equipment type from each carrier at PANYNJ-operated airports. The data is aggregated and forms the basis for estimating flight fees, parking, concession, and PFC revenues at the Port Authority Airports.

--- Original source retains full ownership of the source dataset ---

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 will be periodically included in the dataset until the end of the COVID-19 pandemic.

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)
• 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:
<https://traffic-viz.github.io/scenarios/covid19.html>

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

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 presents monthly statistics on scheduled and non-scheduled aircraft traffic by type, year, and airline in the State of Qatar. It includes the number of arrivals and departures of flights operated by Qatar Airways and other airlines from 2014 to 2015. The data is structured by year, month, airline, and flight type, providing insights into air traffic volume, trends, and the performance of Qatar's aviation sector.

This dataset contain information about: The number of flights executed, the number of buses and their capacity, and the number of passengers between cities for previous years, by year, month, and the city of departure

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

Analysis of ‘Los Angeles International Airport - Flight Operations By Month’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f97af34b-45db-414c-8a1d-964edb72a26f on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Number of Flights That Occurred at the Airport

--- Original source retains full ownership of the source dataset ---

The dataset illustrates the Monthly count of flights per international airport (Abu Dhabi, Al Ain, Al Bateen, Dubai, Al Maktoum, Sharjah, Ras Al Khaima, Fujairah) in the United Arab Emirates for the year of 2017.

Passengers, Freight and Commercial Flights by Airports in Ireland, Country, Direction, Flight Type, Month and Statistic

View data using web pages

Download .px file (Software required)

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.

Data on flight movements, passengers, cargo and mail at Dutch airports.

Summary of the contents of the EU figure in this publication: The composition of the European Union (EU-15) until 2004: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, The Netherlands, Portugal, Spain, Sweden and United Kingdom. In 2005 the European Union (EU-25) expanded with: Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia and Slovenia. In 2007 Bulgaria and Romania have been added (EU-27). In 2014 Croatia was added (EU-28). As of February 2020 the United Kingdom has left the European Union.

Data available from: Annual figures available from 1997; monthly figures available from January 1999.

Status of the figures: The figures are final up to and including 2023, 2024 and for the current year are provisional.

Up to and including December 2020 the figures of EU countries include data for the UK. This to guarantee the comparability of the data. As of January 2021 data for the UK are included in the figures of "other Europe".

As of 8 November 2022 the figures for Eindhoven airport for the reporting period April, May and June 2022 have been adjusted as a result of additional information. As a result, the marginal totals for the months of April, May and June 2022 have also been adjusted.

Due to renovation work on the runway at Maastricht Aachen Airport, there was no air traffic at this airport from 8 May 2023 to 30 June 2023.

Changes as of 4 August 2025: The figures for June 2025 and for the 2nd quarter 2025 have been added.

When will figures become available? The monthly figures are published as a rule 1 month after the end of the reporting month.

The U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT's monthly Air Travel Consumer Report, published about 30 days after the month's end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released.

This version of the dataset was compiled from the Statistical Computing Statistical Graphics 2009 Data Expo and is also available here.

The dataset illustrates the Monthly count of Scheduled and Non Scheduled flights per international airport (Abu Dhabi, Al Ain, Al Bateen, Dubai, Al Maktoom, Sharjah, Ras Al Khaima, Fujairah) in the United Arab Emirates for the year of 2017.