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.

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) |

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 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.

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

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.

Airline on-time performance Have you ever been stuck in an airport because your flight was delayed or canceled and wondered if you could have predicted it if you'd had more data? This is your chance to find out.

The results We had a total of nine entries, and turn out at the poster session at the JSM was great, with plenty of people stopping by to find out why their flights were delayed.

The data The data consists of flight arrival and departure details for all commercial flights within the USA, from October 1987 to April 2008. This is a large dataset: there are nearly 120 million records in total and takes up 1.6 gigabytes of space when compressed and 12 gigabytes when uncompressed.

The challenge The aim of the data expo is to provide a graphical summary of important features of the data set. This is intentionally vague in order to allow different entries to focus on different aspects of the data, but here are a few ideas to get you started:

When is the best time of day/day of week/time of year to fly to minimise delays? Do older planes suffer more delays? How does the number of people flying between different locations change over time? How well does weather predict plane delays? Can you detect cascading failures as delays in one airport create delays in others? Are there critical links in the system? You are also welcome to work with interesting subsets: you might want to compare flight patterns before and after 9/11, or between the pair of cities that you fly between most often, or all flights to and from a major airport like Chicago (ORD). Smaller subsets may also help you to match up the data to other interesting datasets.

Columns | Name|Description| | --- | --- | |year| 1987-2008| |month| 1-12| |day of month| 1-31| |day of week| 1 (Monday) - 7 (Sunday)| |DepTime| actual departure time (minutes)| |CRSDepTime| scheduled departure time (minutes) |ArrTime| actual arrival time (minutes)| |CRSArrTime| scheduled arrival time (minutes)| |UniqueCarrier| unique carrier code| |FlightNum| flight number| |TailNum| plane tail number| |ActualElapsedTime| in minutes| |CRSElapsedTime| in minutes| |AirTime| in minutes| |ArrDelay| arrival delay, in minutes| |DepDelay| departure delay, in minutes| |Origin| origin IATA airport code| |Dest| destination IATA airport code| |Distance| in miles| |TaxiIn| taxi in time, in minutes| |TaxiOut| taxi out time in minutes| |Cancelled| was the flight cancelled?| |CancellationCode| reason for cancellation (A = carrier, B = weather, C = NAS, D = security)| |Diverted| 1 = yes, 0 = no| |CarrierDelay| in minutes| |WeatherDelay| in minutes| |NASDelay| in minutes| |SecurityDelay| in minutes| |LateAircraftDelay| in minutes|

Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Airline Ticket Price dataset concerns the prediction of airline ticket prices. The rows are a sequence of time-ordered observations over several days. Each sample in this dataset represents a set of observations from a specific observation date and departure date pair. The input variables for each sample are values that may be useful for prediction of the airline ticket prices for a specific departure date. The target variables in these datasets are the next day (ATP1D) price or minimum price observed over the next 7 days (ATP7D) for 6 target flight preferences: (1) any airline with any number of stops, (2) any airline non-stop only, (3) Delta Airlines, (4) Continental Airlines, (5) Airtrain Airlines, and (6) United Airlines. The input variables include the following types: the number of days between the observation date and the departure date (1 feature), the boolean variables for day-of-the-week of the observation date (7 features), the complete enumeration of the following 4 values: (1) the minimum price, mean price, and number of quotes from (2) all airlines and from each airline quoting more than 50 % of the observation days (3) for non-stop, one-stop, and two-stop flights, (4) for the current day, previous day, and two days previous. The result is a feature set of 411 variables. For specific details on how these datasets are constructed please consult Groves and Gini (2015). The nature of these datasets is heterogeneous with a mixture of several types of variables including boolean variables, prices, and counts.

The "flights.csv" dataset contains information about the flights of an airport. This dataset includes information such as departure and arrival time, delays, flight company, flight number, flight origin and destination, flight duration, distance, hour and minute of flight, and exact date and time of flight. This data can be used in management analysis and strategies and provide useful information about the performance of flights and placement companies. The analysis of the data in this dataset can be used as a basis for the following activities: - Analysis of time patterns and trends: by examining the departure and arrival time of the aircraft, changes and time changes, patterns and trends in flight behavior can be identified. - Analysis of American companies: By viewing information about airlines such as the number of flights, the impact and overall performance, you can compare and analyze the performance of each company. - Analysis of delays and service quality: By examining delays and arrival time, I can collect and analyze information about the quality of services provided by the airport and companies. - Analysis of flight routes: by checking the origin and destination of flights, distances and flight duration, popular routes and people's choices can be identified and analyzed. - Analysis of airport performance: by observing the characteristics of flights and airport performance, it is possible to identify and analyze the strengths and weaknesses of the airport and suggest improvements.

It provides various tools for data analysis and visualization and can be used as a basis for managerial decisions in the field of aviation industry.

Airline Company Codes (in order of frequency for this dataset)

WN -- Southwest Airlines Co.

DL -- Delta Air Lines Inc.

AA -- American Airlines Inc.

UA -- United Air Lines Inc.

B6 -- JetBlue Airways

AS -- Alaska Airlines Inc.

NK -- Spirit Air Lines

G4 -- Allegiant Air

F9 -- Frontier Airlines Inc.

HA -- Hawaiian Airlines Inc.

SY -- Sun Country Airlines d/b/a MN Airlines

VX -- Virgin America

Have you ever been stuck in an airport because your flight was delayed or cancelled and wondered if you could have predicted it if you'd had more data? This is your chance to find out.

The 2009 ASA Statistical Computing and Graphics Data Expo consisted of flight arrival and departure details for all commercial flights on major carriers within the USA, from October 1987 to April 2008. This is a large dataset containing nearly 120 million records in total.

The aim of the data expo is to provide a graphical summary of important features of the data set. This is intentionally vague in order to allow different entries to focus on different aspects of the data, but here are a few ideas to get you started: •When is the best time of day, day of the week, and time of year to fly to minimise delays? •Do older planes suffer more delays? •How well does weather predict plane delays? •How does the number of people flying between different locations change over time? •Can you detect cascading failures as delays in one airport create delays in others? Are there critical links in the system? •Use the available variables to construct a model that predicts delays.

Our Flight Events data feed combines Spire Global satellite/terrestrial ADS-B flight event data with ch-aviation’s fleet, operator, and airport data providing an overview of all flights operated by airlines, business and general aviation players on a daily basis.

The value of our Flight Events data feed lies in its high-resolution integration of ADS-B flight tracking with ch-aviation’s comprehensive aircraft and operator data, delivering unmatched visibility into global aircraft movements. By identifying the aircraft type and registration for approximately 98% of all ADS-B-tracked flights, we offer an industry-leading solution for lessors, insurers, airports, OEMs, and analysts seeking precise, reliable, and actionable aviation intelligence.

• High-Resolution ADS-B Integration - Satellite and terrestrial ADS-B flight tracking combined with enriched aircraft and operator data for maximum accuracy and visibility • Comprehensive Aircraft Identification - Aircraft type and registration identified for approximately 98% of all ADS-B-tracked flights, using proprietary matching with ch-aviation data and supplementary publicly available authority data sources. • Global Flight Coverage - Tracks approximately 160,000–190,000 flights per day across commercial aviation, business jet, and general aviation sectors worldwide. • ACMI (Wet-Lease) and Cargo Customer Tracking - Detailed monitoring of ACMI operations, including identification of wet-lease activity between different operators as well as cargo customers identifying flights operated for integrators like DHL Express or FedEx as well as cargo customers such as Amazon. • Aircraft Utilisation Tracking - Tracking of flight hours and cycles at both the operator and individual tail number (aircraft) level • Matched Operator and Aircraft Data - Every flight is linked to comprehensive ch-aviation datasets, including aircraft ID, history, operator, variant, callsign, and airport details allowing customers to leverage the industry’s most comprehensive integration between ADS-B flight event and fleet/operator/airport data. • Fallback Data Enrichment - Where ch-aviation data is unavailable, civil aviation authority and ANSP sources are used to ensure continuity in aircraft identification and data accuracy. • Use Case-Driven Insights - Tailored for industry stakeholders like lessors, insurers, OEMs, airports, and analysts seeking operational, commercial, and technical flight data intelligence.

ch-aviation integrates its Commercial Aviation Aircraft Data and Business Jet Aircraft Data with Spire Global’s satellite-based ADS-B data that is fused by Spire with terrestrial feeds from two terrestrial ADS-B data providers.

This data is enriched with mapped callsigns, corrected hexcodes, regional partnership decoding, and identification of wet-leases and cargo customers, enabling detailed insight into each individual flight.

Where ch-aviation data is unavailable, public data from civil aviation authorities and ANSPs is used to ensure broad and reliable aircraft identification and coverage.

The data set is available historically going back to January 1, 2018.

The data set is updated daily.

The sample data shows flights on 2025-03-30, with Swiss, Alaska Airlines, Horizon Air, Jet Aviation Business Jets, and RVR Aviation as operators or wet lease customers.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=flights/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/flights-2/

Passengers enplaned and deplaned at Canadian airports, annual.

This dataset is sourced from the Airline Data Project established by the MIT Global Airline Industry Program. It describes financial metrics for Individual airlines, airline sectors and the industry as a whole for the American commercial airline industry. The Original data from the source is collected in the zip file "Original MIT data" and the data relating to Airline finances and the main industry metrics has been cleaned and written into csv files for ease of use.

MIT ADP Description The U.S. commercial airline industry is one of the most diverse, dynamic and perplexing in the world. It is fast-evolving, labor intensive, capital intensive, hyper-competitive and highly susceptible to the ebb and flow of business cycles as well as being among the most regulated of deregulated businesses.

The Airline Data Project (ADP) was established by the MIT Global Airline Industry Program to better understand the opportunities, risks and challenges facing this vital industry. The ADP presents the most important airline industry data in one location in an easy-to-understand, user-friendly format.

The data on this website is sourced from the U.S. Department of Transportation's Form 41 data product. It has been selected and analyzed to present a view of the industry and its important trends, as well as to identify fundamental drivers of success - and in some cases, the early signs of potential failure.

The ADP is designed to support the goals of the MIT Airline Industry Consortium. It is a unique repository of data and analysis that will allow individuals – from academia to the financial community to the news media – to monitor the evolution of the U.S. commercial airline industry.

The ADP is updated in June of each year pending the release of Form 41 data files by the U.S. Bureau of Transportation Statistics. The last update of the ADP was in June 2020 for calendar year 2019 data. If you have questions about what items are included in various Form 41 data categories, you can refer to the U.S. DOT's Form 41 Financial Reporting Categories Item List Guide.

You are invited to review the data on this site and share your feedback on the wealth of information that is available about this highly visible industry.

Glossary: Aircraft Utilization Measure of aircraft productivity, calculated by dividing aircraft block hours by the number of aircraft days assigned to service on air carrier routes. Typically presented in block hours per day.

Available Seat Miles (ASMs) A common industry measurement of airline output that refers to one aircraft seat flown one mile, whether occupied or not. An aircraft with 100 passenger seats, flown a distance of 100 miles, generates 10,000 available seat miles.

Average Aircraft Capacity Average seating configuration of an airline’s operating fleet. The measure is derived by dividing total available seat miles flown by the number of aircraft miles flown. It is important to understand the average aircraft size as it is an important determinant of employees needed to service the operation of a particular airline.

Block Hour Time from the moment the aircraft door closes at departure of a revenue flight until the moment the aircraft door opens at the arrival gate following its landing. Block hours are the industry standard measure of aircraft utilization (see above).

Cost per Available Seat Mile (CASM) Measure of unit cost in the airline industry. CASM is calculated by taking all of an airline’s operating expenses and dividing it by the total number of available seat miles produced. Sometimes, fuel or transport-related expenses are withheld from CASM calculations to better isolate and directly compare operating expenses.

Unit Cost per Unit of Output A measurement that gauges total operating costs in relation to output.

Form 41 Data Information derived from airline filings with the Bureau of Transportation Statistics. Airline financial data is filed with the BTS quarterly; traffic and employment numbers are filed monthly.

Load Factor The number of Revenue Passenger Miles (RPMs) expressed as a percentage of ASMs, either on a particular flight or for the entire system. Load factor represents the proportion of airline output that is actually consumed. To calculate this figure, divide RPMs by ASMs. Load factor for a single flight can also be calculated by dividing the number of passengers by the number of seats.

Operating Revenue ...

Daily aircraft utilisation is available for all commercial aviation and business jet aircraft showing the number of flight hours and cycles every day (in UTC) time based on a combination of Spire Global satellite/terrestrial ADS-B data and ch-aviation fleet data.

The data set includes hours, cycles, average stage length as well as data quality indicators for each record.

The data set is updated daily.

The sample data shows aircraft flown on 2025-03-30 by Swiss, Alaska Airlines, Horizon Air, Jet Aviation Business Jets, and RVR Aviation, with utilization metrics

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=aircraft_utilisation_daily/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/aircraft-utilisation-daily-ads-b-based-2/

A. SUMMARY This dataset stores the parking reservation information recorded by the Aircraft Parking System at SFO.

The columns in this dataset include details such as the Source ID, Source Type, Start Datetime , End Datetime, Reserving Company, Operator Company, Model, Tail Number and Spot of the aircraft parking activity.

On the 28th day of each month, this dataset is exported from the Aircraft Parking System at SFO.

B. HOW THE DATASET IS CREATED

When airline requests for reservation of aircraft parking stand at SFO, a new aircraft parking reservation is entered to the Aircraft Parking System.

On the 28th day of each month, this dataset is replaced with all the aircraft parking activity records retrieved from the aircraft parking reservation table with reservation Start Date between August 2008 and the previous month of the export run date.

C. UPDATE PROCESS Airside Operations Supervisor updates the aircraft parking activity record as requested by the airline for making changes and cancellations.

When airline requests for changes or cancellation to the previous made aircraft parking reservation, the aircraft parking activity record is updated with the changes in the Aircraft Parking System.

On the 28th day of each month, this dataset is replaced with all the aircraft parking activity records retrieved from the aircraft parking reservation table with reservation Start Date between August 2008 and the previous month of the export run rate.

D. HOW TO USE THIS DATASET Utilize this dataset to determine the parking duration of a reservation by calculating the difference between the reservation's end date and start date.

E. RELATED DATASETS

Aircraft Tail Numbers and Models at SFO

Utilize the Tail Number column of the "Aircraft Parking Activity Records at SFO" dataset to connect with the Tail Number column of the "Aircraft Tail Numbers and Models at SFO" dataset, for referencing the corresponding aircraft record.

Aircraft Parking Location Inventory at SFO

Utilize the Spot column from the "Aircraft Parking Activity Records at SFO" dataset to connect with the Spot Name column in the "Aircraft Parking Locations at SFO" dataset, for referencing the corresponding parking location record.

BACKGROUND The data contained in the compressed file has been extracted from the Marketing Carrier On-Time Performance (Beginning January 2018) data table of the "On-Time" database from the TranStats data library. The time period is indicated in the name of the compressed file; for example, XXX_XXXXX_2001_1 contains data of the first month of the year 2001.

RECORD LAYOUT Below are fields in the order that they appear on the records: Year Year Quarter Quarter (1-4) Month Month DayofMonth Day of Month DayOfWeek Day of Week FlightDate Flight Date (yyyymmdd) Marketing_Airline_Network Unique Marketing Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users, for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. Operated_or_Branded_Code_Share_Partners Reporting Carrier Operated or Branded Code Share Partners DOT_ID_Marketing_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Marketing_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Flight_Number_Marketing_Airline Flight Number Originally_Scheduled_Code_Share_Airline Unique Scheduled Operating Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users,for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. DOT_ID_Originally_Scheduled_Code_Share_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Originally_Scheduled_Code_Share_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Flight_Num_Originally_Scheduled_Code_Share_Airline Flight Number Operating_Airline Unique Carrier Code. When the same code has been used by multiple carriers, a numeric suffix is used for earlier users, for example, PA, PA(1), PA(2). Use this field for analysis across a range of years. DOT_ID_Operating_Airline An identification number assigned by US DOT to identify a unique airline (carrier). A unique airline (carrier) is defined as one holding and reporting under the same DOT certificate regardless of its Code, Name, or holding company/corporation. IATA_Code_Operating_Airline Code assigned by IATA and commonly used to identify a carrier. As the same code may have been assigned to different carriers over time, the code is not always unique. For analysis, use the Unique Carrier Code. Tail_Number Tail Number Flight_Number_Operating_Airline Flight Number OriginAirportID Origin Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused. OriginAirportSeqID Origin Airport, Airport Sequence ID. An identification number assigned by US DOT to identify a unique airport at a given point of time. Airport attributes, such as airport name or coordinates, may change over time. OriginCityMarketID Origin Airport, City Market ID. City Market ID is an identification number assigned by US DOT to identify a city market. Use this field to consolidate airports serving the same city market. Origin Origin Airport OriginCityName Origin Airport, City Name OriginState Origin Airport, State Code OriginStateFips Origin Airport, State Fips OriginStateName Origin Airport, State Name OriginWac Origin Airport, World Area Code DestAirportID Destination Airport, Airport ID. An identification number assigned by US DOT to identify a unique airport. Use this field for airport analysis across a range of years because an airport can change its airport code and airport codes can be reused. DestAirportSeqID Destination Airport, Airport Sequence ID. An identification number assigned by US DOT to identify a unique airport at a given point of time. Airport attributes, such as airport name or coordinates, may change over time. DestCityMarketID Destination Airport, City Market ID. City Market ID is an identification number assigned by US DOT to identify a city market. Use this field to consolidate airports serving the same city market. Dest Destination Airport DestCityName Destination Airport, City Name DestState Destination Airport, State Code DestStateFips De...

A. SUMMARY This dataset contains the aircraft tail and model information recorded by the Aircraft Parking System at SFO. The columns in the dataset include details such as tail number, model, airline, status, as well as the Creation and Modification Dates of the aircraft record. On the 28th day of each month, this dataset is exported from the Aircraft Parking System at SFO. B. HOW THE DATASET IS CREATED When airline requests for reservation of aircraft parking stand at SFO, a new aircraft parking reservation is entered to the Aircraft Parking System. If the requested Aircraft is not found in the Aircraft Parking System, then the requested Aircraft information is added to the aircraft table of the Aircraft Parking System. On the 28th day of each month, this dataset is replaced with all the records retrieved from the aircraft table of the Aircraft Parking System at SFO. C. UPDATE PROCESS When airline companies undergo a merger or acquisition, the transfer of aircraft ownership is recorded by marking the aircraft previously owned by the originating airline as inactive and registering the aircraft now owned by the new airline as active in the aircraft table. On the 28th day of each month, this dataset is replaced with all the records retrieved from the aircraft table of the Aircraft Parking System at SFO. E. RELATED DATASETS Aircraft Parking Activity Records at SFO Aircraft Parking Location Inventory at SFO Utilize the Tail Number column of the "Aircraft Parking Activity Records at SFO" dataset to connect with the Tail Number column of the "Aircraft Tail Numbers and Models at SFO" dataset, for referencing the corresponding aircraft record.

This dataset contains data related to Air Traffic Management hotspots. Hotspots are created in the European airspaces when capacity for some pieces of airspace are foreseen to be infringed due to weather, congestion, strikes, etc. This anonymised dataset records around 5900 hotspots happening at 22 major European airports. These hotspots are generated through a simulator called Mercury that is fed with real data (in particular, real capacity reduction that happened in Europe for over a year, schedules etc) and simulates a day of operation, randomising events like delays, cancellation etc. More details on mercury can be found here [1] and [2]. The data, anonymised in terms of airports and airlines, is a dictionary which is structured as follows: - the top level key is the id of the airport, the value is list a of all regulations available for this airport. - each item of the list is a dictionary, with keys: -- 'slot_times': list of all slots available to flights for this hotspot/regulation, in minutes since midnight. -- 'etas': list of initial estimated arrival times of flights involved in the regulation, in minutes since midnight. -- 'flight_ids': list of flight ids (in the same order than etas) -- 'cost_vectors': list of cost vectors. Each item is a list itself, of length equal to the slot_times list. Each element of that list is the estimated cost that the airline owning the flight would incur, were the flight be assigned to this slot, in terms of: maintenance, crew, rebooking fees, market value loss, and curfew infringement, in 2014 euros. This cost is computed within the Mercury model and is based on [3]. -- 'airlines_flights': dictionary whose keys are airline ids and values are lists of ids of flights owned by the airline. [1] https://www.sciencedirect.com/science/article/abs/pii/S0968090X21003600 [2] G. Gurtner, L. Delgado, and D.Valput, “An agent-based model for air transportation to capture network effects in assessing delay management mechanisms”, Transportation Research Part C: emerging Technologies, 2021. Pre-print available here: https://westminsterresearch.westminster.ac.uk/item/v956w/an-agent-based-model-for-air-transportation-to-capture-network-effects-in-assessing-delay-management-mechanisms [3] A. J. Cook and G. Tanner, “European airline delay cost reference values - updated and extended values (Version 4.1),” University of Westminster, London, 2015a

India All Scheduled Airlines: Domestic: Number of Flight data was reported at 102,319.000 Unit in Mar 2025. This records an increase from the previous number of 92,291.000 Unit for Feb 2025. India All Scheduled Airlines: Domestic: Number of Flight data is updated monthly, averaging 48,100.000 Unit from Apr 2001 (Median) to Mar 2025, with 288 observations. The data reached an all-time high of 102,319.000 Unit in Mar 2025 and a record low of 188.000 Unit in Apr 2020. India All Scheduled Airlines: Domestic: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

Flight Delay Dataset — 2024

Description

This dataset contains detailed flight performance and delay information for domestic flights in 2024, merged from monthly BTS TranStats files into a single cleaned dataset. It includes over 7 million rows and 35 columns, providing comprehensive information on scheduled and actual flight times, delays, cancellations, diversions, and distances between airports. The dataset is suitable for exploratory data analysis (EDA), machine learning tasks such as delay prediction, time series analysis, and airline/airport performance studies.

Monthly CSV files for January–December 2024 were downloaded from the BTS TranStats On-Time Performance database, and 35 relevant columns were selected. The monthly files were merged into a single dataset using pandas, with cleaning steps including standardizing column names to snake_case (e.g., flight_date, dep_delay), converting flight_date to ISO format (YYYY-MM-DD), converting cancelled and diverted to binary indicators (0/1), and filling missing values in delay-related columns (carrier_delay, weather_delay, nas_delay, security_delay, late_aircraft_delay) with 0, while preserving all other values as in the original data.

Source: Available at BTS TranStats

File Description

• flight_data_2024.csv — full cleaned dataset (~7M rows, 35 columns)
• flight_data_2024_sample.csv — sample dataset (10,000 rows)
• flight_data_2024_data_dictionary.csv — column names, data types, null percentage, and example values
• README.md — dataset overview and usage instructions
• LICENSE.txt — CC0 license
• dataset-metadata.json — Kaggle metadata for the dataset

Column Description