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

Data Features:

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

Potential Uses:

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

Percentage of flights arriving on-time. A flight is on-time if it arrives within 15 minutes of the schedule arrival time. Data are available for those carriers that had at least 1% of domestic enplanements in the previous year. The last 25 months of data include only carriers that reported in each of the last 25 months to retain comparability. Earlier data includes all reporting carriers. A scheduled operation consists of any nonstop segment of a flight. The Bureau of Transportation Statistics air collects performance data from U.S. air carriers and international carriers operating within the U.S.

Graph and download economic data for Enplanements for U.S. Air Carrier Domestic, Scheduled Passenger Flights (ENPLANEDD11) from Jan 2000 to May 2025 about flight, passenger, air travel, travel, domestic, and USA.

In 2023, the U.S. airline industry generated 179.2 billion U.S. dollars in revenue from passenger fares. This represented an increase of approximately 16 percent compared to the fare revenue reported a year earlier. The 2023 passenger revenue was also the new peak registered in the given period.

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.

The Airports database is a geographic point database of aircraft landing facilities in the United States and U.S. Territories. Attribute data is provided on the physical and operational characteristics of the landing facility, current usage including enplanements and aircraft operations, congestion levels and usage categories. This geospatial data is derived from the FAA's National Airspace System Resource Aeronautical Data Product.

In 2024, U.S. airlines carried around 852.1 million passengers on domestic flights across the United States. This was an increase from the roughly 819.3 million domestic passengers carried by U.S. airlines in the previous year.

COVID-19(+) Interactions Within Air Travel

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

Acknowledgements

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

Inspiration

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

Methodology

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

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

This dataset contains scheduled and actual departure and arrival times reported by certified US air carriers that account for at least 1% of domestic scheduled passenger revenues. The data was collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS). The dataset contains date, time, origin, destination, airline, distance, and delay status of flights for flights between 2016 and 2018 The report, focusing on data from year 2016-2018, estimated that air transportation delays put a 4 billion dollar dent in the country's gross domestic product that year. Full report can be found here. In order to answer this question, we are going to analyze the provided dataset, containing up to 18 M different internal flights in the US for 2016-2018 and their causes for delay, diversion and cancellation; if any. The data comes from the U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics (BTS).

This dataset is composed by the following variables: Number Column Name Description 1 **Year **2016, 2017, 2018 2 **Month **1-12 3 **DayofMonth **1-31 4 **DayOfWeek **1 (Monday) - 7 (Sunday) 5 DepTime actual departure time (local, hhmm) 6 **CRSDepTime **scheduled departure time (local, hhmm) 7 **ArrTime **actual arrival time (local, hhmm) 8 **CRSArrTime **scheduled arrival time (local, hhmm) 9 **ActualElapsedTime **in minutes 10 **CRSElapsedTime **in minutes 11 **AirTime **in minutes 12 **ArrDelay **arrival delay, in minutes: A flight is counted as "on time" if it operated less than 15 minutes later the scheduled time shown in the carriers' Computerized Reservations Systems (CRS). 13 **DepDelay **departure delay, in minutes 14 **Origin **origin IATA airport code 15 **Dest **destination IATA airport code 16 **Distance **in miles 17 **TaxiIn **taxi in time, in minutes 18 **TaxiOut **taxi out time in minutes 19 **Cancelled ***was the flight cancelled 20 **CancellationCode **reason for cancellation (A = carrier, B = weather, C = NAS, D = security) 21 **Diverted **1 = yes, 0 = no 22 **CarrierDelay **in minutes: Carrier delay is within the control of the air carrier. Examples of occurrences that may determine carrier delay are: aircraft cleaning, aircraft damage, awaiting the arrival of connecting passengers or crew, baggage, bird strike, cargo loading, catering, computer, outage-carrier equipment, crew legality (pilot or attendant rest), damage by hazardous goods, engineering inspection, fuelling, handling disabled passengers, late crew, lavatory servicing, maintenance, oversales, potable water servicing, removal of unruly passenger, slow boarding or seating, stowing carry-on baggage, weight and balance delays. 23 **WeatherDelay **in minutes: Weather delay is caused by extreme or hazardous weather conditions that are forecasted or manifest themselves on point of departure, enrouted, or on point of arrival. 24 **NASDelay **in minutes: Delay that is within the control of the National Airspace System (NAS) may include: non-extreme weather conditions, airport operations, heavy traffic volume, air traffic control, etc. 25 **SecurityDelay **in minutes: Security delay is caused by evacuation of a terminal or concourse, re-boarding of aircraft because of security breach, inoperative screening equipment and/or long lines in excess of 29 minutes at screening areas. 26 **LateAircraftDelay **in minutes: Arrival delay at an airport due to the late arrival of the same aircraft at a previous airport. The ripple effect of an earlier delay at downstream airports is referred to as delay propagation.

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

Graph and download economic data for Load Factor for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights (LOADFACTOR) from Jan 2000 to May 2025 about flight, passenger, air travel, travel, domestic, and USA.

In the fiscal year of 2023, the American Airlines Group reported around 52.8 billion U.S. dollars in operating revenue. This was an increase of about eight percent compared to the previous year. Over the period given, the company's operating revenue decreased dramatically to 17.3 billion U.S. dollars in 2020 due to the coronavirus pandemic.

United States Google Search Trends: Travel & Accommodations: American Airlines data was reported at 7.000 Score in 14 May 2025. This stayed constant from the previous number of 7.000 Score for 13 May 2025. United States Google Search Trends: Travel & Accommodations: American Airlines data is updated daily, averaging 3.000 Score from Dec 2021 (Median) to 14 May 2025, with 1261 observations. The data reached an all-time high of 43.000 Score in 30 Jan 2025 and a record low of 0.000 Score in 22 Jun 2023. United States Google Search Trends: Travel & Accommodations: American Airlines data remains active status in CEIC and is reported by Google Trends. The data is categorized under Global Database’s United States – Table US.Google.GT: Google Search Trends: by Categories.

You can connect to the actual flight routes around the world with your API key at any time with very fast response times. It is possible to view all routes at the same time via a single API key. For your convenience, we have also developed many different filters so that you can pull the exact data you are looking for. This way, you may get data of the routes of a specific airline, routes from or to a specific airport (both IATA and ICAO codes work), or may get an individual flight based on its flight number.

A common use of the air routes API is to develop software in the aviation industry. While Aviation Edge’s focus is to collect and maintaining aviation data, you are free to develop countless applications, tools, and platforms by using our data.

The details included in the routes data are: Departure data: IATA code, ICAO code, terminal, and time. Arrival data: IATA code, ICAO code, terminal, and time. Airline: IATA code of airline. Flight: Flight number. Aircraft: Registration number of the aircraft.

Here's an example response from the API: [ { "departureIata": "OTP", "departureIcao": "LROP", "departureTerminal": 2, "departureTime": "09:15:00", "arrivalIata": "TRN", "arrivalIcao": "LIMF", "arrivalTerminal": 1, "arrivalTime": "10:45:00", "airlineIata": "0B", "airlineIcao": "BMS", "flightNumber": "101", "codeshares": null, "regNumber": "YR-BAP" } ]

Developer information: 1) Available Endpoints &departureIata= &departureIcao= &airlineIata= &airlineIcao= &flightNumber=

2) Output Airports, Airlines or Flights routes output: GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&departureIata=OTP GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&departureIcao=LROP GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&airlineIata=0B GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&airlineIcao=BMS GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&flightNumber=101 For information about a specific route (example). GET http://aviation-edge.com/v2/public/routes?key=[API_KEY]&departureIata=OTP&airlineIata=0B&flightNumber=101

Graph and download economic data for Revenue Passenger Miles for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights (RPM) from Jan 2000 to May 2025 about flight, miles, passenger, air travel, travel, revenue, domestic, and USA.

In the fiscal year 2023, United Airlines Holdings Inc.'s had a total operating revenue of 53.7 billion U.S. dollars. That year, the airline company generated about 32.4 billion U.S. dollars in revenue from activities in North America.

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 T-100 Domestic Market and Segment Data dataset was downloaded on April 08, 2025 from the Bureau of Transportation Statistics (BTS) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). It shows 2024 statistics for all domestic airports operated by US carriers, and all information are totals for the year across all four (4) service classes (F - Scheduled Passenger/ Cargo Service, G - Scheduled All Cargo Service, L - Non-Scheduled Civilian Passenger/ Cargo Service, and P - Non-Scheduled Civilian All Cargo Service). This dataset is a combination of both T-100 Market and T-100 Segments datasets. The T-100 Market includes enplanement data, and T-100 Segment data includes passengers, arrivals, departures, freight, and mail. Data is by origin airport. Along with yearly aggregate totals for these variables, this dataset also provides more granular information for the passenger and freight variable by service class and by scheduled vs non-scheduled statistics where applicable. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529081

Graph and download economic data for Available Seat Miles for U.S. Air Carrier Domestic and International, Scheduled Passenger Flights (ASM) from Jan 2000 to May 2025 about flight, miles, passenger, air travel, travel, domestic, and USA.

This data reports the percent of flights that arrived on time for airports in the U.S. and for the year 2006. The original data comes from monthly numbers "reported by US certified air carriers that account for at least one percent of domestic scheduled passenger revenues--includes scheduled and actual arrival and departure times for flights." www.bts.gov