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.

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.

Russia Number of Flights: Domestic data was reported at 67,658.000 Number in Feb 2022. This records a decrease from the previous number of 71,658.000 Number for Jan 2022. Russia Number of Flights: Domestic data is updated monthly, averaging 55,400.000 Number from Jan 2010 (Median) to Feb 2022, with 146 observations. The data reached an all-time high of 127,409.000 Number in Jul 2021 and a record low of 27,413.000 Number in Feb 2010. Russia Number of Flights: Domestic data remains active status in CEIC and is reported by Federal Agency for Air Transport. The data is categorized under Russia Premium Database’s Transport and Telecommunications Sector – Table RU.TE003: Airlines Statistics: Number of Airlines, Aircrafts, Airports and Flights. [COVID-19-IMPACT]

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.

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

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.

This project is about predicting if a flight will be delayed by over 15 minutes upon arrival, with Scikit-learn Decision Tree Classifier, using US flight data in 2022. Here is the URL of the dataset and variables description: https://www.transtats.bts.gov/DL_SelectFields.aspx?gnoyr_VQ=FGK&QO_fu146_anzr=b0-gvzr

Context The U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics tracks the on-time performance of domestic flights operated by large air carriers. This dataset is collected from the Bureau of Transportation Statistics, Govt. of the USA. This data is open-sourced under U.S. Govt. Works. I dowload 12 csv file that represents each month of 2022. This dataset contains all US domestic flights in 2022.

Description of Columns • Quarter Quarter (1-4) • Month Month • DayofMonth Day of Month • DayOfWeek Day of Week • FlightDate Date of the Flight • Marketing_Airline_Network Airline Identifier • OriginCityName Origin Airport, City Name • DestCityName Destination Airport, City Name • DepDelay Difference in minutes between scheduled and actual departure time. Early departures show negative numbers • ArrDelay Difference in minutes between scheduled and actual arrival time. Early arrivals show negative numbers • Cancelled Cancelled Flight (1=Yes) • Diverted Diverted Flight (1=Yes) • AirTime Flight Time, in Minutes • Distance Distance between airports (miles) • CarrierDelay Delay caused by the airline in minutes • WeatherDelay Delay caused by weather • NASDelay Delay caused by air system • SecurityDelay Delay caused by security reasons • LateAircraftDelay Delay caused as a result of another flight on the same aircraft delayed

The National Security UAS Flight Restrictions in this dataset are currently pending and will become effective on May 05, 2023. The FAA, pursuant to Title 14 of the Code of Federal Regulations (CFR) § 99.7, Special security instructions (SSI), has prohibited all UAS flight operations within the airspace defined under NOTAM FDC 7/7282 . Specific locations are described in the table and on the interactive map provided on this website. The TFRs extend from the surface up to 400 feet Above Ground Level (AGL), apply to all types and purposes of UAS flight operations, and remain in effect 24 hours a day, 7 days a week. WHAT UAS FLIGHT RESTRICTIONS HAVE BEEN PUT INTO PLACE? At the request of and pursuant to agreements with the Department of Defense and U.S. Federal security and intelligence agencies (“sponsoring Federal agencies”), the Federal Aviation Administration (FAA) has implemented Special Security Instructions for Unmanned Aircraft System (UAS), issued as temporary flight restrictions (TFR) over select national security sensitive facilities located throughout the U.S. These TFRs are established within the lateral boundaries of these facilities and extend from surface to 400 feet Above Ground Level (AGL). These TFRs apply to all UAS operations specifically including: · Public aircraft operations conducted in accordance with a Certificate of Authorization or Waiver (COA). · Civil aircraft operations (other than model aircraft), including those conducted in accordance with a COA and those conducted in accordance with the FAA’s small UAS Rule, 14 CFR Part 107. · Model Aircraft operations conducted in accordance with 14 CFR Part 101, Subpart E. UAS operators must comply with these flight restrictions in addition to all other applicable Federal Aviation Regulations, including but not limited to, requirements to secure an FAA airspace authorization and/or waiver prior to flying in the airspace where a TFR is in effect. The information on this website complements Notice to Airmen (NOTAM) NOTAM FDC 7/7282, which generally notifies the public about these temporary flight restrictions (TFR). This website provides UAS operators with more detailed information about these TFRs, including: · An explanation of what is restricted · A table listing the selected facilities over which a TFR has been established · An interactive map providing visual depictions and information about specific TFRs and geospatial (GIS) data that can be downloaded · An explanation of which UAS operations may be able to access the airspace within a TFR, including instructions for submitting a request · Reminders on other requirements for UAS operations WHAT HAPPENS IF I VIOLATE A TEMPORARY FLIGHT RESTRICTION (TFR)? The FAA classifies the airspace encompassed by these temporary flight restrictions (TFRs) as “national defense airspace” in accordance with Title 49 of the United States Code (USC) § 40103(b)(3). Violations of these TFRs may prompt the following enforcement actions: A. The U.S. Government may pursue criminal charges, including charges under Title 49 U.S.C § 46307. B. The FAA may take administrative action, including imposing civil penalties and the revoking FAA certificates and authorizations to operate UAS under Title 49 U.S.C. §§ 44709 and 46301. WHAT ARE THE BASIC FLIGHT RESTRICTIONS? The FAA, pursuant to Title 14 of the Code of Federal Regulations (CFR) § 99.7, Special security instructions (SSI), has prohibited all UAS flight operations within the airspace defined under NOTAM FDC 7/7282. Specific locations are described in the table and on the interactive map provided on this website. The TFRs extend from the surface up to 400 feet Above Ground Level (AGL), apply to all types and purposes of UAS flight operations, and remain in effect 24 hours a day, 7 days a week. See the full text of NOTAM FDC 7/7282 here. ARE THERE EXCEPTIONS FOR UAS OPERATIONS TO ACCESS A TFR? The FAA has authorized UAS operations within the TFRs if those flights are in compliance with the applicable requirements listed below: 1) The UAS flight operation has been pre-approved by the designated facility contact based on criteria established by the sponsoring federal agency in coordination with the FAA. Note: UAS operators seeking approval to operate in one of the TFRs defined in this website under this provision must contact the facility’s designated point of contact identified in the table or interactive map, and secure permission to operate within the airspace prior to entry. Pre-approval from the facility or sponsoring agency does not substitute for compliance with FAA requirements. Depending on the nature of the proposed operation and Class of airspace, waiver or authorization may be needed from the FAA before flight. For more information visit our website at www.faa.gov/uas 2). The UAS flight operation is conducted in direct support of an active national defense, homeland security, law enforcement, firefighting, search and rescue, or disaster response mission, and prior notification has been provided to the designated facility contact. Note: UAS operators seeking approval to operate in one of the TFRs defined in this website under this provision must contact the facility’s designated point of contact identified in the table or interactive map, and provide notification prior to entering the airspace. These operators must make every effort to coordinate with the designated facility to deconflict the UAS flight operation with any safety or security concerns stated by the facility and/or sponsoring Federal agency. 3). The UAS flight operation is conducted in direct support of a significant and urgent governmental interest and is approved by the FAA’s System Operations Support Center (SOSC) in advance of entering the TFR. Note: UAS operators, that meet the criteria for thisprovision , may also qualify for access under provision 2 outlined above and are encouraged to coordinate directly with the facility’s designated point of contact identified in the table or interactive map, by providing notification prior to entering the airspace and taking into consideration any safety or security concerns stated by the facility and/or sponsoring Federal agency. For urgent and time sensitive requests, contact the FAA’s SOSC at (202) 267-8276 for expedited assistance. The FAA’s SOSC will coordinate with the facility and/or sponsoring Federal agency as appropriate. ARE THERE OTHER REQUIREMENTS TO OPERATE IN A TFR IN ADDITION TO THE EXCEPTIONS? Separate and distinct from any of the conditions cited above used to gain access to a TFR defined by NOTAM FDC 7/7282 and described in this website, UAS operators must comply with all applicable Federal Aviation Regulations. For example: For Model Aircraft: · Comply with 14 CFR Part 101, Subpart E NOTE: These provisions require model aircraft operators to notify any airport operator and air traffic control tower within 5 miles of the intended area of flight. For All Other UAS Operators: · Comply with a Public Aircraft Certificate of Authorization or Waiver (COA), or · Comply with 14 CFR Part107, Small Unmanned Aircraft Systems, or · Comply with Section 333 Exemption and a Certificate of Authorization or Waiver (COA) NOTE: Public and civil UAS operators flying under the provisions of a COA or 14 CFR Part 107 may need to secure further airspace authorizations or waivers in order to conduct the proposed flight operation in controlled airspace, which may overlap with one of the TFRs defined by NOTAM FDC 7/7282 and this website. In those cases, these operators should follow the pre-existing procedures outlined below. A. Non-emergency requests for UAS airspace authorizations and waivers must be submitted using the regular process as follows: · 14 CFR Part 107 requests for airspace authorizations and waivers must be submitted to the FAA at https://www.faa.gov/uas/request_waiver/ · Section 333 Exemption holders may request a site specific COA at https://oeaaa.faa.gov/oeaaa/external/uas/portal.jsp · Public aircraft operators without an existing authorization to operate must secure a public COA athttps://ioeaaa.faa.gov/oeaaa/Welcome.jsp B. Emergency requests for UAS authorizations/waivers for missions that directly support significant and urgent governmental interests (e.g., active national defense, homeland security, law enforcement, and emergency operations missions), which cannot be supported by the FAA’s routine authorization/waiver processes should be referred to the SOSC at (202) 267-8276 ADDITIONAL QUESTIONS? If you have any general questions regarding UAS operations, please refer to the following FAA webpage: https://www.faa.gov/uas/ , or contact the FAA by email at uashelp@faa.gov or by phone at (844) FLY-MY-UA. If you have any additional questions regarding the TFRs defined by NOTAM FDC 7/7282 and this website, please contact the FAA SOSC at (202) 267-8276. Disclaimers The restrictions depicted on this site reflect temporary flight restrictions issued for national security reasons at select U.S. Federal facilities. There may be additional temporary flight restrictions that prohibit UAS and manned flight in effect in your area. See https://tfr.faa.gov/tfr2/list.html for additional information on flight restrictions that may be in effect in your area before operating your UAS.

Passengers enplaned and deplaned at Canadian airports, annual.

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.

Hoàn hảo 👍 Dưới đây là bản mô tả (description) hoàn chỉnh — em chỉ cần copy và dán trực tiếp vào phần “Dataset Description” trên Kaggle. Mình đã viết theo chuẩn phong cách Kaggle (ngắn gọn, chuyên nghiệp, có markdown đẹp).

✈️ Flight Delays Dataset (Sample of 10,000 US Flights)

📘 Overview

This dataset contains detailed information on 10,000 domestic flights within the United States during 2014. It was derived from a larger FAA dataset and includes essential flight attributes such as departure and arrival times, delays, carrier codes, origin and destination airports, and distances.

It’s a great dataset for practicing:

• Data cleaning and preprocessing
• Exploratory data analysis (EDA)
• Predictive modeling for flight delay estimation
• Data visualization and dashboard creation

📊 Dataset Structure

📈 Quick Summary

• Rows: 10,000
• Columns: 16
• Time range: Entire year of 2014
• Missing values: Present in dep/arr times, delays, and tail numbers
• Common carriers: Alaska Airlines (AS), Virgin America (VX), Southwest (WN), etc.

💡 Use Cases

• Analyzing flight delay patterns across time, airlines, and routes
• Predicting flight delays using regression or classification models
• Creating interactive dashboards or visualizations of flight traffic
• Integrating with weather or airport datasets for deeper insights

⚙️ Data Source

This dataset is a curated sample inspired by the nycflights13 dataset — a well-known dataset used in many Data Science and Machine Learning tutorials.

📜 License

This dataset is shared for educational and research purposes under the CC BY 4.0 License.

🧠 Keywords

flight delays, aviation, transportation, data analysis, machine learning, EDA, Hadoop, Spark, Big Data

Updates are delayed due to technical difficulties. How many people are staying at home? How far are people traveling when they don’t stay home? Which states and counties have more people taking trips? The Bureau of Transportation Statistics (BTS) now provides answers to those questions through our new mobility statistics. The Trips by Distance data and number of people staying home and not staying home are estimated for the Bureau of Transportation Statistics by the Maryland Transportation Institute and Center for Advanced Transportation Technology Laboratory at the University of Maryland. The travel statistics are produced from an anonymized national panel of mobile device data from multiple sources. All data sources used in the creation of the metrics contain no personal information. Data analysis is conducted at the aggregate national, state, and county levels. A weighting procedure expands the sample of millions of mobile devices, so the results are representative of the entire population in a nation, state, or county. To assure confidentiality and support data quality, no data are reported for a county if it has fewer than 50 devices in the sample on any given day. Trips are defined as movements that include a stay of longer than 10 minutes at an anonymized location away from home. Home locations are imputed on a weekly basis. A movement with multiple stays of longer than 10 minutes before returning home is counted as multiple trips. Trips capture travel by all modes of transportation. including driving, rail, transit, and air. The daily travel estimates are from a mobile device data panel from merged multiple data sources that address the geographic and temporal sample variation issues often observed in a single data source. The merged data panel only includes mobile devices whose anonymized location data meet a set of data quality standards, which further ensures the overall data quality and consistency. The data quality standards consider both temporal frequency and spatial accuracy of anonymized location point observations, temporal coverage and representativeness at the device level, spatial representativeness at the sample and county level, etc. A multi-level weighting method that employs both device and trip-level weights expands the sample to the underlying population at the county and state levels, before travel statistics are computed. These data are experimental and may not meet all of our quality standards. Experimental data products are created using new data sources or methodologies that benefit data users in the absence of other relevant products. We are seeking feedback from data users and stakeholders on the quality and usefulness of these new products. Experimental data products that meet our quality standards and demonstrate sufficient user demand may enter regular production if resources permit.

The Rongowai Level 1 Science Data Record Version 1.0 dataset is generated by the University of Auckland (UoA) Rongowai Science Payloads Operations Centre in New Zealand. This initiative is supported by NASA and the New Zealand Space Agency. The data collection process is conducted using the Next-generation receiver (NgRx) mounted on the Air New Zealand domestic aircraft Q300.This Level 1 (L1) dataset contains the Version 1.0 geo-located Delay Doppler Maps (DDMs) calibrated into Power Received (Watts) and Bistatic Radar Cross Section (BRCS) expressed in units of meters squared from the Delay Doppler Mapping Instrument onboard an Air New Zealand domestic Q300 (tail number ZK-NFA). 20 DDMs are contained within a typical L1 netcdf corresponding to 10 Left-Hand-Circularly Polarized (LHCP) and 10 Right-Hand-Circularly Polarized (RHCP) channels. Other useful scientific and engineering measurement parameters include the co- and cross-polarized Normalized Bistatic Radar Cross Section (NBRCS) of the specular point, the Leading Edge Slope (LES) of the integrated delay waveform and the normalized waveforms. The L1 dataset contains a number of other engineering and science measurement parameters, including coherence detection and a coherence state metric, sets of quality flags/indicators, error estimates, Fresnel-zone geometry estimates (and thereby the estimated per-sample spatial resolution) as well as a variety of timekeeping, and geolocation parameters.Each netCDF data files corresponds to a single flight between airports within New Zealand (flight durations typically range between 45 min and 1hr 30min with a median of 7 flights/day) and measurements are reported at 1 second increments. Latency is approximately 1 days (or better) from the last recorded measurement time.

This table contains information regarding the mobility of the residents of the Netherlands aged 6 or older in private households, so excluding residents of institutions and homes. The table contains per person per day /year an overview of the average number of trips, the average distance travelled and the average time travelled. These are regular trips on Dutch territory, including domestic holiday mobility. The distance travelled is based on stage information. Excluded in this table is mobility based on series of calls trips. The mobility behaviour is broken down by modes of travel, purposes of travel, population and region characteristics. The data used are retrieved from The Dutch National travel survey named Onderweg in Nederland (ODiN).

A methodological break has been identified in the 2024 ODiN file. During the analysis of the data from the "Onderweg in Nederland 2024" (ODiN) survey a methodological break was identified. Several changes were made to the survey in 2024, which likely had an unexpected effect on the 2024 ODiN figures. This means that the 2024 results are not readily comparable with those from previous years. For this reason, the 2024 figures are not being updated in the StatLine tables. More information about the methodological break is available in the ODiN 2024 Plausibility Report (see Chapter 4: "Onderwegen in Nederland" (ODiN) 2024 - Plausibility Report). Due to a revision of the ODiN files, the figures by motive for 2018 have been changed as of February 10, 2022, but the total number of motives in 2018 has remained the same. In 2019, the revision sometimes resulted in minor changes in travel time.

Data available from: 2018

Status of the figures: The figures in this table are final.

Changes as of 4 July 2024: The figures for year 2023 are added.

When will new figures be published? More information will follow in 2026.

The GCIP/GIST National Radar Precipitation Composite contains radar derived percipitation values over the CONUS. These images were prepared at the NASA/Marshal Space Flight Center. One image per day at 12Z is available as a data browse product.

This dataset was collected as part of the AWS Academy's Machine Learning Foundations course. This dataset contains scheduled and actual departure and arrival times reported by certified US air carriers that account for at least 1 percent of domestic scheduled passenger revenues. The data was collected by the U.S. 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 2013 and 2018.

You work for a travel booking website that wants to improve the customer experience for flights that were delayed. The company wants to create a feature to let customers know if the flight will be delayed because of weather when they book a flight to or from the busiest airports for domestic travel in the US.

You are tasked with solving part of this problem by using machine learning (ML) to identify whether the flight will be delayed because of weather. You have been given access to the a dataset about the on-time performance of domestic flights that were operated by large air carriers. You can use this data to train an ML model to predict if the flight is going to be delayed for the busiest airports.

Dataset for Prediction

The flight_delay_predict file is the combination of 60 datasets from 2013 to 2018, but in a reduced version containing only: - Main airports: ATL, ORD, DFW, DEN, CLT, LAX, IAH, PHX, SFO - Top five airlines: UA, OO, WN, AA, DL - Rows with null values were discarded.

Dataset Features

For more information about features in the dataset, see On-time delay dataset features.

Dataset Attribution

Website: Bureau of Transportation Statistics Dataset Compilation: - Compiled by the Office of Airline Information, Bureau of Transportation Statistics (BTS). - Dataset: Airline On-Time Performance Data. - Access at Data Source.

Data Dictionary

1. Year: Year
2. Quarter: Quarter (1-4)
3. Month: Month
4. DayofMonth: Day of Month
5. DayOfWeek: Day of Week
6. FlightDate: Flight Date (yyyymmdd)
7. Reporting_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.
8. DOT_ID_Reporting_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.
9. IATA_CODE_Reporting_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.
10. Tail_Number: Tail Number
11. Flight_Number_Reporting_Airline: Flight Number
12. 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.
13. 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.
14. 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.
15. Origin: Origin Airport
16. OriginCityName: Origin Airport, City Name
17. OriginState: Origin Airport, State Code
18. OriginStateFips: Origin Airport, State Fips
19. OriginStateName: Origin Airport, State Name
20. OriginWac: Origin Airport, World Area Code
21. 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.
22. 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.
23. 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...

Conducted by the Federal Highway Administration, the NHTS is the authoritative source on the travel behavior of the American public. It is the only source of national data that allows one to analyze trends in personal and household travel. It includes daily non-commercial travel by all modes, including characteristics of the people traveling, their household, and their vehicles. The NHTS dataset contains data for all 150,147 completed households in the sample includinghousehold, person, vehicle and daily (travel day) trip level data.This is in part of the same series of surveys that were previously called the Nationwide Personal Transportation Study, but has been renamed into the National Household Travel Survey.

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!

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 their website.

This dataset depicts flight delays and includes various attributes that can be analyzed to understand the patterns and causes of these delays. Each row in the dataset represents a single flight, providing detailed information that can be used to analyze the factors contributing to delays. For instance, the dataset can be used to determine which airlines have the most delays, what times of the day or week delays are most common, and what specific causes (such as weather, carrier issues, or security) are most frequently responsible for delays.

Overall, this comprehensive dataset serves as a valuable resource for analyzing the operational efficiency of airlines and airports, ultimately contributing to enhanced management and planning in the aviation industry.