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 "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

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

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

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.

Using a combination of OAG flight schedule and ch-aviation fleet data, Capacities - Scheduled provides an overview of future flights scheduled per calendar day with a breakdown of seat capacity for five cabin classes (Economy, Economy Plus/Comfort, Premium Economy, Business, First) by operator and route (Continent, Country, Subdivision, Metro Group, Airport).

The data set is updated weekly.

The sample data shows capacity figures for Alaska Airlines, Swiss, and Horizon Air for one week.

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=capacities_scheduled/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/capacities-scheduled/

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

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.

Passengers enplaned and deplaned at Canadian airports, annual.

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/

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.

Dataset

This dataset was created by Surf Trade

Contents

This dataset contains the records of all the flights in the Northern California TRACON. The data was provided by the aircraft noise abatement office (http://www.flyquietsfo.com/) of San Francisco International Airport. The data cover Jan-Mar 2006. It is organized by day and flight. Each record contains some information about the flight and a sequence of 3D position and estimated speed. This data contains thousands of trajectories that can be used for trajectory clustering. The data is used by the Aircraft Noise Abatement Office to analyze the trajectories of aircraft flying in and out SFO. The objective is to minimize the noise pollution due to aircraft in the San Francisco Bay Area The files have the extension "lt6" and are organized as follow, one file per day. line number & explaination 1 TRACK OPNUM (TRACK header word and operation number) 2 eventid (Corralation number) 3 trackstart date (in time since 1900, A8 version four year digit) 4 trackstart time HH:MM:SS 5 trackend time HH:MM:SS 6 airportid 7 ACID (FLIGHTNUM/TAILNUMBER) 8 owner name 9 aircrafttype 10 aircraft category 11 beacon 12 adflag 13 waypoint 14 other_port (dest/origin) 15 runwayname 16 min alt 17 max alt 18 min range 19 max range 20 Count of trackpoints (to follow) 21 x,y,z,v,t (all points is meters relative to MRP, velocity and time from start of track)

Total monthly number of passengers arriving to and departing from Heathrow Airport, including both international and domestic flights.

In prospective human exploration of outer space the need to maintain a species over several generations under changed gravity conditions may arise. This paper reports the analysis of the third generation of fruit fly Drosophila melanogaster obtained during the 44.5-day space flight (Foton-M4 satellite 2014 Russia) followed by the fourth generation on Earth and the fifth generation under conditions of a 12-day space flight (2014 in the Russian Segment of the ISS). The obtained results show that it is possible to obtain the third-fifth generations of a complex multicellular Earth organism under changed gravity conditions (in the cycle weightlessness - Earth - weightlessness) which preserves fertility and normal development. However there were a number of changes in the expression levels and content of cytoskeletal proteins that are the key components of the spindle apparatus and the contractile ring of cells.

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

This paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advancedtechniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. This paper describes the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.