Number of passengers carried (arrivals and departures). Passengers carried are (1) all passengers on a particular flight (with one flight number) counted once only and not repeatedly on each individual stage of that flight, (2) all revenue and non-revenue passengers whose journey begins or terminates at the reporting airport and transfer passengers joining or leaving the flight at the reporting airport. Excludes direct transit passengers. National aggregates, total intra-EU aggregates and total EU aggregates exclude any double counting.

The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached **** million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to **** million in 2020. The flight volume increased again in the following years and was forecasted to reach ** million in 2025.

This Dataset contains year and airline-wise total number of Passengers who are placed in the 'No Fly list'

Note: Data is as per the recommendations of airline's internal committee, constituted in accordance with Civil Aviation Requirements (CAR), Section 3- Air Transport, Series M, and Part VI titled "Handling of unruly / disruptive passengers"

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!

Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

This dataset provides monthly passenger statistics for air travel covering the years 1958, 1959, and 1960. It includes data for each month, detailing the number of passengers in each year.

This project provides a Python script designed to generate horizontal bar charts visualizing statistics for Mexico's 66 civil airports. The visualizations highlight which airports had the most passengers, flights (arrivals + departures), and cargo operations during a given year within the period from 2006 to 2025.Project ContentsPython Script: A Python script to generate horizontal bar charts based on user-specified data inputs (e.g., passengers, flights, or cargo operations) and year.Time Series Dataset: Includes monthly data (2006–2024) for all civil airports in Mexico. The dataset captures:Traffic Types: Passengers, flights, and cargo operations.Traffic Scope: Domestic and international.Sample Outputs: Three example figures demonstrating the generated visualizations.Requirements File: A requirements.txt file listing the Python dependencies needed to run the script.Data SourceAll data in the dataset is sourced from the official Mexican Federal Civil Aviation Agency (AFAC): https://www.gob.mx/afac/acciones-y-programas/estadisticas-280404/.ApplicationsThis tool is useful for researchers, aviation analysts, and policymakers interested in understanding trends and performance in Mexico's civil aviation sector. The script simplifies data visualization and analysis, enabling users to explore traffic patterns over time and identify key hubs for passengers, flights, or cargo operations.

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

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

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

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

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

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

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

Changes as of 3 June 2025: The figures for April 2025 have been added.

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

Passengers enplaned and deplaned at Canadian airports, annual.

Air passenger origin and destination data, for transborder journeys, by outbound and inbound passengers exceeding 400, by city and city-pair, annual.

This dataset contains monthly air travel statistics, detailing the number of passengers in thousands for the years 1958 to 1960. The data is organized by month and includes figures for each year.

The data set indicates the maximum number of seats available for passengers to fly. These are seats scheduled, but not necessarily filled. The success of AUS and all airports is driven by passenger demand, government restrictions, and airline business models. Data on available passenger seats in the Official Airline Guide is collected and distributed by the Campbell-Hill Aviation Schedule Report. The report data is then combined to create the total annual passenger seats for the year. This dataset supports measure M.A.7 of SD23. View more details and insights related to this data set on the story page: https://data.austintexas.gov/stories/s/Number-of-AUS-passenger-seats-available-for-purcha/26rp-vy2b/

Air passenger origin and destination data (passenger numbers, city rank), for transborder journeys, by total outbound and inbound passengers exceeding 4000, by city-pair, annual.

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This physiological data was collected from pilot/copilot pairs in and out of a flight simulator. It was collected to train machine-learning models to aid in the detection of pilot attentive states. The benchmark training set is comprised of a set of controlled experiments collected in a non-flight environment, outside of a flight simulator. The test set (abbreviated LOFT = Line Oriented Flight Training) consists of a full flight (take off, flight, and landing) in a flight simulator. The pilots experienced distractions intended to induce one of the following three cognitive states: Channelized Attention (CA) is the state of being focused on one task to the exclusion of all others. This is induced in benchmarking by having the subjects play an engaging puzzle-based video game. Diverted Attention (DA) is the state of having one’s attention diverted by actions or thought processes associated with a decision. This is induced by having the subjects perform a display monitoring task. Periodically, a math problem showed up which had to be solved before returning to the monitoring task. Startle/Surprise (SS) is induced by having the subjects watch movie clips with jump scares. For each experiment, a pair of pilots (each with its own crew ID) was recorded over time and subjected to the CA, DA, or SS cognitive states. The training set contains three experiments (one for each state) in which the pilots experienced just one of the states. For example, in the experiment labelled CA, the pilots were either in a baseline state (no event) or the CA state. The test set contains a full flight simulation during which the pilots could experience any of the states (but never more than one at a time). Each sensor operated at a sample rate of 256 Hz. Please note that since this is physiological data from real people, there will be noise and artifacts in the data.

Originally sourced by Shubham Bathwal

INTRODUCTION

The objective of the study is to analyse the flight booking dataset obtained from “Ease My Trip” website and to conduct various statistical hypothesis tests in order to get meaningful information from it.

'Easemytrip' is an internet platform for booking flight tickets, and hence a platform that potential passengers use to buy tickets. A thorough study of the data will aid in the discovery of valuable insights that will be of enormous value to both business owners and passengers.

Research Questions

a) Do prices vary with Airlines? b) Does the presence of an alternative flight on the same route affect the price? c) Does the ticket price change based on the departure time and arrival time? d) Is there a significant variation in price across the periods (Month / Day)? e) How does the ticket price vary on a route with respect to Economy and Business class individually? f) Does the duration of the flight have any effect on the price? g) Does the number of stops matter?

DATASET

The Dataset contains information about flight booking options from the website "Easemytrip " for flight travel across India's top 6 metro cities. There are 300257 records and 24 features in the cleaned dataset.

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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.