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.

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.

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 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) |

The average for 2021 based on 152 countries was 15 million passengers. The highest value was in the USA: 666.15 million passengers and the lowest value was in Guatemala: 0 million passengers. The indicator is available from 1970 to 2021. Below is a chart for all countries where data are available.

In 2024, U.S. airlines recorded ****** million passengers on domestic and international flights. The previous year, the number of passengers at U.S. airports officially surpassed the pre-pandemic peak of ***** million passengers recorded in 2019.

Passengers enplaned and deplaned at Canadian airports, annual.

Russia Number of Flights data was reported at 106,742.000 Number in Feb 2022. This records a decrease from the previous number of 119,379.000 Number for Jan 2022. Russia Number of Flights data is updated monthly, averaging 117,995.000 Number from Jan 2010 (Median) to Feb 2022, with 146 observations. The data reached an all-time high of 189,980.000 Number in Aug 2019 and a record low of 53,348.000 Number in Apr 2020. Russia Number of Flights 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]

Dataset

This dataset was created by Surf Trade

Contents

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 cannot 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: 1. 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. 2. 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. 3. 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. 4. 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. 5. 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.

Data dictionary: id: A unique identifier for each flight record in the dataset. year: The year in which the flight took place (2013 in this dataset). month: The month in which the flight took place (1 to 12). day: The day of the month on which the flight took place (1 to 31). dep_time: The actual local departure time of the flight, in 24-hour format (hhmm). sched_dep_time: The scheduled local departure time of the flight, in 24-hour format (hhmm). dep_delay: The difference between the actual and scheduled departure times of the flight, in minutes. A positive value indicates a delayed departure, while a negative value indicates an early departure. arr_time: The actual local arrival time of the flight, in 24-hour format (hhmm). sched_arr_time: The scheduled local arrival time of the flight, in 24-hour format (hhmm). arr_delay: The difference between the actual and scheduled arrival times of the flight, in minutes. A positive value indicates a delayed arrival, while a negative value indicates an early arrival. carrier: The two-letter code of the airline carrier for the flight. flight: The flight number of the flight. tailnum: The unique identifier of the aircraft used for the flight. origin: The three-letter code of the airport of origin for the flight. dest: The three-letter code of the destination airport for the flight. air_time: The duration of the flight, in minutes. distance: The distance between the origin and destination airports, in miles. hour: The hour component of the scheduled departure time, in local time. minute: The minute component of the scheduled departure time, in local time. time_hour: The scheduled departure time of the flight, in local time and format (yyyy-mm-dd hh:mm:ss). name: The name of the airline carrier for the flight.

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]

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.

India All Scheduled Airlines: International: Number of Flight data was reported at 18,502.000 Unit in Mar 2025. This records an increase from the previous number of 16,668.000 Unit for Feb 2025. India All Scheduled Airlines: International: Number of Flight data is updated monthly, averaging 7,797.000 Unit from Apr 2001 (Median) to Mar 2025, with 283 observations. The data reached an all-time high of 18,574.000 Unit in Jan 2025 and a record low of 273.000 Unit in May 2020. India All Scheduled Airlines: International: 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.

Domestic air carriers boarded more than *** million passengers in Russia in 2024. Overall in that year, the passenger traffic increased by around six percent from 2023. In 2025, the passenger count was expected to decline.

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.

On-time data for a random sample of flights that departed NYC (i.e. JFK, LGA or EWR) in 2013. year,month,day Date of departure.

dep_time,arr_time Departure and arrival times, local tz.

dep_delay,arr_delay Departure and arrival delays, in minutes. Negative times represent early departures/arrivals.

hour,minute Time of departure broken in to hour and minutes.

carrier Two letter carrier abbreviation. See airlines in the nycflights13 package for more information or google the airline code.

tailnum Plane tail number.

flight Flight number.

origin,dest Origin and destination. See airports in the nycflights13 package for more information or google airport the code.

air_time Amount of time spent in the air.

distance Distance flown.

Source Hadley Wickham (2014). nycflights13: Data about flights departing NYC in 2013. R package version 0.1.

Formats CSV file Tab-delimited text file

Format A tbl_df with 32,735 rows and 16 variables:

Photo by Phil Mosley on Unsplash

In 2024, more than *********** passengers traveled by air in China, an increase of **** percent compared to the previous year. This indicated a significant recovery in the aviation industry since the end COVID-19 pandemic control measures. Air travel in China As a significant international hub for both business and leisure travel, China’s airports are sites of bustling activity with cargo and passengers being transported in and out of the nation every day. Air travel is a very time-efficient mode of transportation. More and more people in China's socio-economic strata can afford air travel, which has led to the expansion of the nation’s air fleet. Prior to the COVID-19 outbreak, the number of flight hours surged by a significant degree with China Southern which is owned by China Southern Air Holding Group, reaching nearly ************* flight hours in 2019. The aviation industry shows recovery The outbreak of the COVID-19 pandemic in 2020 led to massive losses for the Chinese aviation industry, with the cancellation of flights to and from China. For the first time in the last decade or so, China experienced a decline in the volume of air passengers and air cargo. The industry recovered in 2021 with a *** percent year-over-year increase in air passenger numbers and an *** percent year-over-year increase in air cargo. Furthermore, the industry was predicted to have enhanced employment opportunities between 2021 and 2040, with a growing demand for cabin crews, technicians, and pilots.

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 2020. More data will be periodically included in the dataset until the end of the COVID-19 pandemic.

License

Creative Commons CC-BY

The only difference with the original dataset comes from anonymised aircraft information.

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)

aircraft_uid: a unique anonymised identifier for aircraft;

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 the original OpenSky paper:

Matthias Schäfer, Martin Strohmeier, Vincent Lenders, Ivan Martinovic and Matthias Wilhelm. "Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research". In Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN), pages 83-94, April 2014.

and the traffic library used to derive the data:

Xavier Olive. "traffic, a toolbox for processing and analysing air traffic data." Journal of Open Source Software 4(39), July 2019.