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.

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.

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

This dataset provides valuable insights into the aviation industry's trends and growth patterns, making it a valuable resource for analysts, researchers, and aviation enthusiasts.

1.**Country:** This column represents the name of the country where the airline is based. It provides valuable geographical context to the dataset, allowing users to explore passenger trends on a country-by-country basis.

2.**Airline Passengers Carried:** This column contains the number of passengers carried by each airline. It is a critical metric for evaluating an airline's performance and market share.

3.**Year:** The year column indicates the year to which the data corresponds. It allows users to track changes in passenger numbers over time and observe trends and fluctuations in the airline industry.

This dataset, enables data scientists, analysts, and researchers to conduct a wide range of analyses and create data-driven visualizations to answer various questions related to air travel, such as:

• Which countries have the highest and lowest airline passenger numbers?
• How has the global airline industry evolved over the years in terms of passenger volume?
• Are there any seasonal patterns or trends in airline passenger data?
• What are the top airlines in terms of passenger volume for a specific year or country?
• How does economic or political stability in a country correlate with airline passenger numbers?

By making this dataset available on Kaggle, we aim to foster collaboration and innovation within the data science community, allowing users to extract valuable insights from the world of aviation and contribute to a better understanding of global travel patterns. Researchers, analysts, and data enthusiasts can leverage this dataset to gain a deeper understanding of the dynamics of the airline industry and make informed decisions based on the trends observed in the data.

The Bureau of Transportation Statistics releases non-seasonally adjusted air traffic data based on monthly reports from commercial U.S. air carriers.

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) |

A. SUMMARY San Francisco International Airport Report on Monthly Passenger Traffic Statistics by Airline.

B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level

C. UPDATE PROCESS Data updated quarterly

D. HOW TO USE THIS DATASET Airport data is seasonal in nature, therefore any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Passenger Counts belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Passenger Counts as desired.

In 2024, the global air traffic passenger demand grew by **** percent compared to the previous year, when the passenger demand increased by **** percent. This figure was forecast to grow by eight percent in 2025.

San Francisco International Airport Report on Monthly Passenger Traffic Statistics by Airline. Airport data is seasonal in nature, therefore any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Passenger Counts belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Passenger Counts as desired.

Information on international air traffic between the United States and other countries. Provides publications for a fee.

Graph and download economic data for Air Revenue Passenger Miles (AIRRPMTSID11) from Jan 2000 to Aug 2025 about miles, passenger, air travel, travel, revenue, and USA.

The Flight Prediction Dataset is a comprehensive collection of international airline data focused on predicting various aspects of air travel. This dataset provides valuable insights into flight demand, customer behavior, pricing optimization, route planning, customer segmentation, and churn prediction. With a wide range of attributes and historical flight information, this dataset enables businesses and researchers to develop accurate prediction models and make data-driven decisions in the aviation industry. By leveraging this dataset, stakeholders can enhance operational efficiency, optimize pricing strategies, plan route expansions, and improve customer satisfaction. The dataset offers a valuable resource for analyzing flight patterns, understanding market trends, and unlocking opportunities for growth and innovation in the airline industry.

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.

The United States had the largest commercial air travel market in 2021, with just over *** million passengers boarding planes registered to American and international air carriers. The next largest market was China, with more than *** million passengers, while the Eurozone ranked in third place, with almost *** million passengers. Passenger measurement Measuring the number of passengers boarded by carriers registered in a country provides an indication of the size of that country’s airline industry, but it does not measure the amount of air travel in that country. For example, as Ryanair is registered in Ireland, all Ryanair flights count as Irish, even if the flight was between, say, Berlin and London. One way to measure the number of air passengers within a country is to look at the number of passengers passing through airports in that country. Alternatively, the level of travel within an airline market can be considered at the regional level, in which case North America slips back to third behind the Asia Pacific region and Europe. Erasing two decades of growth in global air travel Regardless of how passenger numbers are measured, global air travel increased rapidly over the last decade. However, this was not the case in 2020, when the COVID-19 pandemic erased two decades of global passenger traffic growth, cutting the number of air passengers to only *** billion and the number of flights globally to **** million. Looking at this period, the Middle East region was affected the most, with seat capacity down ** percent compared to 2019.

About Dataset:

This dataset provides detailed information on airline flight routes, fares, and passenger volumes within the United States from 1993 to 2024.

Data Features:

1. tbl: Table identifier 2. Year: Year of the data record 3. quarter: Quarter of the year (1-4) 4. citymarketid_1: Origin city market ID 5. citymarketid_2: Destination city market ID 6. city1: Origin city name 7. city2: Destination city name 8. airportid_1: Origin airport ID 9. airportid_2: Destination airport ID 10. airport_1: Origin airport code 11. airport_2: Destination airport code 12. nsmiles: Distance between airports in miles 13. passengers: Number of passengers 14. fare: Average fare 15. carrier_lg: Code for the largest carrier by passengers 16. large_ms: Market share of the largest carrier 17. fare_lg: Average fare of the largest carrier 18. carrier_low: Code for the lowest fare carrier 19. lf_ms: Market share of the lowest fare carrier 20. fare_low: Lowest fare 21. Geocoded_City1: Geocoded coordinates for the origin city 22. Geocoded_City2: Geocoded coordinates for the destination city 23. tbl1apk: Unique identifier for the route

Potential Uses: 1. Market Analysis: Assess trends in air travel demand, fare changes, and market share of airlines over time. 2. Price Optimization: Develop models to predict optimal pricing strategies for airlines. 3. Route Planning: Identify profitable routes and underserved markets for new route planning. 4. Economic Studies: Analyze the economic impact of air travel on different cities and regions. 5. Travel Behavior Research: Study changes in passenger preferences and travel behavior over the years. 6. Competitor Analysis: Evaluate the performance of different airlines on various routes.

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

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.

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.

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