Graph and download economic data for Consumer Price Index for All Urban Consumers: Airline Fares in U.S. City Average (CUSR0000SETG01) from Jan 1989 to Sep 2025 about air travel, travel, urban, consumer, CPI, price index, indexes, price, and USA.

United States - Consumer Price Index for All Urban Consumers: Airline Fares in U.S. City Average was 270.33600 Index 1982-84=100 in September of 2025, according to the United States Federal Reserve. Historically, United States - Consumer Price Index for All Urban Consumers: Airline Fares in U.S. City Average reached a record high of 322.64500 in March of 2013 and a record low of 128.00000 in January of 1989. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Consumer Price Index for All Urban Consumers: Airline Fares in U.S. City Average - last updated from the United States Federal Reserve on December of 2025.

Amongst selected European airlines, Ryanair had by far the lowest average passenger fare in 2021, with approximately ** euros per passenger. The low-cost airline is followed by its rivals, Wizz Air and Norwegian, with an average ticket price of ** euros and ** euros respectively.

In 2020, ** percent of respondents who flew from airports in the United Kingdom paid 100 British pounds or less for a singe trip. In comparison, ** percent of individuals bought return trip tickets priced between *** and *** British pounds.

United States - Producer Price Index by Industry: Travel Agencies: Flight Bookings was 85.77100 Index Dec 1989=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Travel Agencies: Flight Bookings reached a record high of 133.20000 in March of 1992 and a record low of 80.89500 in October of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Travel Agencies: Flight Bookings - last updated from the United States Federal Reserve on December of 2025.

In 2024, the consumer price index (CPI) of airplane fares in Japan reached ***** points, increasing by **** points compared to the base year in 2020. This was a significant increase and the highest index during the surveyed period.

Graph and download economic data for Producer Price Index by Industry: Travel Agencies: International Flight Bookings (PCU561510561510112) from Dec 1989 to Sep 2025 about flight, agency, travel, PPI, industry, inflation, price index, indexes, price, and USA.

Participants will use the data provided for 10 markets comprising of all flights operated between these origin and destination cities by a typical carrier to estimate the demand and price by various point in time. To begin with a model of choice can be built and use the 12 months data to train the respective model and then predict the demand & fare for the flights in 13th to 15th months. It is mandatory to estimate the demand for each flight, different price estimation for available for sale at a given time for the respective flights at different days to departure. The model must determine time varying demand, pricing the product at different time points. As an extension of basic modeling, participants are encouraged to show how the respective model can be extended for continuous pricing or classless revenue management as innovation concept. The use of additional concepts like cancellation/no-show rate, overbooking etc can also be included in the model as added features.

Data Description In the airline industry, one of the objectives of the airlines is to correctly predict future bookings on the Flights based on the historical booking behavior. Depending on the expected demand, airlines can choose to sell seats at different fares. 1. Booking Data: The table or data sheet contains historical bookings by Flight ID, departure date, Origin & Destination ID, Cabin, Booking class of service, Dep Time & Arr Time, Booking count, avg booking fare. 2. Final count of flown passenger for the first 12 months of flight is provided along with avg fare (only at cabin level is provided) 3. Use the data for first 12 months to create a forecast for the last 3 months and predict forecasted bookings and corresponding fare. 4. Schedule & Market Data: The table includes flight ID details and corresponding competition flight details for the same Origin & Destination ID, airline market share. 5. Measure forecast accuracy, explain features and errors for different flight at different days to departure.

Quarterly domestic (short and long haul) and international air fares, by fare type group (business class, economy, discounted and other).

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 quarter of respondents to the UK's aviation consumer survey in 2022 indicated that they would reduce the number of flights they take, if the cost of flying increased by *** percent compared to the previous year. Around a fifth of consumers indicated that they will not fly at all due to increasing living costs.

Flight Centre Travel reported AUD660.97M in Operating Expenses for its fiscal semester ending in June of 2025. Data for Flight Centre Travel | FLT - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last December in 2025.

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.

Flight Price Freeze Market Outlook

According to our latest research, the Global Flight Price Freeze market size was valued at $1.2 billion in 2024 and is projected to reach $4.6 billion by 2033, expanding at a CAGR of 16.4% during the forecast period of 2025–2033. The rapid proliferation of online travel platforms and the growing trend of dynamic airline pricing have been major catalysts for the surge in adoption of flight price freeze solutions globally. As consumers increasingly seek flexibility and certainty in air travel booking, these services allow travelers to lock in favorable fares for a specified period, thereby addressing volatility in ticket pricing and enhancing the overall booking experience. This market is further propelled by the integration of advanced analytics and artificial intelligence, enabling more personalized and predictive price freeze offerings across diverse user segments.

Regional Outlook

North America currently dominates the Flight Price Freeze market, accounting for the largest share of global revenue, estimated at over 38% in 2024. The region's leadership stems from its mature digital travel ecosystem, high internet penetration, and the presence of major online travel agencies and airlines that have swiftly adopted price freeze features. Regulatory frameworks that support consumer protection and digital innovation further reinforce market maturity in the United States and Canada. Additionally, North American consumers demonstrate a high propensity for leveraging technology-driven travel solutions, which has fostered robust demand for both software and service components of flight price freeze offerings. The established loyalty programs and frequent flyer bases of North American airlines also contribute to the widespread use of price freeze tools as part of broader customer retention strategies.

The Asia Pacific region is poised to register the fastest growth in the Flight Price Freeze market, with a projected CAGR exceeding 19.5% through 2033. This rapid expansion is underpinned by burgeoning air travel demand, particularly in emerging economies such as India, China, and Southeast Asian countries. The increasing penetration of smartphones and digital payment platforms has enabled a new cohort of tech-savvy travelers to access and utilize flight price freeze services. Investments by regional airlines and online travel agencies in digital infrastructure and customer-centric innovations are accelerating adoption. Furthermore, the region's growing middle class and rising disposable incomes are fueling discretionary travel and, by extension, the need for price assurance in flight bookings.

Emerging economies in Latin America, the Middle East, and Africa are witnessing a gradual uptick in the adoption of flight price freeze solutions, albeit from a lower base. These markets face unique challenges, including limited digital infrastructure, variable internet access, and lower consumer awareness about advanced travel booking tools. Nevertheless, localized travel agencies and airlines are beginning to pilot price freeze offerings, often in partnership with global technology providers. Policy reforms aimed at liberalizing the aviation sector and fostering digital inclusion are expected to gradually improve market penetration. However, the pace of adoption will depend on continued investment in digital transformation and targeted consumer education campaigns in these regions.

Report Scope

Flight Centre Travel reported AUD162.9M in Cost of Sales for its fiscal semester ending in June of 2025. Data for Flight Centre Travel | FLT - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last December in 2025.

United States - Producer Price Index by Industry: Travel Agencies: International Flight Bookings was 73.12900 Index Dec 1989=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Travel Agencies: International Flight Bookings reached a record high of 144.60000 in June of 1997 and a record low of 55.50000 in October of 2002. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Travel Agencies: International Flight Bookings - last updated from the United States Federal Reserve on November of 2025.

United States - Producer Price Index by Industry: Travel Agencies: Domestic Flight Bookings was 68.83300 Index Dec 1989=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Travel Agencies: Domestic Flight Bookings reached a record high of 137.60000 in March of 1992 and a record low of 63.17700 in October of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Travel Agencies: Domestic Flight Bookings - last updated from the United States Federal Reserve on November of 2025.

Context

The European Flights Dataset from 2016 to 2022 provides an extensive record of air traffic activities across various European airports. The data includes essential metrics related to IFR (Instrument Flight Rules) movements, covering both departures and arrivals as reported by the Network Manager and Airport Operator. The dataset is comprehensive, with 688,099 entries and 14 columns, detailing flights over a span of seven years.

Content

Geography: Europe

Time period: Jan 2016- May 2022

Unit of analysis: European Flights Dataset

Variables

Acknowledgements

Datasource: Aviation Intelligence Unit Portal

Inspiration: Commercial air transport in August 2021: in recovery

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This statistic shows the maximum price for a *** way ticket on popular domestic flight routes in Indonesia in 2017. In that year, the maximum price for a *** way ticket from Jakarta to Medan amounted to at most *** million Indonesian rupiah.

this project graph created in Google Sheets:

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Flight Chronicles: An In-depth Analysis of British Airways Reviews (2012-2023)

In the realm of air travel, understanding passenger satisfaction through data analysis is pivotal for enhancing service quality and operational efficiency. This comprehensive exploration dives into a dataset encapsulating British Airways reviews from 2012 to 2023. The dataset, meticulously curated and processed, offers a rich foundation for extracting insights into passenger experiences and airline performance.

The initial phase involved segmenting the dataset into two distinct parts: 'ratings_data' and 'other_data'. The 'ratings_data' part encompasses direct feedback from passengers, including ratings on various service aspects such as comfort, food quality, staff behavior, and overall satisfaction. This feedback is crucial for gauging passenger sentiment and identifying areas for improvement.

The 'other_data' section, on the other hand, includes a wealth of information beyond ratings. This encompasses details on flight routes, aircraft used, and other operational variables that might influence the passenger experience. Processing this segment required a series of meticulous steps to ensure data integrity and usability for analysis or modeling purposes.

One of the first steps in handling 'other_data' was addressing missing values. Missing data can skew analysis and lead to inaccurate conclusions. Therefore, we filled these gaps with the column means for continuous variables, ensuring a coherent dataset without losing the granularity of information. This approach is standard in data preprocessing, allowing for a more accurate representation of the underlying data distribution.

Categorical variables present their own set of challenges, primarily due to their non-numeric nature. To overcome this, we employed encoding techniques, transforming these variables into a format that could be easily interpreted by analysis and modeling tools. This step is crucial for leveraging categorical data, such as airline routes or aircraft types, in predictive modeling or trend analysis.

Numeric features underwent standardization, a process that scales the data to have a mean of zero and a standard deviation of one. This normalization is vital for models that are sensitive to the scale of the data, such as linear regression or neural networks. By standardizing the numeric features, we ensured that our dataset was primed for sophisticated analytical techniques.

An optional yet insightful step was the conversion of date columns into a format conducive to analysis. This transformation allowed us to extract temporal trends, understand seasonal effects on passenger satisfaction, and even predict future performance based on historical data. Additionally, we engaged in feature engineering to create new variables that could offer deeper insights into the data, such as calculating the average delay per route or the impact of specific aircraft models on passenger ratings.

A critical aspect of preprocessing involved scrutinizing the dataset for null values in crucial columns like 'Route' and 'Aircraft'. These columns were integral to our analysis, offering insights into operational aspects that directly impact passenger experience. By removing all null values from these columns, we ensured our dataset's completeness and reliability for subsequent analysis stages.

Unnecessary columns that did not contribute to our analysis objectives were dropped. This step, often overlooked, is vital for focusing the analysis on relevant data, reducing computational load, and improving model accuracy. The result was a leaner, more targeted dataset that encapsulated the most impactful variables.

After thorough preprocessing, we combined 'ratings_data' with the processed 'other_data' to create "processed_airline.csv". This final dataset stands as a testament to meticulous data curation and preprocessing, embodying a comprehensive resource ready for in-depth analysis or modeling. It encapsulates the multifaceted nature of airline service evaluation, from operational efficiency to passenger satisfaction.

In conclusion, the transformation of the British Airways Review Dataset (2012-2023) into "processed_airline.csv" exemplifies the power of data processing in unlocking actionable i...