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 Jul 2025 about air travel, travel, urban, consumer, CPI, price index, indexes, price, and USA.

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.

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

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.

United States - Consumer Price Index for All Urban Consumers: Airline Fares in U.S. City Average was 248.58300 Index 1982-84=100 in July 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 September of 2025.

Airline Ticketing System Market Outlook

In 2023, the global market size for airline ticketing systems was valued at approximately USD 4.5 billion. With a projected compound annual growth rate (CAGR) of 9.6%, the market is forecasted to reach USD 9.7 billion by 2032. This impressive growth is driven by several factors including the increasing digitalization of services, a surge in air travel demand, and the necessity for efficient ticketing solutions to enhance customer experience and operational efficiency. As airlines continue to expand their routes and services, the demand for robust ticketing systems capable of handling high volumes of transactions, providing seamless service, and ensuring data security is expected to grow significantly.

The primary growth factor influencing the airline ticketing system market is the increasing global demand for air travel. As economies grow and globalization intensifies, more individuals and businesses are relying on air transport for travel and logistics. The rise in disposable incomes in emerging markets is also contributing to this demand, as people are more inclined to spend on air travel for leisure and business purposes. Additionally, the decrease in airline ticket prices due to increased competition and the availability of low-cost carriers is making air travel more accessible to a wider population, thereby boosting the need for advanced ticketing systems that can efficiently handle a growing number of passengers.

Technological advancements represent another significant driver of growth in the airline ticketing system market. The integration of artificial intelligence (AI), machine learning (ML), and big data analytics into ticketing systems is transforming the way airlines manage their operations and engage with customers. These technologies enable airlines to offer personalized services, optimize pricing strategies, and enhance customer satisfaction by predicting travel patterns and preferences. Moreover, the shift towards mobile platforms and applications facilitates easier access to services for passengers, allowing them to book, modify, or cancel flights with ease from the comfort of their mobile devices. This technological evolution is expected to further propel the market growth in the coming years.

The need for enhanced security and fraud prevention measures is also driving the adoption of advanced ticketing systems. Airlines are under constant threat from cyber-attacks and fraudulent activities, which can compromise customer data and lead to significant financial losses. Advanced ticketing systems equipped with secure payment gateways and real-time monitoring capabilities help mitigate these risks by providing robust security features. As regulatory bodies enforce stringent compliance standards and data protection laws, there is a growing emphasis on adopting systems that ensure both operational efficiency and data security, thereby contributing to the market's expansion.

The E Ticketing System has revolutionized the way airlines manage their ticketing processes, offering a seamless and efficient solution for both airlines and passengers. This system allows travelers to book, modify, and cancel their flights online, eliminating the need for physical tickets and reducing operational costs for airlines. By integrating with mobile platforms, the E Ticketing System enhances convenience, enabling passengers to access their travel information and boarding passes directly from their smartphones. This digital transformation not only streamlines the check-in process but also supports airlines in providing a more personalized travel experience, as it allows for the collection and analysis of passenger data to tailor services and offers. As the airline industry continues to embrace digitalization, the E Ticketing System is set to play a pivotal role in shaping the future of air travel.

Regionally, the Asia Pacific is expected to witness the highest growth in the airline ticketing system market. The region's burgeoning middle class, coupled with rapid industrialization and urbanization, is leading to a substantial increase in air travel. Furthermore, significant investments in airport infrastructure and the expansion of airline networks are facilitating market growth. In North America and Europe, the market is characterized by the presence of established players and advanced technological infrastructure, which supports the deployment of innovative ticketing solutions. In contrast, the Middle East & Africa and Latin America regions are experiencing steady growth, driven by the d

Amongst low-cost airlines in the United States there is considerable difference in average ticket prices: on one extreme is Spirit, with an average domestic ticket price of ** U.S. dollars, while on the other extreme is JetBlue, whose average domestic ticket price stood at *** U.S. dollars in the 12 months ending December 31, 2020. Ultra-low-cost carriersVariance in ticket prices between low-cost carriers has led some analysts to talk of a new industry segment – ultra-low-cost carriers (ULCC). ULCCs differ in that their business model is aimed at finding untapped locations to create extremely cheap flights. This business model creates new demand through courting consumers who do not normally fly, rather than structuring services according to existing demand. The ULCC model has proved to be successful, with ULCCs such as Allegiant and Frontier recording strong growth in operating revenue over recent years, as has the overall ULCC segment. Low cost carriersMore broadly, the low-cost carrier segment has been consistently expanding its share of the American airline market over the last decade. This trend extends beyond America, with low cost carriers dominating the global list of airlines which launched the newest routes in 2018. The U.S. ULCCs Allegiant, Frontier and Spirit all featured in the top 20 of this list.

Introduction: The aim behind this analysis of flight price prediction is study of flight booking dataset and performing various Exploratory Data Analysis technique.

Research Questions The aim of our study is to answer the below research questions: a) Does price vary with Airlines? b) How is the price affected when tickets are bought in just 1 or 2 days before departure? c) Does ticket price change based on the departure time and arrival time? d) How the price changes with change in Source and Destination? e) How does the ticket price vary between Economy and Business class?

Data collection and methodology

Dataset

Features

📹Project Video available on YouTube - https://youtu.be/gu3Ot78j_Gc

Airlines Flights Dataset for Different Cities

The Flights Booking Dataset of various Airlines is a scraped datewise from a famous website in a structured format. The dataset contains the records of flight travel details between the cities in India. Here, multiple features are present like Source & Destination City, Arrival & Departure Time, Duration & Price of the flight etc.

This data is available as a CSV file. We are going to analyze this data set using the Pandas DataFrame.

This analyse will be helpful for those working in Airlines, Travel domain.

Using this dataset, we answered multiple questions with Python in our Project.

Q.1. What are the airlines in the dataset, accompanied by their frequencies?

Q.2. Show Bar Graphs representing the Departure Time & Arrival Time.

Q.3. Show Bar Graphs representing the Source City & Destination City.

Q.4. Does price varies with airlines ?

Q.5. Does ticket price change based on the departure time and arrival time?

Q.6. How the price changes with change in Source and Destination?

Q.7. How is the price affected when tickets are bought in just 1 or 2 days before departure?

Q.8. How does the ticket price vary between Economy and Business class?

Q.9. What will be the Average Price of Vistara airline for a flight from Delhi to Hyderabad in Business Class ?

These are the main Features/Columns available in the dataset :

1) Airline: The name of the airline company is stored in the airline column. It is a categorical feature having 6 different airlines.

2) Flight: Flight stores information regarding the plane's flight code. It is a categorical feature.

3) Source City: City from which the flight takes off. It is a categorical feature having 6 unique cities.

4) Departure Time: This is a derived categorical feature obtained created by grouping time periods into bins. It stores information about the departure time and have 6 unique time labels.

5) Stops: A categorical feature with 3 distinct values that stores the number of stops between the source and destination cities.

6) Arrival Time: This is a derived categorical feature created by grouping time intervals into bins. It has six distinct time labels and keeps information about the arrival time.

7) Destination City: City where the flight will land. It is a categorical feature having 6 unique cities.

8) Class: A categorical feature that contains information on seat class; it has two distinct values: Business and Economy.

9) Duration: A continuous feature that displays the overall amount of time it takes to travel between cities in hours.

10) Days Left: This is a derived characteristic that is calculated by subtracting the trip date by the booking date.

11) Price: Target variable stores information of the ticket price.

This dataset provides detailed information on airline flight routes, fares, and passenger volumes within the United States from 1993 to 2024. The data includes metrics such as the origin and destination cities, distances between airports, the number of passengers, and fare information segmented by different airline carriers. It serves as a comprehensive resource for analyzing trends in air travel, pricing, and carrier competition over a span of three decades.

Data Features:

• tbl: Table identifier
• Year: Year of the data record
• quarter: Quarter of the year (1-4)
• citymarketid_1: Origin city market ID
• citymarketid_2: Destination city market ID
• city1: Origin city name
• city2: Destination city name
• airportid_1: Origin airport ID
• airportid_2: Destination airport ID
• airport_1: Origin airport code
• airport_2: Destination airport code
• nsmiles: Distance between airports in miles
• passengers: Number of passengers
• fare: Average fare
• carrier_lg: Code for the largest carrier by passengers
• large_ms: Market share of the largest carrier
• fare_lg: Average fare of the largest carrier
• carrier_low: Code for the lowest fare carrier
• lf_ms: Market share of the lowest fare carrier
• fare_low: Lowest fare
• Geocoded_City1: Geocoded coordinates for the origin city
• Geocoded_City2: Geocoded coordinates for the destination city
• tbl1apk: Unique identifier for the route

Potential Uses:

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

This statistic shows the price per route of low cost airlines in Europe in 2013 and 2014. The average cost per route of budget airline Ryanair was 65.67 euros in 2014, up from 58.45 euros the previous year.

Context: This dataset contains flight fare data that was collected from the EaseMyTrip website using web scraping techniques. The data was collected with the goal of providing users with information that could help them make informed decisions about when and where to purchase flight tickets. By analyzing patterns in flight fares over time, users can identify the best times to book tickets and potentially save money.

Sources: 1. Data collected using Python script with Beautiful Soup and Selenium libraries. 2. Script collected data on various flight details such as Date of booking, Date of travel, Airline and class, Departure time and source, Arrival time and destination, Duration, Total stops, Price. 3. The scraping process was designed to collect data for flights departing from a specific set of airports (Top 7 busiest airports in India). Note that the Departure Time feature also includes the Source airport, and the Arrival Time feature also includes the Destination airport. Which is later extracted in Cleaned_dataset. Also both cleaned and scraped datasets have provided so that one can use dataset as per their requirement and convenience.

Inspiration: 1. Dataset created to provide users with valuable resource for analyzing flight fares in India. 2. Detailed information on flight fares over time can be used to develop more accurate pricing models and inform users about best times to book tickets. 3. Data can also be used to study trends and patterns in the travel industry through air can act as a valuable resource for researchers and analysts.

Limitations: 1. This dataset only covers flights departing from specific airports and limited to a certain time period. 2. To perform time series analysis one have gather data for at least top 10 busiest airports for 365 days. 3. This does not cover variations in aviation fuel prices as this is the one of influencing factor for deciding fare, hence the same dataset might not be useful for next year, but I will try to update it twice in an year. 4. Also demand and supply for the particular flight seat is not available in the dataset as this data is not publicly available on any flight booking web site.

Scope of Improvement: 1. The dataset could be enhanced by including additional features such as current aviation fuel prices and the distance between the source and destination in terms of longitude and latitude. 2. The data could also be expanded to include more airlines and more airports, providing a more comprehensive view of the flight market. 3. Additionally, it may be helpful to include data on flight cancellations, delays, and other factors that can impact the price and availability of flights. 4. Finally, while the current dataset provides information on flight prices, it does not include information on the quality of the flight experience, such as legroom, in-flight amenities, and customer reviews. Including this type of data could provide a more complete picture of the flight market and help travelers make more informed decisions.

China Air: Transport Index: Ticket Price: Domestic Line data was reported at 129.600 Jan2004=100 in Jun 2019. This records an increase from the previous number of 127.500 Jan2004=100 for May 2019. China Air: Transport Index: Ticket Price: Domestic Line data is updated monthly, averaging 109.600 Jan2004=100 from Jan 2007 (Median) to Jun 2019, with 149 observations. The data reached an all-time high of 136.800 Jan2004=100 in Aug 2018 and a record low of 78.500 Jan2004=100 in Dec 2008. China Air: Transport Index: Ticket Price: Domestic Line data remains active status in CEIC and is reported by Civil Aviation Administration of China. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TI: Air: Transport Index.

The global flight package ticket market is experiencing robust growth, driven by increasing disposable incomes, a surge in leisure travel, and the convenience offered by bundled travel packages. The market's size in 2025 is estimated at $150 billion USD, projecting a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by several factors: the rising popularity of online booking platforms that offer competitive pricing and customizable packages, the increasing adoption of mobile travel apps, and the expanding reach of budget airlines catering to price-sensitive travelers. Furthermore, strategic partnerships between airlines and travel agencies are creating attractive bundled deals including flights, accommodation, and other travel-related services, thereby further stimulating market expansion. The preference for refundable flight packages is also on the rise, driven by increasing consumer demand for flexibility and peace of mind, especially in uncertain times. However, market growth is not without its constraints. Economic downturns, geopolitical instability, and unforeseen events like pandemics can significantly impact travel demand. Fluctuations in fuel prices also directly affect airline profitability and consequently, ticket prices. Furthermore, increased competition from online travel agencies (OTAs) necessitates continuous innovation and competitive pricing strategies for airlines and travel providers. Segmentation of the market reveals a strong preference for online booking channels, reflecting the growing digitalization of travel planning. The dominance of established airlines like United, British Airways, and others in the market is also noteworthy, although the emergence of low-cost carriers is expected to create further competition in the coming years. Regional variations exist, with North America and Europe currently leading the market, however, the Asia-Pacific region is poised for substantial growth due to its burgeoning middle class and increasing outbound tourism.

This statistic shows the change in domestic corporate ticket prices in the United States from the first quarter of 2015 through the first quarter of 2020, with a breakdown by number of days the ticket was purchased in advance. In the first quarter of 2020, for a ticket purchased between zero and *** days in advance, the U.S. average domestic corporate air fare stood at *** U.S. dollars.

Online Airline Reservation System Market Outlook

The global online airline reservation system market size was valued at approximately USD 5.3 billion in 2023 and is projected to reach USD 9.8 billion by 2032, growing at a compound annual growth rate (CAGR) of around 7.1% during the forecast period. This significant growth trajectory is primarily driven by the increasing reliance of consumers on digital platforms for travel bookings, which is further fueled by the burgeoning adoption of smartphones and internet connectivity worldwide. As digital transformation continues to sweep across industries, the airline sector is increasingly investing in technologically advanced reservation systems to streamline operations, enhance customer experience, and improve overall efficiency.

One major growth factor in the online airline reservation system market is the rising consumer preference for convenience and flexibility in booking travel. With the proliferation of smartphones and the internet, travelers are shifting towards online platforms that allow them to compare prices, view flight schedules, and book tickets instantly, anytime and anywhere. This shift is driving airlines and travel agencies to adopt sophisticated reservation systems that offer seamless user experiences. Furthermore, the increased focus on providing personalized services through AI and machine learning capabilities in these systems is enhancing customer satisfaction, thereby boosting market growth.

Additionally, the rise of low-cost carriers and budget-friendly travel options has significantly contributed to the market's expansion. As budget airlines continue to capture a larger share of the travel market, there is a growing demand for efficient and cost-effective reservation systems that can handle increased booking volumes without compromising on service quality. These systems enable airlines to manage their resources effectively, optimize pricing strategies, and enhance operational efficiency, thus making air travel more accessible and affordable for a broader audience. This trend is expected to continue, driving further growth in the online airline reservation system market.

The integration of advanced technology solutions such as cloud computing and blockchain also plays a pivotal role in the market's growth. Cloud-based reservation systems offer scalable solutions that can accommodate fluctuating demand, reduce IT infrastructure costs, and provide enhanced data security. Moreover, blockchain technology is being explored to improve transparency and security in transactions, which is particularly crucial in the travel industry. These technological advancements are expected to revolutionize the way airline reservations are managed, creating new growth opportunities for market players.

Regionally, North America remains a dominant player in the online airline reservation system market, fueled by the presence of major airlines and the high adoption rate of advanced technologies. However, the Asia Pacific region is anticipated to exhibit the highest growth rate during the forecast period, driven by the increasing middle-class population, rising disposable incomes, and a surge in business and leisure travel. Meanwhile, Europe and Latin America are also witnessing steady growth due to the expansion of airline networks and enhanced online services. The Middle East & Africa region is gradually catching up, with government initiatives aimed at boosting tourism and aviation infrastructure development.

Component Analysis

The online airline reservation system market is segmented into software and services when analyzed by component. Software solutions form the backbone of the reservation system, providing the necessary tools for airlines and travel agencies to manage bookings, customer data, and flight schedules efficiently. The continuous evolution of software technologies has enabled more sophisticated and user-friendly interfaces, offering functionalities such as real-time seat availability, dynamic pricing, and integration with other travel services. As airlines strive to offer seamless and personalized customer experiences, the demand for advanced software solutions is on the rise, driving significant investment in this segment.

In addition to software, services play a crucial role in the adoption and operation of online airline reservation systems. These services encompass implementation, integration, support, and maintenance, ensuring that the systems function optimally and meet the specific needs of airlines and travel agencies. As the complexity of reservation systems increases, there is a

The database consists of a panel of domestic routes in Brazil from January 2018 to December 2019, comprised of passenger flights in the domestic civil aviation market. The flights operated between 30 airports (870 routes) serving Brazil’s capitals. Airfare price data from the "domestic passenger air transport fares" section of ANAC has been grouped into averages by route, month, and airline. Such data are part of the ANAC database of Marketable Airfare Microdata containing airfare information for all domestic and international flights (monetary value and number of seats sold) disaggregated by company, airport pair, and month. Regarding the control variables, data were extracted from the websites of the IBGE , the Ministry of Tourism, and the ANP .

In 2020, the average annual expenditure on airline fares per consumer unit in the United States amounted to ****** U.S. dollars. Amid the COVID-19 pandemic, the average annual expenditure dropped by ** percent compared with 2019 levels.

India Consumer Price Index (CPI): Miscellaneous: Air Fare Normal: Economy Class Adult data was reported at 82.400 2012=100 in Oct 2018. This records an increase from the previous number of 71.100 2012=100 for Sep 2018. India Consumer Price Index (CPI): Miscellaneous: Air Fare Normal: Economy Class Adult data is updated monthly, averaging 81.400 2012=100 from Jan 2014 (Median) to Oct 2018, with 58 observations. The data reached an all-time high of 126.800 2012=100 in May 2014 and a record low of 64.500 2012=100 in Aug 2018. India Consumer Price Index (CPI): Miscellaneous: Air Fare Normal: Economy Class Adult data remains active status in CEIC and is reported by Central Statistics Office. The data is categorized under India Premium Database’s Inflation – Table IN.IA017: Consumer Price Index: 2012=100: Miscellaneous.

Flight Ticket Booking Software Market Outlook

The global flight ticket booking software market size was valued at approximately USD 3 billion in 2023 and is projected to reach around USD 5.4 billion by 2032 with a compound annual growth rate (CAGR) of 6.5% during the forecast period. This growth is driven by the increasing penetration of digital technologies across various sectors, including the travel industry, which has significantly transformed the way consumers book flights. The integration of artificial intelligence (AI) and machine learning (ML) in software solutions has enhanced user experience by offering personalized recommendations and streamlined booking processes. These technological advancements, coupled with the growing trend of online booking, have propelled the demand for efficient and reliable flight ticket booking software solutions worldwide.

The surge in global air travel, fueled by rising disposable incomes and a burgeoning middle class, particularly in emerging economies, is a significant growth factor for this market. As travel becomes more accessible, the demand for user-friendly and efficient booking software is escalating. Airlines and travel agencies are increasingly adopting advanced software solutions to manage bookings, cancellations, and customer interactions efficiently. Additionally, the COVID-19 pandemic has accelerated the digital transformation of the travel industry, as consumers increasingly prefer contactless and digital solutions over traditional methods. This shift in consumer behavior has further amplified the need for sophisticated flight ticket booking software.

Another driving factor is the increasing competition among airlines and travel agencies, compelling them to differentiate themselves through superior customer service and personalized offerings. Flight ticket booking software enables these entities to provide customized travel experiences, manage customer data effectively, and enhance operational efficiency. These solutions help in reducing errors and improving the accuracy of bookings, thereby enhancing customer satisfaction. Moreover, the growing reliance on data analytics tools integrated within booking platforms provides valuable insights into consumer preferences and market trends, allowing businesses to strategize effectively and improve their offerings.

The popularity of mobile platforms has also played a crucial role in the growth of the flight ticket booking software market. With the proliferation of smartphones and mobile internet, consumers now have the convenience of booking flights at their fingertips. Mobile applications offer seamless user interfaces, real-time updates, and personalized alerts, making them a preferred choice for many travelers. As mobile technology continues to advance, the demand for mobile-based booking solutions is expected to rise, further contributing to market growth. This trend is supported by the increasing availability of affordable smartphones and mobile data plans, especially in developing regions.

From a regional perspective, Asia Pacific is expected to witness significant growth during the forecast period, driven by a rapidly expanding middle class and increasing international travel. North America and Europe also hold substantial market shares due to high digital adoption rates and a well-established air travel infrastructure. However, the Middle East & Africa and Latin America are anticipated to experience slower growth due to economic challenges and relatively lower digital penetration. Nonetheless, these regions present potential opportunities for market expansion as travel and tourism sectors begin to recover post-pandemic.

Component Analysis

The flight ticket booking software market is bifurcated into two primary components: software and services. Software solutions encompass a wide range of applications that facilitate booking management, pricing optimization, and customer relationship management (CRM). These solutions are pivotal for airlines and travel agencies as they offer features like real-time booking updates, comprehensive itineraries, and seamless integration with payment gateways. As technology advances, software components are increasingly incorporating AI and machine learning algorithms to enhance personalization and user experience. This trend is compelling vendors to continuously innovate and upgrade their offerings to remain competitive.

On the services front, these include installation, maintenance, consulting, and support services provided by software vendors. As flight ticket booking solutions become more sophisticated, there is a growing n