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.

View monthly updates and historical trends for US Consumer Price Index: Airline Fares. Source: Bureau of Labor Statistics. Track economic data with YChart…

The dataset provides a comprehensive overview of flight details, focusing on various key attributes related to airline operations. It includes information on:

• Airlines: Names of the airlines operating the flights.
• Source and Destination: Cities where flights originate and land.
• Total Stops: Number of stops made by the flights.
• Price: Ticket prices for the respective flights.
• Date, Month, and Year: Specific dates on which the flights are scheduled.
• Departure and Arrival Times: Detailed hours and minutes for both departure and arrival.
• Duration: Total duration of flights in hours and minutes.

This dataset is valuable for analyzing flight pricing trends, travel times, and patterns in airline operations. It offers insights into how different airlines operate across various routes, how prices vary, and the impact of stops on overall travel duration.

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.

Dataset Overview: Flight Price Dataset of Bangladesh

Introduction

The "Bangladesh Flight Fare Dataset" is a synthetic dataset comprising 57,000 flight records tailored to represent air travel scenarios originating from Bangladesh. This dataset simulates realistic flight fare dynamics, capturing key factors such as airline operations, airport specifics, travel classes, booking behaviors, and seasonal variations specific to Bangladesh’s aviation market. It is designed for researchers, data scientists, and analysts interested in flight fare prediction, travel pattern analysis, or machine learning/deep learning applications. By combining real-world inspired statistical distributions and aviation industry standards, this dataset provides a robust foundation for exploring flight economics in a South Asian context.

Dataset Purpose

This dataset aims to: - Facilitate predictive modeling of flight fares, with "Total Fare (BDT)" as the primary target variable. - Enable analysis of travel trends, including the impact of cultural festivals (e.g., Eid, Hajj) and booking timings on pricing. - Serve as a training resource for machine learning (ML) and deep learning (DL) models, with sufficient sample size (50,000) and feature diversity for generalization. - Provide a realistic yet synthetic representation of Bangladesh’s air travel ecosystem, blending domestic and international flight scenarios.

Data Collection and Methodology

The dataset is synthetically generated using Python, with its methodology rooted in real-world aviation data and statistical principles. Below is a detailed breakdown of its construction:

1. Data Components

• Airlines:
  • Count: 25 airlines (21 international, 4 domestic).
  • Source: Compiled from Bangladesh Civil Aviation Authority and Airline History, including major carriers like Emirates, Qatar Airways, and Biman Bangladesh Airlines.
  • Selection: Random uniform choice per flight record to reflect operational diversity.
• Airports:
  • Source Airports: 8 domestic airports (e.g., DAC - Hazrat Shahjalal International Airport, Dhaka).
  • Destination Airports: 20 airports (8 domestic + 12 international, e.g., DXB - Dubai International Airport).
  • Coordinates: Sourced from World Airport Codes, used for distance calculations.
  • Full Names: Added for readability, mapped via a dictionary (e.g., "DAC" → "Hazrat Shahjalal International Airport, Dhaka").
• Travel Classes: Economy, Business, First Class, standard across the industry, randomly assigned with uniform distribution.
• Booking Sources: Direct Booking, Travel Agency, Online Website, reflecting common methods, per Statista, with uniform random selection.
• Aircraft Types: Boeing 777, Airbus A320, Boeing 737, Boeing 787, Airbus A350, assigned based on flight distance, sourced from Boeing and Airbus.

2. Key Calculations

• Distance:

  • Method: Haversine formula calculates great-circle distance: a = sin²(Δφ/2) + cos(φ₁) cos(φ₂) sin²(Δλ/2) c = 2 arctan2(√a, √(1-a)) d = R · c, R = 6371 km

• Purpose: Determines flight duration, aircraft type, and stopovers.

• Source: Wikipedia - Haversine Formula.

• Flight Duration:

• Formula: Duration = max(d/s · U(0.9, 1.1), 0.5), where s is speed (300 km/h for <500 km, 600 km/h for 500-2000 km, 900 km/h for >2000 km), and U is uniform random variation.

• Source: Speeds adjusted from World Atlas, ensuring realism (e.g., DAC to CGP ~45 minutes).

• Fares:

• Base Fares:

• Domestic: Economy (2000-5000 BDT), Business (5000-10000 BDT), First Class (10000-15000 BDT).

• International: Economy (5000-70000 BDT), Business (15000-150000 BDT), First Class (25000-300000 BDT).

• Source: Derived from Trip.com and Expedia, e.g., DAC to LHR ~$380-600 (~41800-66000 BDT at 1 USD = 110 BDT).

• Adjustments:

• Seasonal multipliers (Regular: 1.0, Eid: 1.3, Hajj: 1.5, Winter: 1.2), per demand trends from Timeanddate.com.

• Days Before Departure: 20% discount (60+ days), 10% discount (30-59 days), 20% surge (<5 days), per Skyscanner.

• Taxes: Domestic: 200 BDT; International: 2000-6000 BDT + 15% base fare, per [Bangladesh Civil Aviation Authority](https://www.dgca.g...

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.

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.

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.

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.

China Air: Transport Index: Ticket Price: Domestic Line: Branch Line data was reported at 102.500 Jan2004=100 in Jun 2019. This records a decrease from the previous number of 109.700 Jan2004=100 for May 2019. China Air: Transport Index: Ticket Price: Domestic Line: Branch Line data is updated monthly, averaging 110.100 Jan2004=100 from Jan 2007 (Median) to Jun 2019, with 149 observations. The data reached an all-time high of 146.800 Jan2004=100 in Jul 2012 and a record low of 87.200 Jan2004=100 in Jan 2015. China Air: Transport Index: Ticket Price: Domestic Line: Branch 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.

Airfare Price Drop Protection Market Outlook

According to our latest research, the global airfare price drop protection market size in 2024 stands at USD 1.17 billion, reflecting robust demand for travel cost optimization solutions across the globe. The market is expected to expand at a CAGR of 18.2% from 2025 to 2033, reaching a projected value of USD 5.16 billion by 2033. This remarkable growth is primarily fueled by increasing consumer awareness of dynamic airfare pricing, the proliferation of digital travel platforms, and a heightened focus on user-centric travel experiences.

One of the primary growth drivers for the airfare price drop protection market is the growing volatility and unpredictability of airline ticket prices. As airlines increasingly adopt dynamic pricing algorithms, travelers often face substantial price fluctuations between the time they search for and book tickets. This uncertainty has led to a surge in demand for solutions that can offer financial protection against post-purchase price drops. The integration of advanced analytics and artificial intelligence in travel platforms has further facilitated the development of automated price monitoring and refund mechanisms, making these services more accessible and user-friendly for a broad spectrum of travelers.

The rapid digital transformation of the travel industry has also been a significant catalyst for market expansion. The widespread adoption of online travel agencies (OTAs), mobile travel apps, and meta-search engines has made it easier for consumers to compare prices and access ancillary services, including price drop protection. These platforms have leveraged big data and machine learning to enhance their offerings, providing real-time notifications and seamless refund processes. Additionally, the increasing penetration of smartphones and high-speed internet in emerging economies has expanded the addressable market, enabling even budget-conscious travelers to benefit from airfare price drop protection services.

Another crucial factor propelling the market is the changing expectations of both individual and corporate travelers. With business travel rebounding post-pandemic and leisure travelers seeking greater value for money, there is a heightened emphasis on risk mitigation and cost savings. Corporate travel managers are increasingly integrating airfare price drop protection into their travel policies to optimize budgets and improve employee satisfaction. Simultaneously, leisure travelers, empowered by technology, are demanding more transparent and flexible booking options. This evolving consumer mindset is pushing airlines, OTAs, and travel agencies to differentiate themselves by offering innovative price assurance features.

From a regional perspective, North America remains the largest contributor to the airfare price drop protection market, owing to high digital adoption rates and a mature travel ecosystem. However, Asia Pacific is emerging as the fastest-growing region, driven by a burgeoning middle class, increased international travel, and rapid technological advancements. Europe also holds a significant share, supported by a well-established airline network and a strong culture of travel insurance adoption. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, bolstered by the expansion of low-cost carriers and increased online travel bookings.

Product Type Analysis

The product type segment of the airfare price drop protection market is broadly categorized into Automatic Refund, Manual Claim, Subscription-Based, and Pay-Per-Use models. Automatic refund solutions have gained significant traction due to their seamless, user-friendly experience. These services automatically monitor ticket prices after purchase and initiate refunds or credits if a lower fare becomes available, eliminating the need for customer intervention. The integration of real-time fare tracking algorithms and secure payment gateways has made automatic refund offerings highly attractive to both individual and corporate travelers, driving their adoption across major online travel agencies and airline platforms.

Manual claim models, while less automated, remain relevant, particularly among traditional travel agencies and consumers who prefer a more hands-on approach. In this model, travelers must

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

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

In 2025, the seasonally adjusted consumer price index for all airline fares in the United States was ******. Over the given period, the CPI-U peaked at ***** in 2013, before decreasing significantly to ***** in 2021.

Graph and download economic data for Harmonized Index of Consumer Prices: Passenger Transport by Air for European Union (28 Countries) (CP0733EU28M086NEST) from Dec 2000 to Jan 2020 about passenger, air travel, EU, travel, harmonized, transportation, Europe, CPI, price index, indexes, and price.

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 detailed list of flights from Bengaluru to Delhi extracted from MakeMyTrip via Crawl Feeds. It includes essential flight information for the period between 8th December 2021 and 10th March 2022, making it ideal for analyzing travel trends, airline performance, and pricing patterns during this time frame.

Key Features:

• Flight Routes: All flights operating between Bengaluru (BLR) and Delhi (DEL).
• Dates: Data collected between December 8, 2021, and March 10, 2022.
• Airline Information: Details about different airlines, including flight numbers, departure and arrival times, and layovers (if any).
• Flight Duration: Information on total flight times, including direct and connecting flights.
• Price Data: Collected fare details for various classes (economy, business, premium).
• Data Format: Structured dataset in CSV format for easy import into analysis tools like Excel, Python, or R.

Applications:

• Flight Trend Analysis: Analyze flight availability, pricing, and demand over the given time period.
• Airline Comparison: Compare flight durations, fares, and services offered by various airlines.
• Travel Demand Insights: Evaluate how air travel between Bengaluru and Delhi fluctuated during these months.
• Price Forecasting: Study fare patterns to predict future pricing trends for flights between these cities.

For a more extensive analysis of travel trends and to gain deeper insights into the travel industry, explore our Travel & Tourism Data offerings. Our comprehensive datasets can help you anticipate customer needs, optimize operations, and provide personalized experiences to stay ahead in the competitive travel market.

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.

Discover the booming flight package ticket market! This in-depth analysis reveals a $150 billion USD market in 2025, projecting 7% CAGR growth to 2033. Explore key drivers, trends, regional breakdowns, and leading airlines shaping this dynamic sector. Learn how online booking, refundable options, and strategic partnerships are fueling expansion.

China Air: Transport Index: Ticket Price: Domestic Line: Medium Distance data was reported at 116.200 Jan2004=100 in Jun 2019. This records an increase from the previous number of 112.400 Jan2004=100 for May 2019. China Air: Transport Index: Ticket Price: Domestic Line: Medium Distance data is updated monthly, averaging 109.300 Jan2004=100 from Jan 2007 (Median) to Jun 2019, with 149 observations. The data reached an all-time high of 150.100 Jan2004=100 in Feb 2013 and a record low of 73.300 Jan2004=100 in Dec 2008. China Air: Transport Index: Ticket Price: Domestic Line: Medium Distance 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.