A quick view of the CSV file reveals that it contains customer reviews for the top 10 rated airlines in 2023, including Singapore Airlines, Qatar Airways, ANA (All Nippon Airways), Emirates, Japan Airlines, Turkish Airlines, Air France, Cathay Pacific Airways, EVA Air, and Korean Air. It provides insights into passenger satisfaction and service quality aspects, ranging from seat comfort to inflight entertainment.

The dataset consists of 8,100 reviews with 17 columns, including both numerical and categorical data. Here is a brief overview of the columns:

Title: Title of the review. Name: Name of the reviewer. Review Date: Date when the review was posted. Airline: Airline being reviewed. Verified: Whether the review is verified. Reviews: Text of the review. Type of Traveller: Type of traveler (e.g., Solo Leisure, Family Leisure). Month Flown: Month of the flight. Route: Route of the flight. Class: Class of travel (e.g., Economy Class, Business Class). Seat Comfort: Rating for seat comfort (1-5). Staff Service: Rating for staff service (1-5). Food & Beverages: Rating for food and beverages (1-5). Inflight Entertainment: Rating for inflight entertainment (1-5). Value For Money: Rating for value for money (1-5). Overall Rating: Overall rating for the flight (1-10). Recommended: Whether the reviewer recommends the airline (yes/no)

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.

Context

Have you taken a flight in the U.S. in the past 15 years? If so, then you are a part of monthly data that the U.S. Department of Transportation's TranStats service makes available on various metrics for 15 U.S. airlines and 30 major U.S airports. Their website unfortunately does not include a method for easily downloading and sharing files. Furthermore, the source is built in ASP.NET, so extracting the data is rather cumbersome. To allow easier community access to this rich source of information, I scraped the metrics for every airline / airport combination and stored them in separate CSV files.

Occasionally, an airline doesn't serve a certain airport, or it didn't serve it for the entire duration that the data collection period covers*. In those cases, the data either doesn't exist or is typically too sparse to be of much use. As such, I've only uploaded complete files for airports that an airline served for the entire uninterrupted duration of the collection period. For these files, there should be 174 time series points for one or more of the nine columns below. I recommend any of the files for American, Delta, or United Airlines for outstanding examples of complete and robust airline data.

* No data for Atlas Air exists, and Virgin America commenced service in 2007, so no folders for either airline are included.

Content

There are 13 airlines that have at least one complete dataset. Each airline's folder includes CSV file(s) for each airport that are complete as defined by the above criteria. I've double-checked the files, but if you find one that violates the criteria, please point it out. The file names have the format "AIRLINE-AIRPORT.csv", where both AIRLINE and AIRPORT are IATA codes. For a full listing of the airlines and airports that the codes correspond to, check out the airline_codes.csv or airport_codes.csv files that are included, or perform a lookup here. Note that the data in each airport file represents metrics for flights that originated at the airport.

Among the 13 airlines in data.zip, there are a total of 161 individual datasets. There are also two special folders included - airlines_all_airports.csv and airports_all_airlines.csv. The first contains datasets for each airline aggregated over all airports, while the second contains datasets for each airport aggregated over all airlines. To preview a sample dataset, check out all_airlines_all_airports.csv, which contains industry-wide data.

Each file includes the following metrics for each month from October 2002 to March 2017:

1. Date (YYYY-MM-DD): All dates are set to the first of the month. The day value is just a placeholder and has no significance.
2. ASM_Domestic: Available Seat-Miles in thousands (000s). Number of domestic flights * Number of seats on each flight
3. ASM_International*: Available Seat-Miles in thousands (000s). Number of international flights * Number of seats on each flight
4. Flights_Domestic
5. Flights_International*
6. Passengers_Domestic
7. Passengers_International*
8. RPM_Domestic: Revenue Passenger-Miles in thousands (000s). Number of domestic flights * Number of paying passengers
9. RPM_International*: Revenue Passenger-Miles in thousands (000s). Number of international flights * Number of paying passengers

* Frequently contains missing values

Acknowledgements

Thanks to the U.S. Department of Transportation for collecting this data every month and making it publicly available to us all.

Source: https://www.transtats.bts.gov/Data_Elements.aspx

Inspiration

The airline / airport datasets are perfect for practicing and/or testing time series forecasting with classic statistical models such as autoregressive integrated moving average (ARIMA), or modern deep learning techniques such as long short-term memory (LSTM) networks. The datasets typically show evidence of trends, seasonality, and noise, so modeling and accurate forecasting can be challenging, but still more tractable than time series problems possessing more stochastic elements, e.g. stocks, currencies, commodities, etc. The source releases new data each month, so feel free to check your models' performances against new data as it comes out. I will update the files here every 3 to 6 months depending on how things go.

A future plan is to build a SQLite database so a vast array of queries can be run against the data. The data in it its current time series format is not conducive for this, so coming up with a workable structure for the tables is the first step towards this goal. If you have any suggestions for how I can improve the data presentation, or anything that you would like me to add, please let me know. Looking forward to seeing the questions that we can answer together!

Amazon Airline Data Lake Implementations Market Outlook

According to our latest research, the global Amazon Airline Data Lake Implementations market size in 2024 stands at USD 1.47 billion, reflecting the rapid adoption of advanced data management solutions in the aviation industry. The market is experiencing a robust CAGR of 19.2% and is expected to reach USD 6.18 billion by 2033. This substantial growth is driven by the increasing volume of unstructured data generated by airlines and the pressing need for scalable, real-time analytics to optimize operations, enhance passenger experience, and drive revenue growth.

One of the primary growth factors for the Amazon Airline Data Lake Implementations market is the aviation sector’s digital transformation, which has accelerated over the past few years. Airlines and airports are increasingly leveraging data lakes to break down silos and aggregate data from disparate sources such as IoT sensors, booking systems, flight tracking, and customer interaction points. By centralizing this data on Amazon’s robust cloud infrastructure, organizations gain the ability to perform advanced analytics, machine learning, and predictive maintenance, leading to improved operational efficiency and cost savings. The scalability and flexibility of Amazon’s data lake solutions are particularly attractive to airlines facing fluctuating passenger volumes and evolving regulatory requirements.

Another significant driver is the rising emphasis on enhancing customer experience and personalization in the airline industry. Modern passengers expect seamless, tailored experiences across all touchpoints, from booking to post-flight engagement. Amazon Airline Data Lake Implementations empower airlines to harness large datasets, including customer preferences, travel history, and real-time behavioral data. Advanced analytics and AI models built on these data lakes enable airlines to offer personalized services, targeted promotions, and proactive customer support, resulting in higher customer satisfaction and loyalty. The ability to integrate and analyze data in real time is becoming a key differentiator in an increasingly competitive market.

Furthermore, the need for robust revenue management and cost optimization is propelling the adoption of Amazon Airline Data Lake Implementations. Airlines operate on thin margins and face constant pressure to optimize pricing, route planning, and ancillary revenue streams. Data lakes facilitate the aggregation and analysis of vast volumes of data related to ticket sales, demand trends, competitor pricing, and operational costs. By leveraging Amazon’s analytics and machine learning tools, airlines can make data-driven decisions that maximize revenue and minimize operational inefficiencies. The integration of data lakes with existing airline IT ecosystems ensures seamless data flow and supports agile business strategies.

From a regional perspective, North America leads the market due to the presence of major airlines, advanced IT infrastructure, and a strong focus on digital innovation. Europe follows closely, driven by stringent regulatory requirements and the adoption of smart airport technologies. The Asia Pacific region is witnessing the fastest growth, fueled by rapid air travel expansion, increasing investments in aviation infrastructure, and the proliferation of low-cost carriers. Latin America and the Middle East & Africa are also emerging as promising markets as airlines in these regions modernize their operations and invest in data-driven transformation initiatives.

Component Analysis

The Amazon Airline Data Lake Implementations market is segmented by component into software, hardware, and services, each playing a pivotal role in the successful deployment and operation of data lakes. The software segment dominates the market, accounting for over 48% of the total share in 2024. This dominance is attributed to the growing demand for data integration, management, and analytics tools that can handle the complex and heterogeneous data landscape of the airline industry. Amazon’s suite of software solutions, including AWS Glue, Amazon S3, and Amazon Redshift, enables airlines to ingest, catalog, and analyze vast datasets efficiently. The continuous evolution of software capabilities, such as support for machine learning and real-time analytics, further strengthens this segment’s position.

The hardware segment, altho

Obstacle Database for Aviation Market Outlook

According to our latest research, the global obstacle database for aviation market size reached USD 1.47 billion in 2024, with a robust year-on-year growth reflecting the increasing adoption of digital navigation and safety systems across the aviation sector. The market is projected to expand at a CAGR of 7.8% from 2025 to 2033, reaching an estimated USD 2.92 billion by 2033. This growth is primarily driven by the ongoing modernization of airspace management, the proliferation of unmanned aerial vehicles (UAVs), and stringent regulatory mandates for obstacle data integration to enhance flight safety and operational efficiency.

One of the primary growth factors for the obstacle database for aviation market is the increasing emphasis on flight safety and risk mitigation. As global air traffic continues to rise, the need for accurate, real-time obstacle data has become paramount to avoid mid-air collisions and runway incursions. Regulatory authorities such as the Federal Aviation Administration (FAA) and the European Union Aviation Safety Agency (EASA) have established stringent requirements for obstacle data collection, validation, and dissemination. Airlines and airports are investing heavily in advanced terrain and obstacle databases to comply with these mandates, which in turn is fueling market expansion. Furthermore, the integration of these databases with next-generation avionics and air traffic management systems is enabling more precise navigation, especially in challenging environments such as mountainous regions and urban landscapes.

Another significant driver is the rapid adoption of digital transformation initiatives within the aviation sector. The shift from traditional paper-based obstacle charts to digital obstacle files and aeronautical information databases is streamlining data accessibility and usability for pilots, air traffic controllers, and UAV operators. This digital evolution is not only improving operational efficiency but also facilitating seamless updates and real-time data sharing across multiple stakeholders. The growing use of artificial intelligence (AI) and machine learning for obstacle detection, data validation, and predictive analytics is further enhancing the accuracy and reliability of aviation obstacle databases. As aircraft fleets become more technologically advanced, the demand for integrated, high-fidelity obstacle data is expected to surge, providing a substantial boost to market growth.

The market is also benefiting from the burgeoning UAV and drone industry, which relies heavily on obstacle databases for safe and efficient operations. As commercial and military applications of UAVs expand, there is a pressing need for specialized obstacle data that caters to low-altitude and complex environments. UAV operators require real-time, high-resolution obstacle information to support autonomous navigation, mission planning, and collision avoidance. This has led to the development of customized databases and data services tailored specifically for drone applications. The synergy between manned and unmanned aviation sectors is creating new opportunities for database providers, further propelling the overall market trajectory.

Regionally, North America remains the dominant market for obstacle databases in aviation, accounting for a significant share of global revenue in 2024. This leadership position is attributed to the presence of major aviation technology providers, proactive regulatory frameworks, and substantial investments in airspace modernization projects. Europe follows closely, driven by the implementation of Single European Sky initiatives and the growing adoption of UAVs. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid airport infrastructure development, expanding airline fleets, and increasing air passenger traffic. Latin America and the Middle East & Africa are also witnessing steady growth, supported by ongoing investments in aviation safety and navigation systems. Each region presents unique challenges and opportunities, shaping the competitive landscape and strategic priorities of market participants.

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.

Upvote🙏

The Airlines Reviews and Ratings dataset is a comprehensive collection of passenger feedback on various aspects of their flight experiences across different airlines. This dataset aims to provide insights into passenger satisfaction and airlines' service quality, offering valuable data for analysis in the travel and hospitality industry, customer service improvement, and predictive modeling for customer satisfaction. Airlines Reviews and Ratings Dataset, a rich collection designed to explore the multifaceted aspects of air travel experiences across various airlines worldwide. This dataset encompasses a broad range of data points, from aircraft types and user reviews to detailed service ratings, offering a unique lens through which to analyze and predict airline performance from a passenger perspective.

Column Details:

• Aircraft Type: Type of aircraft used for the flight.
• Users Reviews: Textual reviews provided by the users.
• Country: The country of the airline or the flight origin/destination.
• Type of Travellers: Categorizes travellers (e.g., Solo, Family, Business...).
• Route: The flight route taken.
• Seat Types: Class of the seat (Economy, Business, First Class...).
• Seat Comfort: Rating of the seat comfort.
• Date Flown: When the flight took place./The flight date.
• Cabin Staff Service: Rating of the service provided by the cabin staff.
• Ground Service/Floor: Rating of the ground service, including check-in and boarding.
• Food & Beverages: Rating of the food and beverage quality.
• Wifi & Connectivity: Rating of the wifi and connectivity options available.
• Inflight Entertainment: Rating of the inflight entertainment options.
• Value For Money: Overall value for money rating.
• Recommended: Whether the reviewer recommends the airline or not.

Airline Pricing Optimization AI Market Outlook

According to our latest research, the global Airline Pricing Optimization AI market size reached USD 1.42 billion in 2024, reflecting the rapid digital transformation in the airline industry. The market is expected to advance at a robust CAGR of 18.7% during the forecast period, culminating in a projected value of USD 7.15 billion by 2033. This substantial growth is primarily attributed to the increasing adoption of artificial intelligence (AI) to enhance pricing strategies, maximize revenue, and optimize operational efficiency across airline operations. As airlines continue to face fluctuating demand, volatile fuel prices, and intense competition, the need for advanced pricing optimization solutions powered by AI has never been more critical.

One of the most significant growth drivers for the Airline Pricing Optimization AI market is the rising complexity of fare structures and the proliferation of ancillary revenue streams. Airlines are increasingly moving beyond traditional ticket sales to incorporate dynamic pricing for services such as baggage, seat selection, and in-flight amenities. AI-powered pricing engines analyze vast datasets, including historical booking patterns, competitor pricing, demand fluctuations, and external factors like weather or geopolitical events, to recommend optimal pricing in real time. This enables airlines to respond swiftly to market changes, capture incremental revenue opportunities, and offer personalized pricing to different customer segments, thereby improving load factors and overall profitability.

Another major factor fueling market expansion is the mounting pressure on airlines to maintain profitability amid volatile macroeconomic conditions and shifting consumer behaviors. The COVID-19 pandemic fundamentally altered air travel demand patterns, compelling airlines to become more agile in their pricing strategies. AI-driven demand forecasting and revenue management tools have become indispensable for predicting passenger flows, adjusting prices dynamically, and minimizing unsold inventory. Furthermore, the integration of AI with emerging technologies such as machine learning, big data analytics, and cloud computing is enabling airlines to process and interpret massive volumes of data with unprecedented speed and accuracy, unlocking new opportunities for revenue optimization and cost reduction.

The surge in digital transformation initiatives across the aviation sector is also a pivotal growth catalyst. Airlines are investing heavily in modernizing their IT infrastructure, adopting cloud-based platforms, and leveraging AI to automate and optimize key business processes. These investments are not only enhancing operational efficiency but also enabling airlines to deliver a superior customer experience through hyper-personalized offers and seamless digital interactions. As regulatory bodies increasingly mandate transparency and fairness in pricing, AI-powered solutions are helping airlines ensure compliance while maintaining competitive advantage. The growing collaboration between airlines, technology vendors, and data analytics firms is further accelerating innovation and driving the adoption of AI-based pricing optimization solutions worldwide.

From a regional perspective, North America currently dominates the Airline Pricing Optimization AI market, accounting for the largest revenue share in 2024. This leadership position is underpinned by the presence of major airlines, advanced technology infrastructure, and a high level of digital adoption across the aviation industry. Europe follows closely, driven by strong regulatory frameworks, a competitive airline landscape, and increasing investments in AI-driven revenue management systems. The Asia Pacific region is poised for the fastest growth over the forecast period, fueled by rapid expansion of low-cost carriers, rising air passenger traffic, and increasing focus on operational efficiency. Latin America and the Middle East & Africa are also witnessing steady adoption, supported by ongoing digital transformation and the need to optimize pricing in highly competitive markets.

Component Analysis

The Airline Pricing Optimization AI market by component is segmented into Software and Services. The software segment encompasses AI-powered pricing engines, revenue management platforms, dynamic pricing modules, and demand forecasting tools. In 2024, the softwar

Dynamic Pricing for Airlines Market Outlook

According to our latest research, the global dynamic pricing for airlines market size reached USD 2.37 billion in 2024, reflecting a robust adoption rate across the aviation sector. The market is projected to expand at a CAGR of 17.8% during the forecast period, reaching approximately USD 7.87 billion by 2033. This remarkable growth is primarily fueled by the increasing need for airlines to optimize revenue streams, enhance operational efficiency, and deliver personalized customer experiences in a highly competitive environment. The proliferation of advanced analytics and artificial intelligence (AI) technologies is further accelerating the adoption of dynamic pricing solutions within the airline industry.

A key growth driver for the dynamic pricing for airlines market is the escalating demand for revenue maximization in a volatile and competitive landscape. Airlines are increasingly leveraging dynamic pricing strategies to adjust fares in real-time based on fluctuating demand, competitor pricing, and other market variables. This allows carriers to maximize seat occupancy and optimize ticket revenues, especially during peak travel periods and in response to sudden shifts in demand. The integration of machine learning and predictive analytics has made these pricing models more sophisticated, enabling airlines to analyze vast datasets and make data-driven pricing decisions that directly impact profitability.

Another significant factor contributing to market growth is the surge in digital transformation initiatives within the airline industry. As airlines invest heavily in digital infrastructure, the adoption of cloud-based dynamic pricing platforms has gained momentum. These platforms offer scalability, flexibility, and seamless integration with existing airline reservation and distribution systems. Furthermore, the shift towards personalized pricing models, which consider individual passenger preferences, booking history, and loyalty status, is enhancing customer satisfaction and loyalty. This trend is particularly prevalent among full-service carriers striving to differentiate themselves in a crowded marketplace.

The increasing emphasis on ancillary revenue streams is also propelling the dynamic pricing for airlines market forward. Airlines are now applying dynamic pricing not only to ticket fares but also to ancillary services such as baggage fees, seat selection, in-flight meals, and priority boarding. This holistic approach to revenue management enables carriers to capture additional value from every customer interaction, thereby driving overall profitability. As passenger expectations evolve and competition intensifies, airlines are compelled to adopt innovative pricing strategies that address both primary and ancillary revenue opportunities.

From a regional perspective, North America currently dominates the dynamic pricing for airlines market, owing to the presence of major airlines, advanced technological infrastructure, and a high degree of market maturity. However, the Asia Pacific region is expected to exhibit the fastest growth rate over the forecast period, driven by the rapid expansion of the aviation sector, rising passenger traffic, and increasing adoption of digital technologies by regional carriers. Europe also represents a significant market, characterized by a strong focus on customer experience and regulatory compliance. Latin America and the Middle East & Africa are emerging markets, with potential for substantial growth as airlines in these regions embrace dynamic pricing to enhance competitiveness and profitability.

Component Analysis

The component segment of the dynamic pricing for airlines market is bifurcated into software and services. Software solutions form the backbone of dynamic pricing strategies, providing airlines with the tools and algorithms necessary to analyze market data, forecast demand, and implement real-time price adjustments. These platforms are increasingly incorporating artificial intellige

To assist our country's aviation manufacturers in understanding the historical output value of the domestic aviation industry, a survey has been conducted on our country's aviation industry manufacturers since 1991, and the annual output value of the industry has been compiled to assist relevant manufacturers in upgrading their technical capabilities, enhancing their position in the international supply chain, and promoting the development of our country's aviation industry. In line with the government's promotion of open data measures, the Ministry of Economic Affairs, Industrial Development Bureau, has opened up the historical output value data of our country's aviation industry from today onwards, and welcomes all sectors to download and use it.

Airline Pricing Optimization AI Market Outlook

According to our latest research, the global Airline Pricing Optimization AI market size in 2024 stands at USD 1.17 billion, driven by the increasing adoption of artificial intelligence in aviation for maximizing revenue and operational efficiency. With a robust compound annual growth rate (CAGR) of 17.4%, the market is expected to reach USD 5.19 billion by 2033. This strong growth trajectory is underpinned by the surging demand for dynamic pricing models, real-time data analytics, and the digital transformation of airline revenue management systems, as identified in our comprehensive industry analysis.

One of the primary growth drivers for the Airline Pricing Optimization AI market is the increasing complexity of airline pricing structures in a highly competitive environment. Airlines are under constant pressure to optimize ticket prices in real-time, taking into account fluctuating demand, competitor pricing, seasonality, and macroeconomic factors. The integration of AI-powered pricing engines allows airlines to analyze vast datasets instantly, adapt pricing strategies dynamically, and maximize load factors while ensuring profitability. Furthermore, the proliferation of low-cost carriers and the emergence of new travel patterns post-pandemic have made traditional pricing models obsolete, necessitating the adoption of advanced AI solutions that can deliver granular insights and automate pricing decisions with unprecedented accuracy.

Another significant factor fueling market growth is the rapid advancement of AI and machine learning algorithms, which are now capable of processing unstructured data from multiple sources, including social media, booking platforms, and customer feedback. These sophisticated algorithms empower airlines to anticipate customer behavior, personalize offers, and optimize ancillary revenue streams such as baggage fees, seat selection, and in-flight services. As airlines increasingly focus on improving the passenger experience and capturing additional revenue beyond ticket sales, AI-driven pricing optimization tools are becoming indispensable. The ability to forecast demand with high precision and adjust prices in real-time not only enhances revenue management but also strengthens customer loyalty by offering more relevant and timely pricing options.

The ongoing digital transformation across the aviation sector is also catalyzing the adoption of AI-based pricing optimization. Airlines are investing heavily in cloud-based platforms, big data analytics, and automation technologies to streamline operations and reduce costs. The scalability and flexibility offered by AI-powered pricing solutions enable airlines to respond swiftly to market changes, manage disruptions, and implement agile pricing strategies that align with evolving business objectives. Additionally, regulatory developments and the growing emphasis on transparency and fairness in pricing practices are driving airlines to leverage AI for compliance and risk management. As the industry continues to recover and innovate in the wake of global disruptions, the adoption of AI in pricing optimization is set to accelerate further.

From a regional perspective, North America currently dominates the Airline Pricing Optimization AI market, accounting for the largest share due to its early adoption of advanced technologies and the presence of major airline operators. However, Asia Pacific is expected to witness the fastest growth over the forecast period, fueled by the rapid expansion of the aviation sector, increasing passenger traffic, and rising investments in digital infrastructure. Europe, with its mature airline industry and stringent regulatory environment, is also a significant contributor to market growth. Meanwhile, Latin America and the Middle East & Africa are emerging as promising markets, supported by ongoing modernization efforts and the entry of new airline players. The regional landscape is characterized by varying adoption rates, regulatory frameworks, and market dynamics, which will continue to shape the evolution of the Airline Pricing Optimization AI market globally.

Our Flight Events data feed combines Spire Global satellite/terrestrial ADS-B flight event data with ch-aviation’s fleet, operator, and airport data providing an overview of all flights operated by airlines, business and general aviation players on a daily basis.

The value of our Flight Events data feed lies in its high-resolution integration of ADS-B flight tracking with ch-aviation’s comprehensive aircraft and operator data, delivering unmatched visibility into global aircraft movements. By identifying the aircraft type and registration for approximately 98% of all ADS-B-tracked flights, we offer an industry-leading solution for lessors, insurers, airports, OEMs, and analysts seeking precise, reliable, and actionable aviation intelligence.

• High-Resolution ADS-B Integration - Satellite and terrestrial ADS-B flight tracking combined with enriched aircraft and operator data for maximum accuracy and visibility • Comprehensive Aircraft Identification - Aircraft type and registration identified for approximately 98% of all ADS-B-tracked flights, using proprietary matching with ch-aviation data and supplementary publicly available authority data sources. • Global Flight Coverage - Tracks approximately 160,000–190,000 flights per day across commercial aviation, business jet, and general aviation sectors worldwide. • ACMI (Wet-Lease) and Cargo Customer Tracking - Detailed monitoring of ACMI operations, including identification of wet-lease activity between different operators as well as cargo customers identifying flights operated for integrators like DHL Express or FedEx as well as cargo customers such as Amazon. • Aircraft Utilisation Tracking - Tracking of flight hours and cycles at both the operator and individual tail number (aircraft) level • Matched Operator and Aircraft Data - Every flight is linked to comprehensive ch-aviation datasets, including aircraft ID, history, operator, variant, callsign, and airport details allowing customers to leverage the industry’s most comprehensive integration between ADS-B flight event and fleet/operator/airport data. • Fallback Data Enrichment - Where ch-aviation data is unavailable, civil aviation authority and ANSP sources are used to ensure continuity in aircraft identification and data accuracy. • Use Case-Driven Insights - Tailored for industry stakeholders like lessors, insurers, OEMs, airports, and analysts seeking operational, commercial, and technical flight data intelligence.

ch-aviation integrates its Commercial Aviation Aircraft Data and Business Jet Aircraft Data with Spire Global’s satellite-based ADS-B data that is fused by Spire with terrestrial feeds from two terrestrial ADS-B data providers.

This data is enriched with mapped callsigns, corrected hexcodes, regional partnership decoding, and identification of wet-leases and cargo customers, enabling detailed insight into each individual flight.

Where ch-aviation data is unavailable, public data from civil aviation authorities and ANSPs is used to ensure broad and reliable aircraft identification and coverage.

The data set is available historically going back to January 1, 2018.

The data set is updated daily.

The sample data shows flights on 2025-03-30, with Swiss, Alaska Airlines, Horizon Air, Jet Aviation Business Jets, and RVR Aviation as operators or wet lease customers.

Contact us to get access to ch-aviation's AWS S3 sample data bucket as well allowing you to build proof of concepts with all of our sample data.

The direct bucket URL for this data set is: https://eu-central-1.console.aws.amazon.com/s3/buckets/dataservices-standardised-samples?region=eu-central-1&bucketType=general&prefix=flights/&showversions=false

Full Technical Data Dictionary: https://about.ch-aviation.com/flights-2/

Obstacle Database for Low-Level Flight Market Outlook

According to our latest research, the Global Obstacle Database for Low-Level Flight market size was valued at $1.1 billion in 2024 and is projected to reach $2.4 billion by 2033, expanding at a robust CAGR of 8.9% during 2024–2033. One of the major factors propelling the growth of the Obstacle Database for Low-Level Flight market globally is the rapid proliferation of unmanned aerial vehicles (UAVs) and advanced helicopter operations, which demand real-time, highly accurate obstacle data to ensure safe and efficient low-altitude navigation. As airspace utilization intensifies and urban air mobility concepts mature, the need for comprehensive, up-to-date obstacle databases is becoming critical for both manned and unmanned aviation sectors.

Regional Outlook

North America currently dominates the Obstacle Database for Low-Level Flight market, accounting for the largest share of global revenue, estimated at over 38% in 2024. This region’s leadership can be attributed to its mature aviation infrastructure, significant investments in aerospace technologies, and a proactive regulatory environment that encourages the adoption of advanced navigation and safety systems. The presence of major defense organizations and commercial aviation giants further accelerates the integration of sophisticated obstacle databases across both military and civilian applications. Additionally, North America’s strong ecosystem of software and hardware developers, coupled with robust service providers, ensures continuous innovation and deployment of cutting-edge solutions tailored for low-level flight safety and efficiency.

The Asia Pacific region is projected to be the fastest-growing market for Obstacle Database for Low-Level Flight, with a remarkable CAGR exceeding 11.2% through 2033. This accelerated growth is driven by surging investments in UAV technology, rapid expansion of commercial aviation, and increasing helicopter operations in countries such as China, India, Japan, and South Korea. Governments in this region are actively investing in modernizing their airspace management systems and developing smart city infrastructure, which includes integrating obstacle databases for urban air mobility initiatives. The rise of indigenous aerospace manufacturers and partnerships with global technology leaders are also contributing to the rapid adoption of obstacle database solutions, positioning Asia Pacific as a key engine of market growth.

In emerging economies across Latin America, the Middle East, and Africa, the adoption of obstacle databases for low-level flight is gaining momentum, albeit at a measured pace. These regions face unique challenges such as limited digital infrastructure, regulatory complexities, and budgetary constraints that can slow down the deployment of advanced aviation safety technologies. However, localized demand is increasing, particularly in sectors like defense, resource exploration, and humanitarian logistics, where low-level flight operations are critical. International collaborations, donor-funded modernization projects, and policy reforms aimed at enhancing airspace safety are gradually overcoming adoption barriers, creating new opportunities for market participants willing to tailor solutions to regional requirements.

Report Scope

Datasets for Analysis

🌍 Countries Dataset

Filename: Countries.csv

Description: This dataset contains information about countries around the world, including their respective codes, continents, and regions. It consists of 251 entries and the following columns:

• Country: The name of the country.
• Code: The ISO code for the country (one entry is missing a code).
• Continent: The continent where the country is located.
• Region: A more specific regional classification within the continent.

This dataset can be useful for geographical analysis, regional studies, and other applications that require country-level data.

✈️ British Airways Reviews Dataset

Filename: ba_reviews.csv

Description: This dataset contains reviews of British Airways flights, capturing various aspects of customer experiences. It consists of 1324 entries and the following columns:

• header: The title or headline of the review.
• author: The name of the reviewer.
• date: The date the review was posted.
• place: The reviewer's location.
• content: The main text of the review.
• aircraft: The type of aircraft flown.
• traveller_type: The type of traveler (e.g., business, leisure).
• seat_type: The class of seat (e.g., Economy Class, Business Class).
• route: The flight route taken.
• date_flown: The date the flight took place.
• recommended: Whether the reviewer recommends the airline.
• trip_verified: Whether the trip is verified.
• rating: The overall rating given by the reviewer (out of 5).
• seat_comfort: The rating for seat comfort (out of 5).
• cabin_staff_service: The rating for cabin staff service (out of 5).
• food_beverages: The rating for food and beverages (out of 5).
• ground_service: The rating for ground service (out of 5).
• value_for_money: The rating for value for money (out of 5).
• entertainment: The rating for in-flight entertainment (out of 5, with -1 indicating no rating).

This dataset is useful for analyzing customer satisfaction, identifying trends in airline service quality, and understanding the factors that contribute to positive or negative flight experiences.

Routes database

As of January 2012, the OpenFlights/Airline Route Mapper Route Database contains 59036 routes between 3209 airports on 531 airlines spanning the globe.

Content

The data is ISO 8859-1 (Latin-1) encoded.

Each entry contains the following information:

• Airline 2-letter (IATA) or 3-letter (ICAO) code of the airline.
• Airline ID Unique OpenFlights identifier for airline (see Airline).
• Source airport 3-letter (IATA) or 4-letter (ICAO) code of the source airport.
• Source airport ID Unique OpenFlights identifier for source airport (see Airport)
• Destination airport 3-letter (IATA) or 4-letter (ICAO) code of the destination airport.
• Destination airport ID Unique OpenFlights identifier for destination airport (see Airport)
• Codeshare "Y" if this flight is a codeshare (that is, not operated by Airline, but another carrier), empty otherwise.
• Stops Number of stops on this flight ("0" for direct)
• Equipment 3-letter codes for plane type(s) generally used on this flight, separated by spaces

The special value \N is used for "NULL" to indicate that no value is available.

Notes:

• Routes are directional: if an airline operates services from A to B and from B to A, both A-B and B-A are listed separately.
• Routes where one carrier operates both its own and codeshare flights are listed only once.

Acknowledgements

This dataset was downloaded from Openflights.org under the Open Database license. This is an excellent resource and there is a lot more on their website, so check them out!

Flight Data Analytics Market Outlook

According to our latest research, the global flight data analytics market size reached USD 3.28 billion in 2024, demonstrating robust demand across commercial and military aviation sectors. The market is experiencing a strong growth trajectory, underpinned by increasing emphasis on operational efficiency, safety, and regulatory compliance. The market is projected to expand at a CAGR of 13.7% from 2025 to 2033, reaching a forecasted value of USD 9.13 billion by 2033. This impressive growth is primarily driven by the adoption of advanced analytics solutions and the integration of IoT and AI technologies in aviation, as well as the rising need for real-time data-driven decision-making to enhance flight safety and reduce operational costs.

One of the key growth factors propelling the flight data analytics market is the increasing focus on safety management and regulatory compliance within the aviation industry. Airlines and operators are increasingly leveraging advanced analytics to monitor, analyze, and predict potential safety risks, ensuring adherence to stringent regulations set forth by aviation authorities. The integration of flight data analytics enables real-time monitoring of aircraft systems, early detection of anomalies, and proactive maintenance scheduling, thereby minimizing the risk of in-flight incidents and optimizing overall fleet safety. The growing pressure to reduce accident rates and improve operational transparency is pushing both commercial and military operators to invest in sophisticated analytics platforms, further fueling market expansion.

Another significant driver of market growth is the ongoing digital transformation of the aviation sector, characterized by the widespread adoption of cloud-based analytics solutions and the proliferation of connected aircraft. As airlines strive to enhance operational efficiency and reduce costs, the ability to harness vast volumes of flight data for actionable insights has become a strategic imperative. Cloud deployment models offer scalable, cost-effective, and easily accessible analytics tools, enabling airlines to centralize data management and streamline fleet-wide analytics processes. The integration of AI and machine learning algorithms further enhances predictive maintenance capabilities, fuel optimization, and route planning, delivering tangible benefits in terms of cost savings, reduced downtime, and improved passenger experience.

The increasing complexity of modern aircraft and the exponential growth in data generated by advanced avionics systems are also contributing to the expansion of the flight data analytics market. Aircraft today are equipped with a multitude of sensors and data acquisition systems that capture critical flight parameters, engine performance metrics, and environmental conditions. The ability to analyze this data in real time empowers airlines to optimize flight operations, manage fuel consumption, and extend the lifecycle of key components. As a result, there is a growing demand for comprehensive analytics platforms that can aggregate, process, and visualize large datasets, enabling data-driven decision-making at every level of the aviation value chain.

From a regional perspective, North America continues to dominate the global flight data analytics market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific region. The strong presence of leading airlines, advanced aviation infrastructure, and early adoption of digital technologies are key factors driving market growth in North America. Meanwhile, the Asia Pacific region is expected to exhibit the highest CAGR over the forecast period, supported by rapid fleet expansion, increasing air travel demand, and significant investments in aviation modernization initiatives. Latin America and the Middle East & Africa are also witnessing steady growth, driven by the ongoing expansion of commercial aviation networks and a growing focus on safety and operational excellence.

Component Analysis

The component segment of the flight data analytics market is broadly categorized into software, hardware, and services, each playing a pivotal role in enabling comprehensive analytics solutions for the aviation industry. Software solutions represent the largest share of the market, driven by the increasing adoption of advanced analytics platforms, machine learning algorithms, and real-time data visualization too

INTRODUCTION

The objective of the study is to analyse the flight booking dataset obtained from “Ease My Trip” website and to conduct various statistical hypothesis tests in order to get meaningful information from it. The 'Linear Regression' statistical algorithm would be used to train the dataset and predict a continuous target variable. 'Easemytrip' is an internet platform for booking flight tickets, and hence a platform that potential passengers use to buy tickets. A thorough study of the data will aid in the discovery of valuable insights that will be of enormous value to passengers.

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

Octoparse scraping tool was used to extract data from the website. Data was collected in two parts: one for economy class tickets and another for business class tickets. A total of 300261 distinct flight booking options was extracted from the site. Data was collected for 50 days, from February 11th to March 31st, 2022. Data source was secondary data and was collected from Ease my trip website.

DATASET

Dataset contains information about flight booking options from the website Easemytrip for flight travel between India's top 6 metro cities. There are 300261 datapoints and 11 features in the cleaned dataset.

FEATURES

The various features of the cleaned dataset are explained below: 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.

===================To boost learning, try to create an end-to-end project using the dataset.==================================

Obstacle Database for Aviation Market Outlook

According to our latest research, the global Obstacle Database for Aviation market size in 2024 stands at USD 2.09 billion, with the market expected to reach USD 4.13 billion by 2033, growing at a robust CAGR of 7.8% during the forecast period. The market’s growth is primarily propelled by the increasing emphasis on airspace safety, heightened regulatory requirements, and the rapid adoption of advanced digital navigation systems across both commercial and military aviation sectors.

One of the primary growth factors driving the Obstacle Database for Aviation market is the exponential rise in global air traffic, which necessitates more sophisticated and accurate obstacle databases to ensure the safety of aircraft operations. As airlines and airports strive to optimize flight paths and reduce risks associated with terrain and man-made obstacles, the integration of real-time, high-fidelity obstacle data has become essential. The proliferation of next-generation aircraft equipped with advanced avionics is further accelerating the demand for comprehensive obstacle databases, as these systems rely heavily on precise geospatial information for safe navigation and automated decision-making. Additionally, regulatory bodies such as the International Civil Aviation Organization (ICAO) and Federal Aviation Administration (FAA) have introduced stringent mandates for obstacle data accuracy and timely updates, compelling stakeholders to invest in robust database solutions.

Another significant driver is the ongoing modernization of airport infrastructure worldwide. As airports expand and upgrade their facilities to accommodate larger aircraft and increased passenger volumes, the need for detailed aerodrome obstacle databases becomes increasingly critical. These databases support the design and management of safe approach and departure procedures, helping to minimize the risk of runway incursions and ensure compliance with evolving safety standards. Furthermore, the rise of urban air mobility and unmanned aerial vehicles (UAVs) is creating new market opportunities, as these platforms require dynamic and customizable obstacle data to operate safely in complex urban environments. The integration of artificial intelligence and machine learning technologies into obstacle database management is also enhancing the accuracy and predictive capabilities of these systems, further fueling market growth.

The surge in demand for obstacle databases is not confined to commercial aviation alone; military aviation and general aviation segments are also witnessing substantial adoption. Military operations often involve challenging terrains and require precise obstacle data to support mission planning and execution. Similarly, the general aviation sector, which includes business jets and private aircraft, is increasingly leveraging obstacle databases to enhance flight safety and operational efficiency. The growing trend of digital transformation in aviation, coupled with the increasing use of cloud-based solutions and data analytics, is enabling seamless access to real-time obstacle information, thereby reducing operational risks and improving overall airspace management.

Regionally, North America continues to dominate the Obstacle Database for Aviation market, accounting for the largest share in 2024. This dominance is attributed to the presence of leading aviation technology providers, a well-established regulatory framework, and significant investments in airport infrastructure modernization. Europe follows closely, driven by stringent safety regulations and the rapid adoption of digital navigation systems. The Asia Pacific region is emerging as a high-growth market, fueled by the expansion of commercial aviation, increasing defense budgets, and the proliferation of smart airport projects. Latin America and the Middle East & Africa are also witnessing steady growth, supported by efforts to enhance airspace safety and accommodate rising passenger traffic.

Type Analysis

The Type segment of the Obstacle Database for Aviation market is broadly categorized into Terrain and Obstacle Databases, Aerodrome Obstacle Databases, and Customized Obstacle Databases. Terrain and obstacle databases form the backbone of modern navigation systems, providing essential topographical data and information on natural and man-made obstacles. These databases are critical for both en-route and terminal area operations, enabling pilots and auto

1. Delta Air Lines Delta Air Lines, based in Atlanta, Georgia, is one of the largest and oldest airlines in the world. Delta operates a vast domestic and international network, serving over 300 destinations in more than 50 countries. Known for its reliability and extensive SkyMiles loyalty program, Delta offers multiple cabin classes including Delta One (business/first class on international and transcontinental flights), Delta Premium Select (premium economy), Delta Comfort+ (extra legroom economy), and Main Cabin (economy).

2. American Airlines American Airlines, headquartered in Fort Worth, Texas, is another major airline with a comprehensive route network. It serves nearly 350 destinations in more than 50 countries. The airline's frequent flyer program is AAdvantage. American Airlines offers a range of classes such as Flagship First and Flagship Business on long-haul international flights, Domestic First Class, Premium Economy, Main Cabin Extra (extra legroom economy), and Main Cabin.

3. United Airlines Based in Chicago, Illinois, United Airlines is one of the largest carriers in the world by number of destinations served. United is a founding member of the Star Alliance and operates hubs in several major U.S. cities. Its MileagePlus program is popular among frequent flyers. United offers Polaris (international business class), United First (domestic first class), Premium Plus (premium economy), Economy Plus (extra legroom economy), and Economy.

4. Southwest Airlines Southwest Airlines, headquartered in Dallas, Texas, is known for its low-cost business model and point-to-point routing. It primarily operates within the United States and to some international destinations in Mexico, Central America, and the Caribbean. Southwest does not offer traditional seat classes but provides an all-economy service with benefits such as no change fees and two free checked bags. Its Rapid Rewards program is highly regarded among budget travelers.

5. JetBlue Airways JetBlue Airways, based in New York City, is a low-cost carrier known for its high-quality service and in-flight entertainment. It serves destinations in the United States, Caribbean, and Latin America. JetBlue offers classes such as Mint (premium class on select routes), Even More Space (extra legroom economy), and Core (standard economy). Its TrueBlue loyalty program is popular among passengers seeking flexible rewards.

Aerodrome Mapping Database Services Market Outlook

According to our latest research, the Global Aerodrome Mapping Database Services market size was valued at $1.2 billion in 2024 and is projected to reach $3.8 billion by 2033, expanding at a CAGR of 13.8% during 2024–2033. The primary driver for this robust growth is the increasing emphasis on aviation safety and operational efficiency, compelling airports and aviation authorities worldwide to adopt advanced digital mapping solutions. The integration of aerodrome mapping database services into airport operations, air traffic management, and flight navigation systems is becoming a critical enabler of real-time situational awareness, compliance with international standards, and seamless coordination among stakeholders. As global air traffic continues to rise and airport infrastructure becomes more complex, the demand for accurate, up-to-date, and interoperable mapping databases will only intensify, positioning this market for sustained expansion over the next decade.

Regional Outlook

North America currently dominates the Aerodrome Mapping Database Services market with the largest share, accounting for approximately 38% of global revenue in 2024. This leadership is attributed to the region’s mature aviation ecosystem, high density of commercial and military airports, and early adoption of digital mapping technologies. The United States, in particular, benefits from a strong regulatory framework, such as the Federal Aviation Administration’s (FAA) NextGen initiative, which mandates the use of advanced mapping databases for air navigation and airport management. Furthermore, North American airports are at the forefront of digital transformation, leveraging big data, AI, and IoT to enhance safety, efficiency, and passenger experience. The presence of leading market players and a robust network of technology providers further solidifies the region’s preeminence in this market segment.

Asia Pacific stands out as the fastest-growing region in the Aerodrome Mapping Database Services market, projected to register a remarkable CAGR of 17.2% during the forecast period. This rapid growth is driven by unprecedented investments in airport infrastructure across China, India, Southeast Asia, and Australia, where burgeoning air traffic and government initiatives are fueling demand for state-of-the-art mapping services. Regional governments are prioritizing aviation modernization, with large-scale airport expansions, greenfield projects, and upgrades to meet International Civil Aviation Organization (ICAO) standards. The influx of foreign direct investment, public-private partnerships, and the emergence of local technology providers are also accelerating the adoption of aerodrome mapping database services. As Asia Pacific’s aviation sector continues to evolve, the region is poised to become a global hub for digital aviation solutions.

Emerging economies in Latin America, the Middle East, and Africa are experiencing a gradual but steady rise in the adoption of Aerodrome Mapping Database Services. While these regions currently account for a smaller share of the global market, their growth potential is significant, especially as governments focus on improving aviation safety, enhancing airport infrastructure, and complying with international aviation standards. However, challenges such as limited access to advanced technologies, budget constraints, and a shortage of skilled professionals can impede rapid adoption. In many cases, localized demand is driven by a mix of commercial, military, and private airport projects, with policy reforms and international collaborations serving as key catalysts for market entry. The gradual digital transformation of airports in these regions, coupled with increasing awareness of the benefits of mapping databases, is expected to unlock new opportunities in the medium to long term.

Report Scope