Context

Airline data holds immense importance as it offers insights into the functioning and efficiency of the aviation industry. It provides valuable information about flight routes, schedules, passenger demographics, and preferences, which airlines can leverage to optimize their operations and enhance customer experiences. By analyzing data on delays, cancellations, and on-time performance, airlines can identify trends and implement strategies to improve punctuality and mitigate disruptions. Moreover, regulatory bodies and policymakers rely on this data to ensure safety standards, enforce regulations, and make informed decisions regarding aviation policies. Researchers and analysts use airline data to study market trends, assess environmental impacts, and develop strategies for sustainable growth within the industry. In essence, airline data serves as a foundation for informed decision-making, operational efficiency, and the overall advancement of the aviation sector.

Content

This dataset comprises diverse parameters relating to airline operations on a global scale. The dataset prominently incorporates fields such as Passenger ID, First Name, Last Name, Gender, Age, Nationality, Airport Name, Airport Country Code, Country Name, Airport Continent, Continents, Departure Date, Arrival Airport, Pilot Name, and Flight Status. These columns collectively provide comprehensive insights into passenger demographics, travel details, flight routes, crew information, and flight statuses. Researchers and industry experts can leverage this dataset to analyze trends in passenger behavior, optimize travel experiences, evaluate pilot performance, and enhance overall flight operations.

Dataset Glossary (Column-wise)

• Passenger ID - Unique identifier for each passenger
• First Name - First name of the passenger
• Last Name - Last name of the passenger
• Gender - Gender of the passenger
• Age - Age of the passenger
• Nationality - Nationality of the passenger
• Airport Name - Name of the airport where the passenger boarded
• Airport Country Code - Country code of the airport's location
• Country Name - Name of the country the airport is located in
• Airport Continent - Continent where the airport is situated
• Continents - Continents involved in the flight route
• Departure Date - Date when the flight departed
• Arrival Airport - Destination airport of the flight
• Pilot Name - Name of the pilot operating the flight
• Flight Status - Current status of the flight (e.g., on-time, delayed, canceled)

Structure of the Dataset

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Acknowledgement

The dataset provided here is a simulated example and was generated using the online platform found at Mockaroo. This web-based tool offers a service that enables the creation of customizable Synthetic datasets that closely resemble real data. It is primarily intended for use by developers, testers, and data experts who require sample data for a range of uses, including testing databases, filling applications with demonstration data, and crafting lifelike illustrations for presentations and tutorials. To explore further details, you can visit their website.

Cover Photo by: Kevin Woblick on Unsplash

Thumbnail by: Airplane icons created by Freepik - Flaticon

We'll tailor a bespoke airline dataset to meet your unique needs, encompassing flight details, destinations, pricing, passenger reviews, on-time performance, and other pertinent metrics.

Leverage our airline datasets for diverse applications to bolster strategic planning and market analysis. Scrutinizing these datasets enables organizations to grasp traveler preferences and industry trends, facilitating nuanced operational adaptations and marketing initiatives. Customize your access to the entire dataset or specific subsets as per your business requisites.

Popular use cases involve optimizing route profitability, improving passenger satisfaction, and conducting competitor analysis.

📹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.

Covers monthly punctuality and reliability data of major domestic and regional airlines operating between Australian airports. Details are published for individual airlines on competitive routes and for airports on those routes.

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.

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.

Demand for international airlines has been very volatile over the past five years. Much of the industry benefited from a constant need for travel, both for seasonal vacations and business trips. The ongoing economic recovery from the pandemic and pent-up consumer demand led to revenue spikes. But lately, much of the industry has been dealing with high costs and debt, lowering profit levels. Overall, revenue has expanded at a CAGR of 15.8% to $96.8 billion over the past five years, including a gain of 1.2% in 2025 alone. Industry profit has climbed to 3.0% of revenue in 2025, up from -21.3% in 2020. International tourism from US and non-US residents has rebounded lately, boosting this industry. Charging ancillary fees such as checked baggage fees and seat selection fees have helped airlines generate more revenue in the period, even though the government views these practices with concern. A potential shortage of pilots is a cause for concern for this industry as more pilots are about to reach retirement age. Airlines are countering this problem by hiring more new pilots. Revenue is expected to stagnate in the coming years as geopolitical conflicts restrict where airlines can operate, harming revenue streams. At the same time, regulations regarding charging junk fees are anticipated to continue being scrutinized by the government, which will keep their operations in check. However, climbing international travel activity will help airlines limit revenue declines during the outlook period. A need for labor will maintain high wage costs, and the reality of labor unions representing pilots and mechanics also poses an issue to airlines due to higher wage expenses. Overall, industry revenue is expected to decline at a CAGR of 0.1% to $96.5 billion over the five years to 2030.

Feature description of the Twitter US airlines dataset.

Success.ai’s Aviation Data provides verified access to professionals across the airlines, aviation, and aerospace industries. Leveraging over 700 million LinkedIn profiles, this dataset delivers actionable insights, contact details, and firmographic data for pilots, engineers, airline executives, aerospace manufacturers, and more. Whether your goal is to market aviation technology, recruit aerospace specialists, or analyze industry trends, Success.ai ensures your outreach is powered by accurate, enriched, and continuously updated data.

Why Choose Success.ai’s Aviation Data? Comprehensive Professional Profiles

Access verified LinkedIn profiles of pilots, engineers, flight operations managers, safety specialists, and aviation executives. AI-driven validation ensures 99% accuracy, reducing bounce rates and enhancing communication efficiency. Global Coverage Across Aviation and Aerospace Sectors

Includes professionals from airlines, airport authorities, aerospace manufacturers, and aviation technology providers. Covers key regions such as North America, Europe, APAC, South America, and the Middle East. Continuously Updated Dataset

Real-time updates reflect changes in roles, organizational affiliations, and professional achievements, ensuring relevant targeting. Tailored for Aviation and Aerospace Insights

Enriched profiles include work histories, areas of specialization, professional certifications, and firmographic data. Data Highlights: 700M+ Verified LinkedIn Profiles: Access a vast network of aviation and aerospace professionals worldwide. 100M+ Work Emails: Communicate directly with pilots, engineers, and airline executives. Enriched Professional Histories: Gain insights into career paths, certifications, and organizational roles. Industry-Specific Segmentation: Target professionals in commercial aviation, aerospace R&D, airport management, and more with precision filters. Key Features of the Dataset: Aviation and Aerospace Professional Profiles

Identify and connect with airline CEOs, aerospace engineers, maintenance technicians, flight safety experts, and other key professionals. Engage with individuals responsible for operational decisions, technology adoption, and aviation safety protocols. Detailed Firmographic Data

Leverage insights into company sizes, fleet compositions, geographic operations, and market focus. Align outreach to match specific industry needs and organizational scales. Advanced Filters for Precision Targeting

Refine searches by region, job role, certifications (e.g., FAA, EASA), or years of experience for tailored outreach. Customize campaigns to address unique aviation challenges such as sustainability, fleet modernization, or safety compliance. AI-Driven Enrichment

Enhanced datasets provide actionable insights for personalized campaigns, highlighting certifications, achievements, and career milestones. Strategic Use Cases: Marketing Aviation Products and Services

Promote aviation technology, flight operations software, or aerospace equipment to airline operators and engineers. Engage with professionals responsible for procurement, fleet management, and airport operations. Recruitment and Talent Acquisition

Target HR professionals and aerospace manufacturers seeking pilots, engineers, and aviation specialists. Simplify hiring for roles requiring advanced technical expertise or certifications. Collaboration and Partnerships

Identify aerospace manufacturers, airlines, or airport authorities for joint ventures, technology development, or service agreements. Build partnerships with key players driving innovation and safety in aviation. Market Research and Industry Analysis

Analyze trends in airline operations, aerospace manufacturing, and aviation technology to inform strategy. Use insights to refine product development and marketing efforts tailored to the aviation industry. Why Choose Success.ai? Best Price Guarantee

Access high-quality Aviation Data at unmatched pricing, ensuring cost-effective campaigns and strategies. Seamless Integration

Easily integrate verified aviation data into CRMs, recruitment platforms, or marketing systems using APIs or downloadable formats. AI-Validated Accuracy

Depend on 99% accurate data to minimize wasted efforts and maximize engagement with aviation professionals. Customizable Solutions

Tailor datasets to specific aviation sectors, geographic regions, or professional roles to meet your strategic objectives. Strategic APIs for Enhanced Campaigns: Data Enrichment API

Enhance existing records with verified aviation profiles to refine targeting and engagement. Lead Generation API

Automate lead generation for a consistent pipeline of qualified professionals in the aviation sector, scaling your outreach efficiently. Success.ai’s Aviation Data empowers you to connect with the leaders and innovators shaping the aviation and aerospace industries. With verified conta...

Global AirLine market size 2025 was XX Million. AirLine Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

The industry has navigated a significant recovery, driven by strong rebounds in passenger demand as travel habits returned to pre-pandemic norms. These surges in travel hiked operational pressure, mainly because of persistent delays in aircraft deliveries. As a result, airlines extended aircraft leases to maintain sufficient fleet capacity. This approach offered stable cost management and enabled smoother operations; it also slowed the growth of airfares, as access to additional aircraft softened pricing power across the sector. Balancing regulated airfare growth, airlines shifted their focus toward ancillary revenue streams. Embracing an unbundled pricing model, mirroring the strategy of low-cost carriers (LCCs), allowed traditional airlines to generate extra revenue by charging separately for services like checked baggage and seat selection, in response to intense competition luring travelers toward more affordable LCC options. The industry also grappled with a persistent pilot shortage, affecting the speed of recovery and challenging workforce stability. Training gaps and constraints in bringing new pilots onboard prompted airlines to invest in enhanced in-house training facilities to expedite recruitment and keep up with demand. Technological changes shifted the focus from traditional Global Distribution Systems (GDS) to New Distribution Capabilities (NDC), enabling carriers to offer real-time, dynamically priced fares through more adaptable sales channels. This transition prompted updated fee structures and facilitated commission-based incentives for partners using the NDC platform, providing new profitability streams even as challenges continued. Overall, the industry’s total revenue grew at a CAGR of 23.0% from 2020 to 2025, reaching an estimated $837.0 billion. However, recent yearly growth slowed significantly to just an anticipated 0.1% in 2025. The industry will need to adapt to emerging travel trends, as anticipated growth in travel from emerging economies presents a significant opportunity for increased revenue. By responding to these shifts, the industry can position itself to capture new markets and drive sustained growth over the coming period. Fulfilling overdue aircraft orders should strengthen operational capacity, allowing airlines to better meet consumer needs for profitable routes and lift premiums when justified by high demand. The ongoing rollout of NDC systems will help streamline booking, although rising IT costs, driven by system upgrades and integration, will likely impact the industry’s expense structure soon. Combined with efforts to bring back corporate travel, new revenue channels and tighter cost controls are expected to sustain moderate performance. Industry revenue is estimated to rise at a CAGR of 1.6% to $906.7 billion by 2030, indicating a slower but steady expansion as operational efficiency and diversification shape future growth.

1) Data Introduction • The European Flights Dataset is a tabulated dataset of more than 680,000 air traffic records, including instrument flight (IFR) arrivals and operations at major European airports from January 2016 to May 2022.

2) Data Utilization (1) European Flights Dataset has characteristics that: • Each row contains 14 key items, including year, month, flight date, airport code and name, country name, and number of departures, arrivals, and total flights based on IFR. • The data are segmented by airport, country, and month, so they are well structured to analyze time series and spatial changes in European air traffic. (2) European Flights Dataset can be used to: • Analysis of Air Traffic Trends and Recovery: Using IFR operational performance by year, month, and airport, you can analyze changes in air traffic before and after the pandemic, seasonal trends, and speed of recovery. • Airport and Country Comparison Study: National/Airport performance data can be used to compare and evaluate major hub airports, cross-country aviation network structure, policy effectiveness, and more.

Global Airlines Market was valued at USD 678.55 Billion in 2023 and is expected to reach USD 961.42 Billion by 2029 with a CAGR of 6.03% during the forecast period.

This layer visualizes over 60,000 commercial flight paths. The data was obtained from openflights.org, and was last updated in June 2014. The site states, "The third-party that OpenFlights uses for route data ceased providing updates in June 2014. The current data is of historical value only. As of June 2014, the OpenFlights/Airline Route Mapper Route Database contains 67,663 routes between 3,321 airports on 548 airlines spanning the globe. Creating and maintaining this database has required and continues to require an immense amount of work. We need your support to keep this database up-to-date."To donate, visit the site and click the PayPal link.Routes were created using the XY-to-line tool in ArcGIS Pro, inspired by Kenneth Field's work, and following a modified methodology from Michael Markieta (www.spatialanalysis.ca/2011/global-connectivity-mapping-out-flight-routes).Some cleanup was required in the original data, including adding missing location data for several airports and some missing IATA codes. Before performing the point to line conversion, the key to preserving attributes in the original data is a combination of the INDEX and MATCH functions in Microsoft Excel. Example function: =INDEX(Airlines!$B$2:$B$6200,MATCH(Routes!$A2,Airlines!$D$2:Airlines!$D$6200,0))                                                

Turkish Airlines: AS: International Flights: Europe data was reported at 45,369,748.000 Unit/km th in 2017. This records a decrease from the previous number of 47,590,992.000 Unit/km th for 2016. Turkish Airlines: AS: International Flights: Europe data is updated yearly, averaging 36,721,737.000 Unit/km th from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 47,590,992.000 Unit/km th in 2016 and a record low of 20,646,524.000 Unit/km th in 2009. Turkish Airlines: AS: International Flights: Europe data remains active status in CEIC and is reported by Turkish Airlines, Incorporation. The data is categorized under Global Database’s Turkey – Table TR.TA011: Airlines Statistics: Turkish Airlines .

Global airlines market size and share are estimated to attain USD 473.91 billion by 2032, with a forecasted CAGR of 3.53% during the period. The domestic segment accounted for the largest market share in 2022, which is mainly driven by its low air fares and increasing standard of living.

Number of Businesses statistics on the Global Airlines industry in Global

This dataset provides the monthly enplanement and deplanement statistics since 1986 for all signatory airlines flying to and from the Baton Rouge Metropolitan Airport.

Turkish Airlines: FC: CM: Domestic Flights data was reported at 70,634.000 Ton in 2017. This records an increase from the previous number of 56,453.000 Ton for 2016. Turkish Airlines: FC: CM: Domestic Flights data is updated yearly, averaging 47,472.000 Ton from Dec 2009 (Median) to 2017, with 9 observations. The data reached an all-time high of 70,634.000 Ton in 2017 and a record low of 36,586.000 Ton in 2009. Turkish Airlines: FC: CM: Domestic Flights data remains active status in CEIC and is reported by Turkish Airlines, Incorporation. The data is categorized under Global Database’s Turkey – Table TR.TA011: Airlines Statistics: Turkish Airlines .

This dataset contains Saudi Arabia Traffic of Saudi and Foreign Airlines-International and Domestic Airports for 2008 - 2018. Data from General Authority for Statistics . Export API data for more datasets to advance energy economics research.note: no data for the year 2017.Source : Civil Aviation Presidency.