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.

This record is a global open-source passenger air traffic dataset primarily dedicated to the research community. It gives a seating capacity available on each origin-destination route for a given year, 2019, and the associated aircraft and airline when this information is available. Context on the original work is given in the related article (https://journals.open.tudelft.nl/joas/article/download/7201/5683) and on the associated GitHub page (https://github.com/AeroMAPS/AeroSCOPE/).A simple data exploration interface will be available at www.aeromaps.eu/aeroscope.The dataset was created by aggregating various available open-source databases with limited geographical coverage. It was then completed using a route database created by parsing Wikipedia and Wikidata, on which the traffic volume was estimated using a machine learning algorithm (XGBoost) trained using traffic and socio-economical data. 1- DISCLAIMER The dataset was gathered to allow highly aggregated analyses of the air traffic, at the continental or country levels. At the route level, the accuracy is limited as mentioned in the associated article and improper usage could lead to erroneous analyses. Although all sources used are open to everyone, the Eurocontrol database is only freely available to academic researchers. It is used in this dataset in a very aggregated way and under several levels of abstraction. As a result, it is not distributed in its original format as specified in the contract of use. As a general rule, we decline any responsibility for any use that is contrary to the terms and conditions of the various sources that are used. In case of commercial use of the database, please contact us in advance. 2- DESCRIPTION Each data entry represents an (Origin-Destination-Operator-Aircraft type) tuple. Please refer to the support article for more details (see above). The dataset contains the following columns:

"First column" : index airline_iata : IATA code of the operator in nominal cases. An ICAO -> IATA code conversion was performed for some sources, and the ICAO code was kept if no match was found. acft_icao : ICAO code of the aircraft type acft_class : Aircraft class identifier, own classification.

WB: Wide Body NB: Narrow Body RJ: Regional Jet PJ: Private Jet TP: Turbo Propeller PP: Piston Propeller HE: Helicopter OTHER seymour_proxy: Aircraft code for Seymour Surrogate (https://doi.org/10.1016/j.trd.2020.102528), own classification to derive proxy aircraft when nominal aircraft type unavailable in the aircraft performance model. source: Original data source for the record, before compilation and enrichment.

ANAC: Brasilian Civil Aviation Authorities AUS Stats: Australian Civil Aviation Authorities BTS: US Bureau of Transportation Statistics T100 Estimation: Own model, estimation on Wikipedia-parsed route database Eurocontrol: Aggregation and enrichment of R&D database OpenSky World Bank seats: Number of seats available for the data entry, AFTER airport residual scaling n_flights: Number of flights of the data entry, when available iata_departure, iata_arrival : IATA code of the origin and destination airports. Some BTS inhouse identifiers could remain but it is marginal. departure_lon, departure_lat, arrival_lon, arrival_lat : Origin and destination coordinates, could be NaN if the IATA identifier is erroneous departure_country, arrival_country: Origin and destination country ISO2 code. WARNING: disable NA (Namibia) as default NaN at import departure_continent, arrival_continent: Origin and destination continent code. WARNING: disable NA (North America) as default NaN at import seats_no_est_scaling: Number of seats available for the data entry, BEFORE airport residual scaling distance_km: Flight distance (km) ask: Available Seat Kilometres rpk: Revenue Passenger Kilometres (simple calculation from ASK using IATA average load factor) fuel_burn_seymour: Fuel burn per flight (kg) when seymour proxy available fuel_burn: Total fuel burn of the data entry (kg) co2: Total CO2 emissions of the data entry (kg) domestic: Domestic/international boolean (Domestic=1, International=0)

3- Citation Please cite the support paper instead of the dataset itself.

Salgas, A., Sun, J., Delbecq, S., Planès, T., & Lafforgue, G. (2023). Compilation of an open-source traffic and CO2 emissions dataset for commercial aviation. Journal of Open Aviation Science. https://doi.org/10.59490/joas.2023.7201

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.

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.

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!

Daily data showing UK flight numbers and rolling seven-day average, including flights to, from, and within the UK. These are official statistics in development. Source: EUROCONTROL.

Passengers enplaned and deplaned at Canadian airports, annual.

Percentage of flights arriving on-time. A flight is on-time if it arrives within 15 minutes of the schedule arrival time. Data are available for those carriers that had at least 1% of domestic enplanements in the previous year. The last 25 months of data include only carriers that reported in each of the last 25 months to retain comparability. Earlier data includes all reporting carriers. A scheduled operation consists of any nonstop segment of a flight. The Bureau of Transportation Statistics air collects performance data from U.S. air carriers and international carriers operating within the U.S.

As new technologies are developed to handle the complexities of the Next Generation Air Transportation System (NextGen), it is increasingly important to address both current and future safety concerns along with the operational, environmental, and efficiency issues within the National Airspace System (NAS). In recent years, the Federal Aviation Administration’s (FAA) safety offices have been researching ways to utilize the many safety databases maintained by the FAA, such as those involving flight recorders, radar tracks, weather, and many other high-volume sensors, in order to monitor this unique and complex system. Although a number of current technologies do monitor the frequency of known safety risks in the NAS, very few methods currently exist that are capable of analyzing large data repositories with the purpose of discovering new and previously unmonitored safety risks. While monitoring the frequency of known events in the NAS enables mitigation of already identified problems, a more proactive approach of finding unidentified issues still needs to be addressed. This is especially important in the proactive identification of new, emergent safety issues that may result from the planned introduction of advanced NextGen air traffic management technologies and procedures. Development of an automated tool that continuously evaluates the NAS to discover both events exhibiting flight characteristics indicative of safety-related concerns as well as operational anomalies will heighten the awareness of such situations in the aviation community and serve to increase the overall safety of the NAS. This paper discusses the extension of previous anomaly detection work to identify operationally significant flights within the highly complex airspace encompassing the New York area of operations, focusing on the major airports of Newark International (EWR), LaGuardia International (LGA), and John F. Kennedy International (JFK). In addition, flight traffic in the vicinity of Denver International (DEN) airport/airspace is also investigated to evaluate the impact on operations due to variances in seasonal weather and airport elevation. From our previous research, subject matter experts determined that some of the identified anomalies were significant, but could not reach conclusive findings without additional supportive data. To advance this research further, causal examination using domain experts is continued along with the integration of air traffic control (ATC) voice data to shed much needed insight into resolving which flight characteristic(s) may be impacting an aircraft's unusual profile. Once a flight characteristic is identified, it could be included in a list of potential safety precursors. This paper also describes a process that has been developed and implemented to automatically identify and produce daily reports on flights of interest from the previous day.

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...

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This dataset contains reviews of the top 10 rated airlines in 2023 sourced from the Airline Quality (https://www.airlinequality.com) website. The reviews cover various aspects of the flight experience, including seat comfort, staff service, food and beverages, inflight entertainment, value for money, and overall rating. The dataset is suitable for sentiment analysis, customer satisfaction analysis, and other similar tasks.

Usage - Download the dataset file airlines_reviews.csv. - Use the dataset for analysis, visualization, and machine learning tasks.

List of Airlines 1. Singapore Airlines 2. Qatar Airways 3. All Nippon Airways 4. Emirates 5. Japan Airlines 6. Turkish Airlines 7. Air France 8. Cathay Pacific Airways 9. EVA Air 10.Korean Air

This dataset is provided under the MIT License.

India All Scheduled Airlines: Domestic: Number of Flight data was reported at 102,319.000 Unit in Mar 2025. This records an increase from the previous number of 92,291.000 Unit for Feb 2025. India All Scheduled Airlines: Domestic: Number of Flight data is updated monthly, averaging 48,100.000 Unit from Apr 2001 (Median) to Mar 2025, with 288 observations. The data reached an all-time high of 102,319.000 Unit in Mar 2025 and a record low of 188.000 Unit in Apr 2020. India All Scheduled Airlines: Domestic: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

Motivation

The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members since 1 January 2019. More data has been periodically included in the dataset until the end of the COVID-19 pandemic.

We stopped updating the dataset after December 2022. Previous files have been fixed after a thorough sanity check.

License

See LICENSE.txt

Disclaimer

The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.

Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted. Fields origin or destination are empty when no airport could be found.

Aircraft information come from the OpenSky aircraft database. Fields typecode and registration are empty when the aircraft is not present in the database.

Description of the dataset

One file per month is provided as a csv file with the following features:

callsign: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.)

number: the commercial number of the flight, when available (the matching with the callsign comes from public open API); this field may not be very reliable;

icao24: the transponder unique identification number;

registration: the aircraft tail number (when available);

typecode: the aircraft model type (when available);

origin: a four letter code for the origin airport of the flight (when available);

destination: a four letter code for the destination airport of the flight (when available);

firstseen: the UTC timestamp of the first message received by the OpenSky Network;

lastseen: the UTC timestamp of the last message received by the OpenSky Network;

day: the UTC day of the last message received by the OpenSky Network;

latitude_1, longitude_1, altitude_1: the first detected position of the aircraft;

latitude_2, longitude_2, altitude_2: the last detected position of the aircraft.

Examples

Possible visualisations and a more detailed description of the data are available at the following page:

Credit

If you use this dataset, please cite:

Martin Strohmeier, Xavier Olive, Jannis Lübbe, Matthias Schäfer, and Vincent Lenders "Crowdsourced air traffic data from the OpenSky Network 2019–2020" Earth System Science Data 13(2), 2021 https://doi.org/10.5194/essd-13-357-2021

India All Scheduled Airlines: International: Number of Flight data was reported at 18,502.000 Unit in Mar 2025. This records an increase from the previous number of 16,668.000 Unit for Feb 2025. India All Scheduled Airlines: International: Number of Flight data is updated monthly, averaging 7,797.000 Unit from Apr 2001 (Median) to Mar 2025, with 283 observations. The data reached an all-time high of 18,574.000 Unit in Jan 2025 and a record low of 273.000 Unit in May 2020. India All Scheduled Airlines: International: Number of Flight data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA019: Airline Statistics: All Scheduled Airlines.

This dataset contains (i) a set of worldwide airports that are relevant for the global air cargo transport (ii) a dataset containing aircraft-specific yearly recorded frequencies (referring to the year 2014) for all passenger and cargo airlines (integrators such as FedEx are excluded) for all different origin destination (OD) airport pairs (iii) a dataset for each integrator FedEx, UPS, DHL with yearly estimated cargo capacity (expressed in tonnes) referring to the year 2019 for every OD airport pair. The estimation was based on a dataset containing all recorded flights for each OD airport pair of interest, which was filtered to extrapolate only flights operated by the integrators

Transportation and communication are crucial areas within analytics, especially in tackling safety and environmental challenges associated with the rapid expansion of urban centers and rising air traffic. One of the major hazards in aviation is bird strikes—collisions between aircraft and birds or other wildlife—which present a significant risk. These incidents can inflict severe damage on aircraft, particularly jet engines, and have even led to fatal accidents. Bird strikes are most common during critical flight stages such as takeoff, ascent, approach, and landing, when aircraft operate at lower altitudes where bird activity is more frequent.

The dataset provided by the FAA, covering incidents from 2019 to 2024, offers a comprehensive overview of bird strikes in the U.S. It includes detailed visualizations and analyses across several key areas:

• Trends Over Time: Yearly distribution of bird strike incidents.
• Airline Impact: Analysis of the top 10 U.S. airlines affected by bird strikes.
• Airport Incidents: Identification of the 50 U.S. airports with the highest frequency of bird strike incidents.
• Economic Impact: Yearly costs incurred by airlines and the aviation industry due to bird strikes.
• Timing and Altitude: When and at what altitude most bird strikes occur.
• Flight Phase: The phase of flight during which strikes are most likely to happen.
• Impact Analysis: How bird strikes affect flight operations, including aircraft damage.
• Pilot Awareness: Correlation between pilot knowledge of potential bird strike risks and the severity of the incidents.

This dataset offers valuable insights into bird strike patterns, focusing on factors such as aircraft type, location, flight phase, and the specific species involved. By analyzing these variables, it helps identify risk factors and trends, supporting the development of strategies to reduce the frequency and impact of bird strikes, ultimately enhancing aviation safety and risk mitigation.

Features: - AircraftType: The type of aircraft involved in the bird strike incident (e.g., "Airplane"). - AirportName: The name of the airport where the bird strike occurred (e.g., "LAGUARDIA NY", "DALLAS/FORT WORTH INTL ARPT"). - AltitudeBin: The altitude range (in feet) at which the bird strike occurred, divided into bins (e.g., "(1000, 2000]", "(30, 50]"). - MakeModel: The specific make and model of the aircraft involved (e.g., "B-737-400", "MD-80", "A-300"). - NumberStruck: The number of birds that were struck during the incident (e.g., "Over 100", "1", "26"). - NumberStruckActual: The actual number of birds that were struck during the incident (e.g., 859, 424, 261). - Effect: The effect of the bird strike on the aircraft, indicating whether it caused any damage or not (e.g., "Engine Shut Down", "No damage", "Caused damage"). - FlightDate: The date of the bird strike incident (e.g., "11/23/00 0:00"). - Damage: A description of the damage caused by the bird strike (e.g., "Caused damage", "No damage"). - Engines: The number of engines on the aircraft involved in the bird strike (e.g., 2 engines). - Operator: The airline or operator of the aircraft involved in the bird strike (e.g., "US AIRWAYS", "AMERICAN AIRLINES", "ALASKA AIRLINES"). - OriginState: The U.S. state where the aircraft originated (e.g., "New York", "Texas", "Washington"). - FlightPhase: The phase of flight during which the bird strike occurred (e.g., "Climb", "Landing Roll", "Approach", "Take-off run") - ConditionsPrecipitation: The weather condition related to precipitation at the time of the bird strike (e.g., "None", "Some Cloud"). - RemainsCollected?: Indicates whether bird remains were collected after the strike (e.g., "True" or "False"). - RemainsSentToSmithsonian: Indicates whether the bird remains were sent to the Smithsonian Institution for study (e.g., "True" or "False"). - Remarks: Additional comments or notes related to the incident, including specific details like the number of birds involved, actions taken, or other observations (e.g., "FLYING UNDER A VERY LARGE FLOCK OF BIRDS", "BIRD REMAINS ON F/O WINDSCREEN"). - WildlifeSize: The size of the bird or wildlife involved in the strike (e.g., "Small", "Medium"). - ConditionsSky: The sky condition at the time of the bird strike (e.g., "No Cloud", "Some Cloud"). - WildlifeSpecies: The species of the bird or wildlife involved in the strike (e.g., "European starling", "Rock pigeon", "Unknown bird - medium"). - PilotWarned: Indicates whether the pilot was warned about the potential for a bird strike (e.g., "Y" for Yes, "N" for No). - Cost: The cost incurred as a result of the bird strike (e.g., financial cost to repair damage or related expenses, usually in monetary value like 30,736). - Altitude: The specific altitude at which the bird strike occurred, typically in feet (e.g., 1500 feet, 50 feet). - PeopleInjured: The number of people injure...

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

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

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

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

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

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

Data that looks at how market structure affects delays for US domestic flights between the years 2004 - 2017.

Data on airline delays come from the Airline On-Time Performance Data (OTPD) from the US Bureau of Transportation Statistics. The data on tail numbers and seat capacity come from the Federal Aircraft Administration Aircraft Registry. The data on flight-related whether comes from the Local Climatological Data (LCD) provided by the National Center for Environmental Information.