The "flights.csv" dataset contains information about the flights of an airport. This dataset includes information such as departure and arrival time, delays, flight company, flight number, flight origin and destination, flight duration, distance, hour and minute of flight, and exact date and time of flight. This data can be used in management analysis and strategies and provide useful information about the performance of flights and placement companies. The analysis of the data in this dataset can be used as a basis for the following activities: - Analysis of time patterns and trends: by examining the departure and arrival time of the aircraft, changes and time changes, patterns and trends in flight behavior can be identified. - Analysis of American companies: By viewing information about airlines such as the number of flights, the impact and overall performance, you can compare and analyze the performance of each company. - Analysis of delays and service quality: By examining delays and arrival time, I can collect and analyze information about the quality of services provided by the airport and companies. - Analysis of flight routes: by checking the origin and destination of flights, distances and flight duration, popular routes and people's choices can be identified and analyzed. - Analysis of airport performance: by observing the characteristics of flights and airport performance, it is possible to identify and analyze the strengths and weaknesses of the airport and suggest improvements.

It provides various tools for data analysis and visualization and can be used as a basis for managerial decisions in the field of aviation industry.

Airline Company Codes (in order of frequency for this dataset)

WN -- Southwest Airlines Co.

DL -- Delta Air Lines Inc.

AA -- American Airlines Inc.

UA -- United Air Lines Inc.

B6 -- JetBlue Airways

AS -- Alaska Airlines Inc.

NK -- Spirit Air Lines

G4 -- Allegiant Air

F9 -- Frontier Airlines Inc.

HA -- Hawaiian Airlines Inc.

SY -- Sun Country Airlines d/b/a MN Airlines

VX -- Virgin America

This dataset provides flight track and aircraft navigation data from the NASA Atmospheric Tomography Mission (ATom). Flight track information is available for the four ATom campaigns: ATom-1, ATom-2, ATom-3, and ATom-4. Each ATom campaign consists of multiple individual flights and flight navigational information is recorded in 10-second intervals. Data available for each flight includes research flight number, date, and start and stop time of each 10-second interval. In addition, latitude, longitude, altitude, pressure and temperature is included at each 10-second interval. NASA's ATom campaign deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. During each campaign, flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. One intended use of this flight track data is to facilitate to mapping model results from global models onto the precise ATom flight tracks for comparison.

KEYVAN Aviation offering flight charts including with Hi and Low level airways charts , flight procedure charts ( SID , STAR , APPROACH) in GEO PDF format and digital format. The charts produced according to the specific standards and requirements and our team designed charts layout according to the pilot most required and interested template. Avoiding to add unnecessary data , test and graphic elements on the map will help the pilot for comfortable usage from our generated charts.

KEYVAN Aviation , also offering visualization solutions which is included with the capability to visualize the aeronautical data and charts in any kind of GIS software.

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!

The National Airspace System (NAS) is an ever changing and complex engineering system. As the Next Generation Air Transportation System (NextGen) is developed, there will be an increased emphasis on safety and operational and environmental efficiency. Current operations in the NAS are monitored using a variety of data sources, including data from flight recorders, radar track data, weather data, and other massive data collection systems. Although numerous technologies exist to monitor the frequency of known but undesirable behaviors in the NAS, there are currently few methods that can analyze the large repositories to discover new and previously unknown events in the NAS. Having a tool to discover events that have implications for safety or incidents of operational importance, increases the awareness of such scenarios in the community and helps to broaden the overall safety of the NAS, whereas only monitoring the frequency of known events can only provide mitigations for already established problems. This paper discusses a novel approach for discovering operationally significant events in the NAS that are currently not monitored and have potential safety and/or efficiency implications using radar-track data. This paper will discuss the discovery algorithm and describe in detail some flights of interest with comments from subject matter experts who are familiar with the operations in the airspace that was studied.

You can get all global flight information in 1 API call or track flights based on flight number, airline, departure/arrival airport, and more. The data updates frequently, around every 5 minutes. The details of the data include:

Geography: Location information such as latitude, longitude, altitude, and direction. Speed: Vertical and horizontal speed of aircraft. Departure and arrival: IATA codes and ICAO codes of the departure and arrival airport. Aircraft and flight: IATA and ICAO number of flight and registration number, ICAO code, and ICAO24 code of aircraft. Airline: IATA code, and ICAO code of airline. System information: Squawk, status, and last updated in Epoch.

Here's an example response from the API: [ { "geography": { "latitude": 43.5033, "longitude": -79.1297, "altitude": 7833.36, "direction": 70 }, "speed": { "horizontal": 833.4, "isGround": 0, "vertical": 0 }, "departure": { "iataCode": "YHM", "icaoCode": "CYHM" }, "arrival": { "iataCode": "YQM", "icaoCode": "CYQM" }, "aircraft": { "icaoCode": "B763", "regNumber": "CGYAJ", "icao24": "C08412" }, "airline": { "iataCode": "W8", "icaoCode": "CJT" }, "flight": { "iataNumber": "W8620", "icaoNumber": "CJT620", "number": "620" }, "system": { "updated": 1513148168, "squawk": "0000" }, "status": "en-route" } ]

Developer Information:

1) Available Endpoints &depIata= &depIcao= &arrIata= &arrIcao= &aircraftIcao= &regNum= &aircraftIcao24= &airlineIata= &airlineIcao= &flightIata= &flightIcao= &flightNum= &status= &limit= &lat=&lng=&distance=

2) Flights Tracker API Output

Specific flight based on: Flight IATA Number: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&flightIata=W8519

All flights of a specific Airlines: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&airlineIata=W8

Flights from departure location: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&depIata=MAD

Flights from arrival location: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&arrIata=GIG

Flights within a circle area based on lat and lng values and radius as the distance: GET https://aviation-edge.com/v2/public/flights?key=[API_KEY]&lat=51.5074&lng=0.1278&distance=100&arrIata=LHR

Combinations: two airports and a specific airline flying between them: GET http://aviation-edge.com/v2/public/flights?key=[API_KEY]&depIata=ATL&arrIata=ORD&airlineIata=UA

The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following: -sensor recordings -sampling rate -units -parameter description -parameter ID

The GOES-R PLT ER-2 Flight Navigation Data dataset consists of multiple altitude, pressure, temperature parameters, airspeed, and ground speed measurements collected by the NASA ER-2 high-altitude aircraft for flights that occurred during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT airborne science field campaign took place between March 21 and May 17, 2017 in support of the post-launch product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). ER-2 navigation data files in ASCII-IWG1 format are available for March 21, 2017 through May 17, 2017.

On-time data for a random sample of flights that departed NYC (i.e. JFK, LGA or EWR) in 2013. year,month,day Date of departure.

dep_time,arr_time Departure and arrival times, local tz.

dep_delay,arr_delay Departure and arrival delays, in minutes. Negative times represent early departures/arrivals.

hour,minute Time of departure broken in to hour and minutes.

carrier Two letter carrier abbreviation. See airlines in the nycflights13 package for more information or google the airline code.

tailnum Plane tail number.

flight Flight number.

origin,dest Origin and destination. See airports in the nycflights13 package for more information or google airport the code.

air_time Amount of time spent in the air.

distance Distance flown.

Source Hadley Wickham (2014). nycflights13: Data about flights departing NYC in 2013. R package version 0.1.

Formats CSV file Tab-delimited text file

Format A tbl_df with 32,735 rows and 16 variables:

Photo by Phil Mosley on Unsplash

The Flight Data Monitoring Market Report is Segmented by Installation Type (On-Board and On-Ground), Platform (Fixed-Wing, Rotary-Wing, and More), Component (Hardware, Software and Analytics, and Services), End User (Commercial Airlines, Cargo and Freight Operators. Business Jet Operators, UAV Service Providers, and More), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

About Dataset:

This dataset provides detailed information on airline flight routes, fares, and passenger volumes within the United States from 1993 to 2024.

Data Features:

1. tbl: Table identifier 2. Year: Year of the data record 3. quarter: Quarter of the year (1-4) 4. citymarketid_1: Origin city market ID 5. citymarketid_2: Destination city market ID 6. city1: Origin city name 7. city2: Destination city name 8. airportid_1: Origin airport ID 9. airportid_2: Destination airport ID 10. airport_1: Origin airport code 11. airport_2: Destination airport code 12. nsmiles: Distance between airports in miles 13. passengers: Number of passengers 14. fare: Average fare 15. carrier_lg: Code for the largest carrier by passengers 16. large_ms: Market share of the largest carrier 17. fare_lg: Average fare of the largest carrier 18. carrier_low: Code for the lowest fare carrier 19. lf_ms: Market share of the lowest fare carrier 20. fare_low: Lowest fare 21. Geocoded_City1: Geocoded coordinates for the origin city 22. Geocoded_City2: Geocoded coordinates for the destination city 23. tbl1apk: Unique identifier for the route

Potential Uses: 1. Market Analysis: Assess trends in air travel demand, fare changes, and market share of airlines over time. 2. Price Optimization: Develop models to predict optimal pricing strategies for airlines. 3. Route Planning: Identify profitable routes and underserved markets for new route planning. 4. Economic Studies: Analyze the economic impact of air travel on different cities and regions. 5. Travel Behavior Research: Study changes in passenger preferences and travel behavior over the years. 6. Competitor Analysis: Evaluate the performance of different airlines on various routes.

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

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))                                                

The global flight data monitoring market size is projected to grow from USD 5.3 billion in 2025 to USD 8.02 billion by 2033, exhibiting a CAGR of 5.31%.
Report Scope:

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

Large go-around, also referred to as missed approach, data set. The data set is in support of the paper presented at the OpenSky Symposium on November the 10th.

If you use this data for a scientific publication, please consider citing our paper.

The data set contains landings from 176 (mostly) large airports from 44 different countries. The landings are labelled as performing a go-around (GA) or not. In total, the data set contains almost 9 million landings with more than 33000 GAs. The data was collected from OpenSky Network's historical data base for the year 2019. The published data set contains multiple files:

go_arounds_minimal.csv.gz

Compressed CSV containing the minimal data set. It contains a row for each landing and a minimal amount of information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight

The last two columns, n_approaches and n_rwy_approached, are useful to filter out training and calibration flight. These have usually a large number of n_approaches, so an easy way to exclude them is to filter by n_approaches > 2.

go_arounds_augmented.csv.gz

Compressed CSV containing the augmented data set. It contains a row for each landing and additional information about the landing, and if it was a GA. The data is structured in the following way:

    Column name
    Type
    Description




    time
    date time
    UTC time of landing or first GA attempt


    icao24
    string
    Unique 24-bit (hexadecimal number) ICAO identifier of the aircraft concerned


    callsign
    string
    Aircraft identifier in air-ground communications


    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    has_ga
    string
    "True" if at least one GA was performed, otherwise "False"


    n_approaches
    integer
    Number of approaches identified for this flight


    n_rwy_approached
    integer
    Number of unique runways approached by this flight


    registration
    string
    Aircraft registration


    typecode
    string
    Aircraft ICAO typecode


    icaoaircrafttype
    string
    ICAO aircraft type


    wtc
    string
    ICAO wake turbulence category


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection

string

    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometre


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)


    operator_country
    string
    ISO Alpha-3 country code of the operator


    operator_region
    string
    Geographical region of the operator of the aircraft (either Europe, North America, South America, Asia, Africa, or Oceania)


    wind_speed_knts
    integer
    METAR, surface wind speed in knots


    wind_dir_deg
    integer
    METAR, surface wind direction in degrees


    wind_gust_knts
    integer
    METAR, surface wind gust speed in knots


    visibility_m
    float
    METAR, visibility in m


    temperature_deg
    integer
    METAR, temperature in degrees Celsius


    press_sea_level_p
    float
    METAR, sea level pressure in hPa


    press_p
    float
    METAR, QNH in hPA


    weather_intensity
    list
    METAR, list of present weather codes: qualifier - intensity


    weather_precipitation
    list
    METAR, list of present weather codes: weather phenomena - precipitation


    weather_desc
    list
    METAR, list of present weather codes: qualifier - descriptor


    weather_obscuration
    list
    METAR, list of present weather codes: weather phenomena - obscuration


    weather_other
    list
    METAR, list of present weather codes: weather phenomena - other

This data set is augmented with data from various public data sources. Aircraft related data is mostly from the OpenSky Network's aircraft data base, the METAR information is from the Iowa State University, and the rest is mostly scraped from different web sites. If you need help with the METAR information, you can consult the WMO's Aerodrom Reports and Forecasts handbook.

go_arounds_agg.csv.gz

Compressed CSV containing the aggregated data set. It contains a row for each airport-runway, i.e. every runway at every airport for which data is available. The data is structured in the following way:

    Column name
    Type
    Description




    airport
    string
    ICAO airport code where the aircraft is landing


    runway
    string
    Runway designator on which the aircraft landed


    n_landings
    integer
    Total number of landings observed on this runway in 2019


    ga_rate
    float
    Go-around rate, per 1000 landings


    glide_slope_angle
    float
    Angle of the ILS glide slope in degrees


    has_intersection
    string
    Boolean that is true if the runway has an other runway intersecting it, otherwise false


    rwy_length
    float
    Length of the runway in kilometres


    airport_country
    string
    ISO Alpha-3 country code of the airport


    airport_region
    string
    Geographical region of the airport (either Europe, North America, South America, Asia, Africa, or Oceania)

This aggregated data set is used in the paper for the generalized linear regression model.

Downloading the trajectories

Users of this data set with access to OpenSky Network's Impala shell can download the historical trajectories from the historical data base with a few lines of Python code. For example, you want to get all the go-arounds of the 4th of January 2019 at London City Airport (EGLC). You can use the Traffic library for easy access to the database:

import datetime from tqdm.auto import tqdm import pandas as pd from traffic.data import opensky from traffic.core import Traffic

load minimum data set

df = pd.read_csv("go_arounds_minimal.csv.gz", low_memory=False) df["time"] = pd.to_datetime(df["time"])

select London City Airport, go-arounds, and 2019-01-04

airport = "EGLC" start = datetime.datetime(year=2019, month=1, day=4).replace( tzinfo=datetime.timezone.utc ) stop = datetime.datetime(year=2019, month=1, day=5).replace( tzinfo=datetime.timezone.utc )

df_selection = df.query("airport==@airport & has_ga & (@start <= time <= @stop)")

iterate over flights and pull the data from OpenSky Network

flights = [] delta_time = pd.Timedelta(minutes=10) for _, row in tqdm(df_selection.iterrows(), total=df_selection.shape[0]): # take at most 10 minutes before and 10 minutes after the landing or go-around start_time = row["time"] - delta_time stop_time = row["time"] + delta_time

# fetch the data from OpenSky Network
flights.append(
  opensky.history(
    start=start_time.strftime("%Y-%m-%d %H:%M:%S"),
    stop=stop_time.strftime("%Y-%m-%d %H:%M:%S"),
    callsign=row["callsign"],
    return_flight=True,
  )
)

The flights can be converted into a Traffic object

Traffic.from_flights(flights)

Additional files

Additional files are available to check the quality of the classification into GA/not GA and the selection of the landing runway. These are:

validation_table.xlsx: This Excel sheet was manually completed during the review of the samples for each runway in the data set. It provides an estimate of the false positive and false negative rate of the go-around classification. It also provides an estimate of the runway misclassification rate when the airport has two or more parallel runways. The columns with the headers highlighted in red were filled in manually, the rest is generated automatically.

validation_sample.zip: For each runway, 8 batches of 500 randomly selected trajectories (or as many as available, if fewer than 4000) classified as not having a GA and up to 8 batches of 10 random landings, classified as GA, are plotted. This allows the interested user to visually inspect a random sample of the landings and go-arounds easily.

The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following: -sensor recordings -sampling rate -units -parameter description -parameter ID

This data set contains flight reports from NASA Operation IceBridge Greenland, Arctic, Antarctic, and Alaska missions. Flight reports contain information on region, mission, aircraft model, flight data, purpose of flight, and on-board sensors. The flight reports are collected as part of Operation IceBridge funded aircraft survey campaigns.

The Schedules API service provides real-time data for the flight schedules and timetables of airports and airlines around the world and maintains this for you in our central database, always accessible with your personal API key. This is one of Aviation Edge’s core features. You can build real-time airport departure and arrival tables, keep track of delays and cancellations, track the status of flights by using our API. The data comes in JSON format, making it useful to implement to websites and build applications, tools, software, and more.

The data includes the following: - Flight Status: active, scheduled, landed, cancelled, incident, diverted, redirected. - Airport details: IATA code, ICAO code, Terminal, Gate for both departure and arrival airport - Take-off information: Scheduled, estimated and actual times on runway and that of departure/arrival. - Total delay (updated for departures) - Airline: Name, IATA code and ICAO code. - Flight: Number of Flight, IATA prefix with flight number and ICAO prefix with flight number.

Example response from the API:

[ {"airline": {"iataCode":"DL", "icaoCode":"DAL", "name":"Delta Air Lines"}, "arrival": {"actualRunway":"2021-03-03T04:15:00.000", "actualTime":"2021-03-03T04:15:00.000", "baggage":"T4", "delay":null, "estimatedRunway":"2021-03-03T04:15:00.000", "estimatedTime":"2021-03-03T04:15:00.000", "gate":"B41", "iataCode":"JFK", "icaoCode":"KJFK", "scheduledTime":"2021-03-03T05:05:00.000", "terminal":"4"}, "codeshared":null, "departure": {"actualRunway":"2021-03-03T00:10:00.000", "actualTime":"2021-03-03T00:10:00.000", "baggage":5, "delay":"16", "estimatedRunway":"2021-03-03T00:10:00.000", "estimatedTime":”2021-03-03T00:10:00.000”, "gate":"B06", "iataCode":"TLV", "icaoCode":"LLBG", "scheduledTime":"2021-03-02T23:55:00.000", "terminal":"3"}, "flight": {"iataNumber":"DL235", "icaoNumber":"DAL235", "number":"235"}, "status":"landed", "type":"arrival"} ]

Output:

For the departure schedule of a certain airport. GET http://aviation-edge.com/v2/public/timetable?key=[API_KEY]&iataCode=JFK&type=departure

For the arrival schedule of a certain airport. GET http://aviation-edge.com/v2/public/timetable?key=[API_KEY]&iataCode=JFK&type=arrival

The following zip files contain individual flight recorded data in Matlab file format. There are 186 parameters each with a data structure that contains the following: -sensor recordings -sampling rate -units -parameter description -parameter ID