The historical flight schedule data is perfect to create applications, plugins for websites, running analysis and creating statistics, keeping track of past delays and cancellations for insurance or flight compensation claims, and much more.

We have developed many parameters you can use to pull the exact data you need without having to spend too much time filtering it on your end. We've asked many developers around the world to find out which pieces of data they would need the most, and created the parameters based on this feedback.

The data includes: - Airline: Name, IATA and ICAO codes of the airline. - Departure and arrival: IATA codes and ICAO codes of the departure and arrival location. - Departure and arrival times: Scheduled, estimated and actual arrival and departure times, as well as runway times in local time. - Status: The latest status information of the flight which may be active (for departure schedules), landed (for arrival schedules), cancelled or unknown - Delay: Total delay amount in minutes for delayed flights

Example response from the API: { "type": "departure", "status": "active", "departure": { "iataCode": "jfk", "icaoCode": "kjfk", "terminal": "7", "delay": 10, "scheduledTime": "2020-09-25t20:15:00.000", "estimatedTime": "2020-09-25t20:09:00.000", "actualTime": "2020-09-25t20:25:00.000", "estimatedRunway": "2020-09-25t20:25:00.000", "actualRunway": "2020-09-25t20:25:00.000"}, "arrival": { "iataCode": "lhr", "icaoCode": "egll", "terminal": "5", "scheduledTime": "2020-09-26t08:20:00.000", "estimatedTime": "2020-09-26t07:32:00.000" }, "airline": { "name": "aer lingus", "iataCode": "ei", "icaoCode": "ein" }, "flight": { "number": "8814", "iataNumber": "ei8814", "icaoNumber": "ein8814" }, "codeshared": { "airline": { "name": "british airways", "iataCode": "ba", "icaoCode": "baw" }, "flight": { "number": "114", "iataNumber": "ba114", "icaoNumber": "baw114"} } },

2) Historical Schedules API Output - Developer Information For the departure schedule of a certain airport on a certain date. GET http://aviation-edge.com/v2/public/flightsHistory?key=[API_KEY]&code=JFK&type=departure&date_from=YYYY-MM-DD

For the arrival schedule of a certain airport on a certain date. GET http://aviation-edge.com/v2/public/flightsHistory?key=[API_KEY]&code=JFK&type=arrival&date_from=YYYY-MM-DD

For the schedule of a certain airport of a certain date range (also available for arrival). GET http://aviation-edge.com/v2/public/flightsHistory?key=[API_KEY]&code=JFK&type=departure&date_from=YYYY-MM-DD&date_to=YYYY-MM-DD

For the schedule of a certain airport on a certain date (or range) but only flights with a certain status. GET http://aviation-edge.com/v2/public/flightsHistory?key=[API_KEY]&code=JFK&type=arrival&date_from=YYYY-MM-DD&date_to=YYYY-MM-DD&status=cancelled

For tracking individual historical flights. GET http://aviation-edge.com/v2/public/flightsHistory?key=[API_KEY]&code=JFK&type=departure&date_from=YYYY-MM-DD&date_to=YYYY-MM-DD&flight_number=[1234]

For filtering the flights of a certain airline from the arrival schedule of a certain airport on a certain date (also available for departure schedules and as a date range). GET http://aviation-edge.com/v2/public/flightsHistory?key=[API_KEY]&code=JFK&type=arrival&date_from=YYYY-MM-DD&&airline_iata=TK

Important Tips: - Currently possible to get dates that are up to 1 year earlier than the current date (this will expand soon). - The date range can go up to 28 days for a single API call but may be shorter around 3-5 days for airports with heavy traffic.

Dataset

This dataset was created by Fizza Sarfraz

Released under Other (specified in description)

Contents

Weight of factors for flight delay passenger behavior status.

Dataset

This dataset was created by Jyothindra Pallikonda

Contents

Analysis of ‘New York City Airport Activity’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sveneschlbeck/new-york-city-airport-activity on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

This dataset contains randomly compiled data with detailed information about flights from New York City Airports in 2013 with parameters concerning the airports, the flights and the airlines involved.

Content

The table contains the following parameters:

- year: Year.
- month: Month.
- day: Day.
- dep_time: Departure time, in Eastern time zone.
- dep_delay: Departure delay, in minutes.
- arr_time: Arrival time, in the local time zone.
- arr_delay: Arrival delay, in minutes.
- carrier: Carrier, abbreviated.
- tailnum: Tail number of the airplane.
- flight: Flight number.
- origin: Flight origin, airport code.
- dest: Flight destination, airport code.
- air_time: Time in the air, in minutes.
- distance: Distance between the departure and arrival airports, in miles.
- hour: Scheduled departure hour.
- minute: Scheduled departure minute.

Analysis

Take a look at the notebook "nyc-flights" to get started on how to transform, analyse or visualize the data.

Data Source

Wickham H. 2014. nycflights13: Data about flights departing NYC in 2013. R package version 0.1.

--- Original source retains full ownership of the source dataset ---

Bi-Annual Data Exposition: Every other year, at the Joint Statistical Meetings, the Graphics Section and the Computing Section join in sponsoring a special Poster Session called The Data Exposition , but more commonly known as The Data Expo. All of the papers presented in this Poster Session are reports of analyses of a common data set provided for the occasion. In addition, all papers presented in the session are encouraged to report the use of graphical methods employed during the developme nt of their analysis and to use graphics to convey their findings. Airline on-time performance: Have you ever been stuck in an airport because your flight was delayed or cancelled and wondered if you could have predicted it if you'd had more data? This is your chance to find out. The data: The data consists of flight arrival and departure details for all commercial flights within the USA, from October 1987 to April 2008. This is a large dataset: there are nearly 120 million records in total, and takes up 1.6 gigabytes of space compressed and 12 gigab ytes when uncompressed. The data comes originally from RITA where it is described in detail. You can download the data there, or from the bzipped csv files listed below. These files have derivable variables removed, are packaged in yearly chunks and have been more heavily compressed than the originals. Variable descriptions: Name Description 1 Year 1987-2008 2 Month 1-12 3 DayofMonth 1-31 4 DayOfWeek 1 (Monday) - 7 (Sunday) 5 DepTime actual departure time (local, hhm m) 6 CRSDepTime scheduled departure time (local, hhmm) 7 ArrTime actual arrival time (local, hhmm) 8 CRSArrTime scheduled arrival time (local, hhmm) 9 UniqueCarrier unique carrier code 10 FlightNum flight number 11 TailNum plane tail number 12 ActualElapsedTime in minutes 13 CRSElapsedTime in minutes 14 AirTime in minutes 15 ArrDelay arrival delay, in minutes 16 DepDelay departure delay, in minutes 17 Origin origin IATA airport code 18 Dest des tination IATA airport code 19 Distance in miles 20 TaxiIn taxi in time, in minutes 21 TaxiOut taxi out time in minutes 22 Cancelled was the flight cancelled? 23 CancellationCode reason for cancellation (A = carrier, B = weather, C = NAS, D = security) 24 Diverted 1 = yes, 0 = no 25 CarrierDelay in minutes 26 WeatherDelay in minutes 27 NASDelay in minutes 28 SecurityDelay in minutes 29 LateAircraftDelay in minutes

Average age of delayed flight passengers and the number of flights with passenger disruptions at Shenzhen Airport, 2018.

A. SUMMARY San Francisco International Airport (SFO) keeps track of historical flight operations, also known as aircraft RADAR data for analysis and reporting.

B. HOW THE DATASET IS CREATED Details of flights from the Federal Aviation Administration’s National Offload Program are processed into SFO’s Airport Noise and Operations Management System (ANOMS) where it is correlated with noise reports from the communities and to noise levels collected from noise monitor sites on the San Francisco Peninsula. In ANOMS, various analysis gates (imaginary vertical curtain in space) are used to identify which route flights flew departing and arriving SFO. It serves to quantify, analyze, respond to noise concerns, and report on Runway Use and various programs to reduce aircraft noise in communities surrounding SFO.

C. UPDATE PROCESS Data is available starting in August 2019 and will be updated monthly.

D. HOW TO USE THIS DATASET It is important to note, that this dataset is of flights departing and landing at SFO only and not flight activities associated with other airports in the Bay Area region. This information is the data source used to produce the Flight Operations sections (pages 3-5) of the Airport Director’s Report. These reports are presented at the SFO Airport Community Roundtable Meetings and available online at https://noise.flysfo.com/reports/?category=airport-directors-report

E. RELATED DATASETS Unique Flight Operations - This filtered view contains unique records of flight operations. For example, one record for a flight that departed SFO or one record for a flight that landed at SFO.

Arrival and Departure Routes - This filtered view contains records of flights with details of analysis gate(s) the aircraft flight track penetrates, to derive which route was used to depart and land at SFO.

This dataset contains Operations and Arrival and Departure Routes joined on operation_number. The field gate_penetration is derived by ordering the arrival and departure routes for each operation over gate_penetration_time. Unique_identifier is then created by joining operation_number and gate_penetration.

Other provided datasets are Aircraft Noise Reports, Late Night Aircraft Departures, Air Carrier Runway Use, and Late Night Preferential Runway Use, Aircraft Noise Climates, and Noise Exceedance Rating.

Please contact the Noise Abatement Office at NoiseAbatementOffice@flysfo.com for any questions regarding this data.

Date created: November 17, 2023

Abstract: How is it possible to overcome the challenges of providing public goods that create significant negative local externalities? Sometimes called ‘public bads,’ these goods are characterized by the combination of dispersed benefits and concentrated costs, which often lead to high levels of civil society resistance. Deciding where to locate them and how to expand them can therefore be a difficult political issue. I investigate this theoretical problem through the specific case of airport development. Airports provide crucial transit connections and economic opportunities for cities while also creating additional noise pollution and traffic for nearby residents. They are notoriously difficult to build or expand, often plagued with indecision, delays, and cost overruns. Throughout three papers, my dissertation asks: what makes airport development difficult, and how can these impediments be overcome? Potential explanations I explore include variation in technical characteristics, institutions, levels of civil society resistance, and the political issue space. This dataset contains the replication data for the dissertation's second paper, “The (Non)-Impact of Institutions on Airport Development." This paper examines one possible explanation for difficulty in airport expansion: that institutional variation in airport administration and ownership affects the ease with which development can occur. It focuses on two theoretically-motivated institutional variables: an airport’s level of privatization and its level of government regulation. Using an original dataset of expansion outcomes at the busiest airports in the world in 2012, the paper employs multilevel modeling along with logistic and linear regression techniques to test for a relationship between the aforementioned institutional factors and airports’ runway capacity expansion. In contrast with theoretical expectations, the paper finds no discernible connection between these characteristics. The paper’s conclusions also highlight the need for more nuanced case study analysis of airport development projects. The dataset also contains the replication data for the media graphs in the dissertation's third paper, "London's Airport Capacity Problem." This paper looks at two cases of airport expansion in London between 2008 and 2018, one where party and legislative approval occurred and one where it did not, showing that the reorganization of the issue space caused by the surprise Brexit vote offered a new strategic opportunity to over- come entrenched gridlock by reframing the issue of expansion.

Analysis of ‘Connecticut Airports’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3de5e924-f0a9-4bad-aa9a-36cfaafee8f7 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

Airports Polygon is a 1:24,000-scale, feature-based layer that includes all airport features depicted on all of the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps that cover the State of Connecticut and are listed on the Federal Aviation Administration (FAA) "Airport Data (5010) & Contact Information" June 5, 2008 report. Airports in New York, Massachusetts and Rhode Island that are near the Connecticut state boundary are included. Airports that are listed by FAA and are visible on aerial photography (Connecticut 2004 Orthophotos and Connecticut 2006 NAIP Color Orthophotos from National Agriculture Imagery Program) are included. Airports that are listed by FAA but are not visible on aerial photography are not included. All airports listed by FAA are included in a separate point feature-based layer, Airport FAA CT. The airport point locations were generated from latitude and longitude coordinates contained in the FAA report and all the attribute information in the report was included. The airport layer is based partly on information from USGS topographic quadrangle maps published between 1969 and 1984 which does not represent airports in Connecticut at any one particular point in time. The layer does depict current conditions as to airports listed by FAA and having location identification codes and visible on aerial photography of 2004 and 2006. The layer delineates airports and heliports. It includes airport name, airport location code, type of facility, public or private use of facility and state the airport is located in. It does not include airport elevation, flight schedule, runway capacity, or ownership information. Features are polygonal and generally depict landing strips and perimeters for large and small airports and helicopter landing pads. Attribute information allows to cartographic representation (symbolize) and labeling of these features on a map. This layer was originally published in 1994 and slightly updated in 2005.

--- Original source retains full ownership of the source dataset ---

Model fitting information.

The National Airspace System (NAS) in the United States will become a complex array of commercial and general aviation aircraft, unmanned aircraft systems, reusable launch vehicles, rotorcraft, airports, air traffic control, weather services, and maintenance operations, among others. The Federal Aviation Administration (FAA) projects that by 2025 air traffic will increase by more than 50 percent with 1.1 billion passengers a year and more than 85,000 flights every 24 hours contributing to further delays and congestion in the sky. This increased system complexity necessitates the application of systematic safety risk analysis methods to understand and eliminate where possible, reduce, and/or mitigate risk factors. The product of this effort is the development of an Uninhabited Aerial System (UAS) safety analysis model (USAM). USAM is ground-breaking technology that combines research NASA has already conducted on future UAS flights and UAS performance profiles with technologies developed specifically for UAS flight risk analysis and NAS-wide simulation analysis. The combination of these technologies answers this important question by leveraging on existing work in creative ways and adapting it to the needs of the UAS aviation safety community.

Brazil Aircraft Movement: Iguacu: Departures & Landings in the Month: Regular Transportation: Domestic Cargo: Regional data was reported at 0.000 Unit in Jun 2019. This stayed constant from the previous number of 0.000 Unit for May 2019. Brazil Aircraft Movement: Iguacu: Departures & Landings in the Month: Regular Transportation: Domestic Cargo: Regional data is updated monthly, averaging 0.000 Unit from Jan 2014 (Median) to Jun 2019, with 66 observations. The data reached an all-time high of 0.000 Unit in Jun 2019 and a record low of 0.000 Unit in Jun 2019. Brazil Aircraft Movement: Iguacu: Departures & Landings in the Month: Regular Transportation: Domestic Cargo: Regional data remains active status in CEIC and is reported by Brazilian Airport Infrastructure Company. The data is categorized under Brazil Premium Database’s Transport and Telecommunication Sector – Table BR.TA014: Aircraft Movement: by Airport: Departures & Landings in the Month. Regular Transportation: Flight made with the existence of HOTRAN (existing authorized flights) thus defined by regulator. Non Regular Transportation: Commercial flight made by any aircraft without the existence of HOTRAN. Boarded Passenger: Passengers of origin, who start the trip at the airport, plus passengers on connection. Landed Passenger: Passengers destination, ending the trip at the airport, plus passengers on connection. Cabotage Passenger: Domestic passengers on an international flight. Please be advised that the information provided herein refers to movements of aircraft, passengers, Air Cargo and Mail are provided by the Airline through a form called Summary Passenger Embedded (RPE) Air Cargo: Refer exclusively to loads of basement.

Brazil Aircraft Movement: Iguacu: Departures & Landings in the Month: Regular Transportation: Domestic Cargo: National data was reported at 1,077.000 Unit in Jun 2019. This records a decrease from the previous number of 1,094.000 Unit for May 2019. Brazil Aircraft Movement: Iguacu: Departures & Landings in the Month: Regular Transportation: Domestic Cargo: National data is updated monthly, averaging 1,192.500 Unit from Jan 2014 (Median) to Jun 2019, with 66 observations. The data reached an all-time high of 1,500.000 Unit in Jan 2018 and a record low of 996.000 Unit in Aug 2016. Brazil Aircraft Movement: Iguacu: Departures & Landings in the Month: Regular Transportation: Domestic Cargo: National data remains active status in CEIC and is reported by Brazilian Airport Infrastructure Company. The data is categorized under Brazil Premium Database’s Transport and Telecommunication Sector – Table BR.TA014: Aircraft Movement: by Airport: Departures & Landings in the Month. Regular Transportation: Flight made with the existence of HOTRAN (existing authorized flights) thus defined by regulator. Non Regular Transportation: Commercial flight made by any aircraft without the existence of HOTRAN. Boarded Passenger: Passengers of origin, who start the trip at the airport, plus passengers on connection. Landed Passenger: Passengers destination, ending the trip at the airport, plus passengers on connection. Cabotage Passenger: Domestic passengers on an international flight. Please be advised that the information provided herein refers to movements of aircraft, passengers, Air Cargo and Mail are provided by the Airline through a form called Summary Passenger Embedded (RPE) Air Cargo: Refer exclusively to loads of basement.

Model prediction accuracy.

Brazil Aircraft Movement: Ilheus: Departures & Landings in the Month: Non Regular Transportation data was reported at 0.000 Unit in Dec 2018. This records a decrease from the previous number of 78.000 Unit for Nov 2018. Brazil Aircraft Movement: Ilheus: Departures & Landings in the Month: Non Regular Transportation data is updated monthly, averaging 262.000 Unit from Jan 2014 (Median) to Dec 2018, with 60 observations. The data reached an all-time high of 619.000 Unit in Jan 2017 and a record low of 0.000 Unit in Dec 2018. Brazil Aircraft Movement: Ilheus: Departures & Landings in the Month: Non Regular Transportation data remains active status in CEIC and is reported by Brazilian Airport Infrastructure Company. The data is categorized under Brazil Premium Database’s Transport and Telecommunication Sector – Table BR.TA014: Aircraft Movement: by Airport: Departures & Landings in the Month. Regular Transportation: Flight made with the existence of HOTRAN (existing authorized flights) thus defined by regulator. Non Regular Transportation: Commercial flight made by any aircraft without the existence of HOTRAN. Boarded Passenger: Passengers of origin, who start the trip at the airport, plus passengers on connection. Landed Passenger: Passengers destination, ending the trip at the airport, plus passengers on connection. Cabotage Passenger: Domestic passengers on an international flight. Please be advised that the information provided herein refers to movements of aircraft, passengers, Air Cargo and Mail are provided by the Airline through a form called Summary Passenger Embedded (RPE) Air Cargo: Refer exclusively to loads of basement.

The objective of the Rodent Research-7 mission (RR-7) was to study the impact of the space environment on the gut microbiota of two strains of mice and how any changes in-turn affect the immune system metabolic system and circadian or daily rhythms. To this end ten 11-week-old female C57BL/6J and ten 11-week-old female C3H/HeJ mice were flown to the International Space Station on June 29 2018 on SpaceX-15 and housed in two Rodent Habitats. Samples of food swabs from living surfaces and fecal pellets were collected from each animal before launch and regularly during the mission. The mission also involved extended video collection (48 hr video segments per Habitat) to monitor circadian rhythms and on-orbit mass measurement. After 25 days on-orbit half of the mice of each strain were euthanized on the ISS with Ketamine/Xylazine/Acepromazine and cardiac puncture after which carcasses were segmented in three sections and preserved in RNA later. After 75-76 days the remaining 5 animals from each group were euthanized and processed in the same manner. The 25-day dissected carcasses returned on SpX-15 and the 75-day dissected carcasses returned on SpX-16. In addition to the Flight group three ground control groups were also part of the study: Basal (representing the pre-launch state) Vivarium (standard vivarium housing for the same duration of time as flight) and Ground (same habitat in the International Space Station Environment Simulator ISSES). Twenty mice (10 of each strain) were included in each of these control groups which were euthanized and processed on the same schedule and in the same manner as the flight samples. Dissections for tissues from all experimental groups were completed by the PI groups along with NASA s Biospecimen Sharing Program in February 2019. GeneLab received dorsal skin samples from forty C57BL/6J mice: 10 Basal 5 Ground (25 days) 5 Ground (75 days) 5 Flight (25 days) 5 Flight (75 days) 5 Vivarium (25 days) 5 Vivarium (75 days). GeneLab received dorsal skin samples from forty C3H/HeJ mice: 10 Basal 5 Ground (25 days) 5 Ground (75 days) 5 Flight (25 days) 5 Flight (75 days) 5 Vivarium (25 days) 5 Vivarium (75 days). From these skin samples RNA was extracted libraries generated (stranded ribodepleted) and sequenced (target 60 M clusters at PE 98 bp).

Maximum likelihood ratio test of parameters.

India’s aviation sector had increasingly emerged as a fast-growing industry. The sector had established itself as an affordable and credible alternative to the tedious and long journeys via road or rail. With a visible growth trend, it was estimated that by 2034, India would become one of the largest aviation markets in the world. As of financial year 2024, the passenger carrier IndiGo was the leader in the segment with around 62 percent of the market. IndiGo - the market leader The Indian aviation sector handled over 376 million passengers at Indian airports the same year. Jet Airways held the largest market share after IndiGo as of 2018. But the former passenger carrier had suspended operations in April 2019 following financial difficulties, leaving the field open for the latter, with little competition from other players in the market. A flight for the budget airline market Indigo airline’s low-cost and no-frills approach to domestic flying has been cited as one of the factors leading to its relative success in India. According to the Directorate-General of Civil Aviation, IndiGo airline carried over 85 million passengers during the fiscal year 2023. It ranked third among the country’s most punctual airlines with above 81 percent on-time arrivals. As a carrier that also had the least complaints from the customers, IndiGo’s popularity with the domestic base was high, soaring towards growth in the years to come.

Brazil Aircraft Movement: Sao Luiz: Departures & Landings in the Month: Regular Transportation data was reported at 1,001.000 Unit in Jun 2019. This records a decrease from the previous number of 1,046.000 Unit for May 2019. Brazil Aircraft Movement: Sao Luiz: Departures & Landings in the Month: Regular Transportation data is updated monthly, averaging 1,192.000 Unit from Jan 2014 (Median) to Jun 2019, with 66 observations. The data reached an all-time high of 1,603.000 Unit in Dec 2015 and a record low of 888.000 Unit in May 2016. Brazil Aircraft Movement: Sao Luiz: Departures & Landings in the Month: Regular Transportation data remains active status in CEIC and is reported by Brazilian Airport Infrastructure Company. The data is categorized under Brazil Premium Database’s Transport and Telecommunication Sector – Table BR.TA014: Aircraft Movement: by Airport: Departures & Landings in the Month. Regular Transportation: Flight made with the existence of HOTRAN (existing authorized flights) thus defined by regulator. Non Regular Transportation: Commercial flight made by any aircraft without the existence of HOTRAN. Boarded Passenger: Passengers of origin, who start the trip at the airport, plus passengers on connection. Landed Passenger: Passengers destination, ending the trip at the airport, plus passengers on connection. Cabotage Passenger: Domestic passengers on an international flight. Please be advised that the information provided herein refers to movements of aircraft, passengers, Air Cargo and Mail are provided by the Airline through a form called Summary Passenger Embedded (RPE) Air Cargo: Refer exclusively to loads of basement.