The impact of the novel coronavirus (COVID-19) can be seen on every sector of the most affected countries as well as globally. In the week starting January 4, 2021, the number of scheduled flights worldwide was down by 43.5 percent compared to the week of January 6, 2020. The impact of COVID-19 on the Chinese aviation reached a peak in the week starting February 17, 2020, with flight numbers down by 70.8 percent. Aviation market prior to COVID-19 outbreak Before the coronavirus outbreak hit the globe, the aviation industry was improving at a steady pace across countries. For instance, the projected annual growth of revenue ton-miles (RTM) for international flights by U.S. commercial air carriers was at roughly four percent for the period between 2020 and 2040. Prior to the coronavirus outbreak, the forecasted aircraft maintenance, repair and overhaul (MRO) market size in North America was over 22 billion U.S. dollars in 2020. After the adjustments with respect to radical changes driven by coronavirus shock, the North American MRO market is now estimated to generate roughly 12 billion U.S. dollars during the same period. Besides, it was estimated that between 2019 and 2038 over 260,000 technicians in the aviation industry will be demanded in the Asia Pacific region only. Aviation market after COVID-19 shock Coronavirus pandemic hit the passenger aviation much worse than cargo aviation because of lockdowns and bans restricting international travel across the globe. As a result of persisting COVID-19 shocks, passenger aviation is expected to lose roughly 370 billion U.S. dollars in 2020. Even though some countries started to recover as the coronavirus spread is being contained, the desired level of recovery may take at least several quarters or years. The change of airlines’ capacity will most likely remain at least ten percent below the 2019 levels. The longer recovery periods are attributed to several factors including the COVID-19 economic recession, confidence of people to travel, and stringent travel restrictions. Therefore, some institutions forecast the aviation industry to recover at a much slower pace than what was expected.

Since the arrival of the COVID-19 pandemic in 2020, ** percent of respondents expressed their reluctance to travel in 2020, while ***** percent won't travel in 2021 until COVID-19 disappears. An additional *** percent of respondents confirmed that they would not travel until they've been vaccinated, while ** percent expressed their desire to travel within six months.

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.

Context

This data set was retrieved from the Transtats webpage of the Bureau of Transportation Statistics of the US Department of Transportation. This data was cleaned and made ready for use in an university project where the goal was to compare different database engines in terms of performance and more.

NOTE: December 2020 was not included in the data set since it was not made available by the BTS as of today, 18th Feb 2021.

Content

The data is split into CSV files for each year 2015 to 2020. flights.csv contains all the data in one file. The CSV files contain no headers for the columns. The headers are as follows:

'YEAR', 'MONTH', 'DAY_OF_MONTH', 'DAY_OF_WEEK', 'OP_UNIQUE_CARRIER', 'ORIGIN_CITY_NAME',
'ORIGIN_STATE_ABR', 'DEST_CITY_NAME', 'DEST_STATE_ABR', 'CRS_DEP_TIME', 'DEP_DELAY_NEW',
'CRS_ARR_TIME', 'ARR_DELAY_NEW', 'CANCELLED', 'CANCELLATION_CODE', 'AIR_TIME', 'DISTANCE'

NOTE: The headers were removed due to the requirement of easily importing the data into SQL

Other

If you have any questions about how I retrieved/cleaned the data or anything about my project, feel free to check out my Github repository or shoot me a message.

Dataset have 5 biggest airlines in Vietnam about: flight times, flight number, net revenue, tax...

Since the arrival of the COVID-19 pandemic, ** percent of respondents did not take any airline flight in 2021. Over half of individuals decided to take a flight in the presence of the pandemic, while ** percent of them took at least a flight in 2021.

Context

Data : US Flight Data Period : February 2020

Acknowledgements

This database contains scheduled and actual departure and arrival times reported by certified U.S. air carriers that account for at least one percent of domestic scheduled passenger revenues. The data is collected by the Office of Airline Information, Bureau of Transportation Statistics (BTS).

Context

COVID-19 has severely crippled the global airline industry with air service reductions widespread throughout 2020. This dataset will aid those seeking to visualize the impact that the virus has had on the domestic United States airline industry through detailed flight delay and cancellation data.

Content

The United States Department of Transportation's (DOT) Bureau of Transportation Statistics tracks the on-time performance of domestic flights operated by large air carriers. The data collected is from January - May 2020 (will be updated soon) and contains relevant flight information (on-time, delayed, canceled, diverted flights) from the Top 10 United States flight carriers.

Note: Data is in 47 columns with full column descriptions in attached .txt file.

Acknowledgements

The flight delay and cancellation data were collected and published by the U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics.

As of March 23, 2020, including Emirates Airlines, more than ** airlines suspended their operations by 100 percent, implying all flights were cancelled. Due to coronavirus (COVID-19) outbreak, the aviation industry experiences a huge recession compared to other industries because countries have banned international and domestic travel.

This paper analyzes the impacts of COVID-19 pandemic on the United States air transportation network between March and August 2020. Despite dramatic reductions in flight and passenger volumes, the network remained robust and resilient against perturbation. Although 24% of airports closed, the reduction in network efficiency was only 5.1%, which means airlines continued to serve most destinations. A deeper analysis of airport closures reveals that 1) small peripheral airports were the most likely to be closed; 2) socio-economic and epidemiological factors characterizing the airport’s region such as income, income inequality, political leaning, and the number of observed COVID cases were not predictive of airport closure. Finally, we show that high network robustness has a downside: although emissions from United States air traffic in 2020 fell by 37.4% compared to 2019, mostly due to the drop in the number of flights, emissions per passenger doubled in the period April to August 2020 and increased eightfold in the week of April 5–11. This rise indicates inefficient use of resources by airlines.

This is the third part of flight delay prediction i.e. for the month of March. To check the first and second parts i.e. for the month of January and February, please have a look at these datasets: January Flight Delay Prediction, February Flight Delay Prediction

This data is collected from the Bureau of Transportation Statistics, Govt. of the USA. This data is open-sourced under U.S. Govt. Works. This dataset contains all the flights in the month of March 2019 and March 2020. There are more than 400,000 flights in the month of March itself throughout the United States. The features were manually chosen to do a primary time series analysis. There are several other features available on their website.

This data could well be used to predict the flight delay at the destination airport particularly for the month of March in upcoming years as the data is for March only.

Airline Delays for December 2019 and 2020. Description Summary Data counts for airline per carrier per US City.

Usage airline_delay Format A data frame with 3351 rows and 21 variables.

year Year data collected

month Numeric representation of the month

carrier Carrier.

carrier_name Carrier Name.

airport Airport code.

airport_name Name of airport.

arr_flights Number of flights arriving at airport

arr_del15 Number of flights more than 15 minutes late

carrier_ct Number of flights delayed due to air carrier. (e.g. no crew)

weather_ct Number of flights due to weather.

nas_ct Number of flights delayed due to National Aviation System (e.g. heavy air traffic).

security_ct Number of flights canceled due to a security breach.

late_aircraft_ct Number of flights delayed as a result of another flight on the same aircraft delayed

arr_cancelled Number of cancelled flights

arr_diverted Number of flights that were diverted

arr_delay Total time (minutes) of delayed flight.

carrier_delay Total time (minutes) of delay due to air carrier

weather_delay Total time (minutes) of delay due to inclement weather.

nas_delay Total time (minutes) of delay due to National Aviation System.

security_delay Total time (minutes) of delay as a result of a security issue .

late_aircraft_delay Total time (minutes) of delay flights as a result of a previous flight on the same airplane being late.

The contains flight statistics for all airports in the United States from January 2011 to December 2020. Each observation is reported by month, year, airport, and airline. Flights can be categorized as on time, delayed, canceled, or diverted. Flight delays are attributed to five causes: carrier, weather, NAS, security, and late aircraft. The data was downloaded from the Bureau of Transportation Statistics website https://www.transtats.bts.gov/OT_Delay/OT_DelayCause1.asp.

The accompanying notebook explores commercial airplane flight delays in the United States using Python's visualization capabilities in Matplotlib and Seaborn, through the lenses of seasonality, airport traffic, and airline performance.

The clean data set (delays_clean.csv) is analyzed using the following visualizations:

Bar chart Bar chart subplots Lollipop chart Tree maps Line plot Histogram Histogram subplots Horizontal stacked bar chart Ranked horizontal bar chart Box plot Pareto chart - double axis Marginal histogram Pie charts Scatter plot Violin plot Map chart Linear regression

This is the second part of flight delay prediction i.e. for the month of February. To check the first part i.e. for the month of January, please have a look at this dataset: January Flight Delay Prediction

This data is collected from the Bureau of Transportation Statistics, Govt. of the USA. This data is open-sourced under U.S. Govt. Works. This dataset contains all the flights in the month of February 2019 and February 2020. There are more than 400,000 flights in the month of February itself throughout the United States. The features were manually chosen to do a primary time series analysis. There are several other features available on their website.

This data could well be used to predict the flight delay at the destination airport particularly for the month of February in upcoming years as the data is for February only.

United States No. of Flights: SPN Terminal: Main data was reported at 3.000 Unit in 16 May 2025. This records an increase from the previous number of 2.000 Unit for 15 May 2025. United States No. of Flights: SPN Terminal: Main data is updated daily, averaging 4.000 Unit from May 2020 (Median) to 16 May 2025, with 953 observations. The data reached an all-time high of 8.000 Unit in 26 May 2023 and a record low of 0.000 Unit in 20 Sep 2020. United States No. of Flights: SPN Terminal: Main data remains active status in CEIC and is reported by U.S. Customs and Border Protection. The data is categorized under Global Database’s United States – Table US.TA: Airport Statistics: Number of Flights: by Airport. [COVID-19-IMPACT]

This dataset tracks annual total students amount from 2017 to 2020 for Flight Program

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.

United States No. of Flights: LAS Terminal: 3 data was reported at 14.000 Unit in 16 May 2025. This records a decrease from the previous number of 16.000 Unit for 15 May 2025. United States No. of Flights: LAS Terminal: 3 data is updated daily, averaging 12.000 Unit from May 2008 (Median) to 16 May 2025, with 6104 observations. The data reached an all-time high of 35.000 Unit in 05 Jan 2025 and a record low of 0.000 Unit in 09 Sep 2020. United States No. of Flights: LAS Terminal: 3 data remains active status in CEIC and is reported by U.S. Customs and Border Protection. The data is categorized under Global Database’s United States – Table US.TA: Airport Statistics: Number of Flights: by Airport. [COVID-19-IMPACT]

United States No. of Flights: CVG Terminal: Concourse B data was reported at 0.000 NA in 22 Sep 2020. This stayed constant from the previous number of 0.000 NA for 21 Sep 2020. United States No. of Flights: CVG Terminal: Concourse B data is updated daily, averaging 0.000 NA from May 2020 (Median) to 22 Sep 2020, with 69 observations. The data reached an all-time high of 0.000 NA in 22 Sep 2020 and a record low of 0.000 NA in 22 Sep 2020. United States No. of Flights: CVG Terminal: Concourse B data remains active status in CEIC and is reported by US Customs and Border Protection. The data is categorized under Global Database’s United States – Table US.TA028: Number of Flights: by Airport.

Since the arrival of the COVID-19 pandemic, ** percent of respondents in the United Kingdom took no airline flights in 2020. Of the remaining ** percent who did take flights, only ** percent of individuals took a single flight during this period.