The U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics (BTS) tracks the on-time performance of domestic flights operated by large air carriers. Summary information on the number of on-time, delayed, canceled and diverted flights appears in DOT's monthly Air Travel Consumer Report, published about 30 days after the month's end, as well as in summary tables posted on this website. BTS began collecting details on the causes of flight delays in June 2003. Summary statistics and raw data are made available to the public at the time the Air Travel Consumer Report is released.

This version of the dataset was compiled from the Statistical Computing Statistical Graphics 2009 Data Expo and is also available here.

In 2022, Teesside International Airport had the shortest average delays for scheduled flights at 13 minutes. However, it's worth to mention that, the size of this airport was significantly smaller than some of those listed on the graph. That year, London City Airport had an average of 14 minutes of delays for scheduled flights. On the other hand, Doncaster Sheffield Airport recorded the longest delays at an average of 28 minutes.

About this Dataset

UnDelay’s technology, ?

Deciphers and simplifies radio communication between pilots, ground operators and air traffic control to determine the cause of flight delays, converts it to text and distributes/alerts it in real time to all related airline and airport departments.

?

This enables robust response action from respective departments to fix the cause of the delay faster, saving airlines millions of dollars.

? Airline benefits, ?

Reduces Flight delays

Enables faster aircraft turnarounds.

Improves ground efficiencies.

Saves Money for Airlines & Airports

Airports benefits,

Help in navigate / reduce congestion

Alerts airports on problems related to aircraft delays

Improves airport communication with ground operations

Travel companies benefits, ?

Real Time flight delays

Cause of the delays.

Improves passenger flight choices.

Find out more on www.undelayapp.com!

Category

Business Information & Financials

Keywords

flight delay,airline,airport,travel,flight disruption

Row Count

7254

Price

$10.99

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.

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 statistic shows the leading cause of flight delays by the share of total delay minutes in the U.S. from 2004 to 2019. In 2019, the leading cause of flight delays was late aircraft arrival which accounted for **** percent of the total delay minutes.

Context

Using this data you can find what caused the delay for flight whether it's Security delay, NAS delay or Carrier delay, etc.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Data Dictionary

1. DayOfWeek → 1 (Monday) - 7 (Sunday)
2. Date → Scheduled date
3. DepTime → Actual departure time (local, hhmm)
4. ArrTime → Actual arrival time (local, hhmm)
5. CRSArrTime → Scheduled arrival time (local, hhmm)
6. UniqueCarrier → Unique carrier code
7. Airline → Airline company
8. FlightNum → flight number
9. TailNum → plane tail number
10. ActualElapsedTime → Actual time an airplane spends in the air(in minutes) with TaxiIn/Out
11. CRSElapsedTime → CRS Elapsed Time of Flight (estimated elapse time), in minutes
12. AirTime → Flight Time (in minutes)
13. ArrDelay → Difference in minutes between scheduled and actual arrival time
14. Origin → Origin IATA(International Air Transport Association) airport code
15. Org_Airport → Origin Airport Name
16. Dest → Destination IATA code
17. Dest_Airport → Destination Airport Name
18. Distance → Distance between airports (miles)
19. TaxiIn → Wheels down and arrival at the destination airport gate, in minutes
20. TaxiOut → The time elapsed between departure from the origin airport gate and wheels off, in minutes
21. Cancelled → Was the flight canceled?
22. CancellationCode → Reason for cancellation
23. Diverted → 1 = yes, 0 = no
24. CarrierDelay → Flight delay due to carrier(e.g. maintenance or crew problems, aircraft cleaning, fueling, etc), 0 = No, yes = (in minutes)
25. WeatherDelay → Flight delay due to weather, 0 = No, yes = (in minutes)
26. NASDelay → Flight delay by NSA(National Aviation System), 0 = No, yes = (in minutes)
27. SecurityDelay → Flight delay by this reason, 0 = No, yes = (in minutes)
28. LateAircraftDelay → Flight delay by this reason, 0 = No, yes = (in minutes)

Acknowledgements

Inspired from others DataSet in the same domain. So, tried to create one balanced dataset ready to use for beginners. This is a public dataset so it's not Licensed by anyone.

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.

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 2009 ASA Statistical Computing and Graphics Data Expo consisted of flight arrival and departure details for all commercial flights on major carriers within the USA, from October 1987 to April 2008. This is a large dataset containing nearly 120 million records in total.

The aim of the data expo is to provide a graphical summary of important features of the data set. This is intentionally vague in order to allow different entries to focus on different aspects of the data, but here are a few ideas to get you started: •When is the best time of day, day of the week, and time of year to fly to minimise delays? •Do older planes suffer more delays? •How well does weather predict plane delays? •How does the number of people flying between different locations change over time? •Can you detect cascading failures as delays in one airport create delays in others? Are there critical links in the system? •Use the available variables to construct a model that predicts delays.

This dataset contains various metrics about 40 airports of the United State of America captured from realtime data. It includes airport delays and delay reasons as well as information about airport closure and reopening events. In addition, this dataset provides influence factors such as weather conditions, visibilty information, wind speed/direction and temperature. The dataset includes the following airports given by their IATA code: ATL, BNA, BOS, BWI, CLE, CLT, CVG, DCA, DEN, DFW, DTW, EWR, FLL, IAD, IAH, IND, JFK, LAS, LAX, LGA, MCI, MCO, MDW, MEM, MIA, MSP, ORD, PDX, PHL, PHX, PIT, RDU, SAN, SEA, SFO, SJC, SLC, STL, TEB, TPA. The data was collected from 2015-06-03 until 2015-06-10, every 15 minutes and for each of the airport above defined, by using REST API (http://services.faa.gov/airport/status) provided by the Federal Aviation Administration. API Documentation can be found at: http://services.faa.gov/ MD5 Checksum: 9aee3d984bf25f8867db3ac900442126

description: This database contains scheduled and actual departure and arrival times, reason of delay. 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).; abstract: This database contains scheduled and actual departure and arrival times, reason of delay. 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).

In 2022, roughly ** percent of domestic flights operated by Air Seoul were delayed by ** minutes or more at airports in South Korea. Both Air Seoul and Fly Gangwon were recently established airlines, but appeared on the opposite sides of the spectrum in terms of domestic flight delays.

This graph shows the average time of delays for flights departing from selected major airports across China in 2018. In that year, the average delay time for flights departing from Beijing Capital International Airport was approximately **** minutes.

In 2022, approximately *** percent of international flights operated by domestic airlines in South Korea were delayed one hour or more after the scheduled flight time. This was an increase from the previous year, and a reversal of the continued decrease in the delay rate recorded from 2018 to 2021.

In the fiscal year 2023, Japan Transocean Air Co., Ltd. (JTA) delayed **** percent of its executed domestic flights in Japan, therefore outperforming all other airlines in Japan. The average among all scheduled flights by designated domestic air carriers was around ** percent.

Dataset

This dataset was created by Ioana Gheorghiu

Contents

In 2024, more than half of air passengers who experienced travel disruptions also received information on the reason for this disruption. Passengers traveling in 2020 and 2021 after the outbreak of the COVID-19 pandemic, had reported that they had received information on the cause of travel disruptions far more regularly, in around 73 to 74 percent of cases.

As of August 2019, roughly 24 percent of all flights of the Scandinavian Airline Norwegian Air Shuttle ASA, or shorter Norwegian, were delayed. That was an increase of approximately two percent compared to the same month a year earlier. The highest rate of delayed flights was in July 2019 (35.82 percent) and the lowest one was in April 2019 (18.88 percent)

Delayed flights in contrast to flights arriving on time

Here, delayed flights are all flights with a delay of at least 15 minutes after the expected time of arrival. Flights arriving on time, in contrast, are flights that arrive in 15 minutes or less after it was planned. In comparison to another Nordic airline – SAS Scandinavian Airlines – the punctuality rate of Norwegian was lower, amounting to 75.85 percent in August 2019.

Norwegian Air Shuttle is one of leading Nordic airlines

After the Swedish SAS Scandinavian Airlines, the low-cost carrier Norwegian Air Shuttle ranked second among the most profitable airlines in the Nordic countries as of 2018. The company’s revenue reached roughly 2.9 billion euros that year. According to another source, their highest income was gained within the international market.

Statistics on the time of flight delay and passenger disturbance at Shenzhen Airport, 2018.

In 2023, only 24 percent of passengers in the United Kingdom were very or fairly satisfied with the resolution of postponing or cancellation of their flight. Meanwhile, 45 percent were very dissatisfied with the handling of this situation.