In this article, we will talk about how to predict flight delays with an API and the solutions Aviation Edge APIs offer to achieve this. All the information you are looking for to get started is in one place!
Predicting flight delay for the future depends on the average historical delay rates. Aviation Edge Flight Delay API provides delay data on a historical date or a date range and allows filtering this data based on many factors such as the airline, flight number, airport, flight status, and more. This allows you to collect the data based on an airline, a specific flight, or an airport and determine averages and predictions for market analysis.
The Flight Delay API has global coverage for airports with the exception of private and military airfields and some airports with very little traffic (for example 3-5 flights daily). Every flight in the output has delay data and if the flight was not delayed, the value is “null”. Delay values are in minutes.
Tip
The API can return one airport’s schedule at a time and covers the complete schedule of the requested date in one API call. That’s one of the main advantages of the Aviation Edge Flight Delay API. The API consumes only 1 call without matter the number of flights in the response. Even if there are 5 or 2000 flights in the output, both count as 1 call so you make the best out of your API plan!
Feel free to visit the documentation or the GitHub repo for full input, output, and filter information!
The Flight Delay API also includes the latest recorded flight status for each flight in the response. This means, if a flight was canceled on the given date, the status will appear as “cancelled”. It is possible to filter flights based on the flight status, so with the right parameters in the input, the API can return you:
which is the necessary data to predict average cancellation rates, just like average delays.
The short answer to this would be “not directly”.
However, the Flight Delay Prediction API makes it very easy to predict the future average delay of an airline, a specific route, or all flights of an airport within a certain period in the past – let’s say 1 year backward from the current day. Simply request the flights via the API on the targeted period and analyze how many times they operated and how many times of these were they delayed, and of course, for how long. This will give you a pretty clear understanding of the average delay rates from the current day forward and answer “how to predict flight delays with an API”.
You can always expand the data as the data expands over time too and build a more and more solid average delay rate for future flight delay prediction.
Tip
The date range you can request in 1 API call is normally 30 days at the most, but this can be shorter (down to 3-5 days) if the airport you requested is a busy one with higher traffic, and therefore the data the Flight Delay Prediction API is supposed to return.
In these cases, you may simply make multiple API calls covering shorter periods of time to complete your range.
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