A method for predicting the risk of large-area flight delays at airports

Abstract

The invention discloses a method for predicting the large-area delay risk of airport flights, which specifically includes the following steps: collecting historical data and performing preprocessing; using random forest algorithm to screen key factors; performing category feature numericalization and normalization on the screened data In one process, random sampling is divided into training set and test set; for the above data, use canopy clustering for rough clustering to find the initial cluster; use the cluster found by canopy as the k value, and use the K-means algorithm for multi-feature fine clustering; calculate the occurrence probability of large-area delays in each cluster; classify by inputting airport weather and flight information in a certain hour, and realize the risk prediction of large-area delays in airport flights in that hour. The invention can more accurately predict the risk of large-area delays in the short term at the airport; it improves the disadvantage of only predicting delays for a single flight, and can comprehensively consider many influencing factors to improve the accuracy of prediction; it solves the current risk of large-area delays in airport flights Forecasting problems.

Description

technical field [0001] The invention belongs to the technical field of civil aviation, and in particular relates to a method for predicting the large-area delay risk of airport flights. Background technique [0002] With the rapid development of the civil aviation industry, the problem of delays has become increasingly prominent. There are many reasons for delays, which can be roughly divided into airline reasons, weather reasons and traffic flow control. Flight delays not only have a great negative impact on airlines and passengers, but also have a negative impact on the normal operation of airports. Accurately predicting the risk of large-scale delays at airports is of great practical significance for timely adoption of targeted strategies. The current delay prediction is mainly divided into: delay time prediction, delay quantity prediction and delay level prediction according to the prediction content; according to the prediction object, it is mainly divided into: single...

AI Technical Summary

Problems solved by technology

[0003] However, most of the current forecasts are regression forecasts based on historical data; and most of them are forecasted for a single flight, lacking an overall risk forecast for airport delays within a certain period of time; most of the forecasts are specific forecasts for a single factor of delay, such as Time, quantity, grade, without a risk forecast for the probability of delay

However, in actual operation, large-scale delays at airports are caused by the combined effects of many flights and many influencing factors, and the situation is complex and changeable.

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