Prediction method of aircraft scene taxiing time based on multiple regression analysis

Abstract

The invention discloses a prediction method of aircraft scene taxiing time based on multiple regression analysis. Various factors affecting the aircraft scene taxiing time are comprehensively analyzed, according to different scene taxiing time data transformation ways and independent variable screening strategies, fundamental factors which affect the scene taxiing time are extracted, aircraft scene taxiing time prediction models based on multiple linear regression analysis and multiple curve regression analysis are established, and prediction errors of the established different models are comparatively analyzed, so as to achieve important significance for enhancing the prediction ability of the aircraft scene taxiing process in an airport flight area. The prediction method of the aircraft scene taxiing time based on the multiple regression analysis disclosed by the invention can accurately and fast predict the taxiing process of any approach and departure aircraft in pre-tactical and tactical levels, and can further provide a data reference basis for normal statistical standard formulation of strategic level flights, so that the problem of lower macro prediction ability in the airport aircraft taxiing process is effectively solved.

Description

technical field [0001] The invention relates to a method for predicting taxiing time on aircraft surfaces, in particular to a method for predicting taxiing time on aircraft surfaces based on multiple linear regression analysis and multiple curve regression analysis, and belongs to the field of airport operation information perception and recognition. Background technique [0002] With the increasing topology of the airport network, the increasing density of air traffic operations, and the increasing diversity of airport control rules, the complexity of the aircraft taxiing process is also increasing. Surface taxiing time is an important standard to measure the efficiency of aircraft activities in the airport flight area, as well as the theoretical basis for formulating flight regularity statistical standards. How to accurately predict it in complex taxiing environments has become an urgent task for airport operation command departments and air traffic management departments. ...

AI Technical Summary

Problems solved by technology

However, the ability to accurately predict the taxiing time of aircraft in the airport flight area from a macro level is relatively low

Due to the lack of reliable aircraft surface taxi time prediction information, it is impossible to accurately and scientifically classify airport taxi time levels when formulating flight regularity statistical standards at the strategic level, and it is impossible to accurately classify aircraft taxiing time when controlling airport flight area activities at the pre-tactical and tactical levels Macroscopic measurement of the process

[0004] At present, there are very few technical achievements in the prediction of aircraft taxiing process at home and abroad, and there are still some gaps in the field

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