Disclosed is a layout-variable taxiing-out time prediction system based on big data deep learning. The system comprises: a data set establishment module adapted for acquiring historical operation data and performing data cleaning to obtain a data set; an indicator defining and quantifying module adapted for defining and quantifying a traffic condition indicator of a layout traffic characteristic; a feature set extraction module adapted for analyzing and extracting a feature set, which influences layout taxiing-out time, on the basis of the data set and the traffic condition indicator; a model establishment module adapted for establishing a layout taxiing-out time prediction model by an integrated machine learning method according to the feature set; and a prediction module adapted for completing prediction of airport layout taxiing-out time by means of the layout taxiing-out time prediction model. Airport original record data is processed, modeling is performed on airport layout traffic conditions, taxiing time influence factors are analyzed and extracted, and a GBRT integrated learning model is trained, such that the taxiing-out time prediction model is obtained, and a data basis is provided for management and optimization of airport operations.
LAYOUT-VARIABLE TAXIING-OUT TIME PREDICTION SYSTEM BASED ON BIG DATA DEEP LEARNING