Deep spatio-temporal convolutional networks for flight requirements prediction
LIN Youfang
KANG Youyin
WAN Huaiyu
WU Lina
ZHANG Yuxiang
Abstract：The changes of users' query volume in online fight ticketing systems indicate the changes of requirements in civil aviation market.By analyzing users' online query behaviors,we can accurately predict flight requirements,which is very conducive for airlines and agencies to take effective marketing actions immediately.In this paper,we propose a deep-learning-based approach,called DSTCN-FRP,to forecast flight requirements.We first transform time series data of users' query volumes into grid map,then design multi-layer convolution neural network to capture the time and space dependency between user requirements and query data.In addition,we further add external factors,such as weather and day of the week,to predict a period of time series of flight requirements in the future.Experiments on a real-world users' query dataset collected from an online ticketing site demonstrate that the proposed DSTCN-FRP outperforms other existing forecasting methods,where its MAE falls by 15％ to 50％ than other methods and RMSE falls by 12％ to 28％.
Keywords：flight requirement predictiononline flight ticket querytime series curveconvolutional neural networks
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 1-8 )