北京交通大学学报2018，Vol.42Issue(2)：1-8,8.DOI:10.11860/j.issn.1673-0291.2018.02.001
基于深度时空卷积网络的民航需求预测
Deep spatio-temporal convolutional networks for flight requirements prediction
摘要
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％.关键词
民航需求预测/在线机票查询/时间序列曲线/卷积神经网络Key words
flight requirement prediction/online flight ticket query/time series curve/convolutional neural networks分类
信息技术与安全科学引用本文复制引用
林友芳,康友隐,万怀宇,吴丽娜,张宇翔..基于深度时空卷积网络的民航需求预测[J].北京交通大学学报,2018,42(2):1-8,8.基金项目
国家自然科学基金(61603028,U1533104)National Natural Science Foundation of China(61603028,U1533104) （61603028,U1533104）
