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In this paper, we employ a deep learning (DL) model\u2014 the convolutional long short-term memory network (conv-LSTM), to address the airport delay prediction in network structure. The spatiotemporal variables including flight delays of airport, air route congestion, airport throughput and flow control are input into an end-to-end learning architecture as a spatiotemporal sequence. The future flight delays in airport will be output by the model. Experiments show that conv-LSTM possess stronger ability to capture temporal and spatial characteristic than traditional LSTM.<\/jats:p>","DOI":"10.3233\/jifs-179185","type":"journal-article","created":{"date-parts":[[2019,6,11]],"date-time":"2019-06-11T11:36:59Z","timestamp":1560253019000},"page":"6029-6037","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":24,"title":["A deep learning approach to predict the spatial and temporal distribution of flight delay in network"],"prefix":"10.1177","volume":"37","author":[{"given":"Yi","family":"Ai","sequence":"first","affiliation":[{"name":"Civil Aviation Flight University of China, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijun","family":"Pan","sequence":"additional","affiliation":[{"name":"Civil Aviation Flight University of China, 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