期刊
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
卷 37, 期 5, 页码 6029-6037出版社
IOS PRESS
DOI: 10.3233/JIFS-179185
关键词
Flight delay; civil aviation air traffic network; spatiotemporal distribution prediction; deep learning; convolutional long short-term memory network (conv-LSTM)
资金
- National Natural Science Foundation of China [U1733203]
Along with the rapid increasement of flights and projects of extending and building airports, the probability of flight delays is also increasing. People begin to pay more attention to the prediction of flight delays in a large civil aviation air traffic network. In this paper, we employ a deep learning (DL) model-the convolutional long short-term memory network (conyLSTM), 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.
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