Flight Delay Prediction Model Based on Deep SE-DenseNet

Renbiao WU; Ting ZHAO; Jingyi QU

doi:10.11999/JEIT180644

Volume 41 Issue 6

Jun. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(6): 1510-1517

Renbiao WU, Ting ZHAO, Jingyi QU. Flight Delay Prediction Model Based on Deep SE-DenseNet[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1510-1517. doi: 10.11999/JEIT180644

Citation:

Renbiao WU, Ting ZHAO, Jingyi QU. Flight Delay Prediction Model Based on Deep SE-DenseNet[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1510-1517. doi: 10.11999/JEIT180644

Renbiao WU, Ting ZHAO, Jingyi QU. Flight Delay Prediction Model Based on Deep SE-DenseNet[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1510-1517. doi: 10.11999/JEIT180644

Citation:

Renbiao WU, Ting ZHAO, Jingyi QU. Flight Delay Prediction Model Based on Deep SE-DenseNet[J]. Journal of Electronics & Information Technology, 2019, 41(6): 1510-1517. doi: 10.11999/JEIT180644

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Flight Delay Prediction Model Based on Deep SE-DenseNet

doi: 10.11999/JEIT180644

Tianjin Key Laboratory of Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China

Funds: The National Natural Science Foundation of China (U1833105), The Tianjin Key Laboratory of Advanced Signal Processing Open Project (2017ASP-TJ01)

Received Date: 2018-07-02
Rev Recd Date: 2018-11-16

Available Online: 2018-12-04

Publish Date: 2019-06-01

Abstract

Abstract

Nowadays, the civil aviation industry has a high-precision prediction demand of flight delays, thus a flight delay prediction model based on the deep SE-DenseNet is proposed. Firstly, flight data, associated airport delay information and meteorological data are fused in the model. Then, the improved SE-DenseNet algorithm is used to extract feature automatically based on the fused flight data set. Finally, the softmax classifier is used to predict the delay level of flight. The proposed SE-DenseNet, combing the advantages of DenseNet and SENet, can not only enhance the transmission of deep information, avoid the problem of vanishing gradients, but also achieve feature recalibration by the feature extraction process. The results indicate that after data fusion, the accuracy of the model is improved 1.8% than only considering the characteristics of the flight itself. The improved algorithm can effectively improve the network performance. The final accuracy of the model reaches 93.19%.
- Flight delay prediction,
- SE-DenseNet,
- Data fusion,
- Feature recalibration

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References(16)

References

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