Airport delay prediction model based on regional residual and LSTM network
Nowadays,the civil aviation industry has a high precision requirement of airport delay prediction,so an airport delay prediction model based on the RR-LSTM network was proposed.Firstly,the airport information,meteorological information and related flight information were integrated.Then,the RR-LSTM...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2019-04-01
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| Series: | Tongxin xuebao |
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| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019091/ |
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| author | Jingyi QU Meng YE Xing QU |
| author_facet | Jingyi QU Meng YE Xing QU |
| author_sort | Jingyi QU |
| collection | DOAJ |
| description | Nowadays,the civil aviation industry has a high precision requirement of airport delay prediction,so an airport delay prediction model based on the RR-LSTM network was proposed.Firstly,the airport information,meteorological information and related flight information were integrated.Then,the RR-LSTM network was used to extract the features of the fused airport data set.Finally,the Softmax classifier was adopted to classify and predict the airport delay.The proposed RR-LSTM network model can not only extract the time correlation of airport delay data effectively,but also avoid the gradient disappearance problem of deep LSTM network.The experimental results indicate that the RR-LSTM network model has a prediction accuracy of 95.52%,which achieves better prediction results than the traditional network model.The prediction accuracy can be improved about 11% by fusing the weather information and the flight information of the airport. |
| format | Article |
| id | doaj-art-16f7b79ce72e46e9823df35f5e4c7f65 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2019-04-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-16f7b79ce72e46e9823df35f5e4c7f652025-01-14T07:16:46ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2019-04-014014915959726554Airport delay prediction model based on regional residual and LSTM networkJingyi QUMeng YEXing QUNowadays,the civil aviation industry has a high precision requirement of airport delay prediction,so an airport delay prediction model based on the RR-LSTM network was proposed.Firstly,the airport information,meteorological information and related flight information were integrated.Then,the RR-LSTM network was used to extract the features of the fused airport data set.Finally,the Softmax classifier was adopted to classify and predict the airport delay.The proposed RR-LSTM network model can not only extract the time correlation of airport delay data effectively,but also avoid the gradient disappearance problem of deep LSTM network.The experimental results indicate that the RR-LSTM network model has a prediction accuracy of 95.52%,which achieves better prediction results than the traditional network model.The prediction accuracy can be improved about 11% by fusing the weather information and the flight information of the airport.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019091/regional residual networklong short term memory networkairport delay predictionfeature extraction |
| spellingShingle | Jingyi QU Meng YE Xing QU Airport delay prediction model based on regional residual and LSTM network Tongxin xuebao regional residual network long short term memory network airport delay prediction feature extraction |
| title | Airport delay prediction model based on regional residual and LSTM network |
| title_full | Airport delay prediction model based on regional residual and LSTM network |
| title_fullStr | Airport delay prediction model based on regional residual and LSTM network |
| title_full_unstemmed | Airport delay prediction model based on regional residual and LSTM network |
| title_short | Airport delay prediction model based on regional residual and LSTM network |
| title_sort | airport delay prediction model based on regional residual and lstm network |
| topic | regional residual network long short term memory network airport delay prediction feature extraction |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2019091/ |
| work_keys_str_mv | AT jingyiqu airportdelaypredictionmodelbasedonregionalresidualandlstmnetwork AT mengye airportdelaypredictionmodelbasedonregionalresidualandlstmnetwork AT xingqu airportdelaypredictionmodelbasedonregionalresidualandlstmnetwork |