-
Advanced Search

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

Flight Delay Prediction Model Based on Deep SE-DenseNet

  • Corresponding author: Jingyi QU, qujingyicauc@163.com
  • Received Date: 2018-07-02
    Accepted Date: 2018-11-16
    Available Online: 2019-06-01

Figures(5) / Tables(8)

  • 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%.
  • 加载中
    1. [1]

      BASPINAR B, URE N K, KOYUNCU E, et al. Analysis of delay characteristics of European air traffic through a data-driven airport-centric queuing network model[J]. IFAC-PapersOnLine, 2016, 49(3): 359–364. doi: 10.1016/j.ifacol.2016.07.060

    2. [2]

      KHANMOHAMMAD S, TUTUN S, and KUCUK Y. A new multilevel input layer artificial neural network for predicting flight delays at JFK airport[C]. Complex Adaptive Systems, Los Angeles, USA, 2016: 237–244.

    3. [3]

      程华, 李艳梅, 罗谦, 等. 基于C4.5决策树方法的到港航班延误预测问题研究[J]. 系统工程理论与实践, 2014, 34(S1): 239–247. doi: 10.12011/1000-6788(2014)s1-239
      CHENG Hua, LI Yanmei, LUO Qian, et al. Study on flight delay with C4.5 decision tree based prediction method[J]. System Engineering – Theory &Practice, 2014, 34(S1): 239–247. doi: 10.12011/1000-6788(2014)s1-239

    4. [4]

      徐涛, 丁建立, 顾彬, 等. 基于增量式排列支持向量机的机场航班延误预警[J]. 航空学报, 2009, 30(7): 1256–1263. doi: 10.3321/j.issn:1000-6893.2009.07.014
      XU Tao, DING Jianli, GU Bin, et al. Forecast warning level of flight delays based on incremental ranking support vector machine[J]. Acta Aeronautica et Astronautica Sinica, 2009, 30(7): 1256–1263. doi: 10.3321/j.issn:1000-6893.2009.07.014

    5. [5]

      MANNA S, BISWAS S, KUNDU R, et al. A statistical approach to predict flight delay using gradient boosted decision tree[C]. 2017 International Conference on Computational Intelligence in Data Science, Chennai, India, 2017: 1–5.

    6. [6]

      KIM Y J, CHOI S, BRICENO S, et al. A deep learning approach to flight delay prediction[C]. 35th Digital Avionics Systems Conference, Sacramento, USA, 2016: 1–6.

    7. [7]

      LECUN Y, BENGIO Y, and HINTON G E. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539

    8. [8]

      HUANG Gao, LIU Zhuang, and WEINBERGER K Q. Densely connected convolutional networks[C]. 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, USA, 2017: 2261–2269.

    9. [9]

      HU Jie, SHEN Li, and SUN Gang. Squeeze-and-excitation networks[OL]. https://arxiv.org/pdf/1709.01507.pdf, 2018.4.

    10. [10]

      IOFFE S and SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. 32nd International Conference on Machine Learning, Lile, France, 2015: 448–456.

    11. [11]

      NAIR V and HINTON G E. Rectified linear units improve restricted boltzmann machines[C]. 27th International Conference on Machine Learning, Haifa, Israel, 2010: 807–814.

    12. [12]

      RUMELHART D E, HINTON G E, and WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(9): 533–536. doi: 10.1038/323533a0

    13. [13]

      DUAN Kaibo, KEERTHI S S, CHU Wei, et al. Multi-category classification by soft-max combination of binary classifiers[C]. 4th International Workshop on Multiple Classifier Systems, Guildford, United Kingdom, 2003: 125–134.

    14. [14]

      SHEN Li, LIN Zhouchen, and HUANG Qingming. Relay backpropagation for effective learning of deep convolutional neural networks[C]. European Conference on Computer Vision, Amsterdam, The Netherlands, 2016, 467–482. doi: https://doi.org/10.1007/978-3-319-46478-7_29.

    15. [15]

      HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[C]. 15th IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1026–1034.

    16. [16]

      吴仁彪, 李佳怡, 屈景怡. 基于双通道卷积神经网络的航班延误预测模型[J]. 计算机应用, 2018, 38(7): 2100–2106. doi: 10.11772/j.issn.1001-9081.2018010037
      WU Renbiao, LI Jiayi, and QU Jingyi. Flight delay prediction based on dual-channel convolutional neural networks[J]. Journal of Computer Applications, 2018, 38(7): 2100–2106. doi: 10.11772/j.issn.1001-9081.2018010037

  • 加载中
    1. [1]

      Shouhua WANGMingchi LUXiyan SUNYuanfa JIDingmei HU . IBeacon/INS Data Fusion Location Algorithm Based on Unscented Kalman Filter. Journal of Electronics and Information Technology, 2019, 41(9): 2209-2216. doi: 10.11999/JEIT180748

    2. [2]

      Chao WUYaqian LIYaru ZHANGBing LIU . Correntropy-based Fusion Extreme Learning Machine forRepresentation Level Feature Fusion and Classification. Journal of Electronics and Information Technology, 2019, 41(0): 1-8. doi: 10.11999/JEIT190186

    3. [3]

      You MAShuze JIAXiangang ZHAOXiaohu FENGCunqun FANAijun ZHU . Missing Telemetry Data Prediction Algorithm via Tensor Factorization. Journal of Electronics and Information Technology, 2019, 41(0): 1-7. doi: 10.11999/JEIT180728

    4. [4]

      Fei ZHAOWenkai ZHANGZhiyuan YANHongfeng YUWenhui DIAO . Multi-feature Map Pyramid Fusion Deep Network for Semantic Segmentation on Remote Sensing Data. Journal of Electronics and Information Technology, 2019, 41(10): 2525-2531. doi: 10.11999/JEIT190047

    5. [5]

      Yanjing SUNYunkai SHIXiao YUNXuran ZHUSainan WANG . Adaptive Strategy Fusion Target Tracking Based on Multi-layer Convolutional Features. Journal of Electronics and Information Technology, 2019, 41(10): 2464-2470. doi: 10.11999/JEIT180971

    6. [6]

      Zhengyi LIUQuntao DUANSong SHIPeng ZHAO . RGB-D Image Saliency Detection Based on Multi-modal Feature-fused Supervision. Journal of Electronics and Information Technology, 2019, 41(0): 1-8. doi: 10.11999/JEIT190297

    7. [7]

      Yuan WU . An Airborne SAR Image Target Location Algorithm Based on Parameter Refining. Journal of Electronics and Information Technology, 2019, 41(5): 1063-1068. doi: 10.11999/JEIT180564

    8. [8]

      Di XIONGJunling WANGLizhi ZHAOShan ZHONGMeiguo GAO . Unitary ESPRIT Based Multiband Fusion ISAR Imaging. Journal of Electronics and Information Technology, 2019, 41(2): 285-292. doi: 10.11999/JEIT180438

    9. [9]

      Huilan LUOFei LUYuan YAN . Action Recognition Based on Multi-model Voting with Cross Layer Fusion. Journal of Electronics and Information Technology, 2019, 41(3): 649-655. doi: 10.11999/JEIT180373

    10. [10]

      Xiaoni DUHongxia LÜRong WANG . Construction of a Class of Linear Codes with Four-weight and Six-weight. Journal of Electronics and Information Technology, 2019, 41(0): 1-5. doi: 10.11999/JEIT180939

    11. [11]

      Yihua ZHOUWen JIYuguang YANG . Database Ciphertext Retrieval Scheme Based on f-mOPE. Journal of Electronics and Information Technology, 2019, 41(8): 1793-1799. doi: 10.11999/JEIT180805

    12. [12]

      Zengwei LÜZhenchun WEIJianghong HANRenhao SUNChengkai XIA . A Mobile Charging and Data Collecting Algorithm Based on Multi-objective Optimization. Journal of Electronics and Information Technology, 2019, 41(8): 1877-1884. doi: 10.11999/JEIT180897

    13. [13]

      Deke TANGFeng WANGHongqi WANG . Single-polarization SAR Data Flood Water Detection Method Based on Markov Segmentation. Journal of Electronics and Information Technology, 2019, 41(3): 619-625. doi: 10.11999/JEIT180420

    14. [14]

      Hao WANGXiaonan XUQiming MA . A Robust Broadband Interference Suppression Algorithm Based on Few Snapshots. Journal of Electronics and Information Technology, 2019, 41(4): 851-857. doi: 10.11999/JEIT180505

    15. [15]

      Zhiping ZHOUZhicong LI . Data Anonymous Collection Protocol without Trusted Third Party. Journal of Electronics and Information Technology, 2019, 41(6): 1442-1449. doi: 10.11999/JEIT180595

    16. [16]

      Guosheng ZHAOHui ZHANGJian WANG . A Mobile Crowdsensing Data Security Delivery Model Based on Tangle Network. Journal of Electronics and Information Technology, 2019, 41(0): 1-7. doi: 10.11999/JEIT190370

    17. [17]

      Fangling ZENGXiaofeng OUYANGHao XUDaqian LÜ . Improved Long-code Direct Acquisition Algorithm Based on Time-frequency Fusion. Journal of Electronics and Information Technology, 2019, 41(2): 309-316. doi: 10.11999/JEIT180119

    18. [18]

      Haoran ZHUYunqing LIUWenying ZHANG . Night-vision Image Fusion Based on Intensity Transformation and Two-scale Decomposition. Journal of Electronics and Information Technology, 2019, 41(3): 640-648. doi: 10.11999/JEIT180407

    19. [19]

      Li ZHOUXinming ZHANGWeizhen GUOYan WANG . A Direct Fusion Algorithm for Multiple Pieces of Evidence Based on Improved Conflict Measure. Journal of Electronics and Information Technology, 2019, 41(5): 1145-1151. doi: 10.11999/JEIT180578

    20. [20]

      Ying CHENDandan HE . Spatial-temporal Stream Anomaly Detection Based on Bayesian Fusion. Journal of Electronics and Information Technology, 2019, 41(5): 1137-1144. doi: 10.11999/JEIT180429

Metrics
  • PDF Downloads(48)
  • Abstract views(1429)
  • HTML views(515)
  • Cited By(0)

通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

/

DownLoad:  Full-Size Img  PowerPoint
Return