Advanced Search
Volume 43 Issue 1
Jan.  2021
Turn off MathJax
Article Contents
Renbiao WU, Yaqian ZHAO, Jingyi QU, Aiguo GAO, Wenxiu CHEN. Flight Delay Propagation Prediction Model Based on CBAM-CondenseNet[J]. Journal of Electronics & Information Technology, 2021, 43(1): 187-195. doi: 10.11999/JEIT190794
Citation: Renbiao WU, Yaqian ZHAO, Jingyi QU, Aiguo GAO, Wenxiu CHEN. Flight Delay Propagation Prediction Model Based on CBAM-CondenseNet[J]. Journal of Electronics & Information Technology, 2021, 43(1): 187-195. doi: 10.11999/JEIT190794

Flight Delay Propagation Prediction Model Based on CBAM-CondenseNet

doi: 10.11999/JEIT190794
Funds:  The National Natural Science Foundation of China (U1833105), The Tianjin Natural Science Foundation(19JCYBJC15900)
  • Received Date: 2019-10-16
  • Rev Recd Date: 2020-06-17
  • Available Online: 2020-07-19
  • Publish Date: 2021-01-15
  • For the problem of flight delay propagation caused by flight delay, a flight delay wave prediction model based on CBAM-CondenseNet is presented. Firstly, by analyzing the delays propagation in the aviation network caused by flight delays, the flight chain affected by the pre-order delays is determined; Secondly, the selected flight chain data is cleaned and the flight information and airport information are fused; Finally, an improved CBAM-CondenseNet algorithm is proposed to extract the number of fused flights. According to feature extraction, a Softmax classifier is constructed to predict the delays of the first departure flights and the subsequent flights. The CBAM-CondenseNet algorithm proposed in this paper combines the advantages of CondenseNet and CBAM, and uses channel and spatial attention mechanism to enhance the transmission of deep information in network structure. The experimental results show that the improved algorithm can effectively improve the network performance, and the prediction accuracy can reach 97.55%.

  • loading
  • 丁建立, 陈坦坦, 刘玉洁. 有色-时间Petri网航班延误模型与波及分析[J]. 计算机集成制造系统, 2008, 14(12): 2334–2340. doi: 10.13196/j.cims.2008.12.48.dingjl.001

    DING Jianli, CHEN Tantan, and LIU Yujie. Colored-timed Petri nets model of flight delays and propagated analysis[J]. Computer Integrated Manufacturing Systems, 2008, 14(12): 2334–2340. doi: 10.13196/j.cims.2008.12.48.dingjl.001
    刘玉洁, 何丕廉, 刘春波, 等. 基于贝叶斯网络的航班延误波及研究[J]. 计算机工程与应用, 2006, 44(17): 242–245. doi: 10.3778/j.issn.1002-8331.2008.17.072

    LIU Yujie, HE Pilian, LIU Chunbo, et al. Flight delay propagation research based on Bayesian net[J]. Computer Engineering and Applications, 2006, 44(17): 242–245. doi: 10.3778/j.issn.1002-8331.2008.17.072
    PYRGIOTIS N, MALONE K M, and ODONI A. Modelling delay propagation within an airport network[J]. Transportation Research Part C: Emerging Technologies, 2013, 27: 60–75. doi: 10.1016/j.trc.2011.05.017
    邵荃, 朱燕, 贾萌, 等. 基于复杂网络理论的航班延误波及分析[J]. 航空计算技术, 2015, 45(4): 24–28. doi: 10.3969/j.issn.1671-654X.2015.04.007

    SHAO Quan, ZHU Yan, JIA Meng, et al. Analysis of flight delay propagation based on complex network theory[J]. Aeronautical Computing Technique, 2015, 45(4): 24–28. doi: 10.3969/j.issn.1671-654X.2015.04.007
    CAMPANELLI B, FLEURQUIN P, ARRANZ A, et al. Comparing the modeling of delay propagation in the US and European air traffic networks[J]. Journal of Air Transport Management, 2016, 56: 12–18. doi: 10.1016/j.jairtraman.2016.03.017
    WU Weiwei and WU C L. Enhanced delay propagation tree model with Bayesian network for modelling flight delay propagation[J]. Transportation Planning and Technology, 2018, 41(3): 319–335. doi: 10.1080/03081060.2018.1435453
    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
    KHANMOHAMMADI S, TUTUN S, and KUCUK Y. A new multilevel input layer artificial neural network for predicting flight delays at JFK airport[J]. Procedia Computer Science, 2016, 95: 237–244. doi: 10.1016/j.procs.2016.09.321
    TAKEICHI N, KAIDA R, SHIMOMURA A, et al. Prediction of delay due to air traffic control by machine learning[C]. AIAA Modeling and simulation Technologies Conference. Grapevine, USA, 2017: 191–199.
    HUANG Gao, LIU Shichen, VAN DER MAATEN L, et al. CondenseNet: An efficient DenseNet using learned group convolutions[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 2752–2761.
    WOO S, PARK J, LEE J Y, et al. CBAM: Convolutional block attention module[C].The 15th European Conference on Computer Vision, Munich, Germany, 2018: 3–19.
    盖杉, 鲍中运. 基于改进深度卷积神经网络的纸币识别研究[J]. 电子与信息学报, 2019, 41(8): 1992–2000. doi: 10.11999/JEIT181097

    GAI Shan and BAO Zhongyun. Banknote recognition research based on improved deep convolutional neural network[J]. Journal of Electronics &Information Technology, 2019, 41(8): 1992–2000. doi: 10.11999/JEIT181097
    RUMELHART D E, HINTON G E, and WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533–536. doi: 10.1038/323533a0
    屈景怡, 叶萌, 渠星. 基于区域残差和LSTM网络的机场延误预测模型[J]. 通信学报, 2019, 40(4): 149–159. doi: 10.11959/j.issn.1000-436x.2019091

    QU Jingyi, YE Meng, and QU Xing. Airport delay prediction model based on regional residual and LSTM network[J]. Journal on Communications, 2019, 40(4): 149–159. doi: 10.11959/j.issn.1000-436x.2019091
    吴仁彪, 赵婷, 屈景怡. 基于深度SE-DenseNet的航班延误预测模型[J]. 电子与信息学报, 2019, 41(6): 1510–1517. doi: 10.11999/JEIT180644

    WU Renbiao, ZHAO Ting, and QU Jingyi. Flight delay prediction model based on deep SE-DenseNet[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1510–1517. doi: 10.11999/JEIT180644
  • 加载中

Catalog

    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

    Figures(7)  / Tables(5)

    Article Metrics

    Article views (2657) PDF downloads(160) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return