Prediction on Import and Export Goods Volume of Ports Based on Seq2Sseq Model
CSTR:
Author:
  • Article
  • | |
  • Metrics
  • |
  • Reference [35]
  • |
  • Related [20]
  • | | |
  • Comments
    Abstract:

    The port amount of import and export goods can reflect the congestion of port flow, whose accurate prediction would provide suggestions for port management to make reasonable decisions. In this study, the Seq2Seq model in the field of machine translation is used to model various factors that affect the amount of goods inflow and outflow from the port. An Seq2Seq model can reflect the change of the amount of import and export goods in the time dimension and describe the influence of external factors such as weather and holidays, so as to make accurate predictions. An Seq2Seq model consists of two LSTM, respectively acting as an encoder and a decoder. It can capture the changing trend of containers in the short and long term and predict the amount of goods in the future based on historical import and export volume. Experiments were carried out on a real-world dataset of import and export containers in Tianjin Port. The experimental result reveals that the deep learning prediction model based on Seq2Seq is more effective and efficient than traditional time series model as well as other existing machine learning prediction models.

    Reference
    [1] 岳雷,余丽波,周健,等.全国主要港口一季度综述.中国远洋海运, 2019,(4):86
    [2] 王宇,杨磊.智慧港口建设推动天津港集装箱板块提质增效.中国港口, 2019,(2):23-26
    [3] 刘长俭.发挥港口优势,高质量建设北方国际航运核心区.产业创新研究, 2018,(7):23-27
    [4] 吕晓涵."一带一路"战略下天津港转型与发展.经贸实践, 2018,(9):100, 102
    [5] 佚名.中国主要港口集装箱码头吞吐量快报.集装箱化, 2019, 30(2):32-33
    [6] Zhang J, Man KF. Time series prediction using RNN in multi-dimension embedding phase space. Proceedings of 1998 IEEE International Conference on Systems, Man, and Cybernetics. San Diego, CA, USA. 1998. 1868-1873.
    [7] Huang ZH, Xu W, Yu K. Bidirectional LSTM-CRF models for sequence tagging. arXiv:1508.01991, 2015.
    [8] Cho K, Van Merrienboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078, 2014.
    [9] Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada. 2014. 3104-3112.
    [10] Luong T, Pham H, Manning CD. Effective approaches to attention-based neural machine translation. Proceedings of 2015 Conference on Empirical Methods in Natural Language Processing. Lisbon, Portugal. 2015. 1412-1421.
    [11] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473, 2014.
    [12] Hamilton JD. Time Series Analysis. New Jersey:Princeton University Press, 1994. E
    [13] Box GEP, Jenkins GM, Reinsel GC, et al. Time Series Analysis:Forecasting and Control. 5th ed. Hoboken:John Wiley and Sons, 2015.
    [14] Polson N, Sokolov V. Deep learning predictors for traffic flows. arXiv:1604.04527, 2016.
    [15] Wu YK, Tan HC. Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework. arXiv:1612.01022, 2016.
    [16] Williams BM, Hoel LA. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process:Theoretical basis and empirical results. Journal of Transportation Engineering, 2003, 129(6):664-672.[doi:10.1061/(ASCE)0733-947X (2003)129:6(664)
    [17] Van der Voort M, Dougherty M, Watson S. Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transportation Research Part C:Emerging Technologies, 1996, 4(5):307-318.[doi:10.1016/S0968-090X (97)82903-8
    [18] Bolshinsky E, Friedman R. Traffic flow forecast survey. Computer Science Department, Technion, 2012
    [19] Kamarianakis Y, Prastacos P. Space-time modeling of traffic flow. Computers&Geosciences, 2005, 31(2):119-133
    [20] Williams G, Baxter R, He HX, et al. A comparative study of RNN for outlier detection in data mining. Proceedings of 2002 IEEE International Conference on Data Mining. Maebashi City, Japan. 2002. 709-712.
    [21] 王大荣,张忠占.线性回归模型中变量选择方法综述.数理统计与管理, 2010, 29(4):615-627
    [22] 杨学兵,张俊.决策树算法及其核心技术.计算机技术与发展, 2007, 17(1):43-45.[doi:10.3969/j.issn.1673-629X.2007.01.015
    [23] 王文.线性回归结合季节性复合序列的深圳港集装箱吞吐量预测.中国水运, 2012, 12(12):23-25, 27
    [24] 林慧君,徐荣聪.组合ARMA与SVR模型的时间序列预测.计算机与现代化, 2009,(8):19-22.[doi:10.3969/j.issn.1006-2475.2009.08.006
    [25] 鲁渤,杨显飞,汪寿阳.基于情境变动的港口吞吐量预测模型.管理评论, 2018, 30(1):195-201
    [26] 范莹莹,余思勤.基于NARX神经网络的港口集装箱吞吐量预测.上海海事大学学报, 2015, 36(4):1-5
    [27] 张树奎,肖英杰,鲁子爱.基于灰色神经网络的港口集装箱吞吐量预测模型研究.重庆交通大学学报(自然科学版), 2015, 34(5):135-138
    [28] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature, 2015, 521(7553):436-444.[doi:10.1038/nature14539
    [29] LeCun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11):2278-2324.[doi:10.1109/5.726791
    [30] Williams RJ, Zipser D. A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1989, 1(2):270-280.[doi:10.1162/neco.1989.1.2.270
    [31] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8):1735-1780.[doi:10.1162/neco.1997.9.8.1735
    [32] Sutskever I, Martens J, Hinton G. Generating text with recurrent neural networks. Proceedings of the 28th International Conference on Machine Learning. Bellevue, WA, USA. 2011. 1017-1024.
    [33] Vinyals O, Ravuri S, Povey D. Revisiting recurrent neural networks for robust ASR. Proceedings of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. Kyoto, Japan. 2012. 4085-4088.
    [34] Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv:1409.0473.[2016-05-19/2018-07-05].
    [35] Cai YK. Study on performance of TCP in wireless networks. Proceedings of the 20184th International Conference on Systems, Computing, and Big Data. UK. 2018:5.
    Cited by
    Comments
    Comments
    分享到微博
    Submit
Get Citation

王涛,张伟,贾宇欣,林友芳,万怀宇.基于Seq2Seq模型的港口进出口货物量预测.计算机系统应用,2020,29(3):132-139

Copy
Share
Article Metrics
  • Abstract:2049
  • PDF: 3342
  • HTML: 2075
  • Cited by: 0
History
  • Received:July 17,2019
  • Revised:August 22,2019
  • Online: March 02,2020
  • Published: March 15,2020
Article QR Code
You are the first990414Visitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-3
Address:4# South Fourth Street, Zhongguancun,Haidian, Beijing,Postal Code:100190
Phone:010-62661041 Fax: Email:csa (a) iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063