Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (9): 169-174.doi: 10.11896/jsjkx.190800060

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Improved Sequence-to-Sequence Model for Short-term Vessel Trajectory Prediction Using AIS Data Streams

YOU Lan1, HAN Xue-wei1, HE Zheng-wei2,3,4, XIAO Si-yu1, HE Du5, PAN Xiao-meng1   

  1. 1 School of Computer Science & Information Engineering,Hubei University,Wuhan 430062,China
    2 School of Navigation,Wuhan University of Technology,Wuhan 430063,China
    3 Hubei Key Laboratory of Inland Shipping Technology,Wuhan University of Technology,Wuhan 430063,China
    4 National Engineering Research Center for Water Transportation Safely,Wuhan 430063,China
    5 Hubei Engineering Research Center for Education Information,Wuhan 430071,China
  • Received:2019-08-24 Published:2020-09-10
  • About author:YOU Lan,born in 1978,Ph.D,associate professor.Her main research interests includespatio-temporal data,deep learning and knowledge engineering.
    HE Zheng-wei,born in 1977, Ph.D,associated professor.His main research interests include traffic big data processing and mining, maritime information systems and traffic environment simulation,artificial intelligence application technology,deep learning and smart navigation.
  • Supported by:
    Hubei Province Natural Science Foundation Item (2019CFB757),Open Fund of National Engineering Research Center for Water Transport Safety (A2019011),Fund of Hubei Key Laboratory of Inland Shipping Technology (NHHY2017001),Key Project of Scientific Research Plan of Hubei Ministry of Education (D20161001) and Fundamental Research Funds for the Central Universities (2019III050GX,2019III007GX).

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Abstract: Using deep learning to predict the vessel trajectory is of great significance for the intelligent shipping.AIS (Automatic Identification System) data contain a huge amount of information about vessel trajectory features.The prediction of ship trajectories based on AIS data becomes one of the research hotspots in the intelligent shipping realm.In this paper,an improved sequence-to-sequence model using AIS data streams is proposed for the short-term vessel trajectory prediction.The proposed model utilizes a GRU network to encode the historical spatio-temporal sequence into a context vector,which not only preserves the sequential relationship among those trail points,but also is helpful for the alleviation of the gradient descent problem.Meanwhile,a GRU network is used as a decoder to output target trail points sequence.In this paper,a large scale of real AIS data are used in the experiments.The Chongqing section and the Wuhan section of the Yangzi River are selected as typical experimental areas,which is for the evaluation of the validity and applicability of the model.Experimental results show that the proposed model improves the accuracy and efficiency of short-term ship trajectory prediction.The proposed model provides an effective solution for the intelligent shipping warning in the future.

Key words: Automatic identification system, Recurrent neural network, Sequence-to-sequence model, Spatio-temporal data mining, Trajectory prediction

CLC Number: 

  • TP183
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