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Automatic Maritime Traffic Synthetic Route: A Framework for Route Prediction

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Pervasive Systems, Algorithms and Networks (I-SPAN 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1080))

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Abstract

Ship movement information is becoming increasingly available, resulting in an overwhelming increase of data transmitted to human operators. Understanding the Maritime traffic patterns is important to Maritime Situational Awareness (MSA) applications in particular, to classify and predict trajectories on sea. Therefore, there is need for automatic processing to synthesize the behavior of interest in a simplified, clear, and effective way without any loss of data originality. In this paper, we propose a method to calculate route prediction from a synthetic route representation data once the picture of the maritime traffic is constructed. The synthetic route knowledge based on Automatic Identification System (AIS) is used to classify and predict future routes along which a vessel is going to move. This is in agreement with the partially observed track and given the vessel static and dynamic information. The prediction results do not only reduce data storage space in the database but can also supply data support for traffic management, accident detection, and avoidance of automatic collision and therefore promote the development of maritime intelligent traffic systems. Finally, the simulation results shows a good tradeoff between the predicted and the actual observed vessel routes.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61801166, in part by the Fundamental Research Funds for the Central Universities under Grant 2019B22214, and in part by the Changzhou Sci. and Tech. Program under Grant CJ20180046. This work was supported by Institute for Information and communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2017-0-00255, Autonomous digital companion framework and application).

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Authors and Affiliations

  1. College of IOT Engineering, Hohai University, Changzhou, 213022, China

    Lisa Natswi Tafa & Xin Su

  2. School of Computer Science and Engineering, Soongsil University, Seoul, Korea

    Jiman Hong

  3. IT Research Institute, Chosun University, Gwangju, 61452, South Korea

    Chang Choi

Authors
  1. Lisa Natswi Tafa
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  2. Xin Su
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  3. Jiman Hong
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  4. Chang Choi
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Corresponding author

Correspondence to Xin Su .

Editor information

Editors and Affiliations

  1. University of Naples Federico II, Naples, Napoli, Italy

    Christian Esposito

  2. Soongsil University, Seoul, Korea (Republic of)

    Jiman Hong

  3. The University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

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Tafa, L.N., Su, X., Hong, J., Choi, C. (2019). Automatic Maritime Traffic Synthetic Route: A Framework for Route Prediction. In: Esposito, C., Hong, J., Choo, KK. (eds) Pervasive Systems, Algorithms and Networks. I-SPAN 2019. Communications in Computer and Information Science, vol 1080. Springer, Cham. https://doi.org/10.1007/978-3-030-30143-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-30143-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30142-2

  • Online ISBN: 978-3-030-30143-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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