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Automatic Maritime Traffic Synthetic Route: A Framework for Route Prediction

  • Lisa Natswi Tafa
  • Xin SuEmail author
  • Jiman Hong
  • Chang Choi
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1080)

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.

Keywords

Route prediction Maritime route extraction AIS MSA Maritime traffic representation 

Notes

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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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of IOT EngineeringHohai UniversityChangzhouChina
  2. 2.School of Computer Science and EngineeringSoongsil UniversitySeoulKorea
  3. 3.IT Research InstituteChosun UniversityGwangjuSouth Korea

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