Methods of ship trajectory data processing for applying artificial neural network in port area

  • Authors

    • Kwang II Kim
    • Keon Myung Lee
    • Jang Young Ahn
    2018-04-03
    https://doi.org/10.14419/ijet.v7i2.12.11112
  • Vessel Traffic Service, Categorical data, Route Gate Line, Neural network, Automatic Identification System
  • Background/Objectives: In Vessel Traffic Service (VTS), prediction of the flow of vessel traffic is essential to serve safety information and control ship traffic. However, it is difficult to predict a ship’s speed due to many external forces and environmental conditions. This study proposes a data processing method to convert ship speed data to categorical data by dividing ship navigating routes into several gate lines.

    Methods/Statistical analysis: A ship’s trajectory is converted to each route’s gate line speed. To determine the gate line speed, we convertedthe previous and subsequent gate line speeds into category data. The input and output category data were applied to a multilayer perceptron network using as input variablesthe previous speed variance category, ship type, and ship length, and as output variable the subsequent speed variance.

    Findings: These results are useful because categorical data can be applied to various neural network models. As a result of the conducted experiments, the accuracy of the model improved when many gate lines are included.

    Improvements/Applications: The study results can be applied topredict ship traffic flow for VTS operators.

     

  • References

    1. [1] International Maritime Organization. Guidelines for Vessel Traffic Services. IMO Resolution A.857(20), 1997.

      [2] Dagenais, M. and Martin, F., Forecasting containerized traffic for the port of Montreal (1981–1995), Transportation Research Part A: General. 1987, 21 (1), pp. 1-16.

      [3] Gooiger, J. G. and Klein, A., Forecasting the Antwerp maritime steel traffic flow: a case study. Journal of Forecasting, 1989, 8 (1), pp. 381–398.

      [4] Klein, A. and Verbeke A., The design of an optimal short-term forecasting system for sea port management: an application to the port of Antwerp. International Journal of Transport Economics, 1987, 14 (1), pp.57–70.

      [5] Mohamed M., Forecasting the Suez Canal traffic: a neural network analysis, Maritime Policy & Management, 2014, 31 (2), pp.139-156.

      [6] Guoqiang Peter Zhang, Neural Networks for Classification: A Survey, IEEE Transactions on Systems, Man, and Cybernetics-part C: Applications and Reviews, 2000 November, 30 (4), pp. 451-462.

      [7] AndriusDaranda. A Neural Network Approach to Predict Marine Traffic. Vilnius University Institute of Mathematics and Informatics, 2016.

      [8] Lokukaluge P., Paulo Oliveira, and C. GuedesSoares, Maritime Traffic Monitoring Based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction.IEEE Transactions on Intelligent Transportation Systems, 2012 September, 13 (3), pp.1188-1200.

      [9] Kim K. and Lee K., Ship Encounter Risk Evaluation for Coastal Areas with Holistic Maritime Traffic Data Analysis, Advanced Science Letters, 2017 October, 23 (10), pp.9565-9569.

      [10] International Maritime Organization. Guidelines for the onboard operational use of shipborne automatic identification systems (AIS).IMO Resolution A.917, 2002.

      [11] Kim K., Jeong J., Park G., Development of grid projection algorithm of vessel trajectories for e-Navigation, 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems(SCIS 2014) and 15th International Symposium on Advanced Intelligent Systems (ISIS 2014), 2014. 1 (1), pp.210-213.

      [12] Lee K.,Cluster validity evaluation for small number of clusters, International Journal of Applied Engineering Research, 2014, 19 (21), pp.8933-8940.

      [13] Kim K., Jeong J. and Park G., Analysis of Marine Accident Probability in Mokpo Waterways, Journal of Navigation and Port Research International Edition, 2011, 35 (9), pp.729-733.

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  • How to Cite

    II Kim, K., Myung Lee, K., & Young Ahn, J. (2018). Methods of ship trajectory data processing for applying artificial neural network in port area. International Journal of Engineering & Technology, 7(2.12), 145-146. https://doi.org/10.14419/ijet.v7i2.12.11112