Mining of missing ship trajectory pattern in automatic identification system

  • Authors

    • Kwang Il Kim
    • Keon Myung Lee
    2018-04-03
    https://doi.org/10.14419/ijet.v7i2.12.11117
  • Automatic Identification System, Missing AIS Data, Association Mining, K-Means, Data Mining
  • Background/Objectives: Ship trajectories in Vessel Traffic Service (VTS) system are generated by integrating the Automatic Identification System (AIS) or Radar system. However, the AIS system has missing data section caused by AIS device problems, radio jamming, and so on. These data have been confusing ship navigators and VTS operators.

    Methods/Statistical analysis: In order to extract missing AIS data, time intervals of sequent points from each ship trajectory are calculated. The section with missing AIS data is above a threshold time limit defined by characteristics. Using k-means algorithm, missing AIS data were clustered into several clusters stored by ship’s ID and sailing direction. Using association rule mining analysis, meaningful association pattern were calculated by missing AIS dataset.

    Findings: As a result of the association rule mining, we found several missing AIS situation patterns. In case of the west route, the probability of missing AIS situation is high when they enter the east and passenger routes. Also, the probability of missing AIS situation of passing the passenger route is high when that ship enter the LNG, east and west routes.

    Improvements/Applications: These results can be used to predict the probability of missing AIS data in VTS system.

     

     

  • References

    1. [1] International Maritime Organization. Guidelines for the onboard operational use of shipborne automatic identification systems (AIS). IMO Resolution A.917, 2002.

      [2] F. Heymann, P. Banys and C. Saez, Radar Image Processing and AIS Target Fusion, The International Journalon Marine Navigationand Safety of Sea Transportation, 2015, 9 (3), pp.443-448.

      [3] Abbas H., Alan W., Philip N. and Jon W., Automatic Identification System (AIS): Data Reliability and Human, the Journal of Navigation, 2007 September 60 (3), pp. 373-389.

      [4] Kim K., Jeong J. and 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 February, 1 (1), pp. 210-213.

      [5] 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.

      [6] Jeong J., Park G., Kim K., Risk assessment model of maritime traffic in time-variant CPA environments in waterway, Journal of Advanced Computational Intelligence and Intelligent Informatics, 2012 November, 16 (7), pp.866-873.

      [7] Kim K., Jeong J., Visualization of Ship Collision Risk Based on Near-Miss Accidents, 2016 Joint 8th International Conference on Soft Computing and Intelligent Systems and 2016 17th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2016, 2016 December, 1 (1), pp. 323-327.

      [8] Ingo H., AIS Adding New Quality to VTS Systems,Journal of Navigation,2000 September, 53 (3), pp. 527-539.

      [9] D. M. Kim et al,A music recommendation system with a dynamic k-means clustering algorithm,Sixth International Conference on Machine Learning and Applications, 2008 February, 1(1).

      [10] Jyoti A. , Nidhi B.and Sanjeev R.,A Review on Association Rule Mining Algorithms,International Journal of Innovative Research in Computer and Communication Engineering,2013 July, 1 (5), pp.1246-1251.

      [11] Bayardo Jr, Roberto J.,Efficiently mining long patterns from databases, ACM SIGMOD Record. 1998, 27 (2), pp.85-93.

      [12] Lee S., Lee K. and Lee K, Density and entanglement-based clustering of sequence data, International Conference on Fuzzy Theory and Its Applications, iFUZZY 2015, 2016 January, pp. 40-43.

      [13] K. M. Lee, Mining Generalized Fuzzy Quantitative Association Rules with Fuzzy Generalization Hierarchies,IFSA World Congress and 20th NAFIPS International Conference, 2001, 1 (1), pp. 2977-2982.

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  • How to Cite

    Il Kim, K., & Myung Lee, K. (2018). Mining of missing ship trajectory pattern in automatic identification system. International Journal of Engineering & Technology, 7(2.12), 167-170. https://doi.org/10.14419/ijet.v7i2.12.11117