Journal of Navigation and Port Research (한국항해항만학회지)

Korean Institute of Navigation and Port Research (한국항해항만학회)
권호 표지
DOI QR코드

DOI QR Code

Detection of Ship Movement Anomaly using AIS Data: A Study

AIS 데이터 분석을 통한 이상 거동 선박의 식별에 관한 연구

  • Oh, Jae-Yong (Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering) ;
  • Kim, Hye-Jin (Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering) ;
  • Park, Se-Kil (Maritime Safety and Environmental Research Division, Korea Research Institute of Ships and Ocean Engineering)
  • 오재용 (선박해양플랜트연구소 해양안전환경연구본부) ;
  • 김혜진 (선박해양플랜트연구소 해양안전환경연구본부) ;
  • 박세길 (선박해양플랜트연구소 해양안전환경연구본부)
  • Received : 2018.07.06
  • Accepted : 2018.08.10
  • Published : 2018.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, the Vessel Traffic Service (VTS) coverage has expanded to include coastal areas following the increased attention on vessel traffic safety. However, it has increased the workload on the VTS operators. In some cases, when the traffic volume increases sharply during the rush hour, the VTS operator may not be aware of the risks. Therefore, in this paper, we proposed a new method to recognize ship movement anomalies automatically to support the VTS operator's decision-making. The proposed method generated traffic pattern model without any category information using the unsupervised learning algorithm.. The anomaly score can be calculated by classification and comparison of the trained model. Finally, we reviewed the experimental results using a ship-handling simulator and the actual trajectory data to verify the feasibility of the proposed method.

최근 해상교통량이 증가하고 선박교통 관제구역이 확대됨에 따라 관제사의 업무 부하가 증가하고 있으며, 이로 인해 교통량이 급증하는 경우 관제사가 위험을 인지하지 못하는 상황도 발생하게 된다. 이러한 배경에서 본 논문에서는 관제 업무의 지원을 위해 이상 거동 선박을 자동으로 식별하는 방법을 제안한다. 본 방법은 누적된 AIS 데이터를 이용하여 관제구역 내의 통항 패턴을 학습하고, 학습된 모델과의 비교를 통해 이상치를 계산하여 이상 거동 선박을 식별한다. 특히, 선박의 거동 상태에 대한 분류 정보가 없더라도 비지도 학습법을 기반으로 항적 데이터를 자동으로 분류하여 통항 패턴을 학습할 수 있으며, 항적의 군집화와 분류 과정을 통해 이상 거동 선박을 실시간으로 식별할 수 있는 특징을 가진다. 또한, 본 논문에서는 선박운항 시뮬레이터 및 실제 AIS 항적 데이터를 이용한 식별 실험을 수행하였으며, 이를 통해 선박교통관제 시스템에의 활용 가능성을 고찰하였다.

Keywords

References

  1. Brax, N., Andonoff, E. and Gleizes, M. P.(2012), "A Self-adaptive Multi-Agent System for Abnormal Behavior Detection in Maritime Surveillance", Technologies and Applications. KES-AMSTA 2012. Lecture Notes in Computer Science, Vol. 7327, pp. 174-185.
  2. Canzzanti, L., Millefiori, L. M. and Arcieri, G.(2015), "A document-based data model for large scale computational maritime situational awareness", Proceeding of the IEEE International Conference of Big Data, pp. 1350-1356.
  3. Endsley, M. R. and Bolstad, C. A.(1994), "Individual differences in pilot situation awareness", International Journal of Aviation Psychology, Vol. 4, pp. 241-264. https://doi.org/10.1207/s15327108ijap0403_3
  4. Kowalska K. and Peel L.(2012), "Maritime Anomaly Detection using Gaussian Process Active Learning", Proceedings of the 15th IEEE International Conference on Information Fusion (FUSION), pp. 1164-1171.
  5. IALA(2014), "Use of Decision Support Tools for VTS Personnel", IALA Guideline 1110 Ed. 1.
  6. Liu Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua(2008), "Isolation forest." Data Mining, ICDM'08. Eighth IEEE International Conference on Data Mining, pp. 413-422.
  7. Park, Y. S., Park, S. W. and Cho, I. S.(2015), "A Basic Study on Prediction Module Development of Collision Risk based on Ship's Operator's Consciousness", Journal of Korean Navigation and Port Research, Vol. 39, No. 3, pp. 199-207. https://doi.org/10.5394/KINPR.2015.39.3.199
  8. Park, Y. S., Kim, Y. S. and Jo, S. H.(2014), "A Study on Occupational Stress of the VTS Operators", Journal of Korean Navigation and Port Research, Vol. 38, No. 6, pp. 615-621. https://doi.org/10.5394/KINPR.2014.38.6.615
  9. Rong Zhen, Yongxing Jin, Qinyou Hu, Zheping Shao and Nikitas Nikitakos(2017), "Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naive Bayes Classifier", The Journal of Navigation, Vol. 70, pp. 648-670. https://doi.org/10.1017/S0373463316000850
  10. Smith, M., Reece, S., Roberts, S., Psorakis, I. and Rezek, I.(2014), "Maritime abnormality detection using Gaussian, processes". Knowledge and Information Systems, Vol. 38, pp. 717-741. https://doi.org/10.1007/s10115-013-0685-z
  11. Son, N. S., Oh, J. Y. and Kim, S. Y.(2009), "On an Algorithm for the Assessment of Collision Risk among Multiple Ships based on AIS", Proceeding of the conference of Korean Navigation and Port Research, pp. 62-63.