Journal of the Korean Society of Marine Environment & Safety (해양환경안전학회지)

The Korean Society of Marine Environment and safety (해양환경안전학회)
권호 표지
DOI QR코드

DOI QR Code

Spatiotemporal Analysis of Vessel Trajectory Data using Network Analysis

네트워크 분석 기법을 이용한 항적 데이터의 시공간적 특징 분석

  • Oh, Jaeyong (Korea Research Institute of Ships and Ocean Engineering) ;
  • Kim, Hye-Jin (Korea Research Institute of Ships and Ocean Engineering)
  • 오재용 (한국해양과학기술원 부설 선박해양플랜트연구소) ;
  • 김혜진 (한국해양과학기술원 부설 선박해양플랜트연구소)
  • Received : 2020.10.19
  • Accepted : 2020.12.28
  • Published : 2020.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

In recent years, the maritime traffic environment has been changing in various ways, and the traffic volume has been increasing constantly. Accordingly, the requirements for maritime traffic analysis have become diversified. To this end, traffic characteristics must first be analyzed using vessel trajectory data. However, as the conventional method is mostly manual, it requires a considerable amount of time and effort, and errors may occur during data processing. In addition, ensuring the reliability of the analysis results is difficult, because this method considers the subjective opinion of analysts. Therefore, in this paper, we propose an automated method of traffic network generation for maritime traffic analysis. In the experiment, spatiotemporal features are analyzed using data collected at Mokpo Harbor over six months. The proposed method can automatically generate a traffic network reflecting the traffic characteristics of the experimental area. In addition, it can be applied to a large amount of trajectory data. Finally, as the spatiotemporal characteristics can be analyzed using the traffic network, the proposed method is expected to be used in various maritime traffic analyses.

최근 해상교통 환경의 변화가 다양해지고, 해상 교통량이 지속적으로 증가함에 따라 해상교통 분석에 대한 요구가 다양해지고 있다. 이러한 해상교통 분석 작업은 교통 특성에 대한 모델링이 선행되어야 하지만, 기존의 방법은 자동화되어 있지 않아 전처리 작업에 시간이 많이 소요되고, 분석 결과에 작업자의 주관적인 견해가 포함될 수 있는 문제점이 있었다. 이러한 문제점을 해결하고자 본 논문에서는 해상교통 분석을 위한 자동화된 교통 네트워크 생성 방법을 제안하였으며, 활용 가능성을 검토하기 위해 실제 목포항에서 수집된 6개월간의 항적 데이터를 이용한 실험을 수행하였다. 실험 결과, 대상 해역의 교통 특성을 반영한 교통 네트워크를 자동으로 생성할 수 있었으며, 대용량의 항적 데이터에도 적용할 수 있음을 확인하였다. 또한, 생성된 교통 네트워크는 시공간적 특징 분석이 가능하여 다양한 해상교통 분석에 활용될 수 있을 것으로 기대한다.

Keywords

References

  1. Dobrkovic, A., M. Iacob, and J. van Hillegersberg(2018), Maritime pattern extraction and route reconstruction from incomplete AIS data, International Journal of Data Science and Analytics, Vol. 5, pp. 111-136. https://doi.org/10.1007/s41060-017-0092-8
  2. Ester, M., H. P. Kriegel, J. Sander, and X. Xu(1996), A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226-231.
  3. Filipiak, D., K. Wecel, M. Strozyna, M. Michalak, and W. Abramowicz(2020), Extracting Maritime Traffic Networks from AIS Data Using Evolutionary Algorithm, Business & Information Systems Engineering, Vol. 62, pp. 435-450. https://doi.org/10.1007/s12599-020-00661-0
  4. GateHouse(2018), IWRAP Mk2 User Manual, Engberg GateHouse Logistics A/S.
  5. IALA(2017), The Use of IALA Waterway Risk Assessment Program (IWRAP MKII), IALA Guideline G1123, Edition 1.0.
  6. Kim, H. J. and J. Y. Oh(2018), Research on the Analysis of Maritime Traffic Pattern using Centroid Method, Journal of Navigation and Port Research, Vol. 42, No. 6, pp. 453-458. https://doi.org/10.5394/KINPR.2018.42.6.453
  7. Kim, K. I., G. K. Park, and J. S. Jeong(2011), Analysis of marine accident probability in Mokpo waterways, Journal of Korean Navigation and Port Research, Vol. 35, No. 9, pp. 729-733. https://doi.org/10.5394/KINPR.2011.35.9.729
  8. Kim, K. I., J. S. Jeoung, and J. S. Lee(2019), Analysis of Ship Route Traffic Characteristics using Port-MIS data, Proceedings of the Korean Institute of Navigation and Port Research Conference, pp. 93-95.
  9. Lee, J. H., G. T. Yeo, S. H. Park, and H. C. Lee(2020), Structural analysis of the liner shipping network of the Asia-North America trade route adopting Social Network Analysis, Journal of The Korean Association of Shipping and Logistics, Vol. 36, No. 2, pp. 133-159.
  10. MOF(2014), The implementing guidelines for maritime traffic assessment, No. 2014-164, Ministry of Oceans and Fisheries.
  11. MOF(2016), The Task Instructions of Research project about maritime traffic safety assessment, Ministry of Oceans and Fisheries.
  12. MOF(2020), The summary of the maritime traffic safety assessment project, Ministry of Oceans and Fisheries.
  13. Ramer, U.(1972), An iterative procedure for the polygonal approximation of plane curves, Computer Graphics and Image Processing, Vol. 1, No. 3, pp. 244-256. https://doi.org/10.1016/S0146-664X(72)80017-0
  14. Reynolds, D. A. and R. C. Rose(1995), Robust text-independent speaker identification using Gaussian mixture speaker models, IEEE transactions on speech and audio processing, Vol. 3, No. 1, pp. 72-83. https://doi.org/10.1109/89.365379
  15. Tasseda, E. H. and R. Shoji(2018), Statistical Modeling Framework of Vessel Traffic Streams in Tokyo Bay, Transactions of Navigation, Vol. 3, No. 2, pp. 31-42.
  16. Xiao, F., H. Ligteringen, C. van Gulijk, and B. Ale(2013), Nautical traffic simulation with multi-agent system for safety, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 1245-1252.