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

  • 제27권4호
  • /
  • Pages.483-491
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  • 2021
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  • 1229-3431(pISSN)
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  • 2287-3341(eISSN)
해양환경안전학회 (The Korean Society of Marine Environment and safety)
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계층 클러스터링과 실시간 데이터를 이용한 충돌위험평가

Collision Risk Assessment by using Hierarchical Clustering Method and Real-time Data

  • Vu, Dang-Thai (Division of Transportation System, Mokpo National Maritime University) ;
  • Jeong, Jae-Yong (Division of Transportation System, Mokpo National Maritime University)
  • 투고 : 2021.04.13
  • 심사 : 2021.06.28
  • 발행 : 2021.06.30
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초록

수역 내 충돌 위험 식별은 항해의 안전을 위해 중요하다. 본 연구에서는 거리 요인을 기반으로 한 군집화 방법인 계층 클러스터링을 포함하는 새로운 충돌 위험 평가 방법을 도입했으며, 주변의 선박이 많은 경우 실시간 데이터, 그룹 방법론 및 예비 평가를 사용하여 선박을 분류하고 충돌위험평가를 기반으로 평가하였다(HCAAP 처리라 부른다). 조우하는 선박들의 군집은 계층 프로그램에 의해 모아지고, 예비 평가와 결합되어 상대적으로 안전한 선박을 걸러내었다. 그런 다음, 각 군집 내에서 조우하는 선박 사이의 최근접점(DCPA) 및 최근접점까지의 도착시간(TCPA)까지의 시간을 계산하여 충돌위험지수(CRI)와의 관계를 비교하였다. 조우하는 선박들간의 군집에서 CRI와 DCPA 및 TCPA 수학적 관계는 음의 지수 함수로 구성되었다. 이러한 CRI로부터 운영자는 명시된 해역에서 항해하는 모든 선박의 안전성을 보다 쉽게 평가할 수 있으며, 프레임워크는 해상운송의 안전과 보안을 개선하고 인명 및 재산 손실을 줄일 수 있다. 본 연구에서 제안된 프레임워크의 효과를 설명하기 위해 국내의 목포 연안 해역에서 실험 사례 연구를 수행하였다. 그 결과, 본 연구의 프레임워크가 각 군집 내에서 조우 선박 간의 충돌 위험 지수를 탐지하고 순위를 매기는 데 효과적이고 효율적이라는 것을 보여 주었으며, 추가연구를 위한 자동 위험 우선순위를 지정할 수 있게 해주었다.

The identification of regional collision risks in water areas is significant for the safety of navigation. This paper introduces a new method of collision risk assessment that incorporates a clustering method based on the distance factor - hierarchical clustering - and uses real-time data in case of several surrounding vessels, group methodology and preliminary assessment to classify vessels and evaluate the basis of collision risk evaluation (called HCAAP processing). The vessels are clustered using the hierarchical program to obtain clusters of encounter vessels and are combined with the preliminary assessment to filter relatively safe vessels. Subsequently, the distance at the closest point of approach (DCPA) and time to the closest point of approach (TCPA) between encounter vessels within each cluster are calculated to obtain the relation and comparison with the collision risk index (CRI). The mathematical relationship of CRI for each cluster of encounter vessels with DCPA and TCPA is constructed using a negative exponential function. Operators can easily evaluate the safety of all vessels navigating in the defined area using the calculated CRI. Therefore, this framework can improve the safety and security of vessel traffic transportation and reduce the loss of life and property. To illustrate the effectiveness of the framework proposed, an experimental case study was conducted within the coastal waters of Mokpo, Korea. The results demonstrated that the framework was effective and efficient in detecting and ranking collision risk indexes between encounter vessels within each cluster, which allowed an automatic risk prioritization of encounter vessels for further investigation by operators.

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참고문헌

  1. Aarsaether, K. G. and T. Moan(2009), Estimating navigation patterns from ais, The Journal of Navigation, Vol. 62, No. 4, pp. 587-607. https://doi.org/10.1017/S0373463309990129
  2. Bi, X.(2000), Study on Ship's Collision Risk Index and Collision Avoidance Model. Dalian Maritime University, Dalian. Ph.D. thesis.
  3. Chen, D., Dai, C., Wan, X. and Mou, J.(2015), A research on ais-based embedded system for ship collision avoidance. In: Transportation Information and Safety (ICTIS), International Conference on. IEEE, Vol. 2, pp. 512-517.
  4. Jeon, H. K. and Y. C. Jung(2018), Development of a Collision Risk Assessment System for Optimum Safe Route, Journal of the Korean Society of Marine Environment & Safety Research Paper, Vol. 24, No. 6, pp. 670-678. https://doi.org/10.7837/kosomes.2018.24.6.670
  5. Kim, W. O. and C. J. Kim(2018), Establishment of Navigational Risk Assessment Model Combining Dynamic Ship Domain and Collision Judgement Model, Journal of the Korean Society of Marine Environment & Safety Research Paper, Vol. 24, No. 1, pp. 36-42. https://doi.org/10.7837/kosomes.2018.24.1.036
  6. Koo, B. K.(2018), Analysis of Marine Vessel Collision Risk based on Quantitative Risk Assessment, Journal of the Korean Society of Marine Environment & Safety Research Paper, Vol. 24, No. 3, pp. 319-324. https://doi.org/10.7837/kosomes.2018.24.3.319
  7. Li, B. and F. W. Pang(2013), An approach of vessel collision risk assessment based on the d-s evidence theory, Ocean Engineering, Vol. 74, pp. 16-21. https://doi.org/10.1016/j.oceaneng.2013.09.016
  8. Li, S., Q. Meng, and X. Qu(2012), An overview of maritime waterway quantitative risk assessment models, Risk Analysis, Vol. 32, No. 3, pp. 496-512. https://doi.org/10.1111/j.1539-6924.2011.01697.x
  9. Martin Ester, Hans-Peter Kriegel, Jorg Sander and Xiaowei Xu(1996), A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise, KDD-96, pp. 226-231
  10. Mou, J. M., C. Van der Tak, and H. Ligteringen(2010), Study on collision avoidance in busy waterways by using ais data. Ocean Engineering, Vol. 37, No. 5, pp. 483-490. https://doi.org/10.1016/j.oceaneng.2010.01.012
  11. Naeem, W., G. W. Irwin, and A. Yang(2012), Colregs-based collision avoidance strategies for unmanned surface vehicles. Mechatronics, Vol. 22, No. 6, pp. 669-678. https://doi.org/10.1016/j.mechatronics.2011.09.012
  12. Zhen, R., M. Riveiro, and Y. Jin(2017), A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance, Ocean Engineering, Vol. 145, pp. 492-501. https://doi.org/10.1016/j.oceaneng.2017.09.015
  13. Zhang, J., D. Zhang, X. Yan, S. Haugen, and C. G. Soares(2015), A distributed anti-collision decision support formulation in multi-ship encounter situations under colregs. Ocean Engineering, Vol. 105, pp. 336-348. https://doi.org/10.1016/j.oceaneng.2015.06.054
  14. Zhang, W.,, F. Goerlandt, P. Kujala, and Y. Wang(2016), An advanced method for detecting possible near miss ship collisions from ais data, Ocean Engineering, Vol. 124, pp. 141-156. https://doi.org/10.1016/j.oceaneng.2016.07.059
  15. Zhao, Y., W. Li, and P. Shi(2016), A real-time collision avoidance learning system for unmanned surface vessels, Neurocomputing, Vol. 182, pp. 255-266. https://doi.org/10.1016/j.neucom.2015.12.028