International Journal of Naval Architecture and Ocean Engineering

The Society of Naval Architects of Korea (대한조선학회)
권호 표지
DOI QR코드

DOI QR Code

Collision risk assessment based on the vulnerability of marine accidents using fuzzy logic

  • Hu, Yancai (Navigation College, Shandong Jiaotong University) ;
  • Park, Gyei-Kark (Faculty of International Maritime Transportation Science System, Department of Maritime Transport, MOKPO National Maritime University)
  • Received : 2019.11.12
  • Accepted : 2020.06.10
  • Published : 2020.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Based on the trend, there have been numerous researches analysing the ship collision risk. However, in this scope, the navigational conditions and external environment are ignored or incompletely considered in training or/and real situation. It has been identified as a significant limitation in the navigational collision risk assessment. Therefore, a novel algorithm of the ship navigational collision risk solving system has been proposed based on basic collision risk and vulnerabilities of marine accidents. The vulnerability can increase the possibility of marine collision accidents. The factors of vulnerabilities including bad weather, tidal currents, accidents prone area, traffic congestion, operator fatigue and fishing boat operating area are involved in the fuzzy reasoning engines to evaluate the navigational conditions and environment. Fuzzy logic is employed to reason basic collision risk using Distance to Closest Point of Approach (DCPA) and Time of Closest Point of Approach (TCPA) and the degree of vulnerability in the specific coastal waterways. Analytical Hierarchy Process (AHP) method is used to obtain the integration of vulnerabilities. In this paper, vulnerability factors have been proposed to improve the collision risk assessment especially for non-SOLAS ships such as coastal operating ships and fishing vessels in practice. Simulation is implemented to validate the practicability of the designed navigational collision risk solving system.

Keywords

Acknowledgement

This research is a part of the project titled "SMART-Navigation project," funded by the Ministry of Oceans and Fisheries, the National Natural Science Foundation of China (61873071, 51911540478, G61773015), key research and development plan of Shandong province (2019JZZY020712), Natural Science Foundation of Shandong Jiaotong University [Z201631], Shandong Jiaotong University PhD Startup foundation of Scientific Research.

References

  1. Ahn, J.H., Rhee, K.P., You, Y.J., 2012. A study on the collision avoidance of a ship using neural networks and fuzzy logic. Appl. Ocean Res. 37, 162-173. https://doi.org/10.1016/j.apor.2012.05.008
  2. An, K., 2016. E-navigation services for non-SOLAS ships. Int. J. e-Navig. Mari. Econ. 4, 13-22. https://doi.org/10.1016/j.enavi.2016.06.002
  3. Asbjornslett, B.E., 1999. Assess the vulnerability of your production system. Prod. Plann. Contr. 10 (3), 219-229. https://doi.org/10.1080/095372899233181
  4. Do, T.M.H., Park, G.K., Hong, T.H., Kim, G., 2018. A study on the structure analysis of vulnerability factors of marine accident to coastal cargo ships using FSM. J. Korean Instit. Intell. Syst. 28 (6), 589-594. https://doi.org/10.5391/JKIIS.2018.28.6.589
  5. Emrah, B., Okan, D., Tuba, K., ShigeruY, 2012. Use of consistency index, expert prioritization and direct numerical inputs for generic fuzzy-AHP modeling: a process model for shipping asset management. Expert Syst. Appl. 39 (2), 1911-1923. https://doi.org/10.1016/j.eswa.2011.08.056
  6. Fang, M.C., Tsai, K.Y., Fang, C.C., 2018. A simplified simulation model of ship navigation for safety and collision avoidance in heavy traffic areas. J. Navig. 71 (4), 837-860. https://doi.org/10.1017/S0373463317000923
  7. Hasegawa, K., 1987. Automatic collision avoidance system for ships using fuzzy control. In: Proceedings of the 8th Ship Control System Symposium.
  8. Hasegawa, K., Kouzuki, A., Muramatsu, T., Komine, H., Watabe, Y., 1989. Ship auto navigation fuzzy expert system (SAFES). J. Soc. Nav. Archit. Jpn. 166.
  9. House, D., 2007. Ship Handling. Routledge.
  10. Hwang, C.N., 2002. The integrated design of fuzzy collision-avoidance and H∝-autopilots on ships[J]. J. Navig. 55 (1), 117-136. https://doi.org/10.1017/S0373463301001631
  11. John, A., Osue, U.J., 2017. Collision risk modelling of supply vessels and offshore platforms under uncertainty. J. Navig. 70 (4), 870-886. https://doi.org/10.1017/S0373463317000091
  12. Kao, S.L., Lee, K.T., Chang, K.Y., Ko, M.D., 2007. A fuzzy logic method for collision avoidance in vessel traffic service. J. Navig. 60 (1), 17-31. https://doi.org/10.1017/S0373463307003980
  13. Korea Hydrographic and Oceanographic Administration (KHOA), 2013. Ocean Observa-Tion and Prediction. http://khoa.ko.kr/.
  14. Kim, G.N., 2017. Survey on the Korean VMSs for Fishing Vessels and Challenges for Monitoring the High-Risk Ships. SMART Navigation Project, e-Navigation Underway 2017 AP.
  15. Kim, D.B., Jeong, J.Y., Park, Y.S., 2014. A study on the ship's speed control and ship handling at Myeongnayang waterway. J. Korean Soc. Mar. Environ. Saf. 20 (2), 193-201. https://doi.org/10.7837/kosomes.2014.20.2.193
  16. Korean Marine Security Safety Division, 2016. Statistical Yearbook of Marine Accident Incident.
  17. Korean Marine Security Safety Division, 2017. Statistical Yearbook of Marine Accident Incident.
  18. Lee, H.J., Rhee, K.P., 2001. Development of collision avoidance system by using expert system and search algorithm. Int. Shipbuild. Prog. 48 (3), 197-212.
  19. Lee, S.M., Kwon, K.Y., Joh, J., 2004. A fuzzy logic for autonomous navigation of marine vehicles satisfying COLREG guidelines. Int. J. Contr. Autom. Syst. 2 (2), 171-181.
  20. Lisowski, J., 2001. Determining the optimal ship trajectory in collision situation. In: Proceedings of the IX International Scientific and Technical Conference on Marine Traffic Engineering. Szczecin.
  21. Ministry of Oceans and Fisheries, 2016. Republic of Korea. SMART-Navigation Project. http://www.smartnav.org/eng/html/Index/.
  22. Mou, J.M., Van, D., Tak, C., Ligteringen, H., 2010. Study on collision avoidance in busy waterways by using AIS data. Ocean Eng. 37 (5-6), 483-490. https://doi.org/10.1016/j.oceaneng.2010.01.012
  23. Sahin, B., Senol, Y.E., 2015. A novel process model for marine accident analysis by using generic fuzzy-AHP algorithm. J. Navig. 68 (1), 162-183. https://doi.org/10.1017/S0373463314000514
  24. Sahin, B., Senol, Y.E., 2017. Shipping technology selection for dynamic capability based on improved Gaussian fuzzy AHP model. Ocean Eng. 136 (15), 233-242. https://doi.org/10.1016/j.oceaneng.2017.03.032
  25. Szlapczynski, R., 2006. A unified measure of collision risk derived from the concept of a ship domain. J. Navig. 59, 477-490. https://doi.org/10.1017/S0373463306003833
  26. Zhou, D., Zheng, Z., 2019. Dynamic fuzzy ship domain considering the factors of own ship and other ships. J. Navig. 72 (2), 467-482. https://doi.org/10.1017/S0373463318000802

Cited by

  1. Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach vol.9, pp.1, 2020, https://doi.org/10.3390/jmse9010049
  2. Cognitive Gap and Correlation of Safety-I and Safety-II: A Case of Maritime Shipping Safety Management vol.13, pp.10, 2020, https://doi.org/10.3390/su13105509
  3. A decision support model for ship navigation in Arctic waters based on dynamic risk assessment vol.244, 2022, https://doi.org/10.1016/j.oceaneng.2021.110427
  4. Factors affecting marine ranching risk in China and their hierarchical relationships based on DEMATEL, ISM, and BN vol.549, 2022, https://doi.org/10.1016/j.aquaculture.2021.737802