Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-11T03:42:36.407Z Has data issue: false hasContentIssue false
  • English
  • Français

Performance Evaluation of Maritime Search and Rescue Missions Using Automatic Identification System Data

Published online by Cambridge University Press:  19 May 2020

Fan Zhou ,
Hua Chen  and
Peng Zhang
Fan Zhou*
Affiliation:
(College of Information Engineering, Shanghai Maritime University, Shanghai, PR China)
Hua Chen
Affiliation:
(College of Information Engineering, Shanghai Maritime University, Shanghai, PR China)
Peng Zhang
Affiliation:
(East China Sea Cruise and Law-Enforcement Team of Shanghai Maritime Safety Administration, Maritime Safety Administration of the People's Republic of China)
*
(E-mail: fanzhou_cv@163.com)
Get access Rights & Permissions [Opens in a new window]

Abstract

In maritime search and rescue (SAR), commanders need to understand the task execution efficiency of each SAR unit in real time to improve the overall efficiency of SAR efforts. This study proposes a method to evaluate the progress of maritime SAR missions using automatic identification system (AIS) data. First, the positioning accuracy of the AIS data was improved according to the relationship between position, speed, and course. Second, the historical track of the SAR ship was used to generate the SAR completion area based on a line buffer algorithm. The SAR completion area and SAR mission area were then superimposed to determine the overall progress of the SAR mission. The proposed method has been deployed within the SAR software on-board Haixun01 (China's largest and most advanced large-scale cruise rescue ship) since 2017 and has played an important role in devising SAR strategies and tracking mission progress, during several SAR actions.

Keywords

Marine Search and RescueAutomatic Identification SystemSpatial PositionEfficiency Evaluation
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 6 , November 2020 , pp. 1237 - 1246
Check for updates
Copyright
Copyright © The Royal Institute of Navigation 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Agbissoh Otote, D., Li, B., Ai, B., Gao, S., Xu, J., Chen, X. and Lv, G. (2019). A decision-making algorithm for maritime search and rescue plan. Sustainability, 11, 2084.CrossRefGoogle Scholar
Akbari, A., Pelot, R., Eiselt, H. A. and MacMackin, W. D. (2017). Determining the optimal mix and location of search and rescue vessels for the Canadian coast guard. International Journal of Operations and Quantitative Management, 23, 131–146.Google Scholar
Aydogdu, Y. V. (2014). A comparison of maritime risk perception and accident statistics in the Istanbul Straight The Journal of Navigation, 67(1), 129–144.CrossRefGoogle Scholar
Brushett, B. A., Allen, A. A., King, B. A. and Lemckert, C. J. (2017). Application of leeway drift data to predict the drift of panga skiffs: case study of maritime search and rescue in the Tropical Pacific. Applied Ocean Research, 67, 109–124.CrossRefGoogle Scholar
Duan, B., Wang, J. and Wang, C. (2015). High-precision prediction of ships including speed and orientation. Geomatics and Information Science of Wuhan University, 40(3), 422–426.Google Scholar
Gunnar Aarsæther, K. and Moan, T. (2009). Estimating navigation patterns from AIS. The Journal of Navigation, 62(4), 587–607.CrossRefGoogle Scholar
Harati-Mokhtari, A., Wall, A., Brooks, P. and Wang, J. (2007). Automatic identification system (AIS): data reliability and human error implications. The Journal of Navigation, 60(3), 373–389.Google Scholar
International Maritime Organization. (2002). Safety of Navigation (Chapter V) Regulation 19. International Convention for Safety of Life at Sea (SOLAS).Google Scholar
Kazimierski, W. and Stateczny, A. (2015). Radar and automatic identification system track fusion in an electronic chart display and information system. The Journal of Navigation, 68(6), 1141–1154.CrossRefGoogle Scholar
Kum, S. and Sahin, B. (2015). A root cause analysis for Arctic marine accidents from 1993 to 2011. Safety Science, 74, 206–220.10.1016/j.ssci.2014.12.010CrossRefGoogle Scholar
Li, L., Lu, W., Niu, J., Liu, J. and Liu, D. (2017). AIS data-based decision model for navigation risk in sea areas. The Journal of Navigation, 71, 664–678.10.1017/S0373463317000807CrossRefGoogle Scholar
Li, Y., Chung, K. L., Xie, S., Yang, Y., Wang, M. and Geng, X. (2018). An improved design of automatic-identification-system-based man overboard device: a multidisciplinary product. IEEE Access, 6, 25220–25229.10.1109/ACCESS.2018.2828399CrossRefGoogle Scholar
Park, K. S., Heo, K. Y., Jun, K., Kwon, J. I., Kim, J., Choi, J. Y., Cho, K. H., Choi, B. J., Seo, S. N., Kim, Y. H., Kim, S. D., Yang, C. S., Lee, J. C., Kim, S. I., Kim, S., Choi, J. W. and Jeong, S. H. (2015). Development of the operational oceanographic system of Korea. Ocean Science Journal, 50(2), 353–369.CrossRefGoogle Scholar
Pelot, R., Akbari, A. and Li, L. (2015). Vessel location modeling for maritime search and rescue. In Applications of Location Analysis. Switzerland: Springer International Publishing, pp. 369–402.Google Scholar
Razi, N. and Karatas, M. (2016). A multi-objective model for locating search and rescue boats. European Journal Operational Research, 254(1), 279–293.CrossRefGoogle Scholar
Sang, L., Wall, A., Mao, Z., Yan, X. and Wang, J. (2015). A novel method for restoring the trajectory of the inland waterway ship by using AIS data. Ocean Engineering, 110, 183–194.CrossRefGoogle Scholar
Shchekinova, E. Y., Kumkar, Y. and Coppini, G. (2016). Numerical reconstruction of trajectory of small-size surface drifter in the Mediterranean Sea. Ocean Dynamics, 66(2), 153–161.Google Scholar
Silveira, P. A. M., Teixeira, A. P. and Guedes Soares, C. (2013). Use of AIS data to characterize marine traffic patterns and ship collision risk off the coast of Portugal. The Journal of Navigation, 66, 879–898.Google Scholar
Tong, S. Q., Wang, N. and Song, N. Q. (2017). Emergency evacuation capability evaluation and optimization for an offshore airport: the case of Dalian Offshore Airport, Dalian, China. Safety Science, 92, 128–137.CrossRefGoogle Scholar
Tsou, M.-C. (2010). Discovering knowledge from AIS database for application in VTS. The Journal of Navigation, 63(3), 449–469.CrossRefGoogle Scholar
Varlamis, I., Tserpes, K. and Sardianos, C. (2018). Detecting Search and Rescue Missions from AIS Data. In 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW), pp. 60–65.Google Scholar
Wu, L., Xu, Y., Wang, Q., Wang, F. and Xu, Z. (2016). Mapping global shipping density from AIS data. The Journal of Navigation, 70(1), 67–81.CrossRefGoogle Scholar
Zhang, W., Kopca, C., Tang, J., Ma, D. and Wang, Y. (2017). A systematic approach for collision risk analysis based on AIS data. The Journal of Navigation, 70(5), 1117–1132.10.1017/S0373463317000212CrossRefGoogle Scholar
Zhou, F., Pan, S. and Jiang, J. (2019). Verification of AIS data by using video images taken by a UAV. The Journal of Navigation, 72, 1345–1358.CrossRefGoogle Scholar
Zhou, X., Cheng, L., Zhang, F., Yan, Z., Ruan, X., Min, K. and Li, M. (2019). Integrating island spatial information and integer optimization for locating maritime search and rescue bases: a case study in the South China Sea. ISPRS International Journal of Geo-Information, 8(2), 88.CrossRefGoogle Scholar