Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-27T22:05:00.105Z Has data issue: false hasContentIssue false
  • English
  • Français

Towards a Model of Regional Vessel Near-miss Collision Risk Assessment for Open Waters based on AIS Data

Published online by Cambridge University Press:  22 May 2019

Weibin Zhang ,
Xinyu Feng ,
Yong Qi ,
Feng Shu ,
Yijin Zhang  and
Yinhai Wang
Weibin Zhang
Affiliation:
(Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nangjing 210094, China)
Xinyu Feng
Affiliation:
(Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nangjing 210094, China)
Yong Qi*
Affiliation:
(Nanjing University of Science and Technology, School of Computer Science and Engineering, Nangjing 210094, China)
Feng Shu
Affiliation:
(Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nangjing 210094, China)
Yijin Zhang
Affiliation:
(Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nangjing 210094, China)
Yinhai Wang
Affiliation:
(University of Washington, Department of Civil and Environmental Engineering, Smart Transportation Applications and Research Laboratory, 98195 Seattle, Washington, USA)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

The absence of a regional, open water vessel collision risk assessment system endangers maritime traffic and hampers safety management. Most recent studies have analysed the risk of collision for a pair of vessels and propose micro-level risk models. This study proposes a new method that combines density complexity and a multi-vessel collision risk operator for assessing regional vessel collision risk. This regional model considers spatial and temporal features of vessel trajectory in an open water area and assesses multi-vessel near-miss collision risk through danger probabilities and possible consequences of collision risks via four types of possible relative striking positions. Finally, the clustering method of multi-vessel encountering risk, based on the proposed model, is used to identify high-risk collision areas, which allow reliable and accurate analysis to aid implementation of safety measures.

Keywords

Multi-vessel collision riskDensity complexityAIS dataRegional risk assessmentMaritime traffic safety
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 6 , November 2019 , pp. 1449 - 1468
Copyright
Copyright © The Royal Institute of Navigation 2019 

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

Convertino, M. and Valverde, L. J. (2017). Probabilistic analysis of the impact of vessel speed restrictions on navigational safety: accounting for the right whale rule. The Journal of Navigation, 71(1), 65–82.Google Scholar
Critch, S., Goerlandt, F., Montewka, J. and Kujala, P. (2013). Towards a risk model for the Northern Baltic maritime winter navigation system. In International Workshop on Next Generation Nautical Traffic Models, 21–30.Google Scholar
Delahaye, D., Paimblanc, P., Puechmorel, S., Histon, J. M. and Hansman, R. J. (2002). A new air traffic complexity metric based on dynamical system modelization. In Proceedings of the 21st Digital Avionics Systems Conference, 1, 4A2-1–4A2-12.Google Scholar
Geng, X., Huang, L., Wen, Y. and Zhou, C. (2016). Modelling of the Marine Traffic Situation Complexity. In MATEC Web of Conferences, 68, 15002–15007.Google Scholar
Goerlandt, F., & Kujala, P. (2014). On the reliability and validity of ship–ship collision risk analysis in light of different perspectives on risk. Safety science, 62, 348–365.Google Scholar
Goerlandt, F. and Montewka, J. (2015). A framework for risk analysis of maritime transportation systems: a case study for oil spill from tankers in a ship–ship collision. Safety Science, 76, 42–66.Google Scholar
Hänninen, M., Mazaheri, A., Kujala, P., Montewka, J., Laaksonen, P., Salmiovirta, M. and Klang, M. (2014). Expert elicitation of a navigation service implementation effects on ship groundings and collisions in the Gulf of Finland. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 228(1), 19–28.Google Scholar
Hilburn, B., (2004). Cognitive Complexity in air Traffic Control: A Literature Review. Euro control, Brussels, Belgium, Tech. Rep. 04/04.Google Scholar
Kaplan, S. (1997). The words of risk analysis. Risk Analysis, 17(4), 407–417.Google Scholar
Li, B. and Pang, F. W. (2013). An approach of vessel collision risk assessment based on the d–s evidence theory. Ocean Engineering, 74(2), 16–21.Google Scholar
Masalonis, A. J., Callaham, M. B. and Wanke, C. R. (2003). Dynamic density and complexity metrics for realtime traffic flow management. In Proceedings of the 5th USA/Europe Air Traffic Management R & D Seminar, Budapest, Hungary.Google Scholar
Montewka, J., Ehlers, S., Goerlandt, F., Hinz, T., Tabri, K. and Kujala, P. (2014b). A framework for risk assessment for maritime transportation systems—a case study for open sea collisions involving ROPAX vessels. Reliability Engineering & System Safety, 124(2), 142–157.Google Scholar
Montewka, J., Goerlandt, F. and Kujala, P. (2014a). On a systematic perspective on risk for formal safety assessment (FSA). Reliability Engineering & System Safety, 127(127), 77–85.Google Scholar
Montewka, J., Hinz, T., Kujala, P. and Matusiak, J. (2010). Probability modelling of vessel collisions. Reliability Engineering & System Safety, 95(1), 573–589.Google Scholar
Przywarty, M., Gucma, L., Marcjan, K. and Bąk, A. (2015). Risk analysis of the collision between passenger ferry and chemical tanker in the western zone of the Baltic Sea. Polish Maritime Research, 22(2), 3–8.Google Scholar
Qu, X., Meng, Q. and Suyi, L. (2011). Ship collision risk assessment for the singapore strait. Accident; Analysis and Prevention, 43(6), 2030.Google Scholar
Rausand, M. (2013). Data for Risk Analysis. Risk Assessment. John Wiley & Sons, Inc.Google Scholar
Riveiro, M., Pallotta, G. and Vespe, M. (2018). Maritime anomaly detection: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(1), 1266–1284.Google Scholar
Silveira, P. A. M., Teixeira, A. P. and Guedes, S. C. (2013). Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal. The Journal of Navigation, 66(6), 879–898.Google Scholar
Szlapczynski, R. and Szlapczynska, J. (2017). Review of ship safety domains: models and applications. Ocean Engineering, 145C, 277–289.Google Scholar
Wang, N., Meng, X., Xu, Q. and Wang, Z. (2009). A unified analytical framework for ship domains. The Journal of Navigation, 62(4), 643–655.Google Scholar
Wang, Y. and Chin, H. C. (2016). An empirically-calibrated ship domain as a safety criterion for navigation in confined waters. The Journal of Navigation, 69(2), 257–276.Google Scholar
Wen, Y., Huang, Y., Zhou, C., Yang, J., Xiao, C. and Wu, X. (2015). Modelling of marine traffic flow complexity. Ocean Engineering, 104, 500–510.Google Scholar
Weng, J., Meng, Q. and Qu, X. (2012). Vessel collision frequency estimation in the Singapore Strait. The Journal of Navigation, 65(2), 207–221.Google Scholar
Wilson, P., A., Harris, C., J. and Hong. (2003). A line of sight counteraction navigation algorithm for ship encounter collision avoidance. The Journal of Navigation, 56(1), 111–121.Google Scholar
Wu, X., Mehta, A. L., Zaloom, V. A. and Craig, B. N. (2016). Analysis of waterway transportation in Southeast Texas Waterway based on AIS data. Ocean Engineering, 121, 196–209.Google Scholar
Yahei, F. and Reijiro, S. (1971). The analysis of traffic accidents. The Journal of Navigation, 24(4), 534–543.Google Scholar
You, Y. J., Rhee, K. P., Park, H., Lee, S. K. and Park, J. (2010). A study on the collision avoidance system of a ship considering the effects of speed dependent coefficients. In the Twentieth International Offshore and Polar Engineering Conference. International Society of Offshore and Polar Engineers.Google Scholar
Zhang, J., Zhang, D., Yan, X., Haugen, S. and Soares, C. G. (2015a). A distributed anti-collision decision support formulation in multi-ship encounter situations under COLREGs. Ocean Engineering, 105, 336–348.Google 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(1), 1117–1132.Google Scholar
Zhang, W. B., Goerlandt, F., Montewka, J. and Kujala, P. (2015b). A method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 107(1), 60–69.Google Scholar
Zhang, W. B., Goerlandt, F., Kujala, P. and Wang, Y. (2016). An advanced method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 124, 141–156.Google Scholar
Zhen, R., Riveiro, M. and Jin, Y. (2017). A novel analytic framework of real-time multi-vessel collision risk assessment for maritime traffic surveillance. Ocean Engineering, 145, 492–501.Google Scholar