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A novel regional collision risk assessment method considering aggregation density under multi-ship encounter situations

Published online by Cambridge University Press:  19 November 2021

Rong Zhen Open the ORCID record for Rong Zhen [Opens in a new window] ,
Ziqiang Shi ,
Zheping Shao  and
Jialun Liu
Rong Zhen
Affiliation:
Navigation College, Jimei University, Xiamen, China. Hubei Key Laboratory of Inland Shipping Technology, Wuhan, China Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China
Ziqiang Shi
Affiliation:
Navigation College, Jimei University, Xiamen, China.
Zheping Shao
Affiliation:
Navigation College, Jimei University, Xiamen, China.
Jialun Liu*
Affiliation:
Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, China National Engineering Research Center for Water Transportation Safety, Wuhan, China
*
*Corresponding author. E-mail: [email protected]
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Abstract

The regional ship collision risk assessment for multiple ships in restricted waters is of great significance to the early warning of ship collision risk and the intelligent supervision of maritime traffic. Given the existed method of regional ship collision risk assessment without considering the impact of ship aggregation density, this paper proposes a novel regional ship collision risk assessment method that considers the aggregation density (AD) of the clusters of encounter ships (CES) for intelligent surveillance and navigation. The effectiveness of the proposed method has been examined by the experimental case study in the waters of Xiamen, China, and analysis has been compared with other existed studies to show the advantages of the new proposed algorithm. The results show that the study method can more intuitively and effectively quantify the temporal and spatial distribution of regional collision risks in the restricted sea area. The proposed method can improve the efficiency of traffic management when monitoring the ship collision risks in macroscopic view, and assist the safety of manned and unmanned ship navigation.

Keywords

ship collision riskship aggregation densityclusters of encounter shipsmaritime surveillance
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 1 , January 2022 , pp. 76 - 94
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Copyright
Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Ahn, J. H., Rhee, K. P. and You, Y. J. (2012). A study on the collision avoidance of a ship using neural networks and fuzzy logic. Applied Ocean Research, 37, 162–173.CrossRefGoogle Scholar
Bukhari, A. C., Tusseyeva, I. and Kim, Y. G. (2013). An intelligent real-time multi-vessel collision risk assessment system from VTS viewpoint based on fuzzy inference system. Expert Systems with Applications, 40(4), 1220–1230.CrossRefGoogle Scholar
Chen, P., Li, M. and Mou, J. (2021). A velocity obstacle-based real-time regional ship collision risk analysis method. Journal of Marine Science and Engineering, 9(4), 428.CrossRefGoogle Scholar
Davis, P. V., Dove, M. J. and Stockel, C. T. (1980). A computer simulation of marine traffic using domains and arenas. The Journal of Navigation, 33(2), 215–222.CrossRefGoogle Scholar
Fan, C., Wróbel, K., Montewka, J., Gil, M., Wan, C. and Zhang, D. (2020). A framework to identify factors influencing navigational risk for maritime autonomous surface ships. Ocean Engineering, 202, 107188.CrossRefGoogle Scholar
Fan, S., Zhang, J., Blanco-Davis, E., Yang, Z. and Yan, X. (2020). Maritime accident prevention strategy formulation from a human factor perspective using Bayesian networks and TOPSIS. Ocean Engineering, 210, 107544.CrossRefGoogle Scholar
Feng, Y., X., Zhang, W., Qi, B. Y., et al. Ship Collision Risk Assessment in Open Waters Based on AIS [C]. World Transport Congress 2018, 13, Beijing.Google Scholar
Fujii, Y. and Shiobara, R. (1971). The analysis of traffic accidents. The Journal of Navigation, 24(4), 534–543.CrossRefGoogle Scholar
Fujii, Y. and Tanaka, K. (1971). Traffic capacity. The Journal of Navigation, 24(4), 543–552.CrossRefGoogle Scholar
Gang, L., Wang, Y., Sun, Y., Zhou, L. and Zhang, M. (2016). Estimation of vessel collision risk index based on support vector machine. Advances in Mechanical Engineering, 8(11), 1687814016671250.CrossRefGoogle Scholar
Goerlandt, F. and Montewka, J. (2014). A probabilistic model for accidental cargo oil outflow from product tankers in a ship–ship collision. Marine Pollution Bulletin, 79(1–2), 130–144.CrossRefGoogle Scholar
Huang, Y., Chen, L., Negenborn, R. R. and van Gelder, P. H. A. J. M. (2020a). A ship collision avoidance system for human-machine cooperation during collision avoidance. Ocean Engineering, 217, 107913.CrossRefGoogle Scholar
Huang, Y., Chen, L., Chen, P., Negenborn, R. R. and Van Gelder, P. H. A. J. M. (2020b). Ship collision avoidance methods: State-of-the-art. Safety Science, 121, 451–473.CrossRefGoogle Scholar
Jiang, M., Lu, J., Yang, Z. and Li, J. (2020). Risk analysis of maritime accidents along the main route of the maritime silk road: A Bayesian network approach. Maritime Policy & Management, 47(6), 815–832.CrossRefGoogle Scholar
Kearon, J. (1997). Computer programs for collision avoidance and traffic keeping conference on mathematical aspects on marine traffic.Google Scholar
Kouibia, A. and Pasadas, M. (2012). An approximation problem of noisy data by cubic and bicubic splines. Applied Mathematical Modelling, 36(9), 4135–4145.CrossRefGoogle Scholar
Kujala, P., Hänninen, M., Arola, T. and Ylitalo, J. (2009). Analysis of the marine traffic safety in the gulf of Finland. Reliability Engineering & System Safety, 94(8), 1349–1357.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.CrossRefGoogle Scholar
Li, Y. P. (2019). Research on major characteristics of maritime traffic based on AIS data [D]. Dalian Maritime University.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, 16–21.CrossRefGoogle Scholar
Lisowski, J. (2002). Game control of moving objects. IFAC Proceedings Volumes, 35(1), 373–378.CrossRefGoogle Scholar
Liu, Z., Wu, Z. and Zheng, Z. (2019). A novel framework for regional collision risk identification based on AIS data. Applied Ocean Research, 89, 261–272.CrossRefGoogle Scholar
Liu, Z., Wu, Z. and Zheng, Z. (2020). A molecular dynamics approach for modeling the geographical distribution of ship collision risk. Ocean Engineering, 217, 107991.CrossRefGoogle Scholar
Mou, J. M., Van der Tak, C. and Ligteringen, H. (2010). Study on collision avoidance in busy waterways by using AIS data. Ocean Engineering, 37(5-6), 483–490.CrossRefGoogle Scholar
Ozturk, U. and Cicek, K. (2019). Individual collision risk assessment in ship navigation: A systematic literature review. Ocean Engineering, 180, 130–143.CrossRefGoogle Scholar
Rawson, A. and Brito, M. (2021). A critique of the use of domain analysis for spatial collision risk assessment. Ocean Engineering, 219, 108259.CrossRefGoogle Scholar
Ren, Y., Mou, J., Yan, Q., & Zhang, F. (2011). Study on Assessing Dynamic Risk of Ship Collision. In ICTIS 2011: Multimodal Approach to Sustained Transportation System Development: Information, Technology, Implementation (pp. 2751–2757).Google Scholar
Szlapczynski, R. (2006). A unified measure of collision risk derived from the concept of a ship domain. The Journal of Navigation, 59(3), 477.CrossRefGoogle Scholar
Wang, N. (2010). An intelligent spatial collision risk based on the quaternion ship domain. The Journal of Navigation, 63(4), 733.CrossRefGoogle 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.CrossRefGoogle Scholar
Yip, T. L. (2008). Port traffic risks–A study of accidents in Hong Kong waters. Transportation Research Part E: Logistics and Transportation Review, 44(5), 921–931.CrossRefGoogle Scholar
Yuan, X., Zhang, D., Zhang, J., Zhang, M. and Soares, C. G. (2021). A novel real-time collision risk awareness method based on velocity obstacle considering uncertainties in ship dynamics. Ocean Engineering, 220, 108436.CrossRefGoogle Scholar
Zhang, L. and Meng, Q. (2019). Probabilistic ship domain with applications to ship collision risk assessment. Ocean Engineering, 186, 106130.CrossRefGoogle Scholar
Zhang, J., Zhang, D., Yan, X., Haugen, S. and Soares, C. G. (2015). A distributed anti-collision decision support formulation in multi-ship encounter situations under COLREGs. Ocean Engineering, 105, 336–348.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.CrossRefGoogle Scholar
Zhang, W., Feng, X., Goerlandt, F. and Liu, Q. (2020). Towards a convolutional neural network model for classifying regional ship collision risk levels for waterway risk analysis. Reliability Engineering & System Safety, 204, 107127.CrossRefGoogle Scholar
Zhang, Y., Sun, X., Chen, J. and Cheng, C. (2021). Spatial patterns and characteristics of global maritime accidents. Reliability Engineering & System Safety, 206, 107310.CrossRefGoogle 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.CrossRefGoogle Scholar
Zheng, K., Chen, Y., Jiang, Y. and Qiao, S. (2020). A SVM based ship collision risk assessment algorithm. Ocean Engineering, 202, 107062.CrossRefGoogle Scholar