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A framework of marine collision risk identification strategy using AIS data

Published online by Cambridge University Press:  16 November 2023

Xiaofei Ma ,
Guoyou Shi ,
Jiahui Shi  and
Jiao Liu
Xiaofei Ma
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China Key Laboratory of Navigation Safety Guarantee of Liaoning Province, Dalian, China
Guoyou Shi*
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China Key Laboratory of Navigation Safety Guarantee of Liaoning Province, Dalian, China
Jiahui Shi
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
Jiao Liu
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China Key Laboratory of Navigation Safety Guarantee of Liaoning Province, Dalian, China
*
*Corresponding author: Guoyou Shi; Email: sgydmu@163.com
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Abstract

Collisions are one of the major accidents in the shipping industry, causing significant losses. In this work, a framework of marine collision risk identification strategy was developed to quantitatively analyse collision risks and provide an easy and convenient way to monitor traffic flow in relevant waters to mitigate the chances of collision. The model was verified by using automatic identification system data obtained from Tianjin Port. When compared to previous research, the proposed model can identify risks earlier and give people more time to analyse and take action. The results indicate that it also can provide a visual display to alert relevant personnel. The model can be used as a reference to identify potential collision risks or as an information source for future research.

Keywords

Collision risk identificationAIS dataship domainmaritime safety
Type
Research Article
Information
The Journal of Navigation , Volume 76 , Issue 4-5 , July 2023 , pp. 525 - 544
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Copyright
Copyright © The Author(s), 2023. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Altan, Y. C. and Otay, E. N. (2018). Spatial mapping of encounter probability in congested waterways using AIS. Ocean Engineering, 164, 263–271.CrossRefGoogle Scholar
Baldauf, M., Benedict, K., Fischer, S., Motz, F. and Schröder-Hinrichs, J. U. (2011). Collision avoidance systems in air and maritime traffic. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 225, 333–343.Google Scholar
Bye, R. J. and Aalberg, A. L. (2018). Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports. Reliability Engineering & System Safety, 176, 174–186.CrossRefGoogle Scholar
Bye, R. J. and Almklov, P. G. (2019). Normalization of maritime accident data using AIS. Marine Policy, 109, 103675.CrossRefGoogle Scholar
Chai, T., Weng, J. and De-qi, X. (2017). Development of a quantitative risk assessment model for ship collisions in fairways. Safety Science, 91, 71–83.CrossRefGoogle Scholar
Chen, P., Mou, J. and Van Gelder, P. H. A. J. M. (2019). Integration of individual encounter information into causation probability modelling of ship collision accidents. Safety Science, 120, 636–651.CrossRefGoogle Scholar
Chin, H. C. and Quek, S. T. (1997). Measurement of traffic conflicts. Safety Science, 3, 169–185.Google Scholar
Coldwell, T. G. (1983). Marine traffic behaviour in restricted waters. The Journal of Navigation, 36, 430–444.CrossRefGoogle Scholar
Čorić, M., Mandžuka, S., Gudelj, A. and Lušić, Z. (2021). Quantitative ship collision frequency estimation models: A review. Journal of Marine Science and Engineering, 9, 533.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, 215–222.CrossRefGoogle Scholar
Debnath, A. K. and Chin, H. C. (2010). Navigational traffic conflict technique: A proactive approach to quantitative measurement of collision risks in port waters. The Journal of Navigation, 63, 137–152.CrossRefGoogle Scholar
Du, L., Banda, O. A. V., Huang, Y., Goerlandt, F., Kujala, P. and Zhang, W. (2021). An empirical ship domain based on evasive manoeuvre and perceived collision risk. Reliability Engineering & System Safety, 213, 107752.CrossRefGoogle Scholar
Fujii, Y. and Tanaka, K. (1971). Traffic capacity. The Journal of Navigation, 24, 543–552.CrossRefGoogle Scholar
Gao, M. and Shi, G. Y. (2020). Ship collision avoidance anthropomorphic decision-making for structured learning based on AIS with Seq-CGAN. Ocean Engineering, 217, 107922.CrossRefGoogle Scholar
Gao, D. W., Zhu, Y. S., Zhang, J. F., He, Y. K., Yan, K. and Yan, B. R. (2021). A novel MP-LSTM method for ship trajectory prediction based on AIS data. Ocean Engineering, 228, 108956.CrossRefGoogle Scholar
Goerlandt, F. and Montewka, J. (2015a). 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
Goerlandt, F. and Montewka, J. (2015b). Maritime transportation risk analysis: Review and analysis in light of some foundational issues. Reliability Engineering & System Safety, 138, 115–134.CrossRefGoogle Scholar
Goodwin, E. M. (1975). A statistical study of ship domains. The Journal of Navigation, 28, 328–344.CrossRefGoogle Scholar
Hansen, M. G., Jensen, T. K., Lehn-Schiøler, T., Melchild, K., Rasmussen, F. M. and Ennemark, F. (2013). Empirical ship domain based on AIS data. The Journal of Navigation, 66, 931–940.CrossRefGoogle Scholar
Hörteborn, A. and Ringsberg, J. W. (2021). A method for risk analysis of ship collisions with stationary infrastructure using AIS data and a ship manoeuvring simulator. Ocean Engineering, 235, 109396.CrossRefGoogle Scholar
Jingsong, Z., Zhaolin, W. and Fengchen, W. (1993). Comments on ship domains. The Journal of Navigation, 46, 422–436.CrossRefGoogle Scholar
Jon, M. H., Kim, Y. P. and Choe, U. (2021). Determination of a safety criterion via risk assessment of marine accidents based on a Markov model with five states and MCMC simulation and on three risk factors. Ocean Engineering, 236, 109000.CrossRefGoogle Scholar
Kijima, K. and Furukawa, Y. (2001). Design of automatic collision avoidance system using fuzzy inference. IFAC Proceedings Volumes, 34, 65–70.CrossRefGoogle 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
Lim, G. J., Cho, J., Bora, S., Biobaku, T. and Parsaei, H. (2018). Models and computational algorithms for maritime risk analysis: A review. Annals of Operations Research, 271, 765–786.CrossRefGoogle Scholar
Liu, J., Shi, G. and Zhu, K. (2020). Online multiple outputs least-squares support vector regression model of ship trajectory prediction based on automatic information system data and selection mechanism. IEEE Access, 8, 154727–154745.CrossRefGoogle Scholar
Liu, K., Yuan, Z., Xin, X., Zhang, J. and Wang, W. (2021). Conflict detection method based on dynamic ship domain model for visualization of collision risk hot-spots. Ocean Engineering, 242, 110143.CrossRefGoogle Scholar
Luo, M. and Shin, S. H. (2019). Half-century research developments in maritime accidents: Future directions. Accident Analysis & Prevention, 123, 448–460.CrossRefGoogle ScholarPubMed
Majumdar, A., Manole, I. and Nalty, R. (2021). Analysis of port accidents and calibration of heinrich's pyramid. Transportation Research Record, 2676, 476–489.Google 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, 483–490.Google Scholar
Murray, B. and Perera, L. P. (2021). An AIS-based deep learning framework for regional ship behavior prediction. Reliability Engineering & System Safety, 215, 107819.CrossRefGoogle Scholar
Pietrzykowski, Z. and Uriasz, J. (2004). The Ship Domain in A Deep-Sea Area. Proceeding of the 3rd International Conference on Computer and IT Applications in the Maritime Industries, Siguenza, Spain.Google Scholar
Simsir, U., Amasyalı, M. F., Bal, M., Çelebi, U. B. and Ertugrul, S. (2014). Decision support system for collision avoidance of vessels. Applied Soft Computing, 25, 369–378.CrossRefGoogle Scholar
Smierzchalski, R. (2001). On-Line trajectory planning in collision situations at sea by evolutionary computation-experiments. IFAC Proceedings Volumes, 34, 407–412.CrossRefGoogle Scholar
Szlapczynski, R. and Szlapczynska, J. (2016). An analysis of domain-based ship collision risk parameters. Ocean Engineering, 126, 47–56.CrossRefGoogle Scholar
Wang, N. (2013). A novel analytical framework for dynamic quaternion ship domains. Journal of Navigation, 66, 265–281.CrossRefGoogle Scholar
Wang, Y. and Chin, H. C. (2016). An empirically-calibrated ship domain as a safety criterion for navigation in confined waters. Journal of Navigation, 69, 257–276.CrossRefGoogle Scholar
Wang, N., Meng, X., Xu, Q. and Wang, Z. (2009). A unified analytical framework for ship domains. Journal of Navigation, 62, 643–655.CrossRefGoogle Scholar
Wang, J., Deng, W. and Guo, Y. (2014). New Bayesian combination method for short-term traffic flow forecasting. Transportation Research Part C: Emerging Technologies, 43, 79–94.CrossRefGoogle Scholar
Wei, Z., Xie, X. and Zhang, X. (2020). AIS trajectory simplification algorithm considering ship behaviours. Ocean Engineering, 216, 108086.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, W., Goerlandt, F., Montewka, J. and Kujala, P. (2015). A method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 107, 60–69.CrossRefGoogle Scholar
Zhang, W., 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.CrossRefGoogle Scholar
Zhang, J., Teixeira, ÂP, Soares, C. G. and Yan, X. (2018). Quantitative assessment of collision risk influence factors in the Tianjin port. Safety Science, 110, 363–371.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, F., Peng, X., Huang, L., Zhu, M., Wen, Y. and Zheng, H. (2021). A spatiotemporal statistical method of ship domain in the inland waters driven by trajectory data. Journal of Marine Science and Engineering, 9, 410.CrossRefGoogle Scholar
Zhao, L. and Fu, X. (2021). A novel Index for real-time ship collision risk assessment based on velocity obstacle considering dimension data from AIS. Ocean Engineering, 240, 109913.CrossRefGoogle Scholar
Zhao, L., Shi, G. and Yang, J. (2018). Ship trajectories Pre-processing based on AIS data. Journal of Navigation, 71, 1210–1230.CrossRefGoogle Scholar
Zhao, X., Yuan, H. and Yu, Q. (2021). Autonomous vessels in the Yangtze river: A study on the maritime accidents using data-driven Bayesian networks. Sustainability, 13, 9985.CrossRefGoogle Scholar