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Temporal features of non-accident critical events impact from tides around the Yangtze Estuary and adjacent coastal waters

Published online by Cambridge University Press:  21 January 2022

Chenyang Yao
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
Guoping Gao*
Affiliation:
College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China
Weijiong Chen
Affiliation:
College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China
Tingrong Qin
Affiliation:
Merchant Marine College, Shanghai Maritime University, Shanghai, China.
Zhuang Li
Affiliation:
College of Ocean Science and Engineering, Shanghai Maritime University, Shanghai, China
*
*Corresponding author. E-mail: [email protected]

Abstract

Applying non-accident critical events (NACEs) as an alternative measuring method to assess ship collision risk has become popular in recent years. NACEs has shown temporal features in different waters. This study uses the quaternion ship domain method to identify NACEs around the Yangtze Estuary and adjacent coastal waters from the Automatic Identification System (AIS) data in October 2019. The results indicate that NACEs show different temporal features in estuaries and coastal waters. The relationship between tides, channel types and ships is discussed. In addition, we established a statistical method for the occurrence time of NACEs and the state of tides according to the half-tide level and the nearest time. The outcomes of this study provide a direction for exploring the relationship between NACEs and environmental conditions, which is also instructive for the study of the causes of ship accidents.

Type
Research Article
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

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References

Al Shami, A., Harik, G., Alameddine, I., Bruschi, D., Garcia, D. A. and El-Fadel, M. (2017). Risk assessment of oil spills along the Mediterranean coast: A sensitivity analysis of the choice of hazard quantification. The Science of the Total Environment, 574, 234245.CrossRefGoogle ScholarPubMed
Arici, S. S., Akyuz, E. and Arslan, O. (2020). Application of fuzzy bow-tie risk analysis to maritime transportation: The case of ship collision during the STS operation. Ocean Engineering, 217, 107960.CrossRefGoogle Scholar
Bakdi, A., Glad, I. K., Vanem, E. and Engelhardtsen, Ø. (2019). AIS-Based Multiple vessel collision and grounding risk identification based on adaptive safety domain. Journal of Marine Science and Engineering, 8, 5.CrossRefGoogle Scholar
Bye, R. and Aalberg, A. (2018). Maritime navigation accidents and risk indicators: An exploratory statistical analysis using AIS data and accident reports. Reliability Engineering & System Safety, 176, 174186.CrossRefGoogle Scholar
Chai, T., Weng, J. and Li, G. (2020). Estimation of vessel collision frequency in the Yangtze river estuary considering dynamic ship domains. Journal of Marine Science and Technology, 25, 114.CrossRefGoogle Scholar
Chen, P., Huang, Y., Mou, J. and Gelder, P. H. A. J. M. v. (2019a). Probabilistic risk analysis for ship-ship collision: State-of-the-art. Safety Science, 117, 108122.CrossRefGoogle Scholar
Chen, P., Mou, J. and Gelder, P. H. A. J. M. (2019b). Integration of individual encounter information into causation probability modelling of ship collision accidents. Safety Science, 120, 636651.CrossRefGoogle Scholar
Debnath, A. K. and Chin, H. C. (2006). Analysis of Marine Conflicts. Proceedings of the 19th KKCNN Symposium on Civil Engineering. https://eprints.qut.edu.au/51380/1/Analysis_of_marine_conflicts.pdfGoogle Scholar
Du, Y., Chen, Q., Lam, J. S. L., Xu, Y. and Cao, J. X. (2015). Modeling the impacts of tides and the virtual arrival policy in berth allocation. Transportation Science, 49, 939956.CrossRefGoogle Scholar
Du, L., Goerlandt, F. and Kujala, P. (2020a). Review and analysis of methods for assessing maritime waterway risk based on non-accident critical events detected from AIS data. Reliability Engineering & System Safety, 200, 106933.CrossRefGoogle Scholar
Du, L., Valdez Banda, O. A., Goerlandt, F., Huang, Y. and Kujala, P. (2020b). A COLREG-compliant ship collision alert system for stand-on vessels. Ocean Engineering, 218, 107866.CrossRefGoogle Scholar
EMSA (2019). Annual overview of marine casualties and incidents 2019. Available at: http://www.emsa.europa.eu/emsa-documents/latest/download/5854/3734/23.html [Accessed: 2 July 2020].Google Scholar
Fernandes, R., Braunschweig, F., Lourenço, F. and Neves, R. (2016). Combining operational models and data into a dynamic vessel risk assessment tool for coastal regions. Ocean Science, 12, 285317.CrossRefGoogle Scholar
Goerlandt, F. and Kujala, P. (2011). Traffic simulation based ship collision probability modeling. Reliability Engineering & System Safety, 96, 91107.CrossRefGoogle Scholar
IMO (2001). Guidelines for the onboard operational use of shipborne Automatic Identification System (AIS). Resolution A.917(22). Available at: http://www.imo.org/en/KnowledgeCentre/IndexofIMOResolutions/Assembly/Documents/A.917(22).pdf. [Accessed 5 November 2019].Google Scholar
IMO (2003). Guidelines for the installation of a shipborne Automatic Identification System (AIS). SN/Circ.227. Available at: http://www.imo.org/OurWork/Safety/Navigation/Documents/227.pdf [Accessed 2 November 2019].Google Scholar
Jensen, C., Hines, E., Holzman, B., Moore, T. J., Jahncke, J. and Redfern, J. (2015). Spatial and temporal variability in shipping traffic Off San Francisco, California. Coastal Management, 43, 575588.CrossRefGoogle Scholar
Kujala, P., Hänninen, M. and Arola, T. (2009). Analysis of the marine traffic safety in the gulf of Finland. Reliability Engineering & System Safety, 94, 13491357.CrossRefGoogle Scholar
Kum, S. and Sahin, B. (2015). A root cause analysis for Arctic marine accidents from 1993 to 2011. Safety Science, 74, 206220.CrossRefGoogle Scholar
Lei, P., Tsai, T., Wen, Y. and Peng, W. (2017). A Framework for Discovering Maritime Traffic Conflict From AIS Network. 19th Asia-Pacific Network Operations and Management Symposium (APNOMS), 27-29 Sept. 2017, 16.CrossRefGoogle Scholar
Li, S., Meng, Q. and Qu, X. (2012). An overview of maritime waterway quantitative risk assessment models. Risk Analysis, 32, 496512.CrossRefGoogle ScholarPubMed
Lu, S., Tong, C., Lee, D.-Y., Zheng, J., Shen, J., Zhang, W. and Yan, Y. (2015). Propagation of tidal waves up in Yangtze estuary during the dry season. Journal of Geophysical Research: Oceans, 120, 64456473.CrossRefGoogle Scholar
Luo, M. and Shin, S.-H. (2019). Half-century research developments in maritime accidents: Future directions. Accident Analysis & Prevention, 123, 448460.CrossRefGoogle ScholarPubMed
Ma, S. and Zhu, C. (2018). Extreme cold wave over east Asia in January 2016: A possible response to the larger internal atmospheric variability induced by Arctic warming. Journal of Climate, 32, 12031216.CrossRefGoogle Scholar
MOT (2019). Cargo and container throughput of Chinese Ports. Available at: http://xxgk.mot.gov.cn/jigou/zhghs/202001/t20200120_3326623.html. [Accessed 18 December 2020].Google Scholar
MSA, China. (2018). Sailing direction on Chinese coast (East area) [in Chinese]. Beijing: China Communication Press.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013). Vessel pattern knowledge discovery from AIS data: A framework for anomaly detection and route prediction. Entropy, 15, 22182245.CrossRefGoogle Scholar
Qian, W., Leung, J., Chen, Y. and Huang, S. (2019). Applying anomaly-based weather analysis to the prediction of Low visibility associated with the coastal Fog at ningbo-zhoushan port in east China. Advances in Atmospheric Sciences, 36, 10601077.CrossRefGoogle Scholar
Qu, X., Meng, Q. and Suyi, L. (2011). Ship collision risk assessment for the Singapore strait. Accident Analysis & Prevention, 43, 20302036.CrossRefGoogle ScholarPubMed
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
Rawson, A., Rogers, E., Foster, D. and Phillips, D. (2014). Practical application of domain analysis: Port of London case study. Journal of Navigation, 67, 193209.CrossRefGoogle Scholar
Rezaee, S., Pelot, R. and Finnis, J. (2016). The effect of extratropical cyclone weather conditions on fishing vessel incidents’ severity level in Atlantic Canada. Safety Science, 85, 3340.CrossRefGoogle Scholar
Riveiro, M., Pallotta, G. and Vespe, M. (2018). Maritime anomaly detection: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8, e1266.Google Scholar
Silveira, P., Teixeira, A. P. and Guedes Soares, C. (2013). Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal. Journal of Navigation, 66, 879898.CrossRefGoogle Scholar
Szlapczynski, R. and Szlapczynska, J. (2017). Review of ship safety domains: Models and applications. Ocean Engineering, 145, 277289.CrossRefGoogle Scholar
Wang, N. (2013). Intelligent quaternion ship domains for spatial collision risk assessment. Journal of Ship Research, 56, 170182.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, 257276.CrossRefGoogle Scholar
Wang, D., Guo, W., Kong, S. and Xu, T. (2020). Estimating offshore exposure to oil spill impacts based on a statistical forecast model. Marine Pollution Bulletin, 156, 111213.CrossRefGoogle ScholarPubMed
Weatherall, P., Tozer, B., Arndt, J. E., Bazhenova, E., Bringensparr, C., Castro, C., Dorschel, B., Ferrini, V., Hehemann, L., Jakobsson, M., Johnson, P., Ketter, T., Mackay, K., Martin, T., Mayer, L., McMichael-Phillips, J., Mohammad, R., Nitsche, F., Sandwell, D. and Viquerat, S. (2020). The GEBCO\_2020 Grid - a continuous terrain model of the global oceans and land.Google Scholar
Weng, J., Liao, S., Wu, B. and Yang, D. (2020). Exploring effects of ship traffic characteristics and environmental conditions on ship collision frequency. Maritime Policy & Management, 47, 523543.CrossRefGoogle Scholar
Wu, B., Tian, H., Yan, X. and Guedes Soares, C. (2019). A probabilistic consequence estimation model for collision accidents in the downstream of Yangtze river using Bayesian networks. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 234, 422436.Google Scholar
Xin, X., Liu, K., Yang, Z., Zhang, J. and Wu, X. (2021). A probabilistic risk approach for the collision detection of multi-ships under spatiotemporal movement uncertainty. Reliability Engineering & System Safety, 215, 107772.CrossRefGoogle Scholar
Yao, X., Zhao, D. and Li, Y. (2020). Autumn tropical cyclones over the western north pacific during 1949-2016: A statistical study. Journal of Meteorological Research, 34, 150162.CrossRefGoogle Scholar
Yoo, Y. and Kim, T.-G. (2019). An improved ship collision risk evaluation method for korea maritime safety audit considering traffic flow characteristics. Journal of Marine Science and Engineering, 7, 448.CrossRefGoogle Scholar
Zhang, D., Yan, X., Yang, Z., Wall, A. and Wang, J. (2013). Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River. Reliability Engineering & System Safety, 118, 93105.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, 6069.CrossRefGoogle Scholar
Zhang, Z., Yin, J., Wang, N. and Hui, Z. (2018). Vessel traffic flow analysis and prediction by an improved PSO-BP mechanism based on AIS data. Evolving Systems, 10, 111.Google Scholar
Zhang, W., Feng, X., Qi, Y., Shu, F., Zhang, Y. and Wang, Y. (2019). Towards a model of regional vessel near-miss collision risk assessment for open waters based on AIS data. Journal of Navigation, 72, 14491468.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, L., Liang, Z., Zhuge, D., Lee, L. H. and Chew, E. P. (2017). Daily berth planning in a tidal port with channel flow control. Transportation Research Part B: Methodological, 106, 193217.CrossRefGoogle Scholar