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Social-material aspect of navigation technology: using structural topic models to identify the causes of ship accidents (1973–2018)

Published online by Cambridge University Press:  13 July 2021

Yang Chen ,
Zhenghao Liu Open the ORCID record for Zhenghao Liu [Opens in a new window] ,
Han Zhou ,
Jian Zheng Open the ORCID record for Jian Zheng [Opens in a new window]  and
Likun Wang
Yang Chen
Affiliation:
College of Transport and Communications, Shanghai Maritime University, Shanghai, China.
Zhenghao Liu
Affiliation:
College of Transport and Communications, Shanghai Maritime University, Shanghai, China.
Han Zhou
Affiliation:
iFLYTEK Co., Ltd., Hefei, China
Jian Zheng
Affiliation:
College of Transport and Communications, Shanghai Maritime University, Shanghai, China.
Likun Wang*
Affiliation:
College of Transport and Communications, Shanghai Maritime University, Shanghai, China.
*
*Corresponding author. E-mail: 77912463@qq.com.
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Abstract

What are the major factors contributing to ship accidents, and how do these factors evolve in the long term? This study addresses these two questions by leveraging an unsupervised machine learning method named structural topic modelling to identify the causes of ship accidents. The study analysed 2,341 task errors manually collected from 441 reports issued by four government agencies covering a 45-year time span. The results show that the structure of causes of ship accidents remained essentially the same during this period. This highlights the social-material aspect of navigation technology, indicating that the use of advanced technology may not necessarily lead to safer navigation practices, and the interaction between the technology and human agency must be focused on in the bridge management context. Additionally, the computer-assisted textual data analysis highlights pilot-related factors, which might be rooted in the unsupervised and difficult-to-verify handover procedures between pilots and captains, thereby underlining the importance of appropriate piloting regulations.

Keywords

structural topic modelmaritime safetysocial materialismhuman-related factorstechnology
Type
Research Article
Information
The Journal of Navigation , Volume 75 , Issue 1 , January 2022 , pp. 35 - 56
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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

Airoldi, E. M. and Bischof, J. M. (2016). Improving and evaluating topic models and other models of text. Journal of the American Statistical Association, 111(516), 1381–1403.CrossRefGoogle Scholar
Akhtar, M. J. and Bouwer Utne, I. (2015). Common patterns in aggregated accident analysis charts from human fatigue-related groundings and collisions at sea. Maritime Policy & Management, 42(2), 186–206.CrossRefGoogle Scholar
Allianz. (2020), Safety and Shipping Review 2020. https://www.agcs.allianz.com/news-and-insights/reports/shipping-safety.html [Accessed 17 December 2020].Google Scholar
Awal, Z. I. and Hasegawa, K. (2015). Analysis of ship accidents due to marine engine failure: Application of logic programming technique (LPT). Marine Engineering, 50(6), 744–751.CrossRefGoogle Scholar
Baalisampang, T., Abbassi, R., Garaniya, V., Khan, F. and Dadashzadeh, M. (2018). Review and analysis of fire and explosion accidents in maritime transportation. Ocean Engineering, 158, 350–366.CrossRefGoogle Scholar
Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77–84.CrossRefGoogle 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
Chauvin, C., Lardjane, S., Morel, G., Clostermann, J. P. and Langard, B. (2013). Human and organisational factors in maritime accidents: Analysis of collisions at sea using the HFACS. Accident Analysis & Prevention, 59, 26–37.CrossRefGoogle ScholarPubMed
Chen, J., Bian, W., Wan, Z., Yang, Z., Zheng, H. and Wang, P. (2019). Identifying factors influencing total-loss marine accidents in the world: Analysis and evaluation based on ship types and sea regions. Ocean Engineering, 191, 106495.CrossRefGoogle Scholar
Debnath, A. K. and Chin, H. C. (2009). Hierarchical modeling of perceived collision risks in port fairways. Transportation Research Record, 2100(1), 68–75.CrossRefGoogle Scholar
Eliopoulou, E., Papanikolaou, A. and Voulgarellis, M. (2016). Statistical analysis of ship accidents and review of safety level. Safety Science, 85, 282–292.CrossRefGoogle Scholar
Ellis, J. (2011). Analysis of accidents and incidents occurring during transport of packaged dangerous goods by sea. Safety Science, 49(8-9), 1231–1237.CrossRefGoogle Scholar
EMSA., (2007). Maritime Accident Review 2007. http://www.emsa.europa.eu/damage-stability-study/download/374/216/23.html [Accessed 17 December 2020].Google Scholar
EMSA., (2020). Marine casualties and incidents. Preliminary annual overview of marine casualties and incidents 2014–2019. http://www.emsa.europa.eu/newsroom/latest-news/item/3869-preliminary-annual-overview-of-marine-casualties-and-incidents-2014-2019.html [Accessed 17 December 2020].Google Scholar
Graziano, A., Teixeira, A. P. and Soares, C. G. (2016). Classification of human errors in grounding and collision accidents using the TRACEr taxonomy. Safety Science, 86, 245–257.CrossRefGoogle Scholar
Grimmer, J. and King, G. (2011). General purpose computer-assisted clustering and conceptualization. Proceedings of the National Academy of Sciences of the United States of America, 108(7), 2643–2650.CrossRefGoogle ScholarPubMed
Heij, C. and Knapp, S. (2015). Effects of wind strength and wave height on ship incident risk: Regional trends and seasonality. Transportation Research Part D: Transport and Environment, 37, 29–39.CrossRefGoogle Scholar
Hu, N., Zhang, T., Gao, B. and Bose, I. (2019). What do hotel customers complain about? Text analysis using structural topic model. Tourism Management, 72, 417–426.CrossRefGoogle Scholar
Kuhn, K. D. (2018). Using structural topic modeling to identify latent topics and trends in aviation incident reports. Transportation Research Part C: Emerging Technologies, 87, 105–122.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, K. X., Yin, J. and Fan, L. (2014). Ship safety index. Transportation Research Part A: Policy and Practice, 66, 75–87.Google Scholar
Luo, M. F. and Shin, S. H. (2019). Half-century research developments in maritime accidents: Future directions. Accident Analysis & Prevention, 123, 448–460.CrossRefGoogle ScholarPubMed
Ma, F., Wu, Q., Yan, X., Chu, X. and Zhang, D. (2015). Classification of automatic radar plotting aid targets based on improved fuzzy C-means. Transportation Research Part C: Emerging Technologies, 51, 180–195.CrossRefGoogle Scholar
MAIB. (2012). Casualty Definitions Used by the UK MAIB – from 2012. https://assets.publishing.service.gov.uk/governmen/uploads/system/uploads/attachment_data/file/386365/Supporting_documents_for_Annual_Report_2013.pdf [Accessed 17 December 2020].Google Scholar
MAIIF. (2014) MAIIF Investigation Manual. Marine Accident Investigators’ International Forum. https://maiif.org/wp-content/uploads/2017/08/MAIIF-Manual-2014.pdf [Accessed 17 December 2020].Google Scholar
Mazaheri, A., Montewka, J., Kotilainen, P., Sormunen, O. V. E. and Kujala, P. (2015). Assessing grounding frequency using ship traffic and waterway complexity. The Journal of Navigation, 68(1), 89–106.CrossRefGoogle Scholar
Mimno, D., Wallach, H., Talley, E., Leenders, M. and McCallum, A. (2011). Optimizing Semantic Coherence in Topic Models. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 262–272.Google Scholar
Orlikowski, W. J. and Barley, S. R. (2001). Technology and institutions: What can research on information technology and research on organizations learn from each other? MIS Quarterly, 25(2), 145–165.CrossRefGoogle Scholar
Qiao, W., Liu, Y., Ma, X. and Liu, Y. (2020). Human factors analysis for maritime accidents based on a dynamic fuzzy Bayesian network. Risk Analysis, 40(5), 957–980.CrossRefGoogle ScholarPubMed
Reason, J. (2000). Human error: Models and management. BMJ, 320(7237), 768–770.CrossRefGoogle ScholarPubMed
Roberts, S. E., Pettit, S. J. and Marlow, P. B. (2013). Casualties and loss of life in bulk carriers from 1980 to 2010. Marine Policy, 42, 223–235.CrossRefGoogle Scholar
Roberts, M. E., Stewart, B. M., Tingley, D. and Airoldi, E. M. (2013). The structural topic model and applied social science. Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation, 4, 1–20.Google Scholar
Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B. and Rand, D. G. (2014). Structural topic models for open-ended survey responses. American Journal of Political Science, 58(4), 1064–1082.CrossRefGoogle Scholar
Rothblum, A. M. (2000). Human Error and Marine Safety. In National Safety Council Congress and Expo, Orlando, FL (No. s 7).Google Scholar
Sauer, J., Wastell, D. G., Hockey, G. R., Crawshaw, C. M., Ishak, M. and Downing, J. C. (2002). Effects of display design on performance in a simulated ship navigation environment. Ergonomics, 45(5), 329–347.CrossRefGoogle Scholar
Schröder-Hinrichs, J. U., Baldauf, M. and Ghirxi, K. T. (2011). Accident investigation reporting deficiencies related to organizational factors in machinery space fires and explosions. Accident Analysis & Prevention, 43(3), 1187–1196.CrossRefGoogle ScholarPubMed
Tsou, M. C. (2016). Multi-target collision avoidance route planning under an ECDIS framework. Ocean Engineering, 121, 268–278.CrossRefGoogle Scholar
Uğurlu, Ö, Köse, E., Yıldırım, U. and Yüksekyıldız, E. (2015). Marine accident analysis for collision and grounding in oil tanker using FTA method. Maritime Policy & Management, 42(2), 163–185.CrossRefGoogle Scholar
Vu, H. Q., Li, G. and Law, R. (2019). Discovering implicit activity preferences in travel itineraries by topic modeling. Tourism Management, 75, 435–446.CrossRefGoogle Scholar
Wu, Y., Pelot, R. P. and Hilliard, C. (2009). The influence of weather conditions on the relative incident rate of fishing vessels. Risk Analysis: An International Journal, 29(7), 985–999.CrossRefGoogle ScholarPubMed