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Evaluation of Two-Ship Collision Severity using Ordered Probit Approaches

Published online by Cambridge University Press:  01 February 2018

Jinxian Weng ,
Guorong Li ,
Tian Chai  and
Dong Yang
Jinxian Weng*
Affiliation:
(College of Transport and Communications, Shanghai Maritime University, Shanghai, China 201306)
Guorong Li
Affiliation:
(College of Transport and Communications, Shanghai Maritime University, Shanghai, China 201306)
Tian Chai
Affiliation:
(Navigation Institute, Jimei University, Xiamen Fujian, China, 361021)
Dong Yang
Affiliation:
(Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong, China)
*
(E-mail: jxweng@bjtu.edu.cn)
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Abstract

This study develops an ordered probit model to evaluate the factors influencing two-ship collision severity using ten years’ ship collision accident data from Fujian sea areas. The model results show that the involvement of big ships has the largest impact in increasing the probability of a serious or very serious accident, followed by the involvement of fishing vessels. There will be a bigger probability of a serious accident if both ships involved in the collision are cargo ships. We found that the season of spring, poor visibility and night time periods are more likely to be factors in high severity levels of ship collision. The results also reveal that lookout failure plays a decisive role in increasing serious accident risk compared with other types of human errors. The results of this study may be beneficial for policy-makers in proposing efficient strategies to reduce the likelihood of serious ship collisions.

Keywords

Ship CollisionRiskModelling
Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 4 , July 2018 , pp. 822 - 836
Copyright
Copyright © The Royal Institute of Navigation 2018 

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References

REFERENCES

Akten, N. (2004). Analysis of shipping casualties in the Bosphorus. Journal of Navigation, 57, 345–356.Google Scholar
Aydogdu, Y.V., Yurtoren, C., Park, J.and Park, Y. (2012). A study on local traffic management to improve marine traffic safety in the Istanbul Strait. Journal of Navigation, 65(1), 99–112.Google Scholar
Birpinar, M.E., Talu, G.F.and Gonencgil, B. (2009). Environmental effects of maritime traffic on the Istanbul Strait. Environmental Monitoring and Assessment, 152, 13–23.CrossRefGoogle ScholarPubMed
Debnath, A.K. and Chin, H.C. (2009). Hierarchical modeling of perceived collision risks in port fairways. Transportation Research Record, 2100, 68–75.Google 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. Journal of Navigation, 63(1), 137–152.CrossRefGoogle Scholar
Eliopoulou, E., Hamann, R., Papanikolaou, A.and Golyshev, P. (2013). Casualty analysis of cellular container ships. Proceedings of the IDFS 2013, Shanghai, 25–27.Google Scholar
Eliopoulou, E. and Papanikolaou, A. (2007). Casualty analysis of large tankers. Journal of Marine Science and Technology, 12, 240–250.Google Scholar
Goerlandt, F. and Montewka, J. (2015). Maritime Transportation Risk Analysis: Review and Analysis in Light of Some Foundational Issues. Reliability Engineering and System Safety, 138, 115–34.Google Scholar
Goossens, L.H.J. and Glansdorp, C.C. (1998). Operational benefits and risk reduction of marine accidents. Journal of Navigation, 51, 369–381.Google Scholar
Greene, W.H. (2003). Econometric Analysis 5th edition, Upper Saddle River: Prentice Hall.Google Scholar
Hänninen, M., Banda, O.A.V. and Kujala, P. (2014b). Bayesian Network Model of Maritime Safety Management. Expert Systems with Applications, 41(17), 7837–46.Google Scholar
Hänninen, M. and Kujala, P. (2012). Influences of Variables on Ship Collision Probability in a Bayesian Belief Network Model. Reliability Engineering & System Safety, 102, 27–40.CrossRefGoogle Scholar
Hänninen, M., Mazaheri, A., Kujala, P., Montewka, J., Laaksonen, L., Salmiovirta, M.and Klang, M. (2014a). 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
Hassel, M., Asbjørnslett, B.E. and Hole, L.P. (2011). Underreporting of Maritime Accidents to Vessel Accident Databases. Accident; Analysis and Prevention, 43(6), 2053–2063.Google Scholar
IMO. (2002). Guidelines for formal safety assessment (FSA) for use in the IMO rulemaking process. London: International Maritime Organization, Document No.: MSC/Circular 1023, MEPC/Circular 392, April 5, 2002.Google Scholar
International Regulations for Preventing Collisions at sea. (1972). Convention on the International Regulations for Preventing Collisions at sea 1972. http://www.jag.navy.mil/distrib/instructions/COLREG-1972.pdf.Google Scholar
Jin, D. (2014). The determinants of fishing vessel accident severity. Accident Analysis and Prevention, 66, 1–7.Google Scholar
Jin, D., Kite-Powell, H.L. and Talley, W.K. (2001). The safety of commercial fishing: determinants of vessel total losses and injuries. Journal of Safety Research, 32(2), 209–228.Google Scholar
Jin, D. and Thunberg, E. (2005). An analysis of fishing vessel accidents in fishing areas off the northeastern United States. Safety Science, 43, 523–540.Google Scholar
Karahalios, H. (2014). The contribution of risk management in ship management: the case of ship collision. Safety Science, 63, 104–114.Google Scholar
Kim, J.K., Ulfarsson, G.F., Kim, S.and Shankar, V.N. (2012). Driver-injury severity in single-vehicle crashes in California: A mixed logit analysis of heterogeneity due to age and gender. Accident Analysis and Prevention, 50, 1073–1081.Google Scholar
Kim, T., Nazir, S.and Øvergård, K.I. (2016). A STAMP-Based Causal Analysis of the Korean Sewol Ferry Accident. Safety Science, 83, 93–101.Google Scholar
Lemp, J.D., Kockelman, K.M.and Unnikrishnan, A. (2010). Analysis of large truck crash severity using heteroskedastic ordered probit models. Accident Analysis and Prevention, 43, 370–380.Google Scholar
Long, J.S. and Freese, J. (2006). Regression models for categorical dependent variables using STATA. Second Edition, STATA Press, Texas, USA.Google Scholar
Martins, M.R. and Maturana, M.C. (2010). Human Error Contribution in Collision and Grounding of Oil Tankers. Risk Analysis, 30(4), 674–698.Google Scholar
Mata-Alvarez-Santullano, F. and Souto-Iglesias, . (2013). Fishing effort control policies and ship stability: analysis of a string of accidents in Spain in the period 2004-2007. Marine Policy, 40, 10–17.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. Journal of Navigation, 68(1), 89–106.CrossRefGoogle Scholar
Mazaheri, A., Montewka, J.and Kujala, P. (2016). Towards an Evidence-Based Probabilistic Risk Model for Ship-Grounding Accidents. Safety Science, 86, 195–210.Google Scholar
Ministry of Transport of the People's Republic of China (2014). Statistical methods for collecting maritime traffic accidents. http://www.mot.gov.cn/zfxxgk/bnssj/zcfgs/201410/t20141020_1710151.html accessed 05 June 2016.Google Scholar
Montewka, J., Goerlandt, F., Innes-Jones, G., Owen, D., Hifi, Y.and Puisa, R. (2017). Enhancing Human Performance in Ship Operations by Modifying Global Design Factors at the Design Stage. Reliability Engineering & System Safety, 159, 283–300.Google Scholar
Mullai, A. and Paulsson, U. (2011). A Grounded Theory Model for Analysis of Marine Accidents. Accident Analysis & Prevention, 43(4), 1590–1603.Google Scholar
Ozsoysal, R. and Ozsoysal, O.A. (2006). Maritime casualties through the Bosphorus. Naval Engineers Journal, 118(1), 77–82.Google Scholar
Perez-Labajos, C., Azofra, M., Blanco, B., Achutegui, J.and Gonzalez, J. (2006). Analysis of accident inequality of the Spanish fishing fleet. Accident Analysis and Prevention, 38(6), 1168–1175.Google Scholar
Perez-Labajos, C., Blanco, B., Azofra, M., Achutegui, J.J. and Eguia, E. (2009). Injury and loss concentration by sinkings in fishing fleets. Safety Science, 47(2), 277–284.Google Scholar
Quddus, M. A., Noland, R. B.and Chin, H. C. (2002). An analysis of motorcycle injury and vehicle damage severity using ordered probit models. Accident Analysis and Prevention, 33, 445–462.Google Scholar
Roberts, S.E., Jaremin, B.and Marlow, P.B. (2010). Human and fishing vessel losses in sea accidents in the UK fishing industry from 1948 to 2008. International Maritime Health, 61(3), 143–153.Google Scholar
Sormunen, O-V. E., Castrén, A., Romanoff, J.and Kujala, P. (2016). Estimating Sea Bottom Shapes for Grounding Damage Calculations. Marine Structures, 45, 86–109.Google Scholar
Sormunen, O-V. E., Ehlers, S.and Kujala, P. (2013). Collision Consequence Estimation Model for Chemical Tankers. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 227(2), 98–106.Google Scholar
Sormunen, O-V. E., Goerlandt, F., Hakkinen, J., Posti, A., Hanninen, M., Montewka, J., Stahlberg, K.and Kujala, P. (2014). Uncertainty in Maritime Risk Analysis: Extended Case Study on Chemical Tanker Collisions. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 229(3), 303–320.Google Scholar
Talley, W.K., Jin, D.and Kite-Powell, H. (2006). Determinants of the severity of passenger vessel accidents. Maritime Policy & Management, 33(2), 173–186.CrossRefGoogle Scholar
Tay, R., Choi, J., Kattan, L.and Khan, A. (2011). A multinomial logit model of pedestrian–vehicle crash severity. International Journal of Sustainable Transportation, 5, 233–249.Google Scholar
Weng, J. and Yang, D. (2015). Investigation of shipping accident injury severity and mortality. Accident Analysis and Prevention, 76, 92–101.Google Scholar
Weng, J., Meng, Q.and Qu, X. (2012). Vessel collision frequency estimation in the Singapore Strait. Journal of Navigation, 65, 207–221.Google Scholar
Weng, J., Ge, Y.E. and Han, H. (2016). Evaluation of shipping accident casualties using zero-inflated negative binomial regression technique. Journal of Navigation, 69, 433–448.Google Scholar
Wróbel, K., Montewka, J.and Kujala, P. (2017). Towards the Assessment of Potential Impact of Unmanned Vessels on Maritime Transportation Safety. Reliability Engineering & System Safety, 165, 155–69.Google Scholar
Zhang, D., Yan, X.P., Yang, Z.L., 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, 93–105.Google Scholar
Zheng, Y., Talley, W.K., Jin, D.and Ng, M. (2016). Crew injuries in container vessel accidents. Maritime Policy & Management, 43(5), 541–551.Google Scholar