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A Systematic Approach for Collision Risk Analysis based on AIS Data

Published online by Cambridge University Press:  24 May 2017

Weibin Zhang
(Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Nanjing 210094, China) (University of Washington, Department of Civil and Environmental Engineering, Smart Transportation Applications and Research Laboratory, 98195 Seattle, Washington, USA)
Cole Kopca
(University of Washington, Department of Civil and Environmental Engineering, Smart Transportation Applications and Research Laboratory, 98195 Seattle, Washington, USA)
Jinjun Tang
(Central South University, School of Traffic & Transportation Engineering, Changsha, Hunan 410075, China)
Dongfang Ma
(Zhejiang University, Ocean College, Zhoushan, Zhejiang 316021, China)
Yinhai Wang*
(University of Washington, Department of Civil and Environmental Engineering, Smart Transportation Applications and Research Laboratory, 98195 Seattle, Washington, USA)


Ship collision risk is an important aspect of ship navigation safety. A systematic method to assess collision risk by monitoring parameter states continually is necessary and has proven effective. Another important factor in risk assessment is ship size, but the effect of the size of ship pairs has not been considered properly in many previous studies. This research utilises a systematic perspective to study collision risk of near-misses in ship-ship encounters. This fills a secondary research gap where previous risk assessments only investigated near-misses from the perspective of a single vessel. Following this proposed approach, ship pair encounter states can be continually tracked. Ultimately, a method of improved Vessel Collision Risk Operator (VCRO) to merge risk assessments of both ships is proposed through integration of near-miss collision risks in a systematic way, which overcomes the disadvantages of prior VCROs that only consider the maximum value, from which it is difficult to track and judge the risk trend. Utilising a case study, the effectiveness of the proposed method is validated through analysis of ship encounters, with ships of different sizes in the Baltic Sea.

Research Article
Copyright © The Royal Institute of Navigation 2017 

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Brown, M.J. (2012). John Dewey's logic of science. HOPOS: The Journal of the International Society for the History of Philosophy of Science, 2, 258306.Google Scholar
Bukhari, A.C., Tusseyeva, I. and Kim, Y-G. (2013). An Intelligent Real-Time Multi-Vessel Collision Risk Assessment System from VTS View Point Based on Fuzzy Inference System. Expert Systems with Applications, 40, 1220–30.CrossRefGoogle Scholar
Chauvin, C. and Lardjane, S. (2008). Decision Making and Strategies in an Interaction Situation: Collision Avoidance at Sea. Transportation Research Part F, 11, 259–69.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. Journal of Navigation, 63(1), 137–52.CrossRefGoogle Scholar
Delgado, M., Gomez-Skarmeta, A.F. and Martin, F. (1997). A Fuzzy Clustering-Based Rapid Prototyping for Fuzzy Rule-Based Modeling. IEEE Transactions on Fuzzy Systems, 5(2), 223233.CrossRefGoogle Scholar
Goerlandt, F. and Montewka, J. (2015a). A Framework for Risk Analysis of Maritime Transportation Sytems: A Case Study for Oil Spill from Tankers in a Ship-Ship Collision. Safety Science, 76, 4266.CrossRefGoogle Scholar
Goerlandt, F., Montewka, J., Kuzmin, V. and Kujala, P. (2015). A risk-informed ship collision alert system: Framework and application. Safety Science, 77, 182204.CrossRefGoogle 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, 115–34.CrossRefGoogle Scholar
Goodwin, E.M. (1975). A statistical study of ship domains. The Journal of Navigation, 28(03), 328344.CrossRefGoogle Scholar
Harati-Mokhtari, A., Wall, A., Brooks, P. and Wang, J. (2007). Automatic Identification System (AIS): Data Reliability and Human Error Implications. The Journal of Navigation, 60(03), 373389.CrossRefGoogle Scholar
Hartigan, J.A. and Wong, M.A. (1979). AS136: A K-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100108.Google Scholar
Kaplan, S. and Garrick, J.B. (1981). On the quantitative definition of risk. Risk Analysis, 1(1), 1127.CrossRefGoogle Scholar
Kijima, K. and Furukawa, Y. (2003). Automatic collision avoidance system using the concept of blocking area. Proceeding of IFAC Conference on Manoeuvring and Control of Marine Craft, Girona, Spain.Google Scholar
Laureshyn, A., Svensson, Å., and Hydén, C. (2010). Evaluation of traffic safety, based on micro-level behavioural data: theoretical framework and first implementation. Accident Analysis & Prevention. 42(6), 16371646.CrossRefGoogle Scholar
Li, S., Meng, Q. and Qu, X. (2012). An Overview of Maritime Waterway Quantitative Risk Assessment Models. Risk Analysis, 32(3), 496512.CrossRefGoogle ScholarPubMed
Mou, J.M., Tak, C. and Van der Ligteringen, H. (2010). Study on Collision Avoidance in Busy Waterways by Using AIS Data. Ocean Engineering, 37, 483–90.CrossRefGoogle Scholar
Qu, X., Meng, Q., and Li, S. (2011). Ship Collision Risk Assessment for the Singapore Strait. Accident Analysis & Prevention, 43(6), 2030–36. doi:10.1016/j.aap.2011.05.022.CrossRefGoogle ScholarPubMed
Setnes, M. (2000). Supervised Fuzzy Clustering for Rule Extraction. IEEE Transactions on Fuzzy Systems, 8(4), 416424.CrossRefGoogle Scholar
Tang, J., Liu, F., Zou, Y., Zhang, W. and Wang, Y. (2017). An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic. IEEE Transactions on Intelligent Transportation Systems, PP(99), 111.CrossRefGoogle Scholar
Van Iperen, E. (2012). Detection of Hazardous Encounters at the North Sea from AIS Data. In Proceedings of International Workshop of Next Generation Nautical Traffic Models, 112. Shanghai, China.Google Scholar
Wang, N., Meng, X., Xu, Q. and Wang, Z. (2009). A Unified Analytical Framework for Ship Domains. Journal of Navigation, 62(4), 643–55.CrossRefGoogle Scholar
Zhang, W.B., 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, W.B., Goerlandt, F., Kujala, P. and Wang, Y.H. (2016). An Advanced Method for Detecting Possible Near-Miss Ship Collisions from AIS Data. Ocean Engineering, 124, 141156.CrossRefGoogle Scholar