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AIS Data-based Decision Model for Navigation Risk in Sea Areas

Published online by Cambridge University Press:  07 November 2017

Lianbo Li ,
Wenyu Lu ,
Jiawei Niu ,
Junpo Liu  and
Dexin Liu
Lianbo Li*
Affiliation:
(Navigation College, Dalian Maritime University, Dalian 116026, China)
Wenyu Lu
Affiliation:
(School of Foreign Languages, Dalian Maritime University, Dalian 116026, China)
Jiawei Niu
Affiliation:
(Navigation College, Dalian Maritime University, Dalian 116026, China)
Junpo Liu
Affiliation:
(Navigation College, Dalian Maritime University, Dalian 116026, China)
Dexin Liu
Affiliation:
(Navigation College, Dalian Maritime University, Dalian 116026, China)
*
(E-mail: lilianbodlmu@163.com)
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Abstract

The safety of maritime transportation has become increasingly important in recent decades. In this paper, a decision model (a multi-objective and multi-layer fuzzy optimisation model) for navigation risk in different sea areas is established. This is done according to the evaluation index system based on relative data extracted and analysed from Automatic Identification Systems (AIS) information and the multi-objective and multi-layer fuzzy optimisation theory. Then, sorted by an optimal relative membership degree vector and calculated from lower layer to higher layer, the sea areas which have higher navigation risk are selected. Finally, the decision model is shown to be scientific and practical since the results from it are basically consistent with real traffic in Chengshantou waters and the results from the fuzzy comprehensive evaluation model. With the decision model, navigation risk judgments in different sea areas can be offered. It can also provide decision making references to the design of ship routing systems, the layout of search and rescue sites, the configuration of rescue forces and the administration of navigation safety.

Keywords

Automatic Identification System information (AIS information)Navigation riskEvaluation index systemMulti-objective and multi-layer fuzzy optimisation theory

Information

Type
Research Article
Information
The Journal of Navigation , Volume 71 , Issue 3 , May 2018 , pp. 664 - 678
Copyright
Copyright © The Royal Institute of Navigation 2017 

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References

REFERENCES

Azadeh, A., Gaeini, Z., Motevali Haghighi, S. and Nasirian, B. (2016). A Unique Adaptive Neuro Fuzzy Inference System for Optimum Decision Making Process in a Natural Gas Transmission Unit. Journal of Natural Gas Science and Engineering, 34, 472–485.Google Scholar
Chen, S. (1994). Theory of Fuzzy Optimum Selection for Multistage and Multiobjective Decision Making System. Fuzzy Math, 2 (1), 163–174.Google Scholar
Chen, S., Fu, G., Wang, J.and Liu, G. (2001). Fuzzy Optimum Model of Semi-structural Decision for Lectotype Optimization of Offshore Platforms. China Ocean Engineering, 15 (4), 453–466.Google Scholar
Felski, A. and Jaskólski, K. (2015). Comprehensive Assessment of Automatic Identification System (AIS) Data Application to Anti-collision Manoeuvring. The Journal of Navigation, 68, 697–717.Google Scholar
Guan, Z. (1997). The Analysis of Ship Traffic Accidents. Journal of Dalian Maritime University, 1, 46–51.Google Scholar
Gunnar Aarsæther, K. and Moan, T. (2009). Estimating Navigation Patterns from AIS. The Journal of Navigation, 62(4), 587–607.Google Scholar
Hu, J. and Xu, Z. (2013). Initial Water Rights Allocation Model for Basins Based on the Multilevel and Multi-objective Fuzzy Optimation—A Case Study in Zhangye Municipality. Journal of Glaciology and Geocryology, 35 (3), 776–782.Google Scholar
International Maritime Organization. (2003). COLREG (Consolidated Edition 2003). London: International Maritime Organization.Google Scholar
International Maritime Organization. (2009). SOLAS (Consolidated Edition 2009). London: International Maritime Organization.Google Scholar
Ji, X., Shao, Z., Pan, J.and Tang, C. (2009). A New AIS-Based way to Conduct OLAP of Maritime Traffic Flow. ASCE, Proceeding of ICTE 2009, Chengdu, China.Google Scholar
Lei, D., Wen, Y., Xiao, C., Wang, L., Zhou, C.and Wu, X. (2015). Collision Probability Calculation for Ship Sailing in Free Navigational Sea Area. China Safety Science Journal, 01, 53–59.Google Scholar
Liu, D., Wu, Z.and Jia, C. (2005a). Research on the Decision-making Model of Minimum Safe Passing Distance. Journal of Dalian Maritime University, 31 (1), 13–16.Google Scholar
Liu, D., Wu, Z.and Jia, C. (2005b). Multi-layers and Multi-objects Fuzzy Optimization Model of Main Target Ship. Journal of Traffic and Transportation Engineering, 5 (1), 49–52.Google Scholar
Pan, J., Jiang, Q.and Shao, Z. (2010). Application of Data Mining Technology in Analysis of Marine Traffic Characteristics. Navigation of China, 04, 57–60.Google Scholar
Pan, W., She, K.and Wei, P. (2016). Multi-Granulation Fuzzy Preference Relation Rough Set for Ordinal Decision System. Fuzzy Sets & Systems, 312.Google Scholar
Peng, B. (2013). Maritime Traffic Accident Research in Dalian Jurisdiction. Master Thesis. Dalian Maritime University.Google Scholar
Sang, L., Wall, A., Mao, Z., Yan, X.and Wang, J. (2015). A Novel Method for Restoring the Trajectory of the Inland Waterway Ship by Using AIS Data. Ocean Engineering, 110, 183–194.Google Scholar
Silveira, P. A. M, Teixeira, A. P.and Guedes, Soares, C. (2013). Use of AIS Data to Characterize Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal. The Journal of Navigation, 66 (6), 879–898.CrossRefGoogle Scholar
Tang, C., Shao, Z., Wu, J.and Tang, Q. (2012). Study and Implementation of Vessel Density Distribution Algorithm Based on AIS. Journal of Guangzhou Maritime College, 20 (3), 7–10.Google Scholar
Tsou, M. (2010). Discovering Knowledge from AIS Database for Application in VTS. The Journal of Navigation, 63 (3), 449–469.Google Scholar
Wu, Z. and Zhu, J. (2004). Marine Traffic Engineering (2nd Edition). Dalian: Dalian Maritime University Press.Google Scholar
Xiao, F., Han, L., Gulijk, C.and Ale, B. (2015). Comparison Study on AIS Data of Ship Traffic Behavior. Ocean Engineering, 95 (3), 84–93.Google Scholar
Xiao, X., Zhao, Q., Shao, Z., Ji, X.and Pan, J. (2014). Specific Ship's Encounter Live Distribution Based on AIS. Navigation of China, 37 (3), 50–53.Google Scholar
Zhang, D. (2008). The Environmental Risk Assessment and Forecast Study. Master Thesis. Wuhan University of Technology.Google Scholar
Zhen, R., Shao, Z., Pan, J.and Zhao, Q. (2014). Statistical Analysis of Distribution of Ship Speed within the Fairway Based on AIS Data. Journal of Jimei University (Natural Science), 19 (4), 274–278.Google Scholar