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Volume 18, Issue 35 (5-2022)                   Marine Engineering 2022, 18(35): 111-127 | Back to browse issues page

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ebrahimi mavini M, Shafieefar M. The analysis of the pattern of maritime traffic trajectory using the data mining in the Persian Gulf. Marine Engineering 2022; 18 (35) :111-127
URL: http://marine-eng.ir/article-1-905-en.html
1- Department of Marine Engineering, Tarbiat Modares University
Abstract:   (1101 Views)
Shipping route analysis for maritime traffic management depends on equipments to collect information about ship's behavior. For this purpose, the most reliable data is the Automatic Identification System (AIS) data. The complexity and high volume of AIS data  enhances traditional surveillance operations and makes maritime traffic analysis more difficult. Therefore, an unsupervised approach is desirable for the effective conversion of raw AIS data into regular shipping route patterns. The proposed model for the shipping route analysis consists of four sections: AIS data preprocessing, structural similarity measurement, shipping route clustering and representative trajectory extraction. Experimental evaluation of the proposed model with real AIS data from the studied area shows that it has performed well visually and the expected result has been achieved. The results will contribute to better understanding of shipping route patterns and help maritime authorities in sustainable management of maritime traffic.
Full-Text [PDF 2323 kb]   (578 Downloads)    
Type of Study: Research Paper | Subject: Ship Structure
Received: 2022/01/31 | Accepted: 2022/07/13

1. B. Slack, P. Rodrigue, and C. Comtois, The geography of transport systems, no. January. Routledge Taylor & Francis Group, 2016. [DOI:10.4324/9781315618159]
2. M. Safarzadeh, E. Azizabadi, M. Shahba and H. Hamidi, Maritime Transportation, Asrar Danesh, 2006. (In Persian)
3. S. Pan and Y. Jingbo, "Extracting Shipping Route Patterns by Trajectory Clustering Model Based on Automatic Identification System Data," Sustain. Artic., vol. 13, no. July, pp. 1-13, 2018.
4. Y. Brain, "Predicting vessel trajectories from AIS data using R," NAVAL POSTGRADUATE SCHOOL, 2017.
5. H. Li, J. Liu, K. Wu, Z. Yang, R. W. Liu, and N. Xiong, "Spatio-Temporal Vessel Trajectory Clustering Based on Data Mapping and Density," IEEE Access, vol. 6, no. November, pp. 58939-58954, 2018. [DOI:10.1109/ACCESS.2018.2866364]
6. G. Pallotta, M. Vespe, and K. Bryan, "Traffic knowledge discovery from AIS data," Inf. Fusion (FUSION), 2013 16th Int. Conf., no. July 2015, pp. 1996-2003, 2013.
7. A. Harati-Mokhtari, "Automatic Identification System (AIS): Data Reliability and Human Eror Implications," R. Inst. Navig., vol. 17, pp. 374-389, 2007. [DOI:10.1017/S0373463307004298]
8. A. Sidibé and G. Shu, "Study of Automatic Anomalous Behaviour Detection Techniques for Maritime Vessels," J. Navig., vol. 70, no. 4, pp. 847-858, 2017. [DOI:10.1017/S0373463317000066]
9. Z. Li, J.-G. Lee, X. Li, and J. Han, "Incremental Clustering for Trajectories," in Database Systems for Advanced Applications, 15th International Conference, DASFAA, 2010, pp. 1-15.
10. J. Lee and J. Han, "Trajectory Clustering : A Partition-and-Group Framework," in Proceedings of the 2007 ACM SIGMOD international conference on Management of data, 2007, pp. 1-12.
11. M. Ester, H. Kriegel, X. Xu, and D.- Miinchen, "A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," in the Second International Conference on Knowledge Discovery and Data Mining, 1996, pp. 226-231.
12. S. Gaffney and P. Smyth, "Trajectory Clustering with Mixtures of Regression Models," in the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, 1999, vol. 20, no. June, pp. 1-20. [DOI:10.1145/312129.312198]
13. E. M. Knorr, R. T. Ng, and V. Tucakov, "Distance-based outliers : algorithms and applications," VLDB J., vol. 17, no. February, pp. 237-253, 2000. [DOI:10.1007/s007780050006]
14. N. A. Bomberger, B. J. Rhodes, M. Seibert, and A. M. Waxman, "Associative Learning of Vessel Motion Patterns for Maritime Situation Awareness," in Information Fusion, 2006 9th International Conference, 2006. [DOI:10.1109/ICIF.2006.301661]
15. A. Dahlbom and L. Niklasson, "Trajectory Clustering for Coastal Surveillance," in 10th International Conference on Information Fusion, 2007, pp. 1-8. [DOI:10.1109/ICIF.2007.4408114]
16. B. Auslander, K. M. Gupta, and D. W. Aha, "A Comparative Evaluation of Anomaly Detection Algorithms for Maritime Video Surveillance," in Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense X, edited, 2011, vol. 8019, pp. 1-14. [DOI:10.1117/12.883535]
17. M. Vespe, I. Visentini, K. Bryan, and P. Braca, "unsupervised learning of maritime traffic patterns for anomaly detection," in 9th IET Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 2012, pp. 1-5. [DOI:10.1049/cp.2012.0414]
18. G. Pallotta, M. Vespe, and K. Bryan, "Vessel Pattern Knowledge Discovery from AIS Data," Entropy, vol. 28, no. June, pp. 2219-2245, 2013.
19. B. Liu, E. N. de Souza, S. Matwin, and M. Sydow, "Knowledge-based clustering of ship trajectories using Density-based Approach," in IEEE International Conference on Big Data, 2014, pp. 603-608. [DOI:10.1109/BigData.2014.7004281]
20. S. Spaccapietra et al., "A conceptual view on trajectories," Data Knowl. Eng., vol. 65, pp. 126-146, 2008. [DOI:10.1016/j.datak.2007.10.008]
21. P. Lei, "A framework for anomaly detection in maritime trajectory behavior," Knowl. Inf. Syst., vol. 26, no. May, pp. 189-214, 2015. [DOI:10.1007/s10115-015-0845-4]
22. R. Zhen, Y. Jin, Q. Hu, and Z. Shao, "Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier," R. Inst. Navig., vol. 23, no. February, pp. 1-23, 2017.
23. J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, Third. TheMorgan Kaufmann Series in DataManagement Systems, 2011.
24. M. Sanii Abadeh, S. Mahmoudi and M. Taherparvar, "Applied data mining", Niaz Danesh, 2014.( In Persian)

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International Journal of Maritime Technology is licensed under a

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