| 3/2021 - 9 |
A Novel Approach for Knowledge Discovery from AIS Data: An Application for Transit Marine Traffic in the Sea of MarmaraDOGAN, Y.   , KART, O. , KUNDAKCI, B. , NAS, S. |
View the paper record and citations in  |
| Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF  (3,179 KB) | Citation | Downloads: 789 | Views: 594 |
Author keywords
clustering algorithms, genetic algorithms, knowledge discovery, machine learning, radar signal processing.
References keywords
data(28), maritime(12), traffic(6), icbda(6), ship(5), ocean(5), automatic(5), mining(4), marine(4), identification(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2021-08-31
Volume 21, Issue 3, Year 2021, On page(s): 73 - 80
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.03009
Web of Science Accession Number: 000691632000009
SCOPUS ID: 85114809235
Abstract
This paper addresses the discovery of hidden patterns in the data of Automatic Identification Systems by a novel clustering model using data processing and data mining methods. It reveals the transit tracks and the transit vessels on these tracks in the Sea of Marmara which has a dense marine traffic. In this study, improved Density Based Spatial Clustering of Applications with Noise and KMeans++ clustering algorithms have been used together with complex database queries. This proposed approach has been compared to other clustering algorithms such as Self-Organizing Map, Hierarchical Clustering with Single-Link and Genetic Clustering. It has been observed that these alternative algorithms could not reach high accuracy values and they could not give the expected tracks. The proposed approach has five steps and experimental results demonstrate that when this novel approach has been applied step by step, the results can match the observed data by The Republic of Turkey, Ministry of Transport, Maritime and Communications by 95%. Finally, this novel approach is suggested to maritime authorities for all the seas in the world to manage the vessel traffic which has big and complex data. |
| References | | | Cited By «-- Click to see who has cited this paper |
| [1] J. Hoffmann, "Review of maritime transport 2016," in Proc. United Nations Conf. on Trade and Development, Geneva, Switzerland, pp. 1-118, 2016.
[2] IMO, SOLAS I. "International Convention for the Safety of Life at Sea," Consolidated Edition, London, GBP, 2014, pp. 1-10. [3] P. R. Lei, "Mining maritime traffic conflict trajectories from a massive AIS data," Knowledge and Information Systems, vol. 62, no. 1, pp. 259-285, 2020. [CrossRef] [Web of Science Times Cited 22] [4] M. Liang, R. W. Liu, Q. Zhong, J. Liu, J. Zhang, "Neural network-based automatic reconstruction of missing vessel trajectory data," in Proc. IEEE 4th International Conf. on Big Data Analytics (ICBDA); Suzhou, China, pp. 426-430, 2019. [CrossRef] [Web of Science Times Cited 23] [5] L. Westerdijk, "Classifying vessel types based on AIS data," MSc, Vrije University, Amsterdam, Holland, pp. 49-61, 2019. [6] Y. Zhou, W. Daamen, T. Vellinga, S. P. Hoogendoorn, "Ship classification based on ship behavior clustering from AIS data," Ocean Engineering, vol. 175, pp. 176-187, 2019. [CrossRef] [Web of Science Times Cited 65] [7] Z. Hanyang, S. Xin, Y. Zhenguo, "Vessel sailing patterns analysis from S-AIS data dased on K-means clustering algorithm," in Proc. IEEE 4th International Conference on Big Data Analytics (ICBDA), Suzhou, China, pp. 10-13, 2019. [CrossRef] [Web of Science Times Cited 13] [8] M. Mustaffa, S. Ahmad, A. M. Ali, N. Ahmad, M. H. Mohd Jais, "Data mining analysis on Ships collision risk and marine traffic characteristic of Port Klang Malaysia waterways from automatic identification system (AIS)," in Proc. International MultiConference of Engineers and Computer Scientists; Hong Kong, pp. 242-246, 2019. [9] D. Yang, L. Wu, S. Wang, H. Jia, K. X. Li, "How big data enriches maritime research-a critical review of automatic identification system (AIS) data applications," Transport Reviews, vol. 39, no. 6, pp. 755-773, 2019. [CrossRef] [Web of Science Times Cited 178] [10] R. J. Bye, P. G. Almklov, "Normalization of maritime accident data using AIS," Marine Policy, vol. 109, 103675, 2019. [CrossRef] [Web of Science Times Cited 34] [11] K. Wang, M. Liang, Y. Li, J. Liu, R. W. Liu, "Maritime traffic data visualization: A brief review," in Proc. IEEE 4th International Conference on Big Data Analytics (ICBDA); Suzhou, China, pp. 67-72, 2019. [CrossRef] [Web of Science Times Cited 3] [12] M. Fujii, H. Hashimoto, Y. Taniguchi, E. Kobayashi, "Statistical validation of a voyage simulation model for ocean-going ships using satellite AIS data," Journal of Marine Science and Technology, vol. 24, no. 4, pp. 1297-1307, 2019. [CrossRef] [Web of Science Times Cited 7] [13] Y. Liu, R. Song, R. Bucknall, "Intelligent tracking of moving ships in constrained maritime environments using AIS," Cybernetics and Systems, vol. 50, no. 6, pp. 539-555, 2019. [CrossRef] [Web of Science Times Cited 6] [14] Z. Liu, Z. Wu, Z. Zheng, "A novel framework for regional collision risk identification based on AIS data," Applied Ocean Research, vol. 89, pp. 261-272, 2019. [CrossRef] [Web of Science Times Cited 46] [15] L. Wu, Y. Xu, Q. Wang, F. Wang, Z. Xu, "Mapping global shipping density from AIS data," The Journal of Navigation, vol. 70, no. 1, pp. 67-81, 2017. [CrossRef] [Web of Science Times Cited 114] [16] F. Natale, M. Gibin, A. Alessandrini, M. Vespe, A. Paulrud, "Mapping fishing effort through AIS data," PloS one, vol. 10, no. 6, e0130746, 2015. [CrossRef] [Web of Science Times Cited 172] [17] M. Mustaffa, M. Abas, S. Ahmad, N. Ahmad Aini, W. F. Abbas, S. A. Che Abdullah, M. Y. Darus, "Marine traffic density over Port Klang, Malaysia using statistical analysis Of AIS data: A preliminary study," Journal of ETA Maritime Science, vol. 4, no. 4, pp. 333-341, 2016. [CrossRef] [18] F. Xiao, H. Ligteringen, C. Van Gulijk, B. Ale, "Comparison study on AIS data of ship traffic behavior," Ocean Engineering; vol. 95, pp. 84-93, 2015. [CrossRef] [Web of Science Times Cited 132] [19] S. A. Breithaupt, A. Copping, J. Tagestad, J. Whiting, "Maritime route delineation using AIS data from the Atlantic Coast of the US," The Journal of Navigation, vol. 70, pp. 379-394, 2017. [CrossRef] [Web of Science Times Cited 30] [20] W. Zhang, F. Goerlandt, J. Montewka, P. Kujala, "A method for detecting possible near miss ship collisions from AIS data," Ocean Engineering, vol. 107, pp. 60-69, 2015. [CrossRef] [Web of Science Times Cited 179] [21] J. Li, H. Wang, W. Zhao, Y. Xue, "Ship's trajectory planning based on improved multiobjective algorithm for collision avoidance," Journal of Advanced Transportation, 4068783, 2019. [CrossRef] [Web of Science Times Cited 23] [22] Z. Jiang, D. Chen, Z. Yang, "A synchronous optimization model for multiship shuttle tanker fleet design and scheduling considering hard time window constraint," Journal of Advanced Transportation, 1904340, 2018. [CrossRef] [Web of Science Times Cited 2] [23] P. Jiacai, J. Qingshan, H. Jinxing, S. Zheping, "An AIS data visualization model for assessing maritime traffic situation and its applications," Procedia Engineering, vol. 29, pp. 365-369, 2012. [CrossRef] [Web of Science Times Cited 12] [24] Z. Huang, Z. Shao, J. Pan, X. Ji, Q. Zhao, "Berthing speed control law for large vessels based on AIS data," in Proc. International Conference on Transportation Engineering; Dalian, China, pp. 1322-1330, 2015. [CrossRef] [25] V. F. Arguedas, F. Mazzarella, M. Vespe, "Spatio-temporal data mining for maritime situational awareness," in Proc. OCEANS, Genova, Italy, pp. 1-8, 2015. [CrossRef] [Web of Science Times Cited 12] [26] A. W. Isenor, M. O. St-Hilaire, S. Webb, M. Mayrand, "MSARI: A database for large volume storage and utilisation of maritime data," The Journal of Navigation, vol. 70, no. 2, pp. 276-290, 2017. [CrossRef] [Web of Science Times Cited 7] [27] ITU-R. "Technical characteristics for an automatic identification system using time division multiple access in the VHF maritime mobile frequency band," Recommendation ITU- R M.1371-5. Geneva, Switzerland: Electronic Publication, 2014. [28] D. Arthur, S. Vassilvitskii, "K-means++: The advantages of careful seeding," in Proc. SODA'07: 18th Annual ACM-Society for Industrial and Applied Mathematics Symposium on Discrete Algorithms; Philadelphia, USA, pp. 1027-1035, 2007. [29] G. F. Jenks, F. C. Caspall, "Error on choroplethic maps: definition, measurement, reduction," Annals of the Association of American Geographers, vol. 61, no. 2, pp. 217-244, 1971. [CrossRef] [30] M. Kantardzic, "Data mining: concepts, models, methods, and algorithms," USA: John Wiley & Sons, pp. 289-296, 2011. [31] H. Zou, Y. Yu, W. Tang, H. M. Chen, "Improving I/O performance with adaptive data compression for big data applications," In: IEEE International Parallel & Distributed Processing Symposium Workshops; Phoenix, Arizona, USA, pp. 1228-1237, 2014. [CrossRef] [Web of Science Times Cited 13] [32] S. H. Jung, K. J. Kim, E. C. Lim, C. B. Sim, "A novel on automatic K value for efficiency improvement of KMeans clustering," in Proc. Advanced Multimedia and Ubiquitous Engineering; Singapore, pp. 181-186, 2017. [CrossRef] [33] D. H. Stolfi, E. Alba, X. Yao, "Predicting car park occupancy rates in smart cities," in Proc. International Conference on Smart Cities, Cham, Germany, pp. 107-117, 2017. [CrossRef] [Web of Science Times Cited 52] [34] X. Wang, C. Wang, Z. Chaobiao, "Early warning of debris flow using optimized self-organizing feature mapping network," Water Supply, ws2020142, 2020. [CrossRef] [Web of Science Times Cited 6] [35] P. Vasant, "Handbook of research on modern optimization algorithms and applications in engineering and economics," USA: IGI Global, pp. 682-690, 2016. [36] Z. Halim, J. H. Khattak, "Density-based clustering of big probabilistic graphs," Evolving systems, vol. 10, no. 3, pp. 333-350, 2019. [CrossRef] [Web of Science Times Cited 15] Web of Science® Citations for all references: 1,166 TCR SCOPUS® Citations for all references: 0 Web of Science® Average Citations per reference: 32 ACR SCOPUS® Average Citations per reference: 0 TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2024-04-23 22:53 in 160 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site. |
Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
