Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-25T19:17:32.408Z Has data issue: false hasContentIssue false

Drafting Route Plan Templates for Ships on the Basis of AIS Historical Data

Published online by Cambridge University Press:  23 December 2019

Krzysztof Naus*
Affiliation:
(Polish Naval Academy)
*

Abstract

The paper provides a description of a method of drafting route plan templates on the basis of AIS (automatic identification system) historical data. The first section features a brief background on the problem of drafting route plan templates in the light of international regulations. The main section contains a description of the methods and tools used for processing AIS data into a GRID reference system: ship traffic intensity, average COG (course over ground) and average SOG (speed over ground) as well as route plan templates. The final section includes a presentation of the research method and an analysis of the results, conducted on the basis of maps with charted paths of drafted route plan templates. The summary constitutes a synthesis of general conclusions, the advantages and disadvantages of the solution as well as areas for further research to enhance the solution.

Type
Research Article
Copyright
Copyright © The Royal Institute of Navigation 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anneken, M., Fischer, Y. and Beyerer, J. (2015). Evaluation and Comparison of Anomaly Detection Algorithms in Annotated Datasets from the Maritime Domain. IEEE SAI Intelligent Systems Conference (IntelliSys).CrossRefGoogle Scholar
Borkowski, P. (2017). The ship movement trajectory prediction algorithm using navigational data fusion. Sensors, 17(6), 1432.CrossRefGoogle ScholarPubMed
Breithaupt, S., Copping, A., Tagestad, J. and Whiting, J. (2017). Maritime route delineation using AIS data from the Atlantic coast of the US. The Journal of Navigation, 70(2), 379394.CrossRefGoogle Scholar
Cormen, T. H., Leiserson, H. E., Rivest, R. L. and Stein, C. (2001). Introduction to Algorithms. Greedy Algorithms, Chapter 16.Google Scholar
Douglas, D. H. and Peucker, T. K. (1973). Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartographer, 10(2), 112122.CrossRefGoogle Scholar
Embarcadero. (2018). http://www.embarcadero.com.pl/produkty/cbuilder/. Accessed October 2018.Google Scholar
Garagic, D., Rhodes, B. J., Bomberger, N. A. and Zandipour, M. (2009). Adaptive Mixture-Based Neural Network Approach for Higher-Level Fusion and Automated Behavior Monitoring. NATO Workshop on Data Fusion and Anomaly Detection for Maritime Situational Awareness, La Spezia, Italy.CrossRefGoogle Scholar
George, J., Crassidis, J., Singh, T. and Fosbury, A. M. (2011). Anomaly detection using context-aided target tracking. Journal of Advances in Information Fusion, 6, 3956.Google Scholar
Guo, Y. and Bardera, A. (2019). SHNN-CAD + : An impovement on SHNN-CAD for adaptive online trajectory anomaly detection. Sensors, 19(1), 84.CrossRefGoogle Scholar
IEC. (2014). Future publication. 61162-3: Maritime navigation and radiocommunication equipment and systems – Digital interfaces – Part 3: Multiple Talker and multiple listeners. High speed network bus.Google Scholar
IMO. (1983a). International Convention for the Safety of Life at Sea (SOLAS).Google Scholar
IMO. (1983b). Resolution A.529(13).Google Scholar
IMO. (1995). International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW).Google Scholar
IMO. (1999). Resolution A.893(21), Annex 24 and 25.CrossRefGoogle Scholar
ITU-R. (2014). M.1371-5: Technical characteristics for an automatic identification system using time division multiple access in the VHF maritime mobile frequency band.Google Scholar
Kwang-II, K. and Keon Myung, L. (2018). Deep learning-based caution area traffic prediction with automatic identification system sensor data. Sensors, 18(9), 3172.Google Scholar
Lane, R., Nevell, D., Hayward, S. and Beaney, T. (2010). Maritime anomaly detection and threat assessment. Proceedings of 13th Conference on Information Fusion, Edinburgh, UK.Google Scholar
Laxhammar, R. and Falkman, G. (2011). Sequential Conformal Anomaly Detection in Trajectories Based on Hausdorff Distance. Proceedings of the International Conference on Information Fusion, Chicago, IL, USA, 1–8.Google Scholar
Laxhammar, R. and Falkman, G. (2014). Online learning and sequential anomaly detection in trajectories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36, 11581173.CrossRefGoogle ScholarPubMed
Li, H., Liu, J., Liu, R. W., Xiong, N., Wu, K. and Kim, T.-h. (2017). A dimensionality reduction-based multi-step clustering method from robust vessel trajectory analysis. Sensors, 17(8), 1792.CrossRefGoogle ScholarPubMed
Martineau, E., Roy, J. and DRDC Valcartier. (2011). Maritime Anomaly Detection: Domain Introduction and Review of Selected Literature. www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA554310. Accessed October 2018.Google Scholar
MO. (2018). http://www.umgdy.gov.pl/. Accessed October 2018.Google Scholar
Naus, K. and Wąż, M. (2013). The idea of using the A* algorithm for route planning an unmanned vehicle ‘Edredon’. Scientific Journals University of Szczecin, 36(108), 143147.Google Scholar
Nevell, D. (2009). Anomaly detection in white shipping. Proceedings of 2nd IMA Conference on Mathematics in Defence, Farnborough, UK.Google Scholar
NIMA. (2000). Department of Defense World Geodetic System 1984, Its Definition and Relationships with Local Geodetic Systems.Google Scholar
Obradovic, I., Milicevic, M. and Zubrinic, K. (2014). Machine learning approaches to maritime anomaly detection. Naše more, 61(5–6), 96101.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013a). Traffic Route Extraction and Anomaly Detection (TREAD): Vessel Pattern Knowledge Discovery and Exploitation for Maritime Situational Awareness. NATO Formal Report CMRE-FR-2013-001, NATO Unclassified. Brussels, Belgium: NATO.Google Scholar
Pallotta, G., Vespe, M. and Bryan, K. (2013b). Vessel pattern knowledge discovery from AIS data: a framework for anomaly detection and route prediction. Entropy, 15, 22182245.CrossRefGoogle Scholar
Ristic, B., La Scala, B., Morelande, M. and Gordon, N. (2008). Statistical Analysis of Motion Patterns in AIS Data: Anomaly Detection and Motion Prediction. Proceedings of 11th Conference on Information Fusion, Cologne, Germany.Google Scholar
Sang, L. Z., Yan, X. P., Wall, A., Wang, J. and Mao, Z. (2016). CPA calculation method based on AIS position prediction. The Journal of Navigation, 69, 14091426.CrossRefGoogle Scholar
Seibert, M. (2009). Maritime Anomaly Detection. Workshop on Detection of Anomalous Behaviors in Maritime Environments. Carnegie Mellon University.Google Scholar