Mapping marine traffic density by using AIS data: an application in the Northern Aegean Sea

• Autorzy: Kundakҁi B. , Nas S.

Identyfikatory

DOI

10.2478/pomr-2018-0131

• Języki publikacji: EN

Abstrakty

EN

Automatic Identification System (AIS) data is used for monitoring the movements of vessels live movements through instant transmission of vessel information while, at the same time, historical AIS data is used for marine traffic analysis by researchers. There are several methods and computer programs developed for the analysis of marine traffic by the use of AIS data. Combining the intersection algorithm proposed by Antonio (1992) and distance calculation method, this study develops a method to analyse vessel distribution on a selected cross sectional line (SCS) in the Northern Aegean Sea. As a complementary to the new methods proposed, a desktop application is developed in C# programming language to visualize the vessel distribution on the SCS line. SQL server is used for AIS data storage and analysis. The study is conducted over 7-day AIS data, specifically 2.382.469 rows and 42.884.442 data in total, belonging to the Northern Aegean Sea marine traffic. As a result, the mapping of the movements of different types of vessels in the Northern Aegean Sea is effectively performed and Frequency-Distance, Draught-Distance, SOG-Distance, SOG-COG distributions on the SCS line are successfully analysed by the new method introduced.

Słowa kluczowe

EN

Automatic Identification System; marine traffic; marine traffic analysis

• Wydawca: Gdańsk University of Technology, Faculty of Ocean Engineering & Ship Technology
• Czasopismo: Polish Maritime Research
• Rocznik: 2018
• Tom: nr 4
• Strony: 49--58
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Kundakҁi B.

• Dokuz Eylul University Maritime Faculty, Buca, 35160 Izmir, Turkey

autor

Nas S.

• Dokuz Eylul University Maritime Faculty, Buca, 35160 Izmir, Turkey

Bibliografia

• 1. Altan, Y., & Otay, E. (2017). Maritime Traffic Analysis of the Strait of Istanbul based on AIS data. Journal of Navigation, 70(6), 1367-1382. doi:10.1017/S0373463317000431.
• 2. Antonio, F. (1992). Faster Line Segment Intersection. In D. Kirk (Ed.), Graphics Gems III (1st ed., pp. 199–202). Academic Press, Inc.
• 3. Bowditch, N. (2002). The American Practical Navigator. Bethesda, Maryland: National Imagery and Mapping Agency.
• 4. Eriksen, T., Hoye, G., Narheim, B., and Meland, B. J. (2006). Maritime Traffic Monitoring Using a Space-Based AIS Receiver. Acta Astronautica. 58 (2006), 537–549. https:// doi.org/10.1016/j.actaastro.2005.12.016.
• 5. Greidanus, H., Alvarez, M., Eriksen, T. and Gammieri, V. (2016). Completeness and Accuracy of a Wide-Area Maritime Situational Picture Based on Automatic Ship Reporting Systems. The Journal of Navigation. 69, 156–168. https://doi.org/10.1017/S0373463315000582.
• 6. Hanninen, M. and Kujala, P. (2010). The Effects of Causation Probability on the Ship Collision Statistics in the Gulf of Finland. International Journal on Marine Navigation and Safety of Sea Transportation. 4(1), 79–84.
• 7. 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, 373-389. https://doi.org/10.1017/ S0373463307004298.
• 8. IALA-AISM. (2003). IALA Guidelines on the Universal Automatic Identification System (AIS). Volume 1, Part I – Operational Issues. Edition 1.1.
• 9. ITU-R. (2014). 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: Electronic Publication.
• 10. Kujala, P., Hänninen, M., Arola, T. and Ylitalo, J. (2009). Analysis of the Marine Traffic Safety in the Gulf of Finland. Reliability Engineering and System Safety. 94, 1349–1357. https://doi.org/10.1016/j.ress.2009.02.028.
• 11. Lei, P-R., Tsai, T-H. and Peng, W-C. (2016). Discovering Maritime Traffic Route from AIS Network. The 18th Asia- Pacific Network Operations and Management Symposium (APNOMS). https://doi.org/10.1109/APNOMS.2016.7737223.
• 12. Li, L., Lu, W., Niu, J., Liu, J., & Liu, D. (2017). AIS Databased Decision Model for Navigation Risk in Sea Areas. Journal of Navigation, 1-15. doi:10.1017/S0373463317000807.
• 13. Mazaheri, A., Montewka, J. and Kujala, P. (2013). Correlation between the Ship Grounding Accident and the Ship Traffic – A Case Study Based on the Statistics of the Gulf of Finland. International Journal on Marine Navigation and Safety of Sea Transportation. 7(1), 119–124.
• 14. Mehta, A. L. (2016). Analysis of Waterway Transportation in Southeast Texas Waterway Based on AIS Data. Texas: Lamar University.
• 15. Marine Management Organisation (MMO) (2014). Mapping UK Shipping Density and Routes Technical Annex. A report produced for the Marine Management Organisation, pp 52. MMO Project No: 1066. ISBN: 978-1-909452-26-8.
• 16. Mustaffa, M., Ahmat, N. H. and Ahmad, S. (2015). Mapping Vessel Path of Marine Traffic Density of Port Klang , Malaysia Using Automatic Identification System ( AIS ) Data. International Journal of Science and Research (IJSR). 4(11), 245–248.
• 17. Mustaffa, M., Abas, M., Ahmad, S., Aini, N. A., Abbas, W. F., Abdullah, S. A. C., Razak, N. I. A., Darus, M. Y. (2016). Marine Traffic Density Over Port Klang, Malaysia Using Statistical Analysis of AIS Data: A Preliminary Study. Journal of ETA Maritime Science. 4(4), 333-341. https:// dx.doi.org/10.5505/jems.2016.60352.
• 18. Nas, S. (2014). Deniz Trafiğinde Tehlikesi Olasılığının Analizi: Marmara Denizi Uygulaması. I. Ulusal Gemi Trafik Hizmetleri Kongresi. 08-09 Aralık. İstanbul.
• 19. Natale, F., Gibin, M., Alessandrini, A., Vespe, M. and Paulrud, A. (2015). Mapping Fishing Effort through AIS Data. PLOS ONE. 10(6). https://doi.org/10.1371/journal. pone.0130746.
• 20. PIANC (2014). Harbour Approach Channels Desing Guidelines.
• 21. Seta, T., Matsukura, H., Aratani, T. and Tamura, K. (2016). An Estimation Method of Message Receiving Probability for a Satellite Automatic Identification System Using a Binomial Distribution Model. Scientific Journals of the Maritime University of Szczecin. 46 (118), 101–107. http:// dx.doi.org/10.17402/125.
• 22. Skauen, A. N. (2016). Quantifying the Tracking Capability of Space-Based AIS Systems. Advances in Space Research. 57, 527–542. https://doi.org/10.1016/j.asr.2015.11.028.
• 23. UNCTAD (2016). Review of Maritime Transport 2016. United Nations Publication.
• 24. International Maritime Organization. Automatic Identification Systems (AIS). http://www.imo.org/en/ OurWork/safety/navigation/pages/ais.aspx, Accessed 01June 2017.
• 25. Selçuk NAS, ht tp://www.nasmarit ime.com/ PRESENTATIONS/SN-DEPARTURE.ppt, Accessed 02 April 2017.
• 26. Willems, N., Wetering, H. V. D. and Wijk, J. J. V. (2009). Visualization of Vessel Movements. Eurographics/ IEEEVGTC Symposium on Visualization 2009. 28(3), 959–966.
• 27. Wu, L., Xu, Y., Wang, Q., Wang, F., and Xu, Z. (2017). Mapping Global Shipping Density from AIS Data. The Journal of Navigation, 70(1), 67-81. https://doi.org/10.1017/ S0373463316000345.
• 28. Xiao, F., Ligteringen, H., Gulijk, C. V. and Ale, B. (2015). Comparison Study on AIS Data of Ship Traffic Behavior. Ocean Engineering. 95, 84–93. https://doi.org/10.1016/j. oceaneng.2014.11.020.
• 29. Wise, S. (2014). GIS basics. Taylor & Francis.
• 30. Wolfram Mathworld, Line-Line Intersection, http:// mathworld.wolfram.com/Line-LineIntersection.html, Accessed 25 May 2018.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).