Preliminary inter-comparison of AIS Data and optimal ship tracks

• Autorzy: Mannarini G. , Carelli L. , Zissis D. , Spiliopoulos G. , Chatzikokolakis K.

Identyfikatory

DOI

10.12716/1001.13.01.04

• Języki publikacji: EN

Abstrakty

EN

Optimal ship tracks computed via the VISIR model are compared to tracks recorded by the Automatic Identification System (AIS). The evaluation regards 43 tracks in the Southern Atlantic Ocean, sailed during 2016-2017 by different bulk carriers. In this exercise, VISIR is fed by wave analysis fields from the Copernicus Marine Environment Monitoring Service (CMEMS). In order to reproduce vessel speed loss in waves, a new methodology is developed, where kinematic information from AIS is fusioned with wave information from CMEMS. Resulting VISIR tracks are analyzed along with AIS tracks in terms of their topological features and duration. The tracks exhibit quite diverse topological shapes, including orthodromic, loxodromic, and other paths with complex and dynamic diversions. The distribution of AIS to VISIR track durations is analyzed in terms of several parameters, such as the AIS to VISIR track length and their Fréchet distance. Model features of VISIR affecting the results are discussed and future developments suggested by the results are outlined.

Słowa kluczowe

EN

AIS Data; ship track; Copernicus Marine Environment Monitoring Service (CMEMS); Automatic Identification System (AIS); VISIR (discoVerIng Safe and effIcient Routes); VISIR track; VISIR model; optimal ship track

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2019
• Tom: Vol. 13, no. 1
• Strony: 53--61
• Opis fizyczny: Bibliogr. 12 poz., rys., tab.

Twórcy

autor

Mannarini G.

• Euro-Mediterranean Center for Climate Change, Lecce, Italy

autor

Carelli L.

• CMCC, Lecce, Italy

autor

Zissis D.

• Marine Traffic, London, United Kingdom

autor

Spiliopoulos G.

• Marine Traffic, London, United Kingdom

autor

Chatzikokolakis K.

• Marine Traffic, London, United Kingdom

Bibliografia

• 1. Alexandersson, M. A study of methods to predict added resistance in waves. Master’s thesis, KTH Centre for Naval Architecture, 2009.
• 2. Arguedas et al. 2018. Maritime Traffic Networks: From historical positioning data to unsupervised maritime traffic monitoring. IEEE-ITS, 19(3): pp.722732. - doi:10.1109/TITS.2017.2699635
• 3. Bertram V. and Couser. P. 2017. Computational methods for seakeeping and added resistance in waves. In B. Volker (ed.), 13th International Conference on Computer and IT Applications in the Maritime Industries, Redworth, 12-14 May 2014, pages 8–16. Harburg: Technische Universität Hamburg.
• 4. Fujii M., Hashimoto H., Taniguchi Y.: Analysis of Satellite AIS Data to Derive Weather Judging Criteria for Voyage Route Selection. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 11, No. 2, doi:10.12716/1001.11.02.09, pp. 271-277, 2017
• 5. IMO. MSC.1/Circ.1228 Revised guidance to the Master for avoiding dangerous situations in adverse weather and sea conditions. Technical report, International Maritime Organization, London, UK, 2007.
• 6. M.1371: Technical characteristics for an automatic identification system using time-division multiple access in the VHF maritime mobile band.” [Online]. Available: https://www.itu.int/rec/R-REC- M.1371/en
• 7. Mannarini G., Coppini G., Oddo P., Pinardi N.: A Prototype of Ship Routing Decision Support System for an Operational Oceanographic Service. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 7, No. 1, doi:10.12716/1001.07.01.06, pp. 53-59, 2013
• 8. Mannarini G., Pinardi N., Coppini G., Oddo P. and Iafrati A. 2016. VISIR-I: small vessels – least-time nautical routes using wave forecasts. Geoscientific Model Development, 9(4): 1597–1625. https://www.geosci-model dev.net/9/1597/2016/gmd-9-1597-2016.pdf - doi:10.5194/gmd-9-1597-2016
• 9. Mannarini G. & Carelli L. 2019. VISIR-I.b: waves and ocean currents for energy efficient navigation. Geoscientific Model Development Discussions, (under review) - doi:10.5194/gmd-2018-292
• 10. Spiliopoulos, G., Chatzikokolakis, K., Zissis, D., Biliri, E., Papaspyros, D., Tsapelas, G. and Mouzakitis, S. 2017. Knowledge extraction from maritime spatiotemporal data: An evaluation of clustering algorithms on Big Data. In Big Data (Big Data), 2017 IEEE International Conference on (pp. 1682-1687). IEEE. - doi:10.1109/BigData.2017.8258106
• 11. Tu E., Zhang G., Rachmawati L., Rajabally E., and Huang G. B. 2018, Exploiting AIS Data for Intelligent Maritime Navigation: A Comprehensive Survey From Data to Methodology. IEEE Trans. Intell. Transp. Syst., vol. 19, no. 5, pp. 1559–1582. - doi:10.1109/TITS.2017.2724551
• 12. Tsujimoto, M., Kuroda, M. and Sogihara, N. 2013. Development of a calculation method for fuel consumption of ships in actual seas with performance evaluation. In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering (pp. V009T12A047-V009T12A047). American Society of Mechanical Engineers

Uwagi

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