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Vessel classification method based on vessel behavior in the port of Rotterdam

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
AIS (Automatic Identification System) data have proven to be a valuable source to investigate vessel behavior. The analysis of AIS data provides a possibility to recognize vessel behavior patterns in a waterway area. Furthermore, AIS data can be used to classify vessel behavior into several categories. The analysis results would help the port authority and other equivalent parties in port design and optimization or marine traffic management. For researchers, it provides a systematic way to understand, simulate and predict vessel behavior. This paper focuses on vessel classification in the Botlek area, Rotterdam from the perspective of vessel behavior. In this paper, the vessel properties, including vessel type, GT (Gross Tonnage), length and beam, have been analyzed to investigate the vessel behavior, which is described by four factors including heading, COG (Course over Ground), SOG (Speed over Ground), and position. In order to discover the behavior patterns in normal situations, several thresholds are set in order to filter the collected AIS data to define such situations. By plotting the AIS data, behavioral changes with the changes of properties have been observed. Hence, the correlations between vessel behavior and different vessel properties are investigated. The results reveal that a vessel’s sailing position and COG are both strongly determined by beam, while SOG is affected by GT. For the heading of a vessel, no obvious correlation with any vessel property is found. Each behavioral factor is clustered according to the correlated vessel property. This way, the criteria to classify the vessels are determined. The vessel classification results based on their behavior would likely to lead to more consistency in the analysis, simulation and prediction of the vessel behavior. The reason is that the development of such a simulation model is based on a systematic recognition of the vessel behavior patterns.
Rocznik
Strony
86--92
Opis fizyczny
Bibliogr. 12 poz., rys. tab.
Twórcy
autor
  • Delft University of Technology, Faculty of Civil Engineering and Geosciences, Delft, The Netherlands Department of Hydraulic Engineering, y.zhou-5@tudelft.nl
autor
  • Delft University of Technology, Faculty of Civil Engineering and Geosciences, Delft, The Netherlands Department of Transport & Planning, w.daamen@tudelft.nl
autor
  • Delft University of Technology, Faculty of Civil Engineering and Geosciences, Delft, The Netherlands Department of Hydraulic Engineering, t.vellinga@tudelft.nl
  • Delft University of Technology, Faculty of Civil Engineering and Geosciences, Delft, The Netherlands Department of Transport & Planning, s.p.hoogendoorn@tudelft.nl
Bibliografia
  • 1. De BOER, T. (2010) An analysis of vessel behaviour based on AIS data. TU Delft, Delft University of Technology.
  • 2. FUJII, Y. & TANAKA, K. (1971) Traffic capacity. Journal of Navigation. 24 (04). pp. 543–552.
  • 3. GOERLANDT, F. & KUJALA, P. (2011) Traffic simulation based ship collision probability modeling. Reliability Engineering & System Safety. 96 (1). pp. 91–107.
  • 4. AARSÆTHER, G.K. & MOAN, T. (2009) Estimating navigation patterns from AIS. Journal of Navigation. 62 (04). pp. 587–607.
  • 5. MASCARO, S., KORB, K.B. & NICHOLSON, A.E. (2010) Learning abnormal vessel behaviour from ais data with bayesian networks at two time scales. Tracks a Journal of Artists Writings.
  • 6. PALLOTTA, G., VESPE, M. & BRYAN, K. (2013) Vessel pattern knowledge discovery from ais data: A framework for anomaly detection and route prediction. Entropy. 15 (6). pp. 2218–2245.
  • 7. Port Authority, R. (2012) HydroMeteoBundel, No. 4, ed, Rotterdam, the Netherlands, Dutch.
  • 8. RISTIC, B., La SCALA, B., MORELANDE, M. & GORDON, N. (2008) Statistical analysis of motion patterns in AIS data: Anomaly detection and motion prediction. Information Fusion, 2008. 11th International Conference, 30 June – 3 July 2008, Cologne. IEEE. pp. 1–7.
  • 9. SHU, Y., DAAMEN, W., LIGTERINGEN, H. & HOOGENDOORN, S. (2013) Vessel Speed, Course, and Path Analysis in the Botlek Area of the Port of Rotterdam, Netherlands. Transportation Research Record: Journal of the Transportation Research Board. 2330 (1). pp. 63–72.
  • 10. SILVEIRA, P., TEIXEIRA, A. & SOARES, C.G. (2013) Use of AIS data to characterise marine traffic patterns and ship collision risk off the coast of Portugal. Journal of Navigation. 66 (06). pp. 879–898.
  • 11. XIAO, F. (2014) Ships in an Artificial Force Field: A Multiagent System for Nautical Traffic and Safety. TU Delft, Delft University of Technology.
  • 12. ZHU, F. (2011) Mining ship spatial trajectory patterns from AIS database for maritime surveillance, Emergency Management and Management Sciences (ICEMMS). 2 nd IEEE International Conference, 8–10 August 2011, Beijing. IEEE. pp. 772–775.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9cba62cc-1ac0-4682-983b-488d3e01050a
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