Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-27T12:04:33.240Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal

Published online by Cambridge University Press:  09 August 2013

P.A.M. Silveira ,
A.P. Teixeira  and
C. Guedes Soares
P.A.M. Silveira
Affiliation:
(Centre for Marine Technology and Engineering (CENTEC), Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal)
A.P. Teixeira
Affiliation:
(Centre for Marine Technology and Engineering (CENTEC), Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal)
C. Guedes Soares*
Affiliation:
(Centre for Marine Technology and Engineering (CENTEC), Instituto Superior Técnico, Technical University of Lisbon, Lisbon, Portugal)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper studies the risk of ship collision off the coast of Portugal based on Automatic Identification System (AIS) data, which is recorded and maintained by the Portuguese coastal Vessel Traffic Service (VTS) control centre (CCTMC). Computer programs for decoding, visualization and analysis of the AIS data have been developed. From analysis of the AIS data available, maritime traffic off the coast of Portugal is characterized and a statistical analysis of traffic in the Traffic Separation Schemes is provided. An algorithm has been developed to assess the risk profile and the relative importance of routes associated with ports. A method is proposed to calculate the collision risk from the assessment of the number of collision candidates by estimating future distances between ships based on their previous positions, courses and speeds, and comparing those distances with a defined collision diameter. Values of causation probability suggested in several studies are used to calculate the expected number of collisions in the period of time under investigation based on the number of collision candidates. The results of this study are then compared with the number of collisions that have occurred between 1997–2006, registered and maintained by the Portuguese Maritime Authority.

Keywords

AIS dataTraffic assessmentRisk of ship collisionCollision candidates
Type
Research Article
Information
The Journal of Navigation , Volume 66 , Issue 6 , November 2013 , pp. 879 - 898
Copyright
Copyright © The Royal Institute of Navigation 2013 

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

Aarsaether, K. and Moan, T. (2009). Estimating Navigation Patterns from AIS. Journal of Navigation, 62, 587–607.CrossRefGoogle Scholar
Antão, P., Grande, O., Trucco, P. and Guedes Soares, C. (2008). Analysis of maritime accident data with BBN modelling. Safety, Reliability and Risk Analysis – Theory, Methods and Applications. In Martorell, S., Guedes Soares, C. and Barnett, J., Eds. Balkema, Taylor & Francis Group, Volume II, pp. 3265–3274.Google Scholar
Coldwell, T. (1983). Marine Traffic Behaviour in Restricted Waters. Journal of Navigation, 36, 431–444.CrossRefGoogle Scholar
Degré, T., Glansdorp, C., van der Tak, C. (2003). The importance of a risk based index for vessels to enhance maritime safety, In: Proceedings of the 10th IFAC. Symposium on Control in Transportation System, CTS2003, Tokyo, Japan.Google Scholar
Fowler, T.G. and Sørgård, E. (2000). Modeling Ship Transportation Risk. Risk Analysis, 20(2), 225–244.CrossRefGoogle ScholarPubMed
Friis-Hansen, P. and Simonsen, B. (2001). GRACAT: software for grounding and collision risk analysis. Marine Structures, 15(4), 383–401.CrossRefGoogle Scholar
Fujii, Y. and Tanaka, K. (1971). Traffic capacity. Journal of Navigation, 24, 543–552.CrossRefGoogle Scholar
Fujii, Y., Yamanouchi, H. and Mizuki, N. (1974). Some factors affecting the frequency of accidents in marine traffic. II—the probability of stranding and III—the effect of darkness on the probability of collision and stranding. Journal of Navigation, 27, 239–247.CrossRefGoogle Scholar
Goerlandt, F. and Kujala, P. (2010). Traffic Simulation Based Ship Collision Probability Modeling. Reliability Engineering and System Safety, 96, 91–107.CrossRefGoogle Scholar
Goodwin, E. M. (1975). A statistical study of ship domains. Journal of Navigation, 28, 328–344.CrossRefGoogle Scholar
Gouveia, J., Antão, P. and Guedes Soares, C. (2007). Accidents in sea areas of Portuguese jurisdiction (in Portuguese), Riscos Públicos e Industriais. In Guedes Soares, C., Teixeira, A.P.Antão, e P. Eds. Edições Salamandra, Lisboa, Vol. I, pp: 499–516.Google Scholar
Graveson, A. (2004). AIS-An Inexact Science. Journal of Navigation, 57, 339–343.CrossRefGoogle Scholar
Guedes Soares, C. and Teixeira, A.P. (2001). Risk assessment in maritime transportation. Reliability Engineering and System Safety, 74, 299–309.CrossRefGoogle Scholar
Hänninen, M. and Kujala, P. (2010). The Effects of Causation Probability on the Ship Collision Statistics in the Gulf of Finland. TransNav – International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 4, No. 1, pp. 79–84.Google Scholar
IMO. (2007). Consolidated text of the guidelines for formal safety assessment (FSA) for use in the IMO rule-making process (MSC/Circ.1023-MEPC/Circ.392), MSC 83/INF.2, London, 14 May 2007.Google Scholar
Jansen, F. (2001). Bayesian Networks and Decision Graphs. Springer-Verlag, New York.CrossRefGoogle Scholar
Kujala, P., Hanninen, 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.CrossRefGoogle Scholar
MacDuff, T. (1974). The probability of vessel collisions. Ocean Industry, 144–8.Google Scholar
Martins, M. R. and Maturana, M. C. (2010). Human error contribution in collision and grounding of oil tankers. Risk Analysis, 30(4), 674–698.CrossRefGoogle ScholarPubMed
Montewka, J., Hinz, T., Kujala, P. and Matusiak, J. (2010). Probability modelling of vessel collisions. Reliability Engineering and System Safety, 95, 573–589.CrossRefGoogle Scholar
Mou, J. M., Tak, C. and Ligteringen, H. (2010). Study on Collision Avoidance in Busy Waterways by Using AIS Data. Ocean Engineering, 37, 483–490.CrossRefGoogle Scholar
Norris, A. (2007). AIS implementation – success or failure. Journal of Navigation, 60, 373–389.CrossRefGoogle Scholar
Otto, S., Pedersen, P.T., Samuelides, M. and Sames, P.C. (2002). Elements of risk analysis for collision and grounding of a RoRo passenger ferry. Marine Structures, 15(4), 461–474.CrossRefGoogle Scholar
Pedersen, P.T. (1995). Collision and Grounding Mechanics, The Danish Society of Naval Architects and Marine Engineers, 125–57.Google Scholar
Perera, L. P., Carvalho, J. and Guedes Soares, C. (2011). Fuzzy-logic based decision making system for collision avoidance of ocean navigation under critical collision conditions. Journal of Marine Science and Technology, 16(1), 84–99.CrossRefGoogle Scholar
Perera, L. P., Carvalho, J. P. and Guedes Soares, C. (2012a). Intelligent Ocean Navigation and Fuzzy-Bayesian Decision-Action Formulation. IEEE Journal of Oceanic Engineering, 37(2), 4–219.CrossRefGoogle Scholar
Perera, L. P., Oliveira, P. and Guedes Soares, C. (2012b). Maritime Traffic Monitoring based on Vessel Detection, Tracking, State Estimation, and Trajectory Prediction. IEEE Transactions on Intelligent Transportation Systems, 13(3), 88–1200.CrossRefGoogle Scholar
Pietrzykowski, Z. (2008). Ship's Fuzzy Domain – a Criterion for Navigational Safety in Narrow Fairways. Journal of Navigation, 62, 499–514.CrossRefGoogle Scholar
Pietrzykowski, Z., Uriasz, J. (2009). The ship domain – a criterion of navigational safety assessment in an open sea area. Journal of Navigation, 61, 93–108.CrossRefGoogle Scholar
Rosqvist, T., Nyman, T., Sonninen, S. and Tuominen, R. (2002). The implementation of the VTMIS system for the Gulf of Finland – a FSA study. RINA International Conference on Formal Safety Assessment, London, UK, 151–164.CrossRefGoogle Scholar
Trucco, P., Cagno, E., Ruggeri, F. and Grande, O. (2007). A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation. Reliability Engineering and System Safety, 93, 823–834.Google Scholar
Wang, N., Meng, X., Xu, Q., Wang, Z. (2009). A Unified Analytical Framework for Ship Domains. Journal of Navigation, 62, 643–655.CrossRefGoogle Scholar
Weng, J., Meng, Q. and Qu, X. (2012). Vessel Collision Frequency Estimation in the Singapore Strait. Journal of Navigation, 65, 207–221.CrossRefGoogle Scholar
Willems, N., Wetering, H. and Wijke, J. (2009). Visualization of Vessel Movements. Eurographics/IEEE-VGTC Symposium on Visualization 2009. Vol. 28, no. 3.Google Scholar
Zhao, J., Wu, Z., Wang, F. (1993). Comments on ship domains. Journal of Navigation, 46, 422–436.Google Scholar