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Maritime Route Delineation using AIS Data from the Atlantic Coast of the US

Published online by Cambridge University Press:  28 September 2016

Stephen A. Breithaupt ,
Andrea Copping ,
Jerry Tagestad  and
Jonathan Whiting
Stephen A. Breithaupt*
Affiliation:
(Coastal Division, Pacific Northwest National Laboratory, Seattle, Washington, USA)
Andrea Copping
Affiliation:
(Coastal Division, Pacific Northwest National Laboratory, Seattle, Washington, USA)
Jerry Tagestad
Affiliation:
(Paradigm ISR, Bend, Oregon, USA)
Jonathan Whiting
Affiliation:
(Coastal Division, Pacific Northwest National Laboratory, Seattle, Washington, USA)
*
(E-mail: [email protected])
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Abstract

This study examines maritime routes between ports along the Atlantic coast of the US, utilising Automated Identification System (AIS) data for the years 2010 through 2012. The delineation of vessel routes conducted in this study was motivated by development planned for offshore Wind Energy Areas (WEAs) along the Atlantic coast of the US and the need to evaluate the effect of these development areas on commercial shipping. To this end, available AIS data were processed to generate commercial vessel tracks for individual vessels, though cargo vessels are the focus in this study. The individual vessel tracks were sampled at transects placed along the Atlantic coast. The transect samples were analysed and partitioned by voyages between Atlantic ports to facilitate computation of vessel routes between ports. The route boundary analysis utilised a definition from UK guidance in which routes' boundaries encompassed 95% of the vessel traffic between ports. In addition to delineating route boundaries, we found multi-modal transverse distributions of vessels for well-travelled routes, which indicated preference for lanes of travel within the delineated routes.

Keywords

Marine TrafficVector TrackingCoastalWind Energy Areas
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 2 , March 2017 , pp. 379 - 394
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

REFERENCES

AISM-IALA (Association of Internationale de Signalisation Maritime - International Association of Marine Aids to Navigation and Lighthouse Authorities). (2004). IALA Guideline No. 1028 – On The Automatic Identification (AIS) Volume 1, Part I Operational Issues Edition 1·3. Saint Germain en Laye, France.Google Scholar
Chen, J., Lu, F. and Peng, G. (2015). A quantitative approach for delineating principal fairways of ship passages through a strait. Ocean Engineering, 103, 188–197.Google Scholar
Christensen, C.F. (2006). Navigational Risk Assessment Frequency analysis Wind Farm Horns Rev 2. Det Norske Veritas Report No. 643233- Rep 01, Revision 1. Prepared for Energi E2.Google Scholar
Copping, A., Breithaupt, S.A., Whiting, J., Grear, M., Tagestad, J. and Shelton, G. (2015). Likelihood of a Marine Vessel Accident from Wind Energy Development in the Atlantic. Wind Energy, DOI: 10.1002/we.1935.Google Scholar
Debnath, A.K. and Chin, H.C. (2010). Navigational traffic conflict technique: a proactive approach to quantitative measurement of collision risks in port waters. Journal of Navigation, 63, 137–152.CrossRefGoogle Scholar
Department of Trade Industry (DTI). (2005). Guidance on the Assessment of the Impact of Offshore Wind Farms: Methodology for Assessing the Marine Navigational Safety Risks of Offshore Wind Farms.Google Scholar
Goerlandt, F. and Kujala, P. (2011). Traffic simulation based ship collision probability modelling. Reliability Engineering and System Safety, 96, 91–107.Google Scholar
Goerlandt, F., Montewka, J., Ravn, E.S., Hänninen, M. and Mazaheri, A. (2012). Analysis of the near-collisions using AIS data for the selected locations in the Baltic Sea. Deliverable No. D_WP6_2_03. Report prepared for Baltic Sea Region Programme 2007–2013.Google Scholar
Goerlandt, F. and Kujala, P. (2014). On the reliability and validity of ship–ship collision risk analysis in light of different perspectives on risk. Safety Science, 62, 348–365.Google 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.Google Scholar
Montewka, J., Krata, P., Goerlandt, F., Mazaheri, A. and Kujala, P. (2011). Marine traffic risk modelling – an innovative approach and a case study. Proceedings of the Institute of Mechanical Engineering. Part O J. Risk Reliability, 225(3), 307–322.Google Scholar
Montewka, J., Goerlandt, F. and Kujala, P. (2012). Determination of collision criteria and causation factors appropriate to a model for estimating the probability of maritime accidents. Ocean Engineering, 40, 50–61.CrossRefGoogle Scholar
Mulyadi, Y., Kobayashi, E., Wakabayashi, N. and Pitana, T. (2014). Development of ship sinking frequency model over subsea pipeline for Madura Strait using AIS data. WMU Journal of Maritime Affairs, 13, 43–59. DOI 10.1007/s13437-013-0049-2.Google Scholar
Qu, X. and Meng, Q. (2012). Development and applications of a simulation model for vessels in the Singapore Straits. Expert Systems with Applications. 39, 8430–8438.Google Scholar
Rawson, A. and Rogers, E. (2015). Assessing the impacts to vessel traffic from offshore wind farms in the Thames Estuary. Scientific Journals of the Maritime University of Szczecin, 43(115), 99–107.Google Scholar
Rong, H., Teixeira, A. and Guedes Soares, C. (2015). Evaluation of near-collisions in the Tagus River Estuary using a marine traffic simulation model. Scientific Journals of the Maritime University of Szczecin, 43(115), 68–78.Google Scholar
Shelmerdine, R.L. (2015). Teasing out the detail: How our understanding of marine AIS data can better inform industries, developments, and planning. Marine Policy, 54, 17–25.Google Scholar
Shu, Y., Daamen, W., Ligteringen, H. and Hoogendoon, S. (2012). AIS Based Vessel Speed, Course and Path Analysis in the Botlek Area in the Port of Rotterdam. The International Workshop on Next Generation of Nautical Traffic Model, Shanghai, China.Google Scholar
Silveira, P.A.M., Teixeira, A.P. and Guedes Soares, C. (2013). Use of AIS Data to Characterise Marine Traffic Patterns and Ship Collision Risk off the Coast of Portugal. Journal of Navigation, 66(6), 879–898.Google Scholar
Sormunen, O.-V.E., Goerlandt, F., Häkkinen, J., Posti, A., Hänninen, M., Montewka, J., Ståhlberg, K. and Kujala, P. (2013). Uncertainty in maritime risk analysis: Extended case study on chemical tanker collisions. Proceedings of the Institute of Mechanical Engineering. Part M Journal of Engineering in the Maritime Environment, 229, 303–320.Google Scholar
Van Dorp, R. and Merrick, J.R.W. (2011). On a risk management analysis of oil spill risk using maritime transportation system simulation. Annals of Operational Resesearch, 187, 249–277. DOI 10.1007/s10479-009-0678-1.Google Scholar
Weng, J., Meng, Q. and Qu, X. (2012). Vessel Collision Frequency Estimation in the Singapore Strait. Journal of Navigation, 65, 207–221.Google Scholar
Xiao, F., Ligteringen, H., van Gulijk, C. and Ale, B. (2015). Comparison study on AIS data of ship traffic behaviour. Ocean Engineering, 95, 84–93.Google Scholar
Zhang, W., Goerlandt, F., Montewka, J. and Kujala, P. (2015). A method for detecting possible near miss ship collisions from AIS data. Ocean Engineering, 107, 60–69.CrossRefGoogle Scholar