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Assessment of the Influence of Offshore Wind Farms on Ship Traffic Flow Based on AIS Data

Published online by Cambridge University Press:  04 June 2019

Qing Yu ,
Kezhong Liu ,
A.P. Teixeira  and
C. Guedes Soares Open the ORCID record for C. Guedes Soares [Opens in a new window]
Qing Yu
Affiliation:
(School of Navigation, Wuhan University of Technology, Wuhan, China) (Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Portugal)
Kezhong Liu
Affiliation:
(School of Navigation, Wuhan University of Technology, Wuhan, China)
A.P. Teixeira
Affiliation:
(Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Portugal)
C. Guedes Soares*
Affiliation:
(Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Técnico, Universidade de Lisboa, Portugal)
*
(E-mail: c.guedes.soares@centec.tecnico.ulisboa.pt)
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Abstract

This paper proposes a framework to assess the influence of Offshore Wind Farms (OWFs) on maritime traffic flow based on raw Automatic Identification System (AIS) data collected before and after the installation of the offshore wind turbines. The framework includes modules for data acquisition, data filtering and statistical analysis. The statistical analysis characterises the influence of an OWF on maritime traffic in terms of minimum passing distances and lateral distribution of the ship trajectories near the OWF. The framework is applied to a specific route for which AIS data is available before and after an OWF installation. The impacts of the OWF on marine traffic are diverse and depend on the ship type categories. This paper quantitatively characterises an OWF's influence on a specific route that is probabilistically modelled, which is important for further studies on OWF site selection and maritime traffic risk assessment and management.

Keywords

Marine trafficOffshore Wind FarmAIS dataStatistical analysis
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 1 , January 2020 , pp. 131 - 148
Copyright
Copyright © The Royal Institute of Navigation 2019 

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