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

2019 ◽  
Vol 73 (1) ◽  
pp. 131-148 ◽  
Author(s):  
Qing Yu ◽  
Kezhong Liu ◽  
A.P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Statistical Analysis ◽  
Traffic Flow ◽  
Wind Farms ◽  
Offshore Wind ◽  
Automatic Identification ◽  
Identification System ◽  
Offshore Wind Farms ◽  
Ship Traffic ◽  
Maritime Traffic ◽  
Before And After

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.

scholarly journals The Methodology for Assessing the Impact of Offshore Wind Farms on Navigation, Based on the Automatic Identification System Historical Data

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6559
Author(s):  
Krzysztof Naus ◽  
Katarzyna Banaszak ◽  
Piotr Szymak
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Automatic Identification ◽  
Identification System ◽  
Large Set ◽  
Data Set ◽  
Offshore Wind Farms ◽  
Automatic Identification System ◽  
The Impact

Mounting offshore renewable energy installations often involves extra risk regarding the safety of navigation, especially for areas with high traffic intensity. The decision-makers planning such projects need to anticipate and plan appropriate solutions in order to manage navigation risks. This process is referred to as “environmental impact assessment”. In what way can these threats be reduced using the available Automatic Identification System (AIS) tool? This paper presents a study of the concept for the methodology of an a posteriori vessel traffic description in the form of quantitative and qualitative characteristics created based on a large set of historical AIS data (big data). The research was oriented primarily towards the practical application and verification of the methodology used when assessing the impact of the planned Offshore Wind Farm (OWF) Baltic II on the safety of ships in Polish Marine Areas, and on the effectiveness of navigation, taking into account the existing shipping routes and customary and traffic separation systems. The research results (e.g., a significant distance of the Baltic II from the nearest customary shipping route equal to 3 Nm, a small number of vessels in its area in 2017 amounting to only 930) obtained on the basis of the annual AIS data set allowed for an unambiguous and reliable assessment of the impact of OWFs on shipping, thus confirming the suitability of the methodology for MREI spatial planning.

scholarly journals Semi-Continuous Spatial Statistical Analysis Using AIS Data for Vessel Traffic Flow Characteristics in Fairway

2021 ◽  
Vol 9 (4) ◽  
pp. 378
Author(s):  
Jong Kwan Kim
Keyword(s):  
Statistical Analysis ◽  
Traffic Flow ◽  
Flow Characteristics ◽  
Automatic Identification ◽  
Identification System ◽  
Spatial Statistical Analysis ◽  
Traffic Characteristics ◽  
Traffic Distribution ◽  
Traffic Flow Characteristics ◽  
Marine Accidents

As high vessel traffic in fairways is likely to cause frequent marine accidents, understanding vessel traffic flow characteristics is necessary to prevent marine accidents in fairways. Therefore, this study conducted semi-continuous spatial statistical analysis tests (the normal distribution test, kurtosis test and skewness test) to understand vessel traffic flow characteristics. First, a vessel traffic survey was conducted in a designated area (Busan North Port) for seven days. The data were collected using an automatic identification system and subsequently converted using semi-continuous processing methods. Thereafter, the converted data were used to conduct three methods of spatial statistical analysis. The analysis results revealed the vessel traffic distribution and its characteristics, such as the degree of use and lateral positioning on the fairway based on the size of the vessel. In addition, the generalization of the results of this study along with that of further studies will aid in deriving the traffic characteristics of vessels on the fairway. Moreover, these characteristics will reduce maritime accidents on the fairway, in addition to establishing the foundation for research on autonomous ships.

Monitoring fisheries resources at offshore wind farms: BACI vs. BAG designs

2020 ◽  
Vol 77 (3) ◽  
pp. 890-900
Author(s):  
Elizabeth T Methratta
Keyword(s):  
Statistical Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Cultural Value ◽  
Analytical Models ◽  
Offshore Wind Farms ◽  
Fisheries Resources ◽  
Unaffected Control ◽  
Before And After

Abstract Offshore wind farms often co-occur with biodiverse marine ecosystems with high ecological, economic, and cultural value. Yet there are many uncertainties about how wind farms affect marine organisms and their environment. The before–after–control–impact (BACI) design, an approach that compares an impact location with an unaffected control both before and after the intervention, is the most common method used to study how offshore wind farms affect finfish. Unfortunately, this design has several methodological limitations that undermine its ability to detect effects in these studies. An alternative approach, the before–after-gradient (BAG) design, would sample along a gradient with increasing distance from the turbines both before and after the intervention, and could overcome many of the limitations of BACI. The BAG design would eliminate the difficult task of finding a suitable control, allow for the assessment of the spatial scale and extent of wind farm effects, and improve statistical power by incorporating distance as an independent variable in analytical models rather than relegating it to the error term. This article explores the strengths and weaknesses of the BACI and BAG designs in the context of offshore wind development and suggests an approach to incorporating the BAG design into existing fisheries surveys and a regional monitoring framework.

Operational offshore wind farms and associated ship traffic cause profound changes in distribution patterns of Loons (Gavia spp.)

2019 ◽  
Vol 231 ◽  
pp. 429-438 ◽  
Author(s):  
Bettina Mendel ◽  
Philipp Schwemmer ◽  
Verena Peschko ◽  
Sabine Müller ◽  
Henriette Schwemmer ◽  
...  
Keyword(s):  
Wind Farms ◽  
Distribution Patterns ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
Ship Traffic

scholarly journals Modeling of Vessel Traffic Flow for Waterway Design–Port of Świnoujście Case Study

2021 ◽  
Vol 11 (17) ◽  
pp. 8126
Author(s):  
Agnieszka Nowy ◽  
Kinga Łazuga ◽  
Lucjan Gucma ◽  
Andrej Androjna ◽  
Marko Perkovič ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Traffic Flow ◽  
Probabilistic Models ◽  
Historical Data ◽  
Automatic Identification ◽  
Identification System ◽  
Practical Applications ◽  
Ship Traffic ◽  
Navigation Patterns

The paper presents an analysis of ship traffic using the port of Świnoujście and the problems associated with modelling vessel traffic flows. Navigation patterns were studied using the Automatic Identification System (AIS); an analysis of vessel traffic was performed with statistical methods using historical data; and the paper presents probabilistic models of the spatial distribution of vessel traffic and its parameters. The factors that influence the spatial distribution were considered to be the types of vessels, dimensions, and distances to hazards. The results show a correlation between the standard deviation of the traffic flow, the vessel sizes, and the distance to the hazard. These can be used in practice to determine the safety of navigation and the design of non-existing waterways and to create a general model of vessel traffic flow. The creation of the practical applications is intended to improve navigation efficiency, safety, and risk analysis in any particular area.

scholarly journals Assessment of fresh water wind resources on Lake Erie

2019 ◽  
Vol 43 (1) ◽  
pp. 83-91
Author(s):  
Jiale (Jerry) Li ◽  
Xiong (Bill) Yu
Keyword(s):  
Statistical Analysis ◽  
Wind Energy ◽  
Land Surface ◽  
Lake Erie ◽  
Wind Farms ◽  
Offshore Wind ◽  
Energy Output ◽  
Energy Potential ◽  
Offshore Wind Farms ◽  
Onshore Wind

Wind farms are better been built at locations with higher wind resource potentials. As the appropriate locations become fewer and fewer to build onshore wind farms, significant attention has been drawn to the wind energy industry to build offshore wind farms. The terrain effect has fewer effects offshore than onshore since the sea level is flat and no artificial buildings are built there. The coastal line of the Great Lakes is one of those areas that not only has great wind energy potential but is also near the high population coastal cities which is short of the land surface. This article makes the detailed statistical analysis of 1-year offshore wind data in Lake Erie from a Light Detection and Ranging system placed on a water intake crib 4 miles away from near the coast of Cleveland. For comparison purpose, a nearby onshore wind monitoring station’s data have also been analyzed to study the wind and power characteristics. Specifically, the statistical analysis of the data includes Weibull shape and scale factors, the monthly average of the wind speed, turbulence intensity, and wind power density. In addition, two site-matching commercial wind turbines with 50 (Vestas® 39) and 80 m (Vestas® V90) hub heights have been chosen to estimate the 1-year energy output. The result shows great preponderances of building offshore wind farms than building onshore wind farms. This study gives guidance to the cost-benefit analysis to build the offshore wind farms in Lake Erie.

scholarly journals The research of traffic flow in LaoTieshan channel based on AIS data

2020 ◽  
Vol 325 ◽  
pp. 02002
Author(s):  
Qiang Tu ◽  
Zhongyi Zheng
Keyword(s):  
Traffic Flow ◽  
Actual Condition ◽  
Flow Density ◽  
Automatic Identification ◽  
Identification System ◽  
Adaptation Mechanism ◽  
Ship Traffic ◽  
System Data ◽  
Actual Flow ◽  
Improved Model

In order to study the ship traffic flow in LaoTieshan channel, this paper rewrites the rules of NS (Nagel-Schreckenberg) model of Cellular Automata (CA) based on AIS（Automatic Identification System） data. By referring to the idea of synchronous flow in three-phase traffic flow theory and introducing the speed adaptation mechanism into the simulation, the new model reproduces the traffic flow of LaoTieshan channel and gets the relevant fundamental diagrams. Compared with the actual flow-density diagram drawn from AIS data, it is found that the simulated flow generated by the improved model is more in line with the actual condition, and also conforms to the phase transition of traffic flow in LaoTieshan channel. The method and idea of the simulation are helpful to explain and investigate some complex situations in LaoTieshan channel.

scholarly journals Extracting the Maritime Traffic Route in Korea Based on Probabilistic Approach Using Automatic Identification System Big Data

2022 ◽  
Vol 12 (2) ◽  
pp. 635
Author(s):  
Jeong-Seok Lee ◽  
Ik-Soon Cho
Keyword(s):  
Big Data ◽  
Wind Farm ◽  
Marine Pollution ◽  
Probabilistic Approach ◽  
Offshore Wind ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Traffic ◽  
Energy Projects ◽  
Separate Scheme

To protect the environment around the world, we are actively developing ecofriendly energy. Offshore wind farm generation installed in the sea is extremely large among various energies, and friction with ships occurs regularly. Other than the traffic designated area and the traffic separate scheme, traffic routes in other sea areas are not protected in Korea. Furthermore, due to increased cargo volume and ship size, there is a risk of collisions with marine facilities and marine pollution. In this study, maritime safety traffic routes that must be preserved are created to ensure the safety of maritime traffic and to prevent accidents with ecofriendly energy projects. To construct maritime traffic routes, the analysis area is divided, and ships are classified using big data. These data are used to estimate density, and 50% maritime traffic is chosen. This result is obtained by categorizing the main route, inner branch route, and outer branch route. The Korean maritime traffic route is constructed, and the width of the route is indicated. Furthermore, this route can be applied as a navigation route for maritime autonomous surface ships.

Assessing Grounding Frequency using Ship Traffic and Waterway Complexity

2014 ◽  
Vol 68 (1) ◽  
pp. 89-106 ◽  
Author(s):  
Arsham Mazaheri ◽  
Jakub Montewka ◽  
Pentti Kotilainen ◽  
Otto-Ville Edvard Sormunen ◽  
Pentti Kujala
Keyword(s):  
Expert Elicitation ◽  
Traffic Density ◽  
Automatic Identification ◽  
Identification System ◽  
Factors Affecting ◽  
Ship Traffic ◽  
Maritime Traffic ◽  
Statistical Dependency ◽  
Almost All ◽  
Sea Area

Ship traffic is the factor that presents in almost all of the existing grounding risk models. It is considered to be one of the main factors affecting the expected frequency of ship groundings. This is mostly accepted by experts as common sense. However, there is no research available on the actual dependency between ship traffic and grounding accidents. In this paper, we conduct a study aimed at determining the statistical dependency between the density and distribution of traffic, the number and frequency of grounding accidents and the dependency between the complexity of waterways and an actual accident. For this purpose we utilise statistical analysis of maritime traffic, obtained from Automatic Identification System (AIS) data and grounding accidents, enhanced with the expert elicitation techniques delivering the waterway complexity index. The sea area under investigation is the Gulf of Finland. The results show statistical dependency between frequency of grounding and waterway complexity as well as the traffic distribution. However, the study does not reveal any significant dependency between grounding and traffic density.

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