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.

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.

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.

Supply chain integration opportunities for the offshore wind industry

2016 ◽  
Vol 10 (2) ◽  
pp. 191-220 ◽  
Author(s):  
Ivan Francisco Martinez Neri
Keyword(s):  
Supply Chain ◽  
Wind Farm ◽  
Supply Chain Integration ◽  
Offshore Wind ◽  
Unit Of Analysis ◽  
Content Type ◽  
Energy Projects ◽  
State Of Research ◽  
And Performance ◽  
Manufacturing Environments

Purpose This paper surveys the literature on supply chain integration (SCI) to identify the state of research in the various types of studied industries and manufacturing environments. The purpose of this paper is to identify academic discoveries that could provide offshore wind projects with means to overcome their current supply chain challenges. Design/methodology/approach A comprehensive literature review was conducted involving 162 articles published in 29 peer-reviewed journals. The papers were analyzed in terms of the dimensions of SCI, research methodology, unit of analysis, level of analysis, type of industry and manufacturing environment being studied, integrative practices, integrative barriers and the link between SCI and performance. Findings While SCI has been evolving to become an influential topic in the field of supply chain management, scholars have overlooked industrial contingencies by ignoring the differences between the studied industrial contexts, especially project-based manufacturing environments. The present review also reveals that no study of SCI has been conducted on the construction of renewable energy projects. Another finding is that case studies and research articles using networks as a unit of analysis are underrepresented. Originality/value This is the first work to advocate for an industrial contingency approach in the analysis of SCI. Thus, it proposes the offshore wind farm-construction industry as a potential study subject to broaden the knowledge in SCI in project manufacturing environments.

scholarly journals Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

2021 ◽  
Vol 10 (11) ◽  
pp. 757
Author(s):  
Pin Nie ◽  
Zhenjie Chen ◽  
Nan Xia ◽  
Qiuhao Huang ◽  
Feixue Li
Keyword(s):  
Traffic Management ◽  
Similarity Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Motion Direction ◽  
Maritime Traffic ◽  
A Cell ◽  
Robustness To Noise ◽  
Trajectory Similarity

Automatic Identification System (AIS) data have been widely used in many fields, such as collision detection, navigation, and maritime traffic management. Similarity analysis is an important process for most AIS trajectory analysis topics. However, most traditional AIS trajectory similarity analysis methods calculate the distance between trajectory points, which requires complex and time-consuming calculations, often leading to substantial errors when processing AIS trajectory data characterized by substantial differences in length or uneven trajectory points. Therefore, we propose a cell-based similarity analysis method that combines the weight of the direction and k-neighborhood (WDN-SIM). This method quantifies the similarity between trajectories based on the degree of proximity and differences in motion direction. In terms of its effectiveness and efficiency, WDN-SIM outperformed seven traditional methods for trajectory similarity analysis. Particularly, WDN-SIM has a high robustness to noise and can distinguish the similarities between trajectories under complex situations, such as when there are opposing directions of motion, large differences in length, and uneven point distributions.

Ship Operational Efficiency from AIS Data Using Big Data Technology

2022 ◽  
Vol 10 (1) ◽  
pp. 89-97
Author(s):  
Suraj Ingle
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Operational Efficiency ◽  
Fuel Oil ◽  
Automatic Identification ◽  
Identification System ◽  
Oil Consumption ◽  
Automatic Identification System ◽  
Public Data ◽  
Ship Operational Efficiency

Abstract: The Energy Efficiency Design Index (EEDI) is a necessary benchmark for all new ships to prevent pollution from ships. MARPOL has also applied the Ship Energy Efficiency Management Plan (SEEMP) to all existing ships. The Energy Efficiency Operational Indicator (EEOI) provided by SEEMP is used to measure a ship's operational efficiency. The shipowner or operator can make strategic plans, such as routing, hull cleaning, decommissioning, new construction, and so on, by monitoring the EEOI. Fuel Oil Consumption is the most important factor in calculating EEOI (FOC). It is possible to measure it when a ship is in operation. This means that the EEOI of a ship can only be calculated by the shipowner or operator. Other stakeholders, such as the shipbuilding firm and Class, or those who do not have the measured FOC, can assess how efficiently their ships are working relative to other ships if the EEOI can be determined without the real FOC. We present a method to estimate the EEOI without requiring the actual FOC in this paper. The EEOI is calculated using data from the Automatic Identification System (AIS), ship static data, and publicly available environmental data. Big data technologies, notably Hadoop and Spark, are used because the public data is huge. We test the suggested method with real data, and the results show that it can predict EEOI from public data without having to use actual FOC Keywords: Ship operational efficiency, Energy Efficiency Operational Indicator (EEOI), Fuel Oil Consumption (FOC), Automatic Identification System (AIS), Big data

scholarly journals Deep Learning in Unmanned Surface Vehicles Collision-Avoidance Pattern Based on AIS Big Data with Double GRU-RNN

2020 ◽  
Vol 8 (9) ◽  
pp. 682
Author(s):  
Jia-hui Shi ◽  
Zheng-jiang Liu
Keyword(s):  
Big Data ◽  
Collision Avoidance ◽  
Difficult Problem ◽  
Automatic Identification ◽  
Identification System ◽  
Unmanned Surface Vehicle ◽  
Effective Learning ◽  
Maritime Navigation ◽  
Ship Collision ◽  
Gated Recurrent Unit

There is a collection of a large amount of automatic identification system (AIS) data that contains ship encounter information, but mining the collision avoidance knowledge from AIS big data and carrying out effective machine learning is a difficult problem in current maritime field. Herein, first the Douglas–Peucker (DP) algorithm was used to preprocess the AIS data. Then, based on the ship domain the risk of collision was identified. Finally, a double-gated recurrent unit neural network (GRU-RNN) was constructed to learn unmanned surface vehicle (USV) collision avoidance decision from the extracted data of successful encounters of ships. The double GRU-RNN was trained on the 2015 Tianjin Port AIS dataset to realize the effective learning of ship encounter data. The results indicated that the double GRU-RNN could effectively learn the collision avoidance pattern hidden in AIS big data, and generate corresponding ship collision-avoidance decisions for different maritime navigation states. This study contributes significantly to the increased efficiency and safety of sea operations. The proposed method could be potentially applied to USV technology and intelligence collision avoidance.

Research on Vessel Trajectory Multi-Dimensional Compression Algorithm Based on Douglas-Peucker Theory

2014 ◽  
Vol 694 ◽  
pp. 59-62 ◽  
Author(s):  
Fei Xiang Zhu ◽  
Li Ming Miao ◽  
Wen Liu
Keyword(s):  
Compression Algorithm ◽  
Spatial Distance ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Compression Process ◽  
Maritime Traffic ◽  
Offset Distance ◽  
Improved Algorithm

Currently, maritime safety administrations or shipping company had received a large number of vessel trajectory data from Automatic Identification System (AIS). In order to more efficiently carry out research of maritime traffic flow, ship behavior and maritime investigation, it is important to ensure the quality of the vessel trajectory data under compression condition. In classic Douglas-Peucker vector data compression algorithm, offset spatial distance of each point was the single factor in compression process. In order to overcome the shortcomings of classic Douglas-Peucker, a vessel trajectory multi-dimensional compression improved algorithm is proposed. In improved algorithm, the concept of single trajectory point importance which considers the point offset distance and other vessel handling factors, such as the vessel turning angle, speed variation, is proposed to as the compression index. Compared to classic Douglas-Peucker algorithm, experiment results show that the proposed multi-dimensional vessel trajectory compression improved algorithms can effectively retain characteristics of navigation.

A Statistical Model for Vessel-to-Vessel Distances to Evaluate Radar Interference

2017 ◽  
Vol 70 (5) ◽  
pp. 1098-1116 ◽  
Author(s):  
Gaspare Galati ◽  
Gabriele Pavan ◽  
Francesco De Palo ◽  
Giuseppe Ragonesi
Keyword(s):  
Solid State ◽  
Density Function ◽  
Coast Guard ◽  
Automatic Identification ◽  
Identification System ◽  
Limited Area ◽  
Maritime Traffic ◽  
The Mean ◽  
New Generation ◽  
Italian Coast

Maritime traffic has significantly increased in recent decades due to its advantageous costs, delivery rate and environmental compatibility. With the advent of the new generation of marine radars, based on the solid-state transmitter technology that calls for much longer transmitted pulses, the interference problem can become critical. Knowing the positions and the heights of the ships, the mean number of the vessels in radar range can be estimated to evaluate the effects of their mutual radar interferences. This paper aims to estimate the probability density function of the mutual distances. The truncation of the density function within a limited area related to horizon visibility leads to a simple single-parameter expression, useful to classify the ships as either randomly distributed or following a defined route. Practical results have been obtained using Automatic Identification System (AIS) data provided by the Italian Coast Guard in the Mediterranean Sea.

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