Use of ship traffic density from Automatic Identification System (AIS) and Vessel Monitoring Systems (VMS) in marine spatial planning: a case study in New England coast, USA

2014 ◽  
Vol 6 (1) ◽  
pp. 39-52 ◽  
Author(s):  
Yong Hoon Kim ◽  
Eoin Howlett ◽  
Jin Hwan Hwang ◽  
Young Gyu Park
Keyword(s):  
New England ◽  
Spatial Planning ◽  
Traffic Density ◽  
Monitoring Systems ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Spatial Planning ◽  
Automatic Identification System ◽  
Ship Traffic

scholarly journals How can Automatic Identification System (AIS) data be used for maritime spatial planning?

2018 ◽  
Author(s):  
Matthieu Le Tixerant ◽  
Damien Le Guyader ◽  
Françoise Gourmelon ◽  
Betty Queffelec
Keyword(s):  
Spatial Planning ◽  
Legal Framework ◽  
Traffic Density ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Legal Questions ◽  
Pertinent Data ◽  
Spatio Temporal ◽  
Maritime Spatial Planning

Although the importance of Maritime Spatial Planning (MSP) as a concept is know acknowledged and the legal framework is in place, the task of applying it remains a delicate one. One of the keys to success is having pertinent data. Knowing how maritime uses unfold in a spatio-temporal context, and what conflicting or synergistic interactions exist between activities, is crucial. However, this information is especially hard to obtain in a marine environment. As a result this information has often been identified as the missing layer in information systems developed by maritime stakeholders. Since 2002, the Automatic Identification System (AIS) has been undergoing a major development. Allowing for real time geo-tracking and identification for equipped vessels, the data that issues from AIS data promises to map and describe certain marine human activities.After recapitulating the main characteristics of AIS and the data it provides, this article proposes to evaluate how AIS is currently used in MSP at a European level, and to concisely present a series of methods and results obtained within the framework of several operational research projects. The objective is to illustrate how the AIS data processing and analysis can produce adequate information for MSP: maritime traffic density, shipping lanes and navigation flows, hierarchical network of maritime routes, alleged fishing zones, spatio-temporal interactions between activities (potential conflicting uses or synergies). The conclusion looks in particular at the legal questions concerning the use of AIS.

Automatic identification system data-driven model for analysis of ship domain near bridge-waters

2021 ◽  
pp. 1-22
Author(s):  
Lei Jinyu ◽  
Liu Lei ◽  
Chu Xiumin ◽  
He Wei ◽  
Liu Xinglong ◽  
...  
Keyword(s):  
Least Squares Method ◽  
Automatic Identification ◽  
Identification System ◽  
Collision Risk ◽  
Automatic Identification System ◽  
Domain Models ◽  
System Data ◽  
Quantification Model ◽  
Highly Correlated

Abstract The ship safety domain plays a significant role in collision risk assessment. However, few studies take the practical considerations of implementing this method in the vicinity of bridge-waters into account. Therefore, historical automatic identification system data is utilised to construct and analyse ship domains considering ship–ship and ship–bridge collisions. A method for determining the closest boundary is proposed, and the boundary of the ship domain is fitted by the least squares method. The ship domains near bridge-waters are constructed as ellipse models, the characteristics of which are discussed. Novel fuzzy quaternion ship domain models are established respectively for inland ships and bridge piers, which would assist in the construction of a risk quantification model and the calculation of a grid ship collision index. A case study is carried out on the multi-bridge waterway of the Yangtze River in Wuhan, China. The results show that the size of the ship domain is highly correlated with the ship's speed and length, and analysis of collision risk can reflect the real situation near bridge-waters, which is helpful to demonstrate the application of the ship domain in quantifying the collision risk and to characterise the collision risk distribution near bridge-waters.

scholarly journals AIS and VMS Ensemble Can Address Data Gaps on Fisheries for Marine Spatial Planning

2021 ◽  
Vol 13 (7) ◽  
pp. 3769
Author(s):  
Pascal Thoya ◽  
Joseph Maina ◽  
Christian Möllmann ◽  
Kerstin S. Schiele
Keyword(s):  
Case Studies ◽  
Spatial Planning ◽  
Fishing Effort ◽  
Rapid Expansion ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Spatial Planning ◽  
The North ◽  
Address Data ◽  
The Baltic

Spatially explicit records of fishing activities’ distribution are fundamental for effective marine spatial planning (MSP) because they can help to identify principal fishing areas. However, in numerous case studies, MSP has ignored fishing activities due to data scarcity. The vessel monitoring system (VMS) and the automatic identification system (AIS) are two commonly known technologies used to observe fishing activities. However, both technologies generate data that have several limitations, making them ineffective when used in isolation. Here, we evaluate both datasets’ limitations and strengths, measure the drawbacks of using any single dataset and propose a method for combining both technologies for a more precise estimation of the distribution of fishing activities. Using the Baltic Sea and the North Sea–Celtic Sea regions as case studies, we compare the spatial distribution of fishing effort from International Council for the Exploration of the Seas (ICES) VMS data and global fishing watch AIS data. We show that using either dataset in isolation can lead to a significant underestimation of fishing effort. We also demonstrate that integrating both datasets in an ensemble approach can provide more accurate fisheries information for MSP. Given the rapid expansion of MSP activities globally, our approach can be utilised in data-limited regions to improve cross border spatial planning.

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.

A Feature Encoding Approach and a Cloud Computing Architecture to Map Fishing Activities

2021 ◽  
Author(s):  
A. Galdelli ◽  
A. Mancini ◽  
E. Frontoni ◽  
A. N. Tassetti
Keyword(s):  
Spatial Planning ◽  
Low Cost ◽  
Small Scale ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Spatial Planning ◽  
Fishing Activity ◽  
Feature Encoding ◽  
Encoding Method ◽  
New Policies

Abstract Monitoring fish stocks and fleets’ activities is key for Marine Spatial Planning. In recent years Vessel Monitoring System and Automatic Identification System have been developed for vessels longer than 12 and 15m in length, respectively, while small scale vessels (< 12m in length) remain untracked and largely unregulated, even though they account for 83% of all fishing activity in the Mediterranean Sea. In this paper we present an architecture that makes use of a low-cost LoRa/cellular network to acquire and process positioning data from small scale vessels, and a feature encoding approach that can be easily extended to process and map small scale fisheries. The feature encoding method uses a Markov chain to model transitions between successive behavioural states (e.g., fishing, steaming) of each vessel and classify its activity. The approach is evaluated using k-fold and Leave One Boat Out cross-validations and, in both cases, it results in significant improvements in the classification of fishing activities. The use of a such low-cost and open source technology coupled to artificial intelligence could open up potential for more integrated and transparent platforms to inform coastal resource and fisheries management, and cross-border marine spatial planning. It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to the optimal use of marine resources.

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