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.

Analysis of Tugboat Activities using AIS Data for the Tianjin Port

2020 ◽  
Vol 2674 (5) ◽  
pp. 498-509
Author(s):  
Shukai Chen ◽  
Feng Wang ◽  
Xiaoyang Wei ◽  
Zhijia Tan ◽  
Hua Wang
Keyword(s):  
Spatial Distribution ◽  
Spatial Analysis ◽  
Service Time ◽  
Automatic Identification ◽  
Identification System ◽  
Crucial Importance ◽  
Two Stage ◽  
Temporal And Spatial ◽  
Shed Light

The tugboat is the vessel that helps to maneuver large ships for berthing and un-berthing operations. To achieve efficient tugboat operations, investigating the features of tugboat activities is of crucial importance. This study aims to use automatic identification system (AIS) data to identify the maneuver services and analyze the characteristics of tugboat activities. A two-stage algorithm is developed to extract the time, locations, and involved tugboats for berthing and un-berthing operations from AIS data. The AIS data from Tianjin port, China, are used in the case study to demonstrate the effectiveness of the proposed method and analyze the pattern of tugboat activities. First, some important features of tugboat jobs are presented, such as the daily number of jobs and the spatial distribution of jobs. Then, a temporal and spatial analysis is conducted to investigate tugboat assignment, service time, tugboat utilization, and locations of berthing and un-berthing operations. The obtained results and implications could shed light on the deployment of tugboat berths, tugboat scheduling, and evaluation of tugboat fleet operation.

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.

Estimating Navigation Patterns from AIS

2009 ◽  
Vol 62 (4) ◽  
pp. 587-607 ◽  
Author(s):  
Karl Gunnar Aarsæther ◽  
Torgeir Moan
Keyword(s):  
Time Series ◽  
Traffic Model ◽  
Traffic Information ◽  
Automatic Identification ◽  
Identification System ◽  
Statistical Distributions ◽  
Ship Traffic ◽  
Precise Data ◽  
Decision Variables ◽  
Navigation Patterns

The Automatic Identification System (AIS) has proven itself to be a valuable source for ship traffic information. Its introduction has reversed the previous situation with scarcity of precise data from ship traffic and has instead posed the reverse challenge of coping with an overabundance of data. The number of time-series available for ship traffic and manoeuvring analysis has increased from tens, or hundreds, to several thousands. Sifting through these data manually, either to find the salient features of traffic, or to provide statistical distributions of decision variables is an extremely time consuming procedure. In this paper we present the results of applying computer vision techniques to this problem and show how it is possible to automatically separate AIS data in order to obtain traffic statistics and prevailing features down to the scale of individual manoeuvres and how this procedure enables the production of a simplified ship traffic model.

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 Extracting Global Shipping Networks from Massive Historical Automatic Identification System Sensor Data: A Bottom-Up Approach

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3363 ◽  
Author(s):  
Zhihuan Wang ◽  
Christophe Claramunt ◽  
Yinhai Wang
Keyword(s):  
Multiple Scales ◽  
Spatial Clustering ◽  
Sensor Data ◽  
Automatic Identification ◽  
Identification System ◽  
Bottom Up ◽  
Ship Traffic ◽  
Terminal Level ◽  
Shipping Networks

The increasing availability of big Automatic Identification Systems (AIS) sensor data offers great opportunities to track ship activities and mine spatial-temporal patterns of ship traffic worldwide. This research proposes a data integration approach to construct Global Shipping Networks (GSN) from massive historical ship AIS trajectories in a completely bottom-up way. First, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm is applied to temporally identify relevant stop locations, such as marine terminals and their associated events. Second, the semantic meanings of these locations are obtained by mapping them to real ports as identified by the World Port Index (WPI). Stop events are leveraged to develop travel sequences of any ship between stop locations at multiple scales. Last, a GSN is constructed by considering stop locations as nodes and journeys between nodes as links. This approach generates different levels of shipping networks from the terminal, port, and country levels. It is illustrated by a case study that extracts country, port, and terminal level Global Container Shipping Networks (GCSN) from AIS trajectories of more than 4000 container ships in 2015. The main features of these GCSNs and the limitations of this work are finally discussed.

Drafting Route Plan Templates for Ships on the Basis of AIS Historical Data

2019 ◽  
Vol 73 (3) ◽  
pp. 726-745
Author(s):  
Krzysztof Naus
Keyword(s):  
Research Method ◽  
Final Section ◽  
Historical Data ◽  
Automatic Identification ◽  
Identification System ◽  
Traffic Intensity ◽  
Main Section ◽  
Ship Traffic ◽  
Advantages And Disadvantages ◽  
International Regulations

The paper provides a description of a method of drafting route plan templates on the basis of AIS (automatic identification system) historical data. The first section features a brief background on the problem of drafting route plan templates in the light of international regulations. The main section contains a description of the methods and tools used for processing AIS data into a GRID reference system: ship traffic intensity, average COG (course over ground) and average SOG (speed over ground) as well as route plan templates. The final section includes a presentation of the research method and an analysis of the results, conducted on the basis of maps with charted paths of drafted route plan templates. The summary constitutes a synthesis of general conclusions, the advantages and disadvantages of the solution as well as areas for further research to enhance the solution.

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 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.

Research on Ship Classification Based on Trajectory Features

2017 ◽  
Vol 71 (1) ◽  
pp. 100-116 ◽  
Author(s):  
Kai Sheng ◽  
Zhong Liu ◽  
Dechao Zhou ◽  
Ailin He ◽  
Chengxu Feng
Keyword(s):  
Pattern Mining ◽  
Extraction Methods ◽  
Behaviour Pattern ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Trajectory Features ◽  
Ship Classification ◽  
Historical Trajectory

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

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