AIS Database Powered by GIS Technology for Maritime Safety and Security

2008 ◽  
Vol 61 (4) ◽  
pp. 655-665 ◽  
Author(s):  
Ziqiang Ou ◽  
Jianjun Zhu
Keyword(s):  
Traffic Management ◽  
Surveillance Program ◽  
Automatic Identification ◽  
Identification System ◽  
Gis Technology ◽  
Aerial Surveillance ◽  
Maritime Traffic ◽  
Land Control ◽  
Maritime Domain Awareness ◽  
Fundamental Requirement

The Automatic Identification System (AIS) is an efficient tool to exchange positioning data among participating naval units and land control centres. It was developed primarily as an advanced tool for assistance to sailors during navigation and for the safety of the life at sea. Maritime security has become a major concern for all coastal nations, especially after September 11, 2001. The fundamental requirement is maritime domain awareness via identification, tracking and monitoring of vessels within their waters and this is exactly what an AIS could bring. This paper will be focused on how the AIS-derived information could be used for coastal security, maritime traffic management, vessel tracking and monitoring with the help of GIS technology. The AIS data used in this paper was collected by the Canadian national aerial surveillance program.

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.

MSARI: A Database for Large Volume Storage and Utilisation of Maritime Data

2016 ◽  
Vol 70 (2) ◽  
pp. 276-290 ◽  
Author(s):  
Anthony W. Isenor ◽  
Marie-Odette St-Hilaire ◽  
Sean Webb ◽  
Michel Mayrand
Keyword(s):  
Situational Awareness ◽  
Automatic Identification ◽  
Identification System ◽  
Product Management ◽  
Long Term Storage ◽  
Maritime Traffic ◽  
Data Volume ◽  
Maritime Domain Awareness ◽  
Meet Data

The volume of maritime vessel data, such as available from the Automatic Identification System (AIS), places considerable burden on systems designed and developed to manage data pertaining to maritime traffic. A properly designed and implemented data management infrastructure can provide benefits to the maritime domain awareness research community by supporting data volumes, diverse user needs, and product management. Such an infrastructure has been constructed on a modest budget by utilising open-source technologies. This paper describes the Maritime Situational Awareness Research Infrastructure (MSARI), and the design of the underlying database to meet data volume and user analysis needs. The resulting infrastructure currently handles input rates of approximately two billion vessel reports per month. This work is of potential benefit to those in the navigational community interested in the long-term storage and usage of global vessel data such as that available from AIS.

scholarly journals Navigation Safety for Marine Traffic in the Malacca Strait using AIS Data

2019 ◽  
Vol 7 (4) ◽  
Author(s):  
Muhammad Badrus Zaman
Keyword(s):  
Traffic Safety ◽  
Traffic Management ◽  
Automatic Identification ◽  
Identification System ◽  
Maritime Traffic ◽  
Marine Traffic ◽  
International Regulations ◽  
Ship Safety ◽  
Malacca Strait ◽  
Navigation Safety

The Malacca Strait experiences high-density vessel traffic, and therefore is a busy area with high potential for collisions. Analyses of marine traffic that reflect the real conditions of ship navigation are performed to enhance maritime traffic safety. An automatic identification system (AIS) allows for the accurate investigation of actual ship encounters, ship collisions, and sea traffic management systems. For this study, an AIS receiver installed at the Universiti Teknologi Malaysia (UTM) provided AIS data, which focused on a selected area in the Malacca Strait. The 1972 International Regulations for Preventing Collisions at Sea (COLREG) guided the assessment of navigation safety based on real conditions using AIS and geographic identification systems (GIS). Based on estimates of the probability and consequence indices from a risk matrix, the time and encounter conditions determined the level of risk. This study also conducted safety measurements. The analysis indicated that ship safety would improve significantly if the vessels followed the guidelines established in this study

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.

scholarly journals Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8133
Author(s):  
Clara I. Valero ◽  
Enrique Ivancos Pla ◽  
Rafael Vaño ◽  
Eduardo Garro ◽  
Fernando Boronat ◽  
...  
Keyword(s):  
Traffic Management ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Legacy System ◽  
Cognitive Component ◽  
Cognitive Services ◽  
Oceanographic Data ◽  
Reducing Costs ◽  
Easy Integration

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

scholarly journals The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation

2019 ◽  
Vol 7 (9) ◽  
pp. 287 ◽  
Author(s):  
EunSu Lee ◽  
Amit J. Mokashi ◽  
Sang Young Moon ◽  
GeunSub Kim
Keyword(s):  
Traffic Management ◽  
International Maritime Organization ◽  
Future Research ◽  
Automatic Identification ◽  
Public Agencies ◽  
Member States ◽  
The Public ◽  
Maritime Traffic ◽  
The Future ◽  
Access To Data

The member states of International Maritime Organization (IMO) have been leading in and enforcing the use of automatic identification systems (AIS) in the analysis of ship-to-ship collisions, vessel monitoring, and maritime traffic management offshore. This study will help non-federal stakeholders understand the AIS data and contribute to future research by assessing difficulties and improving access to data and applications. This study introduces the basics of AIS materials, shared channels, and currently developed applications, and discusses areas where they can be incorporated in the future. The literature revealed that using AIS data will be beneficial to the public as well as to business and public agencies.

Risk of Maritime Traffic in Coastal Waters

2018 ◽  
Author(s):  
A. P. Teixeira ◽  
C. Guedes Soares
Keyword(s):  
Coastal Waters ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Risk Models ◽  
Effective Response ◽  
Maritime Traffic ◽  
Accident Probability ◽  
Probability Prediction ◽  
Organisational Factors

This paper addresses the broad aspects of safety of maritime transportation from the identification to the management of risks related particularly to the maritime traffic in coastal waters. A brief overview of present-day maritime accident statistics are presented and the methodologies that have been adopted in the maritime sector to analyze ship accidents are reviewed. The paper also reviews the models and tools that have been used for simulation of ship navigation and for accident probability prediction based on Automatic Identification System (AIS) data and the analysis and modelling of the influence of human and organisational factors on ship accidents. The development of maritime risk models based on Bayesian Networks and the various elements that influence an effective response to maritime accidents are also addressed.

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