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 Positioning Error Analysis of Ranging-Mode Using AIS Signals in China

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Zheng ◽  
Qing Hu ◽  
Jingbo Zhang
Keyword(s):  
Measurement Errors ◽  
Phase Locking ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Positioning Error ◽  
North East ◽  
The North ◽  
Error Distributions ◽  
Shift Keying

In order to provide resilient position, navigation, and time (PNT) information forE-Navigation, the ranging-mode (R-Mode) positioning using automatic identification system (AIS) signals is encouraged. As the accuracy is the key for the positioning system, this paper investigates the position error of the R-Mode positioning based on AIS shore-based station in China. The measurement errors of Gaussian filtered minimum shift keying (GMSK) demodulation based on carrier phase locking loop are investigated in theory. The dilution of precision (DOP) for time of arrival (TOA) and time difference of arrival (TDOA) used in R-Mode positioning of AIS is discussed in two measurement mechanisms. The positioning error distributions in the North, East, and South Sea regions of China based on the existing AIS shore-based stations are evaluated. The positioning accuracy is at the meter level in the most traffic dense areas to meet the requirements for vessel navigation.

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.

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 Near-Optimal Weather Routing by Using Improved A* Algorithm

2020 ◽  
Vol 10 (17) ◽  
pp. 6010
Author(s):  
Yong Woo Shin ◽  
Misganaw Abebe ◽  
Yoojeong Noh ◽  
Sangbong Lee ◽  
Inwon Lee ◽  
...  
Keyword(s):  
Operating Conditions ◽  
Weather Data ◽  
Computational Time ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Climate Data ◽  
A Algorithm ◽  
Grid Deformation ◽  
Ship Operation

With soaring oil prices worldwide, determining the most optimal routes for economical ship operation has become an important issue. Optimizing ship routes is economically important for ship operation, but it is also essential to meet the standards of environmental regulations recently imposed by the International Maritime Organization. For this purpose, various algorithms for determining ship routes have been developed to ensure the economical operation of ships via utilization of marine climate data and Automatic Identification System (AIS) data. However, such algorithms require a large amount of computational time and do not provide optimal routes because they do not consider practical operating conditions, such as weather and ocean conditions. In this study, an improved A* algorithm using AIS and weather data is proposed to overcome the limitation of the original A* algorithm, one of the most widely used path-finding algorithms. The improved A* algorithm uses an adaptive grid system that efficiently explores nodes according to map grid deformation by latitude. It finds economical routes by minimizing the estimated time of arrival generated by machine learning through 16-way node exploration. For verification of the proposed method, the original A* algorithm and improved A* algorithm were compared through a case study.

scholarly journals Ship AIS Trajectory Clustering: An HDBSCAN-Based Approach

2021 ◽  
Vol 9 (6) ◽  
pp. 566
Author(s):  
Lianhui Wang ◽  
Pengfei Chen ◽  
Linying Chen ◽  
Junmin Mou
Keyword(s):  
Traffic Management ◽  
Performance Metrics ◽  
Spatial Clustering ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Clustering Methods ◽  
Trajectory Clustering ◽  
Clustering Method ◽  
Shape Characteristics

The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability. On this basis, a re-clustering method is proposed and comprehensive clustering performance metrics are introduced to optimize the clustering results. The AIS data of the estuary waters of the Yangtze River in China has been utilized to conduct a case study and compare the results with three popular clustering methods. Experimental results prove that this method has good clustering results on ship trajectories in complex waters.

Early Adoption Technology Performance Impact: AIS on the St. Lawrence Seaway

2005 ◽  
Vol 58 (1) ◽  
pp. 17-30
Author(s):  
Martha Grabowski ◽  
Hemil Dhami
Keyword(s):  
Traffic Management ◽  
System Analysis ◽  
Automatic Identification ◽  
Identification System ◽  
Performance Impact ◽  
Critical System ◽  
Early Adoption ◽  
State Evaluation ◽  
Traffic Management System ◽  
The Impact

An Automatic Identification System (AIS) was implemented in the St. Lawrence Seaway during 2003. This paper reports the results of a trial conducted pre- and post-AIS implementation to examine the impact of AIS adoption in a safety-critical system. Analysis of the impact on three types of operators, ship's masters, mates and shore-based traffic management system operators showed that overall AIS significantly improved voyage plan monitoring, contributed to improved monitoring vigilance and offered significant aid to decision making. Recommendations include follow-on studies to include a steady state evaluation of the technology impact once the system is mature and a broadening of the pool of subjects to include a less experienced, more international and less well educated group of operators.

scholarly journals Assessment of AIS Vessel Position Report Under the Aspect of Data Reliability

2012 ◽  
Vol 19 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Paweł Banyś ◽  
Thoralf Noack ◽  
Stefan Gewies
Keyword(s):  
Data Analysis ◽  
Baltic Sea ◽  
Traffic Management ◽  
Automatic Identification ◽  
Identification System ◽  
Detailed Data ◽  
Data Reliability ◽  
The Baltic Sea ◽  
Data Set ◽  
The Baltic

Abstract Since its introduction the Automatic Identification System (AIS) has played an important part in improving safety at sea, making bridge watchkeeping duties more comfortable and enhancing vessel traffic management ashore. However the analysis of a AIS data set describing the vessel traffic of the Baltic Sea came to conclusion, that specific parameters with relevance to navigation seemed to be defective or implausible. Essentially, it concerned the true heading (THDG) and the rate of turn (ROT) parameters. With the paper we are trying to clarify, which parameters of the AIS position report and to what extent, are affected. The detailed data analysis gives answers on how reliable the AIS data in different traffic areas is.

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

scholarly journals Risk Assessment Methodology for Vessel Traffic in Ports by Defining the Nautical Port Risk Index

2019 ◽  
Vol 8 (1) ◽  
pp. 10 ◽  
Author(s):  
Xavier Bellsolà Olba ◽  
Winnie Daamen ◽  
Tiedo Vellinga ◽  
Serge P. Hoogendoorn
Keyword(s):  
Risk Assessment ◽  
Traffic Management ◽  
Risk Index ◽  
Analytic Network Process ◽  
Assessment Method ◽  
Maritime Transportation ◽  
Automatic Identification ◽  
Identification System ◽  
Assessment Methodology ◽  
Network Process

Ports represent a key element in the maritime transportation chain. Larger vessels and higher traffic volumes in ports might result in higher risks at the navigational level. Thus, the dire need for a comprehensive and efficient risk assessment method for ports is felt. Many methodologies have been proposed so far, but their application to aggregated vessel traffic risks for the overall assessment of ports is not developed yet. Hence, the development of an approach for the appraisal of the vessel traffic risks is still a challenging issue. This research aims to develop an assessment methodology to appraise the potential risk of accident occurrence in port areas at an aggregated level by creating a ‘Nautical Port Risk Index’ (NPRI). After identifying the main nautical risks in ports, the Analytic Network Process (ANP) is used to derive the risk perception (RP) weights for each criterion from data collected through surveys to expert navigators. The consequences related to each nautical risk are identified in consultation with risk experts. By combining the RP values and the consequence of each criterion for a time period, the NPRI is calculated. The risks in the Port of Rotterdam are presented in a case study, and the method has been validated by checking the results with experts in assessing nautical port risks from the Port of Rotterdam Authority. This method can be used to assess any new port design, the performance of different vessel traffic management measures, changes in the fleet composition, or existent ports using the Automatic Identification System (AIS) data.

Capacity Analysis for Bifurcated Estuaries Based on Ship Domain Theory and its Applications

2017 ◽  
Vol 2611 (1) ◽  
pp. 56-64 ◽  
Author(s):  
Xingjian Zhang ◽  
Junmin Mou ◽  
Jianfeng Zhu ◽  
Pengfei Chen ◽  
Rongfang (Rachel) Liu
Keyword(s):  
Traffic Management ◽  
River Estuary ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Water Levels ◽  
Domain Theory ◽  
Yangtze River Estuary ◽  
Automatic Identification ◽  
Identification System ◽  
Estimation Model

The bifurcated estuary is an important segment of marine transportation systems that are themselves becoming increasingly important. Because of branching channels, the cyclical change of water levels, and sophisticated operating rules in many large bifurcated estuaries, it is often difficult to estimate the traffic capacity and simulate ships’ motions, even though it is critically important for traffic management and efficiency. In recent years, the increasing number of ships that collect and contribute to the Automatic Identification System (AIS) have made it possible to monitor traffic flow along waterways, including bifurcated estuaries. This study developed a typical capacity estimation model based on ship domain theory. By using AIS data collected in the Yangtze River estuary, a typical bifurcated estuary system, the study analyzed various physical characteristics, weather conditions, and vessel characteristics to derive related impacts of each on overall capacity of the bifurcated estuary. Validated with practical observations, the method can be applied to similar estuary channel systems to improve waterway operations and management.

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