scholarly journals Modelling the Risk of Imported COVID-19 Infections at Maritime Ports Based on the Mobility of International-Going Ships

2022 ◽  
Vol 11 (1) ◽  
pp. 60
Author(s):  
Zhihuan Wang ◽  
Chenguang Meng ◽  
Mengyuan Yao ◽  
Christophe Claramunt
Keyword(s):  
Clustering Algorithm ◽  
Infection Risk ◽  
Data Driven ◽  
Risk Level ◽  
Automatic Identification ◽  
Identification System ◽  
Prevention Measures ◽  
Increase Rate ◽  
Spatio Temporal ◽  
Imported Infections

Maritime ports are critical logistics hubs that play an important role when preventing the transmission of COVID-19-imported infections from incoming international-going ships. This study introduces a data-driven method to dynamically model infection risks of international ports from imported COVID-19 cases. The approach is based on global Automatic Identification System (AIS) data and a spatio-temporal clustering algorithm that both automatically identifies ports and countries approached by ships and correlates them with country COVID-19 statistics and stopover dates. The infection risk of an individual ship is firstly modeled by considering the current number of COVID-19 cases of the approached countries, increase rate of the new cases, and ship capacity. The infection risk of a maritime port is mainly calculated as the aggregation of the risks of all of the ships stopovering at a specific date. This method is applied to track the risk of the imported COVID-19 of the main cruise ports worldwide. The results show that the proposed method dynamically estimates the risk level of the overseas imported COVID-19 of cruise ports and has the potential to provide valuable support to improve prevention measures and reduce the risk of imported COVID-19 cases in seaports.

scholarly journals Risk Assessment of the Overseas Imported COVID-19 of Ocean-Going Ships Based on AIS and Infection Data

2020 ◽  
Vol 9 (6) ◽  
pp. 351 ◽  
Author(s):  
Zhihuan Wang ◽  
Mengyuan Yao ◽  
Chenguang Meng ◽  
Christophe Claramunt
Keyword(s):  
Spatial Clustering ◽  
Infection Risk ◽  
Data Driven ◽  
Automatic Identification ◽  
Quick Response ◽  
Support Mechanism ◽  
Risk Levels ◽  
Data Driven Approach ◽  
Spatio Temporal ◽  
And Control

Preventing and controlling the risk of importing the coronavirus disease (COVID-19) has rapidly become a major concern. In addition to air freight, ocean-going ships play a non-negligible role in spreading COVID-19 due to frequent visits to countries with infected populations. This research introduces a method to dynamically assess the infection risk of ships based on a data-driven approach. It automatically identifies the ports and countries these ships approach based on their Automatic Identification Systems (AIS) data and a spatio-temporal density-based spatial clustering of applications with noise (ST_DBSCAN) algorithm. We derive daily and 14 day cumulative ship exposure indexes based on a series of country-based indices, such as population density, cumulative confirmed cases, and increased rate of confirmed cases. These indexes are classified into high-, middle-, and low-risk levels that are then coded as red, yellow, and green according to the health Quick Response (QR) code based on the reference exposure index of Wuhan on April 8, 2020. This method was applied to a real container ship deployed along a Eurasian route. The results showed that the proposed method can trace ship infection risk and provide a decision support mechanism to prevent and control overseas imported COVID-19 cases from international shipping.

scholarly journals Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining

2021 ◽  
Vol 13 (3) ◽  
pp. 1089
Author(s):  
Hailin Zheng ◽  
Qinyou Hu ◽  
Chun Yang ◽  
Jinhai Chen ◽  
Qiang Mei
Keyword(s):  
Clustering Algorithm ◽  
Pattern Mining ◽  
Transportation Networks ◽  
Automatic Identification ◽  
Identification System ◽  
Classification Models ◽  
Transmission Path ◽  
The Hierarchical Structure ◽  
Hierarchical Clustering Algorithm ◽  
And Control

Since the spread of the coronavirus disease 2019 (COVID-19) pandemic, the transportation of cargo by ship has been seriously impacted. In order to prevent and control maritime COVID-19 transmission, it is of great significance to track and predict ship sailing behavior. As the nodes of cargo ship transportation networks, ports of call can reflect the sailing behavior of the cargo ship. Accurate hierarchical division of ports of call can help to clarify the navigation law of ships with different ship types and scales. For typical cargo ships, ships with deadweight over 10,000 tonnages account for 95.77% of total deadweight, and 592,244 berthing ships’ records were mined from automatic identification system (AIS) from January to October 2020. Considering ship type and ship scale, port hierarchy classification models are constructed to divide these ports into three kinds of specialized ports, including bulk, container, and tanker ports. For all types of specialized ports (considering ship scale), port call probability for corresponding ship type is higher than other ships, positively correlated with the ship deadweight if port scale is bigger than ship scale, and negatively correlated with the ship deadweight if port scale is smaller than ship scale. Moreover, port call probability for its corresponding ship type is positively correlated with ship deadweight, while port call probability for other ship types is negatively correlated with ship deadweight. Results indicate that a specialized port hierarchical clustering algorithm can divide the hierarchical structure of typical cargo ship calling ports, and is an effective method to track the maritime transmission path of the COVID-19 pandemic.

scholarly journals SPATIO TEMPORAL DATA CUBE APPLIED TO AIS CONTAINERSHIPS TREND ANALYSIS IN THE EARLY YEARS OF THE BELT AND ROAD INITIATIVE – FROM GLOBAL TO LOCAL SCALE

2021 ◽  
Vol XLIII-B4-2021 ◽  
pp. 71-78
Author(s):  
E. Arco ◽  
A. Ajmar ◽  
F. Cremaschini ◽  
C. Monaco
Keyword(s):  
Trend Analysis ◽  
Value Added ◽  
Data Cube ◽  
Early Years ◽  
Automatic Identification ◽  
Identification System ◽  
Export Trade ◽  
Time Data ◽  
Belt And Road ◽  
Spatio Temporal

Abstract. Maritime trade represents a significant part of all global import-export trade. The traffic of containerships can be monitored through Automatic Identification System (AIS), due to the fact that the International Maritime Organization (IMO) regulation requires AIS to be fitted aboard all ships of 300 gross tonnage and upwards engaged on international voyages. The approach proposed by the authors aimed to extract value added information from an AIS dataset, with a focus on maritime economy. Using an AIS dataset of global position of containerships from 01/01/2012 to 31/12/2016, the paper focuses on space-time data cube creation and analysis for a better understanding of maritime trades trends. Data cube creation has been tested at different spatio-temporal bins dimension and on different specific topics (TEU classes, alliances, chokepoints and port areas), analysing the sensitivity on trend results, and highlighting how appropriate spatio-temporal bins dimensions are important to effectively highlight relevant trends. Results of the trend analysis are discussed and validated with the main data and information found over the period 2012–2016. The aim of this paper is to demonstrate the suitability of this approach applied to AIS data and to highlight its limitations. The authors can conclude that the approach used has proved to be adequate in describing the evolution of the global import-export trade.

Vessel Spatio-temporal Knowledge Discovery with AIS Trajectories Using Co-clustering

2017 ◽  
Vol 70 (6) ◽  
pp. 1383-1400 ◽  
Author(s):  
Jiang Wang ◽  
Cheng Zhu ◽  
Yun Zhou ◽  
Weiming Zhang
Keyword(s):  
Knowledge Discovery ◽  
Situational Awareness ◽  
Automatic Identification ◽  
Identification System ◽  
Mobility Patterns ◽  
Multidimensional Arrays ◽  
Temporal Knowledge Discovery ◽  
Spatio Temporal ◽  
Vessel Motion ◽  
Occurrence Patterns

Large volumes of data collected by the Automatic Identification System (AIS) provide opportunities for studying both single vessel motion behaviours and collective mobility patterns on the sea. Understanding these behaviours or patterns is of great importance to maritime situational awareness applications. In this paper, we leveraged AIS trajectories to discover vessel spatio-temporal co-occurrence patterns, which distinguish vessel behaviours simultaneously in terms of space, time and other dimensions (such as ship type, speed, width etc.). To this end, available AIS data were processed to generate spatio-temporal matrices and spatio-temporal tensors (i.e., multidimensional arrays). We then imposed a sparse bilinear decomposition on the matrices and a sparse multi-linear decomposition on the tensors. Experimental results on a real-world dataset demonstrated the effectiveness of this methodology, with which we show the existence of connection among regions, time, and vessel attributes.

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.

scholarly journals A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2756
Author(s):  
Alessandro Galdelli ◽  
Adriano Mancini ◽  
Carmen Ferrà ◽  
Anna Nora Tassetti
Keyword(s):  
Large Scale ◽  
Automatic Identification ◽  
Identification System ◽  
Distributed Data ◽  
Close Proximity ◽  
Geographically Distributed ◽  
Remote Sensing Systems ◽  
Spatio Temporal ◽  
Sar Imagery ◽  
Point To Point

Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight “suspicious” AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel—and the gear it adopts—is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas.

Fire Seat Intelligent Identification System

2014 ◽  
Vol 536-537 ◽  
pp. 421-425
Author(s):  
Jun Hui Zheng ◽  
Bing Li
Keyword(s):  
High Speed ◽  
Clustering Algorithm ◽  
Data Transfer ◽  
High Sensitivity ◽  
Automatic Identification ◽  
Identification System ◽  
Data Set ◽  
Ignition Point ◽  
Control Center ◽  
Fire Alarm

Current fire alarm system can only send fire alarms but failed to report accurately ignition point. So a fire seat intelligent identification system was designed based on wireless sensor networks. The system collects data from wireless sensors using ZigBee wireless communication technology, and carries on cluster analysis on the data set using the improved fuzzy kernel clustering algorithm, and gets accurate clustering results. Finally the ignition source location information is reported to the fire alarm control center. The experimental results show that, compared with other methods, this system realizes real-time monitoring and automatic identification of fire seats, which has virtues of high sensitivity and accuracy and high-speed data transfer.

scholarly journals Ship Trajectory Prediction Based on Bi-LSTM Using Spectral-Clustered AIS Data

2021 ◽  
Vol 9 (9) ◽  
pp. 1037
Author(s):  
Jinwan Park ◽  
Jungsik Jeong ◽  
Youngsoo Park
Keyword(s):  
Clustering Algorithm ◽  
Short Term Memory ◽  
Spatial Clustering ◽  
Longest Common Subsequence ◽  
Automatic Identification ◽  
Identification System ◽  
Short Term ◽  
Term Memory ◽  
Common Subsequence ◽  
Long Short Term Memory

According to the statistics of maritime accidents, most collision accidents have been caused by human factors. In an encounter situation, the prediction of ship’s trajectory is a good way to notice the intention of the other ship. This paper proposes a methodology for predicting the ship’s trajectory that can be used for an intelligent collision avoidance algorithm at sea. To improve the prediction performance, the density-based spatial clustering of applications with noise (DBSCAN) has been used to recognize the pattern of the ship trajectory. Since the DBSCAN is a clustering algorithm based on the density of data points, it has limitations in clustering the trajectories with nonlinear curves. Thus, we applied the spectral clustering method that can reflect a similarity between individual trajectories. The similarity measured by the longest common subsequence (LCSS) distance. Based on the clustering results, the prediction model of ship trajectory was developed using the bidirectional long short-term memory (Bi-LSTM). Moreover, the performance of the proposed model was compared with that of the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. The input data was obtained by preprocessing techniques such as filtering, grouping, and interpolation of the automatic identification system (AIS) data. As a result of the experiment, the prediction accuracy of Bi-LSTM was found to be the highest compared to that of LSTM and GRU.

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