Vessel Traffic Service System Research Articles

Enhancing target detection capability of the vessel traffic service (VTS) system on the Cai Mep - Thi Vai channel

This study addresses the limitations of the current Vessel Traffic Service (VTS) system in monitoring and ensuring navigational safety on the Cai Mep-Thi Vai channel, a crucial waterway in Vietnam's maritime transport system. This research aims to propose solutions to improve the target detection capability of the VTS system in this area. Through surveys and analysis of the current status, this study identifies the system's shortcomings and proposes solutions such as installing additional radar stations and cameras. The proposed solution offers a systematic approach to enhance the effectiveness of the VTS system in managing and monitoring maritime traffic on the Cai Mep-Thi Vai channel, thereby contributing to navigational safety and security. This study provides feasible solutions that can be implemented in practice to address the limitations of the current VTS system.

The model of vessel trajectory abnormal behavior detection based on graph attention prediction and reconstruction network

The identification of atypical vessel behavior is a crucial factor in ensuring the safety of maritime navigation and enhancing the effectiveness of waterway oversight. The current approach to detecting vessel abnormal behavior lacks consideration for the inter-dependency among different time series and various characteristics associated with abnormal vessel behavior. Additionally, the subjective setting of anomaly detection threshold adversely affects the real-time performance of anomaly detection, resulting in poor detection accuracy. In order to tackle these issues, this paper explores the monitoring and detection mechanism of vessel abnormal behavior based on Graph Attention Prediction and Reconstruction Network. The proposed mechanism employs a sliding window to generate fixed data input. It effectively captures the inter-dependencies among vessel behavior characteristics. Additionally, it dynamically adjusts the threshold for identifying anomalies based on variations in the marine environment, utilizing the Peak Over Threshold (POT) method. In order to achieve real-time detection of abnormal vessel behavior in multivariate, multi-dimensional time series data, it is necessary to complete the task. The results of the paper are derived from an analysis of the AIS dataset encompassing vessel movements between Dalian and Yantai. The temporal scope of the dataset spans from November 1, 2021, to December 31, 2021. The detection accuracy, detection precision and recall rate are 98.29%, 92.44%, 87.09% respectively. The results indicate that there is an enhancement in the accuracy of detecting abnormal behavior in vessels, leading to a significant reduction in the blind area of VTS (Vessel traffic services) system monitoring.

Next-Generation Vessel Traffic Services Systems—From “Passive” to “Proactive”

Vessel traffic services (VTS) systems are critical to safeguard maritime traffic safety in which sensing and information technologies are implemented to assist in gathering, storing, processing, and visualizing maritime traffic data and are inclusively produced as a system solution. Mariners all over the world recognize VTS systems as aids to navigation. For instance, they use the services provided by the system to request traffic data and availability of pilots when vessels are arriving at a port or while transiting in waterways closer to the shoreline of a country. However, current VTS systems still work in a “passive” mode, which requires tedious human labor, and especially relies on a VTS officer’s professional experience and skills. Today, industrial customers (like port authorities, maritime traffic management agencies, and so forth) have higher expectations for “proactive” decision support features that enhance VTS intelligence and help reduce manual workloads and human errors. In this article, we focus on an overall framework including software technologies and functional features that may transform VTS systems from passive to proactive, which is highlighted in line with the technologies and toolkits we developed in several industrial projects for maritime traffic safety management.

Research on detection mechanism of vessel abnormal behavior based on immune genetic spectral clustering algorithm

Maritime transport accounts for over 90% of global trade, and maritime safety has been confirmed as a vital issue of maritime transport. Accordingly, vessel traffic service system (VTS) is capable of assisting the maritime department to complete real-time supervision of a certain water area and increasing the efficiency of water supervision. However, the VTS system has low effectiveness in vessel anomaly detection and supervision, thus resulting in regulatory blind spot in the VTS system. In this study, a vessel trajectory anomaly detection mechanism is developed using the immune genetic spectral clustering method. Moreover, the contour coefficient of scatter clustering is accurately analyzed, and the clustering center is optimized using the adopted method. On that basis, different abnormal features can be detected, and abnormal detection accuracy can be increased. In this study, the vessel trajectory anomaly detection model is built in accordance with the set sliding window and abnormal threshold parameters of vessel trajectory characteristics. The AIS data of Lianyungang to Qingdao port from March 2021 are selected for experimental analysis. The results suggest that the proposed method outperforms the conventional method in the detection accuracy and the false alarm rate, it facilitates the intelligent and automatic management of vessels by the VTS system.

Generation of Ship’s Passage Plan Using Data-Driven Shortest Path Algorithms

In this study, an approach for generating the shortest-distance passage plan for a ship is proposed considering the navigable area obtained based on automatic identification system data. The navigable area is designated based on the information from the electronic navigational chart and by considering the ship traffic density using Jenks natural breaks classification. The shortest-path algorithms used in our experiment are the Dijkstra, A*, and improved A*. Then, the Douglas–Peucker algorithm is applied to generate an optimal result by supplementing the passage plan generated by the best-performing shortest-path algorithm. The experiment was divided into two cases depending on whether or not the passage plan covered the target area of this study, namely, the vessel traffic service system control area of Busan Port between Busan New Port and Ulsan Port. The improved A* performed better than Dijkstra and A* in these two cases. Subsequently, the distance of the final passage plan was 98.649 km in the case of passing through the control area and 105.365 km in the case of bypassing, which were less than the distances in the passage plan of the actual ship by approximately 13.18% and 7.27%, respectively. The experimental results of the proposed approach show the possibility of automatically establishing the shortest-distance passage plan for a ship considering the navigable area. In addition, this study suggests an effective approach for coastal navigation, which is more complex than ocean navigation. Further, this study can serve as a basis for generating passage plans for maritime autonomous surface ships.

Investigation on Improving Strategies for Navigation Safety in the Offshore Wind Farm in Taiwan Strait

The Taiwan Strait, to the west of Taiwan, is rich in wind energy resources and has the greatest offshore wind power potential in the world. Therefore, Taiwan has been actively expanding its offshore wind power industry in this area in recent years and expects to achieve the total installed capacity to 15.6 GW by 2035. Due to the large vessel traffic flow in Western Taiwan’s sea area, wind farms will inevitably reduce the navigable space and shadow some existing marine aids to navigation, thus worsening navigation safety. An approach using a fault tree analysis was used to carry out analysis of collision risk between ship-to-ship and ship-to-turbine. The vessel density distribution and traffic flow within the open sea of offshore wind farms would further increase to curtail the available navigable space. The shadowing effects along navigation channels would thereafter be worsened to raise the probability of collision risks in the sea. The results of the fault tree analysis revealed that if the ship is out of control, the time allowed to provide assistance is rather short, leading to the increase of collision risk extent between ships and wind turbines. Moreover, the study also found that unfit functions of the Vessel Traffic Service System and navigation aids and frequently and arbitrarily crossing the navigation channel of fishery vessels are the main causes of ship collisions. In order to effectively improve the navigation safety, competitive strategies for navigation safety are investigated and evaluated in this study. These strategies include making a complete plan for utilizing the whole sea, integrating the offshore vessel traffic service and management system, providing remote pilotage services, and building salvage vessels. The above promising strategies would enhance the navigation safety within the open sea. Collision risk might occur once marine accident occurs and no salvage vessel is available.

Lightweight deep network-enabled real-time low-visibility enhancement for promoting vessel detection in maritime video surveillance

AbstractMaritime video surveillance has become an essential part of the vessel traffic services system, intended to guarantee vessel traffic safety and security in maritime applications. To make maritime surveillance more feasible and practicable, many intelligent vision-empowered technologies have been developed to automatically detect moving vessels from maritime visual sensing data (i.e., maritime surveillance videos). However, when visual data is collected in a low-visibility environment, the essential optical information is often hidden in the dark, potentially resulting in decreased accuracy of vessel detection. To guarantee reliable vessel detection under low-visibility conditions, the paper proposes a low-visibility enhancement network (termed LVENet) based on Retinex theory to enhance imaging quality in maritime video surveillance. LVENet is a lightweight deep neural network incorporating a depthwise separable convolution. The synthetically-degraded image generation and hybrid loss function are further presented to enhance the robustness and generalisation capacities of LVENet. Both full-reference and no-reference evaluation experiments demonstrate that LVENet could yield comparable or even better visual qualities than other state-of-the-art methods. In addition, it takes LVENet just 0⋅0045 s to restore degraded images with size 1920 × 1080 pixels on an NVIDIA 2080Ti GPU, which can adequately meet real-time requirements. Using LVENet, vessel detection performance can be greatly improved with enhanced visibility under low-light imaging conditions.

A Method of Increasing the Accuracy of Radar Distance Measurement in VTS Systems for Vessels with Very Large Dimensions

The navigation information about a vessel’s position in the waters covered by the vessel traffic system operation is obtained through supervisory services, mainly from coastal navigation radars. Although today it is possible to simultaneously acquire data from many independent radars, the obtained radar image is inconsistent and consists of several echoes next to each other. This makes it difficult to establish which echo represents the monitored unit. Another problem is the method of determining radar distances, which significantly affect the quality of determining the observation position. Errors in radar distance may occur when determining the radar echoes from large vessels, where the position of the unit is not the same as the edge of the radar echo to which the observation is made. In this article, the authors present a method of improving the measured radar distance. The presented proposal was verified in navigation and maneuvering simulation conditions. It could support the process of determining the ship position in vessel traffic service (VTS) systems with increased accuracy.

A prediction model of vessel trajectory based on generative adversarial network

AbstractTrajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

Maritime Aerial Surveillance: Integration Manual Identification System to Automatic Identification System

Indonesia has an almost 100,000 km coastline with a very busy shipping and maritime activity. To regulate ship traffic, the government relies on Vessel Traffic Service (VTS) and Automatic Identification System (AIS). VTS functions like Air Traffic Control (ATC) in commercial flight traffic control, which relies on radio conversations to communicate between VTS officers and the ship’s captain. While AIS uses transponders mounted on ships to send ship position data which are then displayed virtually in the form of ship icons. However, both VTS and AIS do not display the real picture of the ship’s description. VTS, AIS Receiver and Electronic Chart Display Information System (ECDIS only presented the ship’s movement through radio voice and the ship’s icon on the monitor. With Reporting Interval from 2 seconds until 180 seconds. It is because AIS is designated to send data every 2 – 180 seconds depending on vessel moving status. But within the reporting interval 180 seconds, the condition of the ship can change a lot. This is the motivation of this paper was written to propose monitoring vessel traffic through an aerial flying camera that can monitor real ships with cinematic class video quality in real-time because the video latency can be achieved faster than 10 milliseconds. The research is conduct by simultaneous monitoring the marine traffic through web and launching the flying camera at the same time and place. And the results is very surprises, because many ship found not AIS detected compare to flying camera results video.

Marine Vessel Re-Identification: A Large-Scale Dataset and Global-and-Local Fusion-Based Discriminative Feature Learning

A marine vessel re-identification system has to determine whether or not different images represent the same vessel. Accurate vessel re-identification improves onshore closed-circuit television monitoring in a vessel traffic services system as well as onboard surveillance of surrounding vessels. However, because ships are rigid bodies and the marine environment is harsh, the accurate re-identification of vessels at sea can be very difficult. We describe a marine vessel-re-identification framework, Global-and-Local Fusion-based Multi-view Feature Learning (GLF-MVFL), which is based on a combination of global and fine-grained local features. GLF-MVFL combines cross-entropy loss with our newly-developed orientation-guided quintuplet loss. We exploit intrinsic features of marine vessels to optimize multi-view representation learning for re-identification. GLF-MVFL uses ResNet-50 as the backbone network to extract features for simultaneous quintuple input. It detects and discriminates between features and estimates viewpoints to form a comprehensive re-identification framework. We created an annotated large-scale vessel retrieval dataset, VesselID-539, which contains images from viewpoints similar to those of an autonomous surface vessel, to use in evaluating the performance of the model. Extensive experiments and analysis of the results obtained from using VesselID-539 demonstrate that our approach significantly increases the accuracy of vessel re-identification and is more effective and robust for images from different viewpoints than other approaches.

A Novel Approach of Using Selected Unconventional Geodesic Methods of Estimation on VTS Areas

The Vessel Traffic Service (VTS) systems belong to the fundamental tools used in ensuring a high level of safety across sea basins with heavy traffic, where the presence of navigational hazards poses a great risk of collision or a ship running aground. In order to determine the mutual location of ships, VTS systems obtain information from different facilities, such as coastal radar stations, AIS, and vision systems. Fixing a ship’s position is always accompanied by a degree of error, but the degree of error depends on the specific position fixing system used. In order to increase the accuracy of position fixing in VTS systems, the authors propose the use of contemporary geodetic methods. The article presents the theoretical fundamentals of two unconventional geodesic methods of estimation: M estimation and Msplit(q)estimation and the authors’ own proposal of their application in VTS systems. The article concludes with a test verifying theoretical assumptions, performed with the use of a computer application allowing position fixing with the use of selected robust adjustment methods. Furthermore, an example is presented of Msplit(q)estimation as used for the acquisition of low-signal radar echoes, which in VTS systems are not automatically identified for tracking. The level of the echo signal makes it difficult for a VTS controller to separate these objects from typical radar noise and interference.

Usage of Digital Twin Technologies during System Modeling and Testing in Vessel Traffic Services System Project

AbstractDesign and Integration of a Vessel Traffic Services (VTS) System with the implementation of a newly VTS software is a highly complex project with some challenges on interfaces and timing. The involvement of directives of command operation center personal and information from shipleader as stakeholder as well as different systems as automatic identification system, radar systems, electro optical systems, physical security systems beside the traffic services system makes is difficult manage the overall system design. A system of systems approach with MBSE techniques and usage of tools as designing with digital twin technology helps to simplify overviewing system design and checking interface definitions on the left side of the Vee model before implementation and integration process starts. A software intensive system with involvement of electrical and mechanical engineering including structural engineering for measurement of tolerances of sensors installed on towers gives an opportunistic theme for digital twin modeling of devices.

Adaptive Douglas-Peucker Algorithm With Automatic Thresholding for AIS-Based Vessel Trajectory Compression

Automatic identification system (AIS) is an important part of perfecting terrestrial networks, radar systems and satellite constellations. It has been widely used in vessel traffic service system to improve navigational safety. Following the explosion in vessel AIS data, the issues of data storing, processing, and analysis arise as emerging research topics in recent years. Vessel trajectory compression is used to eliminate the redundant information, preserve the key features, and simplify information for further data mining, thus correspondingly improving data quality and guaranteeing accurate measurement for ensuring navigational safety. It is well known that trajectory compression quality significantly depends on the threshold selection. We propose an Adaptive Douglas-Peucker (ADP) algorithm with automatic thresholding for AIS-based vessel trajectory compression. In particular, the optimal threshold is adaptively calculated using a novel automatic threshold selection method for each trajectory, as an improvement and complement of original Douglas-Peucker (DP) algorithm. It is developed based on the channel and trajectory characteristics, segmentation framework, and mean distance. The proposed method is able to simplify vessel trajectory data and extract useful information effectively. The time series trajectory classification and clustering are discussed and analysed based on ADP algorithm in this paper. To verify the reasonability and effectiveness of the proposed method, experiments are conducted on two different trajectory data sets in inland waterway of Yangtze River for trajectory classification based on the nearest neighbor classifier, and for trajectory clustering based on the spectral clustering. Comprehensive results demonstrate that the proposed algorithm can reduce the computational cost while ensuring the clustering and classification accuracy.

Mining of missing ship trajectory pattern in automatic identification system

Background/Objectives: Ship trajectories in Vessel Traffic Service (VTS) system are generated by integrating the Automatic Identification System (AIS) or Radar system. However, the AIS system has missing data section caused by AIS device problems, radio jamming, and so on. These data have been confusing ship navigators and VTS operators.Methods/Statistical analysis: In order to extract missing AIS data, time intervals of sequent points from each ship trajectory are calculated. The section with missing AIS data is above a threshold time limit defined by characteristics. Using k-means algorithm, missing AIS data were clustered into several clusters stored by ship’s ID and sailing direction. Using association rule mining analysis, meaningful association pattern were calculated by missing AIS dataset.Findings: As a result of the association rule mining, we found several missing AIS situation patterns. In case of the west route, the probability of missing AIS situation is high when they enter the east and passenger routes. Also, the probability of missing AIS situation of passing the passenger route is high when that ship enter the LNG, east and west routes.Improvements/Applications: These results can be used to predict the probability of missing AIS data in VTS system.

A Recognition Approach of Radar Blips Based on Improved Fuzzy C Means

Maritime radar is the kernel sensor for tracking vessels in Vessel Traffic Service system, it is important for Maritime Situation Awareness. However, the images collected by the maritime radar are inundated with excessive noise blips, which bring variety of troubles in extraction of ship targets from the images. This paper proposes a radar target recognition method based on fuzzy C-means. First, the attributes of the blips in the sequential radar images, such as speed, direction and size, are quantified as three pieces of evidence to determine whether a radar blip is a moving vessel. Then, an artificial intelligence was built based on FCM. According to the three pieces of evidence, the possibility of a blip being a real vessel is computed with FCM. The main difficulty in building the FCM framework is to find an appropriate way to provide the classification coefficient C and the fuzzy coefficient m. Since the C in classification is finite, this study proposes a method to obtain C by assessing the Euclidean distance of the expected result. Since m is related to the discreteness of evidence and results, the coefficient can be assessed by Shannon entropy and gain. Field experiments suggest that the improved FCM is capable of classifying the radar blips accurately, and reducing the operational strength of the ship operators and improving the safety.

Enhancing Automatic Maritime Surveillance Systems With Visual Information

Automatic surveillance systems for the maritime domain are becoming more and more important due to a constant increase of naval traffic and to the simultaneous reduction of crews on decks. However, available technology still provides only a limited support to this kind of applications. In this paper, a modular system for intelligent maritime surveillance, capable of fusing information from heterogeneous sources, is described. The system is designed to enhance the functions of the existing vessel traffic services systems and to be deployable in populated areas, where radar-based systems cannot be used due to the high electromagnetic radiation emissions. A quantitative evaluation of the proposed approach has been carried out on a large and publicly available data set of images and videos, which are collected from multiple real sites, with different light, weather, and traffic conditions.

Turkish Republic of Northern Cyprus Vessel Traffic Services (TRNC-VTS)

The first Vessel Traffic Service (VTS) started in 1949 in the Liverpool Port (UK) and it continued in Netherlands in 1956. In Turkey, planning and management of the marine traffic using the waterways and ports in Turkey started with Turkish Straits VTS which came into service in 2003 due to the increase in traffic density enhance its effectiveness and necessity every year. Feasibility studies in five new areas have been initiated for the establishment of the VTS system by force of the strategic decision taken by the maritime authority in 2008. These areas are; Izmit Bay, Izmir Bay and Aliaga Region, Gulf of Iskenderun and Mersin. Monitoring the marine traffic has an important place as the Turkish Republic of Northern Cyprus (TRNC) is an important transition point in the Eastern Mediterranean region. For this reason, in this study the impact assessment and necessity of the establishment of a VTS to be located in the TRNC were evaluated by using Environmental (PEST: Political, Economic, Social, Technological) and SWOT (Strengths, Weakness, Opportunities, Threats) Analyses. In addition, the suitability of the possible locations of Traffic Monitoring Stations (TMS) has been examined by the field study. Evaluation of TRNC VTS in geographical and strategic terms will benefit from the opportunities and facilities that both the Republic of Turkey and the TRNC will have as a part of the effect of VTS against the embargo imposed on the TRNC in the world. Three appropriate VTS TMSs have been chosen as a result of the field study carried out for 10 determined TMSs; Cape Kormakitis, Cape Apostolos Andreas and Famagusta. It is also concluded that it would be appropriate to plan the Famagusta Station as VTS Centre. It is assumed to monitor and track the vessels in the zones out of the coverage area of these stations by Automatic Identification System (AIS). Safety and security in the shipping, protection of navigation, life, property and the marine environment of the region will be enhanced by monitoring the vessels passing particularly through this region and by making use of the opportunities provided by technology in accordance with national and international regulations.

M-Estimation as a Tool Supporting a Vessel Traffic Controller in the VTS System

Abstract In order to improve maritime safety and the efficiency of vessel traffic, systems supervising vessel traffic, i.e. VTS (Vessel Traffic Service), started to be created. These systems are aimed to control vessel traffic in waters where traffic congestion, a large concentration of vessels or the presence of navigational hazards creates a risk of collision or stranding. VTS systems constitute maritime safety centres and they must be equipped with appropriate devices in order to be fully functional. Among devices that provide information about vessels are coastal radar stations which are located around a monitored sea area. This kind of spatial arrangement of these stations can be used to simultaneously obtain information about every vessel, but such observations may be fraught with serious errors. Therefore, the estimation methods that are employed and developed in geodesy can be used to improve the accuracy with which a vessel’s position is determined. The Interactive Navigational Structure, i.e. IANS, is an example of how these methods can be applied in navigation; this term has already been introduced into the literature (Czaplewski, 2004). The text below presents the theoretical assumptions underlying the use of IANS as a tool supporting a vessel traffic controller using the VTS system in his/her work. This presentation is supported by a numerical test that was performed in the waters of the Bay of Gdańsk which are covered by the VTS system.

선박운항자 의식 기반 충돌 위험도 예측 모듈 개발에 관한 연구

우리나라 항구 주변에는 입 출항하는 선박으로 인하여 해상교통흐름이 복잡하다. 이러한 선박통항의 안전과 효율성을 증진하기 위해 우리나라에서는 해상교통관제 서비스를 시행하고 있다. 24시간 쉴 틈이 없는 해상교통 관제사들의 노력에도 불구하고 관제구역 내에서의 충돌사고는 지속적으로 발생하고 있으며, 위험 상황이 약 20분에 1회씩 발생하고 있는 것으로 분석되어 그 위험성은 크다고 할 수 있다. 이러한 사고는 선박운항 및 해상교통관제 정보 제공 시 충돌 위험에 대한 안전 기준을 적시에 제공함으로써 사고를 감소시킬 수 있을 것으로 조사되었다. 이에 본 연구는 선박의 충돌위험도를 선박운항자의 관점에서 평가할 수 있는 모델을 이용하여 그 위험을 선박의 속도, 침로 등을 조정하여 각 교통 상황별 충돌 위험도를 사전에 예측 할 수 있는 위험도 예측 모듈을 개발하였다. 이 모듈을 이용하여 선박운항자 및 관제사는 복잡한 교통 상황에서 위험요소를 쉽게 식별 할 수 있어, 가까운 장래의 위험 정도의 변화를 선박침로 및 속력변경 등을 제시할 수 있는 등의 적절한 피항조치를 취할 수 있다. 이 모듈의 효용성을 검증하기 위해 부산항 해역을 대상으로 조우 상황별 위험도를 예측 후 구체적인 침로 및 속력 변경에 따른 위험도 변화를 식별할 수 있는 것으로 확인되었다. In ports of Korea, the marine traffic flow is congested due to a large number of vessels coming in and going out. In order to improve the safety and efficiency of these vessels, South Korea is operating with a Vessel Traffic Service System, which is monitoring its waters for 24 hours. However despite these efforts of the VTS (Vessel Traffic Service) officers, collisions are occurring continuously, the risk situation is analyzed that occurs once in about 20 minutes, the risk may be greater. It investigated to reduce these accidents by providing a safety standard for collision danger in a timely manner. Thus, this study has developed a risk prediction module to predict risk in advance. This module can avoid collision risk to adjust the speed and course of ship using a risk evaluation model based on ship operator's risk perspective. Using this module, the ship operators and VTS officers can easily be identified risks in complex traffic situations, so they can take an appropriate action against danger in near future including course and speed change. To verify the effectiveness of this module, this paper predicted the risk of each encounter situation and confirmed to be capable of identifying a risk changes in specific course and speed changes at Busan coastal water.