AIS 데이터 분석을 통한 이상 거동 선박의 식별에 관한 연구

The Sea Traffic Management (STM) Validation Project is a European-based initiative with ambitions to improve maritime safety and efficiency through information sharing in real time. The purpose of this paper is to evaluate the “STM services,” which can be categorized as low-level automated functions designed to improve information exchange between ship and shore. Full-scale simulated scenarios were developed and tested on 16 professional vessel traffic service (VTS) operators comparing VTS operations as they are today with the added STM functionality. Data collection involved observations which assessed the frequency and type of interactions between ships and VTS, followed by questionnaires to provide an overall assessment of the user experience. The results indicate that the frequency and method of communication patterns between VTS operators and ships will be affected by the integration of the STM services. Additional access to navigational information could change the role of VTS operators in traffic situations compared with traditional operations. This paper discusses the findings from a socio-technical systems perspective while also addressing the individual STM services and their potential impact on VTS operations. This research provides valuable information for European VTS centers that could be affected by the implementation of e-Navigation and, specifically, the STM services.

Maritime accident prediction in busan port using machine learning: An integrated approach with maritime accident reports and VTS data

This study presents enhanced collision detection model in a Vessel Traffic Services (VTS) area. The proposed detection method of collision situations is based on the assumption that VTS station is provided with passage plans of all the vessels in a monitored area. By using an early detection model for prediction of possible collision situations, VTS stations could switch from the area-monitoring concept to the passage-monitoring system. An early detection model of collision risks in a VTS area uses vessels’ dynamic characteristics as inputs (vessels’ position, course over ground and speed), and delivers prediction of their future positions as output. In order to achieve the desired accuracy, the model takes into the account the intended course alterations and the impending environmental loads. The model is able to provide the outputs as early as the passage plans are submitted to a VTS monitored area. Hence, when discussing model’s capability for early detection of collision situations, improved VTS operating standards could be developed in order to achieve safer passages through enhanced collision avoidance strategies. Simulation results clearly show the advantages of the proposed model as a decision support tool for a VTS operator when combining passage plans with the analysis of environmental loads.

Vessel Traffic Services (VTS) is one of the most important aids for ships in the dangerous waters. Due to the ever-increasing sea transport and thus the number of ships in this service, the importance of the services offered by VTS has increased more in the course of time. Despite the advanced technological infrastructure and the improved VTS, however the safety of sea traffic is still mostly based on human factor. Although in the relevant literature there have been some studies revealing the effects of VTS on the safety, there has been no research on the service quality. The aim of this study is, considering the views of ship masters, to analyze the effects of the VTS service quality perceptions on the corporate image. The process of this study has been two-fold; developing a VTS service quality scale for shipmasters and the required through this scale measuring the perceptions of the service quality of the Turkish Straits Vessel Traffic Services (TSVTS) and the effects of these perceptions on the corporate image. Developing the model of study has been based on the relevant literature, and SERVQUAL model has been chosen. Firstly, in analyzing the perceptions, 20 experts from four diverse field have been interviewed and a pilot study has been carried out with shipmasters (n=72). The questionnaire, which has been conducted through shipmasters (n=192) who have used the Turkish Straits, has comprised 25 statements constituting two dimensions as “Perceived Service Quality (PSQ)” and “Corporate Image (CI)”. The results reveal that there is a strong interrelationship between the perceptions of the TSVTS’ service quality and corporate image for shipmasters. The VTS-PSQ scale develop through this research is expected to contribute to evaluating the VTS service quality and thus to the safe navigation through the relevant sea ways.

With the fast development of transportation at sea, establishing vessel traffic services (VTS) is reaching a new high tide in order to more effectively guarantee vessel traffic safety, protect maritime environment and increase traffic efficiency. Formal safety assessment (FSA) has been considered a powerful tool that can help ensure that limited resources are allocated scientifically for these purposes. This paper introduces the application of FSA on planning VTS.

The functions of VTS are to ensure the security of waterways in the port, improve the efficiency of vessel navigation and avoid the pollution of the water, which is an important approach to water traffic management. It has apparent achievements in enhancing port operation efficiency and promoting the development of related industries. As a public service, the investment and operation cost of VTS is high. It has significant indirect economic and social benefits but relatively small direct economic benefits or profits. There is a lack of a unified method for the quantitative assessment of the VTS economic benefits. It is a meaningful topic that should be further explored in theoretical research. This paper analyzes and evaluates the connotation of VTS economic benefits and defines the scope of direct and indirect economic benefits. The direct economic benefits are classified with the specific business of VTS to establish a basic model, and sampling analysis is used to measure them in the application. For indirect economic benefits, the Input-Output Analysis (I-O) is used to measure them. The operational data of the Tianjin VTS is used as an example for verification. The correct understanding and evaluation of direct and indirect economic benefits generated by VTS is an inherent requirement to reflect the operational performance of VTS objectively and provides a scientific decision-making basis for the continuous investment and operation of VTS.

Vessel Traffic Service (VTS) plays an important role in maintaining the safety and order of ship traffic, especially in congested water areas such as the West Shipping Channel of Surabaya. With this system, it is hoped that the movement of ships can be more organized, so that the risk of accidents and violations can be minimized. However, the effectiveness of VTS in supporting the monitoring of the order of ship movement and berth areas still needs to be further studied. This study aims to analyze the effectiveness of Vessel Traffic Service (VTS) in supporting the monitoring of the order of ship movement and the berth area in the West Shipping Channel of Surabaya. This study uses a quantitative method with a simple linear regression approach. Data was collected through observation and analysis of secondary data, then processed using the SPSS 22 application. The results showed that the effectiveness of VTS had a significant effect on order monitoring, with a significance value of 0.001 less than 0.05. The value of the determination coefficient is 29.1% which means that the effectiveness of VTS contributes to the orderliness of ship movements and the berth area, and from the regression equation obtained it shows that every increase in the effectiveness of VTS by one unit will increase the monitoring of order by 0.470 units.

The Istanbul Strait is a challenging waterway for maritime traffic due to its rough topology, moderate to severe environmental conditions, and heavy local traffic. In particular, a total of 232 maritime accidents took place there between 2000 and 2011. In this study, generic fuzzy analytic hierarchy processes were used to assess the risk perception of stakeholders in the Istanbul Strait, including ship captains, maritime pilots and Vessel Traffic Services operators. These risk perceptions were then compared to the statistical maritime accident data, revealing a fundamental discrepancy between the risk perception and statistical data. Specifically, the area of the Straight with the highest number of accidents is perceived as relatively low-risk, whereas areas perceived as high-risk experience a lower number of accidents. Our results have implications for stakeholders as well as government agencies responsible for the safety of the Straight.

There are enormous challenges in the Istanbul Strait- one of the most important, congested and narrow waterways in the world - from the view point of risk determination and risk mitigation for the local traffic. Previously several traffic parameters such as; traffic volume for local vessels, traffic flow and potential encounters of local traffic, in addition to the possibility of collision, were investigated in order to determine the degree of dangers in the southern entrance of the Istanbul Strait. Furthermore, risky zones were also determined in this waterway. On the basis of the results of those, a group of expert was surveyed. These experts were pilots, Vessel Traffic Services Operators (VTS-O), Local Traffic Vessel Captains and Master Mariners who had several experience of navigation through the Istanbul Strait. In order to assess experts perceptions of danger and to propose further studies based on this survey. The questionnaire was analyzed by using SPSS (Statistical Package for the Social Sciences) program version 13.0. Finally, some differences and/or shares on risk perceptions of expert in the Istanbul Strait are considered.

선박교통류 평가는 대상해역 해상교통특성 파악을 통하여 정량화된 체계화된 교통관리를 수행함으로써 해양사고를 예방하는데 중요하다. 해상교통류의 특성은 선박자동식별장치(AIS, Automatic Identification System)가 선박에 설치됨에 따라 이 장치에서 수신된 정보를 토대로 평가할 수 있다. 본 연구에서는 해상교통관제센터(VTS, Vessel Traffic Services)에서 선박으로부터 수집된 위치, 속력, 침로, 시간 정보로부터 항로상에 해상교통류를 평가하기 위한 시뮬레이터를 구현하였다. 그 결과 해상교통관제센터에서 수집된 데이터로부터 선박 통항량, 통항분포, 선박속력분포, 기하학적 충돌빈도가 계산되어 표시된다. 이러한 해상교통류평가 평가는 해상교통관제사가 해상교통관리의 관점에서 해상안전을 위한 서비스를 제공하는데 유효하게 활용될 수 있을 것으로 사료된다. Maritime traffic assessment is important to understand the characteristics of maritime traffic and to prevent maritime accidents. The maritime traffic assessment can be calculated from the ship trajectory data observed by using AIS(Automatic Identification System). This paper developes a maritime traffic assessment tool using ship's position and speed, course, time data from ships navigating waterways. The results are represented in terms of the number of traffic quantity and traffic distribution, speed distribution, geometric collision candidates. The developed tool will contributes to advance maritime traffic safety by VTS(Vessel Traffic Services).

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.

The maritime transport system is one of the major means for transporting goods as safely, efficiently and environmentally friendly as possible. In this system, ports represent hubs connecting maritime to other transport modes such as railway, road and aviation. Therefore ports and their services can be considered as critical bottlenecks where operations need to be sustained in a large variety of operating conditions. This chapter presents an analysis of the Vessel Traffic Service (VTS), a maritime shore-based information service which is part of a port’s infrastructure. VTS is a key service in guaranteeing safe, fluent and efficient traffic flows in and out of a port, therefore making it a crucial part of a port’s ability to provide a constant service performance despite the large uncertainties that are inherent to maritime operations. Functional Resonance Analysis Method (FRAM) is used to build a functional model of the VTS to analyse the system design and its impact on the service supply chain’s ability to operate in a resilient manner, i.e. being able to sustain required operations prior, during and after disturbances or changes of operating conditions. While the chapter focuses on the maritime domain, it also provides an example on how a function-based approach can be used to understand and design service supply chains with a focus on how they achieve successful adaption to the large variety of operating conditions.

This paper describes the application method of bumper area defined in the ship domain theory and it is to identify risky sectors in VTS(Vessel Traffic Services) area. The final goal of this work is to develop early warning system providing the location information with high traffic risks in Mokpo VTS area and to prevent the human errors of VTS Officer(VTSO). The current goal of this paper is to find evaluation and detection method of risky sectors. The ratio between overlapped bumper area of each vessels and the summing area of a designated sector, Ratio to Evaluate Risk(RER) <TEX>${\gamma}$</TEX> is used as one of evaluation and detection parameter. The usability of overlapped bumper area is testified through three kinds of scenarios for various traffic situations. The marine traffic data used in the experiments is collected by AIS(Automatic Identification System) receiver and then compiled in the SQL(Structured Query Language) Server. Through the analysis of passing vessel's tracks within the boundary of Mokpo VTS area, the total of 11 sectors are identified as evaluation unit sector. As experiment results from risk evaluation for the 11 sectors, it is clearly known that the proposed method with RER <TEX>${\gamma}$</TEX> can provide the location information of high risky sectors which are need to keep traffic tracks of vessel movements and to maintain traffic monitoring by VTSO.

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.

The advent of IoT technology and the increase in wireless networking devices has led to an enormous increase in network attacks from different sources. To maintain networks as safe and secure, the Intrusion Detection System (IDS) have become very critical. Intrusion Detection Systems (IDS) are designed to protect the network by identifying anomaly behaviors or improper uses. Intrusion Detection systems provide more meticulous security functionality than access control barriers by detecting attempted and successful attacks at the end-point or within the network. Intrusion prevention systems are the next logical step to this approach as they can take real-time action against breaches. To have an accurate IDS, detailed visibility is required into the network traffic. The intrusion detection system should be able to detect inside the network threats as well as access control breaches. IDS has been around for a very long time now. These traditional IDS were rules and signature-based. Though they were able to reduce false positives they were not able to detect new attacks. In today's world due to the growth of connectivity, attacks have increased at an exponential rate and it has become essential to use a data-driven approach to tackle these issues. In this paper, the KDD data set was used to train the unsupervised machine learning algorithm called Isolation Forest. The data set is highly imbalanced and contains various attacks such as DOS, Probe, U2R, R2L. Since this data set suffers from a redundancy of values and class imbalance, the data preprocessing will be performed first and also used unsupervised learning. For this network traffic based anomaly detection model isolation forest was used to detect outliers and probable attacks the results were evaluated using the anomaly score.