해상교통류 평가모델 개발에 관한 연구

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.

This study developed a decision‐making support system (DMSS) for automatically determining the route priority of vessels entering/exiting the waters surrounding the port monitored by vessel traffic services (VTS). The developed DMSS was combined with marine geographic information systems (GIS), automatic identification systems (AIS), and fuzzy logic theory. The simulations of case studies for the ports of Keelung and Singapore were shown to illustrate the fitness of the developed DMSS. Based on the vessel information received/transmitted by AIS within the waters monitored by VTS, the developed DMSS can compute and output the route priority values for the captains on board and the operators in VTS stations as a point of reference. In other words, each ship in the waters surrounding the port can know the route priority for entering/exiting the port. The simulation results of case studies showed that the developed DMSS seems promising. The developed DMSS could be a useful tool in improving a port's navigation safety.

Navigation safety has become one of the important issues to the entire world community. Automatic Identification System (AIS) firstly has been used to comply with safety and security regulations, functioning as collision avoidance, vessel traffic services, maritime security, aids to navigation, search and rescue and accident investigation. This paper presents marine navigation collision preventing system between ships and ships using AIS. In the system, the raw data from AIS is crossed with database based MMSI number to find detail information then, location of each ship was plotted on to Google Map. The safety distance is assessed based actual and stopping distance. The system was tested in the Batam and Singapore Channel.

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.

This paper presents an intellectual decision making system for the vessel traffic service (VTS). Its aim is to increase the safety and efficiency of the traffic and the effectiveness of VTS. In general, the navigational decision making process in a traffic control system is based on radar and navigation information although other systems, such as automatic identification systems, GPS can be used with good effect. The application of an intellectual component during a decision making process increases safety and economic effects of the shipping as well as reduces the operator's workload.

Development of a Gridded Maritime Traffic DB for e-Navigation

Radars are already used for collision avoidance during more than 50 years. During this period many proposal for collision avoidance by using radar have been made, and the experience of their use have been gained. However the effectiveness of these proposals for collision avoidance is not enough, especially in congested water. The Automatic Identification System (AIS) will be equipped all ships of 300 gross tonnage and upwards engaged on international voyages. The AIS is expected to be very useful for collision avoidance. Even if AIS is used, it is necessary to consider the effect of predicted error of ship's position on the evaluation of collision risk. In this paper an approach to improve the evaluation and expression of collision risk is shown. This method is able to show the obstacle zone by target (OZT), considered an estimated error of ship's speed, and it will be useful for collision avoidance in congested water. Expression of OZT shown in this paper will be easy to trace the origin target of OZT and to check the fault information transmitted by the target.

Vessel losses of command (e.g., propulsion, electrical power, or steering failure) can serve as the initiating event for significant maritime accidents, such as grounding. It is therefore important to estimate the frequency of such events for navigational risk assessments. Despite the existence of previous studies, new data have become available that may reveal more precise and useful information for risk estimations and accident prevention. This paper analyzes two databases of vessel losses of command within Norway’s economic exclusive area over a five year period. The study utilizes a novel database of incidents observed by the Norwegian Coastal Administration’s (NCA) Vessel Traffic Services (VTS), in addition to incidents reported to IHS Markit over the same five year period. Automatic identification system (AIS) data is used to construct activity metrics of vessel activity, which are used to compute vessel loss of command incidence rates for ship types. To account for under-reporting, capture–recapture methods are used to calculate adjusted incidence-rates. The duration of each incident is recorded and used to construct distributions of repair times. Probabilities of towing and anchoring are computed for ship types and incident location. The results indicate that previous studies may have underestimated the incidence of vessel loss of command. Using the best-case scenario, cargo ships and tankers suffer losses of command more frequently than other ship types. Significant under-reporting of losses of command was observed in the IHS Markit database.

The Utilization of AIS (Automatic Identification System) and VTS (Vessel Traffic Services) in a Wide Range of Fields, Not Only the Safety of Maritime Navigation in Congested Sea Areas but also Anti-piracy Measures

As a core component of the maritime Internet of Things (IoT), the Automatic Identification System (AIS) continuously collects dynamic vessel navigation data, providing a solid foundation for addressing complex maritime traffic prediction tasks that support intelligent Vessel Traffic Services (VTS), such as vessel trajectory prediction and vessel arrival time (VAT) estimation. However, existing methods typically focus on single prediction objectives, falling short of meeting practical multi-task requirements. To address this gap, this study proposes a geospatial grid-constrained deep learning framework based on AIS data to simultaneously handle three key prediction tasks: vessel trajectory prediction, whether the vessel arrives within the specified time, and VAT. The framework incorporates a dynamic patch construction method and a Graph Soft Evolution (GSE) module to capture temporal correlations among observations under spatial grid constraints. An encoder-decoder architecture is introduced, where the encoder employs a Squeeze-and-Excitation (SE) block to adaptively select feature channels, and the decoder models dependencies across both variable and temporal dimensions. In a case study of New York Harbor, the model achieved an R² of 0.8386 and RMSE of 0.0329 for latitude increment prediction, and an R² of 0.8432 with RMSE of 0.0322 for longitude increment prediction. It also attained 99.83% accuracy in arrival status prediction and an R² of 0.9350 with RMSE of 0.0701 for VAT prediction. The framework demonstrated effectiveness in port scheduling and robust generalizability in cross-validation experiments at the Port of Los Angeles, thereby demonstrating its substantial potential to enhance the operational efficiency of VTS within maritime IoT systems.

The automatic identification system (AIS) provides detailed information about vessel movement that has gained wide application in areas such as ship behavior analysis over the past decade. Based on the AIS data in January and February of the lunar calendar in 2019 and 2020, this study analyzed the number and size of ships, the spatial distribution characteristics, such as ship position and density, ship speed, as well as the temporal characteristics of daily and monthly changes of ship flow, and then evaluated the impact of the COVID-19 epidemic on ship activities in Dongying Port waters. The results show that the number of tankers declined to a lesser extent compared to fishing and cargo ships after the outbreak of the COVID-19 epidemic. Because berthing activities would require extra operating time due to the epidemic, ships may require longer turnaround times. In general, tankers in Dongying Port waters were less affected by the epidemic than cargo and fishing ships.

This is a combination of YOLO (you only look once) target recognition detection algorithm and AIS (Automatic Identification System) automatic ship identification system to assist vessels in inland navigation to carry out vessels and obstacle avoidance. This system proposes a ship-assisted collision avoidance system for ship collision accidents caused by a narrow inland waterway and complex navigation channel. The system can process the image and video information acquired by the high-definition camera mounted on the ship, and frame and mark the ships in the current sea area, and combine the AIS system to realize the visualization of the ship information and improve the global view. The experiment shows that the system can effectively identify and frame the ship and realize the visualization of AIS information, and the transformed image and video information can be used for decision-making by the ship's driver.

The AIS (Automatic Identification System) became a standard under the International Convention for the Safety of Life at Sea (SOLAS) in December 2001. Using the AIS transponder, the vessel will report its position, identity heading and other information to other ship traffic in the same area. The AIS is a significant contribution to the control and safety of shipping. Transport Canada (TC) is a pioneer internationally in using the AIS (R4A airborne AIS transponder) integrated with the remote sensing system (MSS 6000 from Swedish Space Corporation) on board their aircraft, in order to add the information on ship identity and voyage to the information from other sensors. The AIS integration provides a quantum leap in the effectiveness of the surveillance mission. In the Canadian system integration, detailed vessel information obtained through the AIS system is displayed and correlated with information received from other sensors. All information is also presented together with a high-resolution digital map including nautical chart data. The AIS provides the operators with vessel voyage and identity information. It is very useful for vectoring the aircraft to areas where ships have been reported. The advent of AIS simplifies surveillance procedures and makes each hour of surveillance more productive. During last year TC reported that over 9700 vessels were over flown. It is anticipated that over 20,000 AIS targets will be acquired using AIS in 2007–2008. The experience from Canada also indicates an outstanding VHF range. During missions, vessels have been identified as far away as 200 nautical miles (n.m.) from the aircraft. The AIS information together with other sensor images gives a comprehensive image that will dramatically improve the chance to tie a ship to a detected illegal oil spill. Receiving ship identity information will enable aircrews to conduct covert operations and contact the suspect ship from far standoff distances. This will also act as a deterrent to potential polluters. Integrating AIS within the operational requirements of surveillance programs, dedicated to minimizing the adverse effects of shipping on the marine environment, will make a significant contribution towards improving the effectiveness of surveillance missions.

Recently, the Vessel Traffic Service (VTS) coverage has expanded to include coastal areas following the increased attention on vessel traffic safety. However, it has increased the workload on the VTS operators. In some cases, when the traffic volume increases sharply during the rush hour, the VTS operator may not be aware of the risks. Therefore, in this paper, we proposed a new method to recognize ship movement anomalies automatically to support the VTS operator’s decision-making. The proposed method generated traffic pattern model without any category information using the unsupervised learning algorithm.. The anomaly score can be calculated by classification and comparison of the trained model. Finally, we reviewed the experimental results using a ship-handling simulator and the actual trajectory data to verify the feasibility of the proposed method.

Maritime traffic pattern extraction is a fundamental and crucial factor for maritime surveillance and anomaly detection. Emerging technologies like Automatic Identification System (AIS) provides multi-dimensional data which is used to construct a maritime traffic model. In this paper, we propose a framework of maritime traffic pattern extraction from vessel AIS information, which learns a traffic pattern using an unsupervised technique, and can be applied on historical Automatic Identification System data. AIS data is a kind of spatio-temporal data that contains information of location data, as well as time stamps. In this way, traffic pattern is described by AIS data. Furthermore, we conduct a simulation experiment that extracts traffic pattern from the AIS data through the unsupervised technique. The proposed framework takes advantage of AIS data, which is a type of the spatiotemporal data that consists of vessel motion information, to perform the experiment. The result shows that the unsupervised framework converts useful information from raw AIS data to effective traffic pattern. The proposed method strongly supports the further research on maritime traffic pattern extraction of AIS data. Besides, an overview of the framework and the unsupervised technique for high-level maritime situation awareness is presented.