GNSS relies on weak radio signals from distant satellites in space and has been noted to be vulnerable to positioning failures caused by radio jamming and the output of erroneous position information due to spoofed signals. GNSS jamming and spoofing can render a vessel's positioning impossible or display incorrect locations, potentially causing confusion among seafarers. Therefore, effective education and training on countermeasures are important for vessel safety. However, effective methods for providing demonstration-based education on GNSS jamming and spoofing, or experiential training using ship-handling simulators, have not yet been established. This study developed and validated a training method aimed at enhancing the awareness and understanding of seafarers operating vessels and maritime students regarding the risks of GNSS displaying incorrect positions due to intentional interference signals or spoofing signals, and the countermeasures against them. The results show that instances of maneuvering based on erroneous positions were observed, and positive feedback regarding the training was received from participants.

Based on the Port Regulations Act, various powers are granted to the captain of the port, such as the requirement that event organizers must obtain the permission from the captain of the port when holding an event in the specified port. However, for the events, marine fireworks festivals and drone shows, held in the ports where the Port Regulations Act Applies, excluding specific port, the current Port Regulations Act restricts the measures that the Commander of the Regional Coast Guard Headquarters having jurisdiction over the location of the relevant port, which is specified by Order of the Ministry of Land, Infrastructure, Transport and Tourism can take to ensure the view of navigational safety, despite the possibility that an accident that occurred during an event could have a negative impact on port traffic. In this paper, we point out the 4 problems with the Port Regulations Act, such as article 32, and propose improvements, referring to the foreign example of fireworks festivals that was held on the East Coast of the United States of America.

As a countermeasure of a quick evacuation from ports and harbors for large ships against a great earthquake and tsunami, it is important to achieve a head-out mooring, instead of a head-in mooring ordinarily conducted in larger ships. It is also a key issue to obtain a property of yaw moment acting on the ship in shallow water during a turning short around assisted by tugs. From several pure yaw rotating tests in a water tank for a model ship conducted in this study, it is found that the yaw moment in shallow water where the ratio(h/d) of water depth(h) and draft(d) is h/d=1.2, is decreased by digging down the sea bottom of the turning basin only until the ratio of h/d=2.0, as well as that in the ratio of h/d ≧ 2.0.

As container ships become larger with the expansion of trade volume, the importance of efficient container handling at container terminals (CTs) is increasing. One way to improve the efficiency of container handling is to reduce the number of relocations that occur at container yards (CYs). Terminal operators need to relocate containers because containers are stacked in multiple tiers, although the work is often considered unproductive. This study considers the container relocation problem to minimize the total number of containers relocated and the total move distance caused by container relocations. Also, we develop a model that simulates a real-world CY operation, and clarify the degree of improvement in the number of containers relocated and the container move distance.

This study aims to construct a system that evaluates work safety one week in advance by combining wave prediction with motion response prediction of workvessel using machine learning. We enhanced the accuracy of wave prediction by utilizing data from NOWPHAS's GPS wave gauges and meteorological information from ERA5. The Long Short Term Memory (LSTM) algorithm was adopted, and two approaches were undertaken: “point prediction” and “historical prediction”. Point prediction targeted the prediction of wave conditions at specific times, while historical prediction forecast time-series data up to one week ahead. Historical prediction proved advantageous for work planning because it could simultaneously learn and reflect dependencies among multiple future time points. The computational results successfully reduced the probability of prediction failures, specifically underestimations in wave predictions, to approximately 20%. This reduction is critical because underestimating wave heights can lead to unsafe operations. Furthermore, by calculating the prediction accuracy of the motion response of workvessel, we developed an operational system that enables safe offshore operations.

The subject research proposed the positive pressure design concept of ventilation system for ship engine control room when adopting ammonia as the new future fuel. The research considered the assumption scenario of ammonia leakage in engine room, and built the CFD simulation model and numerical analysis method for the ventilation operation to analyze the air flow distribution and differential pressure in the engine control room. The efficiency of setting chamber room to improve the gas safety is shown. The operation scenario of chamber room is suggested based on the CFD analysis result of differential pressure and ammonia concentration.

In this study, we aimed to improve maritime traffic safety by focusing on the problems of approach and navigation between ships in traffic separation lane routes (Uraga Suido Traffic Route, Akashi Kaikyo Traffic Route, Bisan Seto East Traffic Route) under the Maritime Traffic Safety Act, and aimed to make new proposals by grasping the current situation and pointing out the problems.

For this purpose, the approach limit of distances for vessels navigating in front of and behind their own ship, left and right, were investigated by means of a questionnaire. The results showed that the values of the approach distance required by vessels in the separated traffic lane were smaller than those in other routes, and the ratio of each direction showed that the limit distance in the front-rear area was almost three times that in the left-right area, which is different from previous studies and characteristic of separated traffic lane routes.

This study examined the feasibility of a quantitative measurement method for cutter boat racing. A pilot system was developed, integrating RTK-GNSS, an IMU with a fiber-optic gyro, and an onboard camera. Motion and video data were synchronized to extract stroke indicators, such as the proportion of oar-in-water time, peak drive acceleration, and jerk. Field trials demonstrated that the system could capture boat trajectory (latitude/longitude), attitude (heading, roll, pitch), speed, and key stroke events (oar entry/exit, stroke phases). The trials also identified practical challenges, including unstable heading estimation and increased GNSS course-over-ground error at low speeds, as well as the need for precise time synchronization between sensors. The findings clarify the measurable parameters and technical issues to be addressed for applying this approach to performance analysis in cutter racing.

Remote ship operation is emerging as a practical solution for autonomous ships. However, since human intervention is still required in emergencies, safety risks due to human error remain a concern. In particular, knowledge about remote operation is limited, and few relevant human performance factors have been identified. To address this, the present study analyzes how human factors influence operator performance in remote ship operations. As a representative task, course recovery was examined, assuming that manual intervention is needed to regain control of the ship. First, task analysis was conducted to decompose the task, model task relationships, and identify factors related to performance. Potential human errors and associated factors were then identified, and a Bayesian network was developed to analyze their probabilistic impact. The results indicate that operator experience and human-machine interface design are critical, suggesting that improvements in these areas should be prioritized.

In response to the increasing volatility of liquefied natural gas (LNG) spot freight rates, this study applies deep learning models to improve forecasting accuracy. The performance of standalone models—Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN)—is compared with that of hybrid models. The results show that the LSTM+CNN model achieved the highest prediction accuracy, reaching approximately 88% for short-term forecasts. While the standalone CNN model performed poorly in both short- and long-term predictions, its combination with LSTM significantly improved prediction accuracy. This improvement is attributed to CNN’s ability to extract local patterns and LSTM’s strength in capturing long-term dependencies, demonstrating a complementary effect. Furthermore, incorporating the attention mechanism (ATT), which has proven effective in previous studies, enhanced predictive performance, particularly in long-term forecasting. The LSTM+ATT model showed strong results due to the attention mechanism's capability to assign greater weight to important time steps and reduce information loss over longer sequences.

In congested maritime traffic areas, it is essential for navigators and vessel traffic service operators to accurately understand the navigational intent of vessels. One key piece of information indicating such intent is the vessel’s destination. Although this information is generally provided through the Automatic Identification System (AIS), it is manually entered and may contain errors such as typos or omissions. Moreover, AIS does not include a standard means of reporting intermediate waypoints, which makes it difficult to capture a vessel’s intended route. This study proposes a method for predicting a vessel’s behavior based on its observed trajectory captured within a specific area. Using geographical trajectory data extracted from AIS as input, we developed a destination prediction model based on a Hidden Markov Model (HMM), that captures the relationship between vessel movement patterns and destinations. The model estimates the likelihood of each destination based on the vessel’s partial trajectory. We applied the proposed method to vessel traffic data with three target destinations in Tokyo Bay. The results indicate that, when estimating a specific destination, the model can provide a reasonable estimate of vessel destinations based on partial trajectories.

Many ship collision accidents are attributed to human factors such as a lack of lookout and improper maneuvering. In accident investigations, information related to human factors is qualitative; therefore, analysis is required to be conducted about quantitative indicators. In response, methods have been developed to evaluate ship operators’ awareness by linking it with a collision risk index based on the vessel’s physical state. In a previous study, the authors quantified the relative position from their own ship heading to the expected collision point, defined it as an index indicating the possibility of collision, and compared the index values with the operators’ perceptions. There were cases where the expected collision point may not have been appropriately assessed, and the range of positions to be included in the evaluation remained ambiguous. In this study, indices were calculated based on each of heading and course over ground (COG) and then compared to clarify the range of positions that should be included in the evaluation. The relationship between these indices and the operators’ situational awareness was subsequently examined. In this study, it was observed that based on COG is often more appropriate for evaluation. Additionally, when the heading-based approach is high, it may be more effective to collect further information on human factors.

Research on autonomous ships has been progressing rapidly in recent years, aiming to enhance safety and efficiency in marine transportation.

However, collision avoidance with fishing vessels remains a particularly difficult challenge for autonomous navigation algorithms because of their irregular movements and diverse operating patterns.

This study focuses on evaluating the maneuvering behavior of a powered vessel encountering fishing vessels.

Simulator-based experiments were conducted using multiple encounter scenarios that reflected actual fishing activities, and avoidance maneuvers were analyzed based on the responses of student participants.

The results and expert evaluations were compared to identify tendencies in appropriate and inappropriate avoidance behaviors.

These findings are expected to provide fundamental insights for establishing evaluation criteria and improving future collision-avoidance algorithms for autonomous ships.