Search Results (36)

Due to the unique physicochemical properties of ammonia fuel, any leakages in the engine room will inevitably endanger ship safety. This study focuses on investigating the diffusion behavior of ammonia fuel within the engine room during ship navigation after leakage, aiming to identify hazardous points and implement measures, such as installing air-blowing and extraction devices, to mitigate the risks. To address potential leakage risks in ammonia-fueled ships, a simplified three-dimensional computational model was developed based on ship design drawings and field investigations. ANSYS Fluent software (2024 R2) was employed to simulate ammonia fuel leakage from pipelines and equipment, analyzing the diffusion patterns of leakage at different locations and evaluating the impact of adding air-blowing and extraction devices on leaked fuel in the engine room. The simulation results demonstrate that leakage at point 3 poses the greatest operational hazard, and ammonia fuel leakage during navigation generates combustible gas mixtures within the explosion limit range around the main engine, severely threatening both vessel safety and crew lives. Installing air-blowing and extraction devices in high-risk areas effectively reduces the explosion limit range of ammonia fuel, with air outlet 3 showing optimal mitigation effectiveness against ammonia fuel leakage during ship transportation. Full article

High-completeness ship trajectory data are critical for analyzing navigation behavior characteristics and enhancing effective maritime management. To address the common issues of prolonged AIS data loss for small coastal vessels in nearshore waters, an intelligent trajectory repairing model based on the artificial potential field-enhanced A* algorithm (APF-A*) has been proposed. Kernel density estimation was utilized to quantify the distribution characteristics of vessels, thereby constructing an attractive potential field based on historical trajectories and a repulsive potential field based on coastal terrain. Speed distribution characteristics were extracted from historical trajectory points in different regions; on the basis of this, the A* algorithm, integrated with attractive and repulsive fields, was proposed to repair missing trajectory segments. Based on the speed distribution characteristics, time intervals, and distance information, the temporal information of the vessel trajectories was effectively reconstructed. The present study fills the research gap in AIS data reconstruction for small coastal vessels in complex coastal waters. A case study has been conducted in Luoyuan Bay, Fujian Province, China, to further validate the proposed model. The results demonstrate that the trajectory repairing model based on the artificial potential field-enhanced A* algorithm outperformed other models. More specifically, the Hausdorff Distance and Dynamic Time Warping (DTW) metrics decreased by 81.67% and 91.56%, respectively. The present study shares useful insights into intelligent maritime management and further supports accident prevention in coastal waters. Full article

During icebreaker navigation in ice-covered waters, icebreaking resistance and dynamic ice loads acting on the bow critically determine the vessel’s icebreaking performance. Quantitative characterization of the icebreaking resistance behavior and ice load distribution on the bow is essential for elucidating ship-ice interaction mechanisms, assessing icebreaking capability, and optimizing structural design. This study conducted comparative icebreaking tests on two icebreaker bow models with distinct geometries in the small ice model basin of China Ship Scientific Research Center (CSSRC SIMB). Systematic measurements were performed to quantify icebreaking resistance, capture spatiotemporal ice load distributions, and document ice failure patterns under level ice conditions. The analysis reveals that bow geometry profoundly influences icebreaking efficiency: the stem angle governs the proportion of bending failure during vertical ice penetration, while the flare angle modulates circumferential failure modes along the hull-ice interface. Notably, the sunken keel configuration enhances ice clearance by mechanically expelling fractured ice blocks. Ice load distributions exhibit pronounced nonlinearity, with localized pressure concentrations and stochastic load center migration driven by ice fracture dynamics. Furthermore, icebreaking patterns—such as fractured ice dimensions and kinematic behavior during ship-ice interaction—are quantitatively correlated with the bow designs. These experimentally validated findings provide critical insights into ice-structure interaction physics, offering an empirical foundation for performance prediction and bow-form optimization in the preliminary design of icebreakers. Full article

Despite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that govern vessel conduct to avoid collisions. Borderline encounter situations—such as those between head-on and crossing, or overtaking and crossing—pose particular challenges, often resulting in inconsistent or ambiguous interpretations. This study models navigator awareness as a probabilistic function of encounter angle, aiming to identify interpretive transition zones and cognitive uncertainty in rule application. A structured survey was conducted with 101 licensed navigators, each evaluating simulated ship encounter scenarios with varying relative bearings. Responses were collected using a Likert scale and analyzed in angular sectors known for interpretational ambiguity: 006–012° for head on to crossing (HC) and 100–160° for overtaking to crossing (OC). Gaussian curve fitting was applied to the response distributions, with the awareness center (μ) and standard deviation (σ) serving as indicators of consensus and ambiguity. The results reveal sharp shifts in awareness near 008° and 160°, suggesting cognitively unstable zones. Risk-averse interpretation patterns were also observed, where navigators tended to classify borderline situations more conservatively under uncertainty. These findings suggest that navigator awareness is not deterministic but probabilistically structured and context sensitive. The proposed awareness modeling framework helps bridge the gap between regulatory prescriptions and real world navigator behavior, offering practical implications for MASS algorithm design and COLREGs refinement. Full article

Inland waterway navigation involves complex traffic conditions with frequent multi-ship encounters. Benefiting from its straightforward structure and robust adaptability, reinforcement learning has found applications in navigation. This article proposes a deep actor–critic collision avoidance model which is based on the weighted summation of intrinsic reward and extrinsic reward, overcoming the sparsity of the reward function in navigation tasks. For the proposed algorithm, the extrinsic reward considers factors of collision risk, economic reward, and penalties for violating collision avoidance rules, while the intrinsic reward explores the novelty of agent states. The optimization of the own ship’s actions is achieved through the utilization of a weighted summation of these two types of rewards, providing valuable guidance for decision-making in a symmetrical interaction framework. To validate the performance of the proposed multi-ship collision avoidance model, simulations of both two-ship encounters and complex multi-ship scenarios involving dynamic and static obstacles are conducted. The following conclusions can be drawn: (1) The proposed model could provide effective decisions for ship navigation in inland waterways, maintaining symmetrical coordination between vessels. (2) The hybrid reward mechanism successfully guides ship behavior in collision avoidance scenarios. Full article

Under complex hydrodynamic conditions, Unmanned Surface Vessel (USV) exhibits non-stationary and nonlinear dynamic behaviors. Extreme short-term prediction of such nonlinear motion is therefore critical for ensuring navigational safety. To improve the prediction accuracy, a VMD-CNN-LSTM combined prediction model was applied based on Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Long Short-term Memory (LSTM) neural network. The methodology employs VMD to decompose the nonlinear motion time series data of the USV obtained by numerical simulation into stationary Intrinsic Mode Functions (IMFs), subsequently extracting spatial features from these IMFs using CNN layers, and, finally, predicts temporal sequence via the LSTM module. Comparative analyses highlight the better performance of the VMD-CNN-LSTM model over standalone LSTM and CNN-LSTM models in predicting nonlinear motion under varying significant wave heights. At a Prediction Advance Time (PAT) of 3.7 s, the VMD-CNN-LSTM model improves prediction accuracy by 13.3% for a wave height of 1.015 m (Case I) and 54.9% for a wave height of 1.998 m (Case II) compared to the CNN-LSTM model. With a PAT of 5.6 s, the accuracy gains increase to 32.9% for Case I and 94.6% for Case II, demonstrating the model’s robustness in extended prediction scenarios. Full article

Maritime behavior detection is vital for maritime surveillance and management, ensuring safe ship navigation, normal port operations, marine environmental protection, and the prevention of illegal activities on water. Current methods for detecting anomalous vessel behaviors primarily rely on single time series data or feature point analysis, which struggle to capture the relationships between vessel behaviors, limiting anomaly identification accuracy. To address this challenge, we proposed a novel vessel anomaly detection framework, which is called the BPEF-TSD framework. It integrates a ship behavior pattern recognition algorithm, Smith–Waterman, and text similarity measurement methods. Specifically, we first introduced the BPEF mining framework to extract vessel behavior events from AIS data, then generated complete vessel behavior sequence chains through temporal combinations. Simultaneously, we employed the Smith–Waterman algorithm to achieve local alignment between the test vessel and known anomalous vessel behavior sequences. Finally, we evaluated the overall similarity between behavior chains based on the text similarity measure strategy, with vessels exceeding a predefined threshold being flagged as anomalous. The results demonstrate that the BPEF-TSD framework achieves over 90% accuracy in detecting abnormal trajectories in the waters of Xiamen Port, outperforming alternative methods such as LSTM, iForest, and HDBSCAN. This study contributes valuable insights for enhancing maritime safety and advancing intelligent supervision while introducing a novel research perspective on detecting anomalous vessel behavior through maritime big data mining. Full article

In the field of research into vessel type recognition utilizing trajectory data, researchers have primarily concentrated on developing models based on trajectory sequences to extract the relevant information. However, this approach often overlooks the crucial significance of the spatial dependency relationships among trajectory points, posing challenges for comprehensively capturing the intricate features of vessel travel patterns. To address this limitation, our study introduces a novel multi-graph fusion representation method that integrates both trajectory sequences and dependency relationships to optimize the task of vessel type recognition. The proposed method initially extracts the spatiotemporal features and behavioral semantic features from vessel trajectories. By utilizing these behavioral semantic features, the key nodes within the trajectory that exhibit dependencies are identified. Subsequently, graph structures are constructed to represent the intricate dependencies between these nodes and the sequences of trajectory points. These graph structures are then processed through graph convolutional networks (GCNs), which integrate various sources of information within the graphs to obtain behavioral representations of vessel trajectories. Finally, these representations are applied to the task of vessel type recognition for experimental validation. The experimental results indicate that this method significantly enhances vessel type recognition performance when compared to other baseline methods. Additionally, ablation experiments have been conducted to validate the effectiveness of each component of the method. This innovative approach not only delves deeply into the behavioral representations of vessel trajectories but also contributes to advancements in intelligent water traffic control. Full article

Accident analysis models are crucial tools for understanding and preventing accidents in the maritime industry. Despite the advances in ship technology and regulatory frameworks, human factors remain a leading cause of marine accidents. The complexity of human behavior, influenced by social, technical, and psychological aspects, makes accident analysis challenging. Various methods are used to analyze accidents, but no single approach is universally chosen for use as the most effective. Traditional methods often emphasize human errors, technical failures, and mechanical breakdowns. However, hybrid models, which combine different approaches, are increasingly recognized for providing more accurate predictions by addressing multiple causal factors. In this study, a dynamic hybrid model based on the Human Factors Analysis and Classification System (HFACS) and Bayesian Networks is proposed to predict and estimate accident risks in narrow waterways. The model utilizes past accident data and expert judgment to assess the potential risks ships encounter when navigating these confined areas. Uniquely, this approach enables the prediction of accident probabilities under varying operational conditions, offering practical applications such as real-time risk estimation for vessels before entering the Istanbul Strait. By offering real-time insights, the proposed model supports traffic operators in implementing preventive measures before ships enter high-risk zones. The results of this study can serve as a decision-support system not only for VTS operators, shipmasters, and company representatives but also for national and international stakeholders in the maritime industry, aiding in both accident probability prediction and the development of preventive measures. Full article

With the rapid growth of the global shipping industry, the increasing number of vessels has brought significant challenges to navigation safety and management. Vessel trajectory prediction technology plays a crucial role in route optimization and collision avoidance. However, current prediction methods face limitations when dealing with complex vessel interactions and multi-dimensional attribute information. Most models rely solely on global modeling in the temporal dimension, considering spatial interactions only later, failing to capture dynamic changes in trajectory interactions at different time points. Additionally, these methods do not fully utilize the multi-attribute information in AIS data, and the simple concatenation of attributes limits the model’s potential. To address these issues, this paper proposes a dual spacial–temporal attention network with multi-attribute information (DualSTMA). This network models vessel behavior and interactions through two distinct paths, comprehensively considering individual vessel intentions and dynamic interactions. Moreover, we divide vessel attributes into dynamic and static categories, with dynamic attributes fused during feature preprocessing, and with static attributes being controlled through a gating mechanism during spatial interactions to regulate the importance of neighboring vessel features. Benchmark tests on real AIS data show that DualSTMA significantly outperforms existing methods in prediction accuracy. Ablation studies and visual analyses further validate the model’s reliability and advantages. Full article

Overtaking situations are commonly encountered in maritime navigation, and the overtaking process involves various risk factors that significantly contribute to collision incidents. It is crucial to conduct research on the maneuvering behaviors and decision-making processes associated with ship overtaking. This paper proposes a method based on the analysis of ship maneuvering performance to investigate overtaking behaviors in navigational channels. A relative motion model is established for both the overtaking and the overtaken vessels, and the inter-vessel distance is calculated, taking into account the psychological perceptions of the ship’s driver. A decision-making model for ship overtaking is presented to provide a safety protocol for overtaking maneuvers. Applying this method to overtaking data from the South Channel shows that it effectively characterizes both the permissible overtaking space and the driver’s overtaking desire. Additionally, it enables the prediction of optimal overtaking timing and strategies based on short-term trajectory forecasts. Thus, this method not only offers a safe overtaking plan for vessels but also provides auxiliary information for decision making in intelligent ship navigation. Full article

The increasing congestion in major global maritime routes poses significant threats to international maritime safety, exacerbated by the proliferation of large, high-speed vessels. To improve the detection of abnormal ship behavior, this research employed automatic identification system (AIS) data for ship trajectory forecasting. Traditional methods primarily focus on spatial and temporal correlations but often lack accuracy and reliability. In this study, ship path predictions were enhanced using the WTG model, which combines wavelet transform, temporal convolutional networks (TCN), and gated recurrent units (GRU). Initially, wavelet decomposition was applied to deconstruct the input trajectory time series. The TCN and GRU modules then extracted features from both the time series and the decomposed data. The predicted elements were reassembled using a multi-head attention mechanism and a pooling layer to produce the final predictions. Comparative experiments demonstrated that the WTG model surpasses other models in the accuracy of ship trajectory prediction. The model proposed in this study proves to be reliable for forecasting ship paths, which is crucial for marine traffic management and ensuring safe navigation. Full article

The continuous growth of global trade and maritime transport has significantly heightened the challenges of managing ship traffic in port waters, particularly within fairways. Effective traffic management in these channels is crucial not only for ensuring navigational safety but also for optimizing port efficiency. A deep understanding of ship behavior within fairways is essential for effective traffic management. This paper applies machine learning techniques, including Decision Tree, Random Forest, and Gradient Boosting Regression, to model and analyze the behavior of various types of ships at specific moments within fairways. The study focuses on predicting four key behavioral parameters: latitude, longitude, speed, and heading. The experimental results reveal that the Random Forest model achieves adjusted $R^2$ scores of 0.9999 for both longitude and latitude, 0.9957 for speed, and 0.9727 for heading. All three models perform well in accurately predicting ship positions at different times, with the Random Forest model particularly excelling in speed and heading predictions. It effectively captures the behavior of ships within fairways and provides accurate predictions for different types and sizes of vessels, especially in terms of speed and heading variations as they approach or leave berths. This model offers valuable support for predicting ship behavior, enhancing ship traffic management, optimizing port scheduling, and detecting anomalies. Full article

The melting of Arctic ice has facilitated the successful navigation of merchant ships through the Arctic route, often requiring icebreakers for assistance. To reduce the risk of accidents between merchant vessels and icebreakers stemming from human errors during operations, this paper introduces an enhanced human reliability assessment approach. This method utilizes the Dynamic Bayesian Network (DBN) model, integrated with the information, decision, and action in crew context (IDAC) framework. First, a qualitative analysis of crew maneuvering behavior in scenarios involving a collision with the preceding vessel during icebreaker assistance is conducted using the IDAC model. Second, the D–S evidence theory and cloud models are integrated to process multi-source subjective data. Finally, the human error probability of crew members is quantified using the DBN. The research results indicate that during convoy operations, the maximum probability that the officer on watch (OOW) chooses an incorrect deceleration strategy is $8.259\times {10}^{-2}$ and the collision probability is $4.129\times {10}^{-3}$. Furthermore, this study also found that the factors of Team Effectiveness and Knowledge/Abilities during convoy operations have the greatest impact on collision occurrence. This research provides important guidance and recommendations for the safe navigation of merchant ships in the Arctic waters. By reducing human errors and adopting appropriate preventive measures, the risk of collisions between merchant ships and icebreakers can be significantly decreased. Full article

Maritime traffic is essential for global trade but faces significant challenges, including navigation safety, environmental protection, and the prevention of illicit activities. This work presents a framework for detecting illegal activities carried out by vessels, combining navigation behavior detection models with rules based on expert knowledge. Using synthetic and real datasets based on the Automatic Identification System (AIS), we structured our framework into five levels based on the Joint Directors of Laboratories (JDL) model, efficiently integrating data from multiple sources. Activities are classified into four categories: illegal fishing, suspicious activity, anomalous activity, and normal activity. To address the issue of a lack of labels and integrate data-driven detection with expert knowledge, we employed a stack ensemble model along with active learning. The results showed that the framework was highly effective, achieving 99% accuracy in detecting illegal fishing and 92% in detecting suspicious activities. Furthermore, it drastically reduced the need for manual checks by specialists, transforming experts’ tacit knowledge into explicit knowledge through the models and allowing continuous updates of maritime domain rules. This work significantly contributes to maritime surveillance, offering a scalable and efficient solution for detecting illegal activities in the maritime domain. Full article