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This study proposes a novel and feasible framework for assessing maritime traffic complexity, utilising historical Automatic Identification System data to extract dynamic traffic characteristics of sea waters and quantify the corresponding maritime traffic complexity. The processed historical statistics are initially segmented into subsets according to different geographic traffic cells based on traffic zones. Subsequently, a Radial Basis Function regression model with a Gaussian kernel is employed to extract dynamic traffic parameters at the central coordinates of each geographic traffic zone based on subsets. The dynamic parameters derived from nonlinear regression and complexity sub-models are ultimately applied in maritime traffic complexity identification. The effectiveness of the proposed framework is validated through complexity assessments in selected areas of Malacca Strait. Empirical results are significant for maritime shipping research on data-driven traffic complexity monitoring and digitalised decision support. A novel method to assess maritime traffic complexity, utilising historical AIS data is proposed. The processed historical statistics are initially segmented into geographic traffic cells. A Radial Basis Function regression model with a Gaussian kernel are employed to extract dynamic traffic parameters. The dynamic parameters are derived from nonlinear regression and complexity sub-models. The effectiveness of the proposed framework is validated through complexity assessments in selected areas of Malacca Strait. [ABSTRACT FROM AUTHOR]

Baffles are essential safety devices for mobile pressure vessels on land and at sea. Under the most hazardous transport condition, Violent sloshing of the liquid inside the tank can damage the tank structure and baffles. To solve this problem, a multi-objective optimization method based on a combined approximate model and NSGA-II was proposed. The goals are to minimize the maximum impact force on the baffles and the front head of the LNG tank and to achieve a multi-objective optimization of the baffles. Firstly, based on the VOF method a dynamic model of liquid sloshing in the LNG tank was established. Then, the changes in the impact force on the baffles and front head of the LNG tank truck under different filling ratios and two extreme transport conditions were analyzed to determine the most hazardous transport condition. Finally, a novel multi-objective optimization method was proposed to reduce the maximum impact force of the baffles and the front head of the LNG tank. The results show that the two optimization goals were reduced by 117% and 85% respectively. These findings establish a theoretical foundation and provide data support for baffles design in mobile pressure vessels. This improves the safety of land and sea transportation. • LNG tank truck in the most hazardous transport condition, the multi-objective optimization method proposed for the baffles structure effectively reduces the maximum impact force on the front head of the tank and the baffles and suppresses the liquid sloshing inside the tank to a large extent. • When the filling ratio is K = 0.9, the LNG tank truck accelerates after emergency braking (Working Condition 2), which is the most hazardous transport condition for LNG tank trucks. • Based on the VOF and dynamic mesh adaptation CFD gas-liquid two-phase flow simulation method to establish a high-precision dynamics model of the liquid sloshing in the tank. • For the low prediction accuracy of a single approximate model, a combined approximate model is proposed to improve the prediction accuracy. [ABSTRACT FROM AUTHOR]

With the continuous increase of maritime traffic, developing an efficient and accurate model for ship trajectory anomaly detection has become crucial for ensuring maritime transportation safety. The high complexity and variability of the marine environment lead to diverse ship trajectory patterns, making it challenging to learn effective trajectory representations for accurately identifying anomalies. We thus proposed an unsupervised deep learning model called STAD for ship trajectory anomaly detection in ocean to address this challenge. Concretely, STAD leverages offset reconstruction-based representation learning and a deep Gaussian Mixture Model (GMM) estimation network to learn the underlying complex clustering patterns of ship trajectories and utilize the learned patterns to enhance trajectory anomaly detection. Extensive experiments on multiple AIS datasets indicate that our model significantly outperforms existing methods in detecting multiple representative types of ship trajectory anomalies, including shift deviation, abnormal heading, and abnormal speeding. This study could help closely monitor the status of ship movement and detect abnormal behaviors in advance, thus benefiting maritime safety. • A STAD model is proposed to effectively detect abnormal maritime ship trajectories. • Unidirectional attention and a deep GMM estimation network are developed for effective representation learning. • A sliding anomaly scoring method is proposed to enhance trajectory anomaly detection performance. • The proposed STAD model outperforms all the baseline methods, including OCSVM, GRU-AE, LSTM-AE, and GeoTrackNet. [ABSTRACT FROM AUTHOR]

Owing to the increasingly stringent environmental requirements and low emission targets, the entire shipping industry is developing in a green direction. The world's major shipbuilding powers have actively invested in the development of green ship technology and equipment. The utilization of wind energy in the development of traditional wind assisted vessels has become increasingly popular in numerous countries, because it aligns with the specific conditions present at sea. This study investigates the optimal design methodology for a low-speed and full-formed sail-assisted very large crude carrier (VLCC) considering its unique characteristics. Based on a high-precision numerical simulation, the influence of the hull form on the resistance and wake field is analysed comprehensively, and then the appropriate local detailed parameters are selected for the hull form parameterisation modelling of the low-speed and full-formed VLCC. Combined with the optimization method based on the approximation model, an optimization design method of the hull form of the fore-and-aft based on the characteristics of the low-speed and full-formed VLCC is established. Finally, according to a towing tank model test, the average resistance of the optimized ship model is reduced by 3.42% in the full speed section, while the unevenness of the wake field is reduced by 9.46% on average. • Analyzing the characteristics of the VLCC and the influence of principal dimensions on sail boosting capacity. • Multi-objective optimization of resistance and wake field of the sail-assisted VLCC. • Calculation and verification of resistance performance of the sail-assisted VLCC. • A Neural Network model is used to complete the multi-objective optimization of sail-assisted VLCC, which greatly improves the optimization efficiency. • The average resistance of the optimized ship is reduced by 3.42% and the wake is increased by 9.46% in the full speed section. [ABSTRACT FROM AUTHOR]

Maritime traffic safety is of utmost importance in the shipping industry. It is common to avoid potential traffic accident via the support of varied kinematic maritime data (e.g., ship position, ship speed). The study aims to fulfill maritime traffic safety enhancement by extracting multiple ship trajectories from maritime video data. The proposed framework is implemented with three steps. Firstly, the framework obtains initial ship imaging positions by proposing an ensemble YOLOX (You Only Look Once X) with CenterNet model. Secondly, ship contours from each maritime frame are explored with the deep snake module. Thirdly, consecutive ship imaging trajectory is identified with an enhanced Bytetrack multi-ship target tracking algorithm. Experimental results demonstrate that our model outperforms other ship trajectory extraction-like models considering that the identification F-Score (IDF1), IDP (identification Precision), IDR (identification recall), and MOTA (multi-object tracking accuracy) for our proposed framework were 0.965, 0.995, 0.933, and 0.925, respectively. The research findings can help ship crew better aware on-site traffic situations, and thus make optimal ship maneuvering operations for the purpose of enhancing maritime traffic system safety and sustainability. • we propose a novel trajectory fusion framework by integrating YOLOX and CenterNet modules. • we simultaneously track ships from maritime images in a pixel-wise manner with instance segmentation mechanism. • we propose an improved Bytetrack model with the help of irregular polygon detector (i.e., mask of the instance segmentation output) to fulfill ship trajectory data association. [ABSTRACT FROM AUTHOR]

The marine transportation of platforms and electrical equipment is crucial for the development of deep-sea wind farms. Although previous studies have investigated the land transportation of equipment, there have been few studies on the challenges posed by marine environments, which differ significantly from land environments. Electrical equipment is the core component of a transmission system. It is easily tipped or damaged under the complex conditions of marine transportation. This study aimed to fill the knowledge gap in the rapid calculation of the electrical equipment response during marine transportation by applying the theory of the equipment response spectrum. A hydrodynamic analysis of the barge motion response was first performed. Based on the load transfer process, a simplified vibration system for marine transport equipment was established, and various factors affecting the response of the equipment were discussed. Finally, an empirically fitted formula for the equipment acceleration response spectrum was obtained, which quickly estimated the absolute acceleration of the electrical equipment on the platform. The accuracy of the proposed formula was verified by comparing it with the results of power spectrum analysis. The conclusions of this study are significant for the safety assessment and design of electrical equipment in actual marine transportation projects. • The simplified vibration system for the electrical equipment during marine transport is established. • The knowledge gap in the study of the acceleration response spectrum of electrical equipment on the platform during marine transport is filled. • The effects of environmental parameters and structural parameters on the electrical equipment on the platform during marine transport are revealed. • The proposed formulas can estimate the absolute acceleration of the electrical equipment on the platform during marine transport. [ABSTRACT FROM AUTHOR]

This study proposes a novel physics-guided metamodel to predict vertical bending-induced fatigue damage in a 2800TEU container vessel navigating the North Atlantic, based on data from the vessel's hull monitoring system. The metamodel combines two XGBoost-based base learners: a black-box model utilizing ship heave and pitch motion measurements, and a gray-box model using spectral moments from numerical analysis. Predictions from both models are refined through a meta learner Gaussian process regression to enhance accuracy. The metamodel was evaluated against black-box and gray-box models across various training data volumes. The proposed model adapts to varying data volumes, from months to over 2 years, effectively integrating the strengths of both base learners to provide reliable predictions in both seen and unseen scenarios. The model consistently demonstrated superior performance, enhancing fatigue damage accumulation accuracy by up to 35% over traditional machine learning methods. This advancement can aid the maritime industry in effectively monitoring ship fatigue and implementing predictive maintenance strategies, marking a significant step forward in applying data-driven techniques in shipping. • A novel physics-guided metamodel is proposed for fatigue damage monitoring in container vessels. • The metamodel integrates a purely data-driven BBM with a physics-guided GBM. • The proposed metamodel effectively adapts to varying available data volumes. • The proposed metamodel demonstrated up to 35% accuracy increase in fatigue damage accumulation than BBM. [ABSTRACT FROM AUTHOR]

Improving ship energy efficiency has been a crucial part for energy preservation and emission control of the shipping industry. However, the current optimization technologies mainly concentrate on a single optimization of navigation speed, route and trim. There is still a shortage of an effective collaborative optimization method to enhance the ship energy efficiency. In this regard, it is necessary to carry out more efficient optimization approach to further enhance the ship fuel efficiency. Therefore, a new collaborative optimization approach for energy efficiency optimization considering the coupling effects of navigation route, speed, trim and various environmental variables is proposed in this study. Firstly, a predictive model for ship energy consumption, which considers the sailing route, speed, trim and various environmental factors, is established by using Genetic Algorithm (GA) improved Long Short-Term Memory (LSTM) approach. On these bases, a collaborative optimization method based on the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is proposed. The results of a case study show that the proposed collaborative optimization strategy can save fuel consumption by as much as 4.54%, compared with the original operational mode. Therefore, it holds significant importance for further enhancing ship fuel efficiency and promoting the advancement of low-carbon shipping. Improving ship energy efficiency has been a crucial part for energy preservation and emission control of the shipping industry. However, the current optimization technologies mainly concentrate on a single optimization of navigation speed, route and trim. There is still a shortage of an effective collaborative optimization method to enhance the ship energy efficiency. In this regard, it is necessary to carry out more efficient optimization approach to further enhance the ship fuel efficiency. Therefore, a new collaborative optimization approach for energy efficiency optimization considering the coupling effects of navigation route, speed, trim and various environmental variables is proposed in this study. Firstly, a predictive model for ship energy consumption, which considers the sailing route, speed, trim and various environmental factors, is established by using Genetic Algorithm (GA) improved Long Short-Term Memory (LSTM) approach. On these bases, a collaborative optimization method based on the Non-dominated Sorting Genetic Algorithm III is proposed. The results of a case study show that the proposed collaborative optimization strategy can save fuel consumption by as much as 4.54%, compared with the original operational mode. Therefore, it holds significant importance for further enhancing ship fuel efficiency and promoting the advancement of low-carbon shipping. [Display omitted] • A collaborative optimization model of sailing route, speed and trim based on the GA-LSTM and NSGA-III is established. • A collaborative optimization method for the ship energy efficiency improvement is proposed. • The proposed collaborative optimization method can reduce fuel usage as much as by 4.54%. • The proposed method can be regarded as an essential guidance for the ship energy efficiency improvement, thereby promoting the low-carbon shipping development. [ABSTRACT FROM AUTHOR]

To gain a comprehensive understanding of formation patterns for various maritime accidents in Arctic waters, the present study integrates human and organizational failures (HOFs) with accident risk factors (AIFs) to examine the individual or collective impacts of different AIFs and HOFs on Arctic maritime accidents. Firstly, a novel database is established within the improved Human Factors Analysis and Classification System (HFACS), encompassing 14 AIFs with multiple states and 33 HOFs with binary states. Secondly, the Association rule mining (ARM) is employed to capture the causal relationships among HOFs, while Augmented Naive Bayes (ANB) is introduced to account for the interdependency of AIFs, thereby developing the data-driven BN model for analyzing and predicting Arctic maritime accidents. Finally, the criticality and sensitivity of AIFs and HOFs across different Arctic maritime accidents are identified and ranked by BN forward and backward inference. Furthermore, three scenarios are simulated to demonstrate effective management strategies for prevention and mitigation of Arctic maritime accidents. Additionally, a correlation graph depicting the relationship between HOFs and AIFs is generated for each accident scenario, facilitating crew onboard ships as well as shipping companies and maritime authorities in customizing targeted safety measures aimed at preventing Arctic maritime accidents. • A comprehensive database encompassing HOFs and AIFs associated with Arctic maritime accidents is established. • A data-driven approach integrating improved HFACS, ARM, and data-driven BN is proposed. • The impact of different AIFs and HOFs individually or collectively on Arctic maritime accidents is investigated. [ABSTRACT FROM AUTHOR]

Collision avoidance algorithms are critical for enhancing ship navigation safety. To ensure the reliability of these algorithms, comprehensive testing is imperative prior to their deployment. A pivotal aspect of such testing is the strategic formulation of testing scenarios, which plays a vital role in securing the trust of testing phase results. This study proposes a systematic method for generating ship encounter scenarios and analyzing their complexity using Automatic Identification System (AIS) data, to support the implementation of collision avoidance algorithm testing. Firstly, real-world ship encounter scenarios are extracted from AIS data by considering the spatial and temporal dependencies among ships. Subsequently, a complexity evaluation model based on encounter topology evolution is designed to assess the complexity of these scenarios. Finally, detailed strategies for using these scenarios in collision avoidance algorithm testing are explored based on the complexity evaluation results. Real AIS data from the waters off Ningbo-Zhoushan Port is collected to validate the performance of the proposed method. Research findings indicate that the proposed method successfully separates numerous ship encounter scenarios from historical AIS data and comprehensively evaluates their complexity. Based on this foundation, valuable guidance is provided for the application of extracted scenarios in collision avoidance algorithm testing. • A new method to generate and evaluate ship encounter scenarios using AIS data for collision avoidance testing. • A methodical approach to extract ship encounter scenarios from AIS data, considering spatial and temporal dependencies. • A novel model to evaluate the complexity of ship encounter scenarios based on encounter topology evolution. • A detailed strategy for applying generated ship encounter scenarios in collision avoidance algorithm testing. [ABSTRACT FROM AUTHOR]

Ship behaviors refer to the operational process such as sailing, entering into port/departure, etc., which indicate by their position, speed, and so on. The collected big data normally have been treated by unsupervised Machine Learning methods. However, the process is time consuming and lacks consideration of time continuity. From the unknown data to recognize and recur the ship behaviors is still a complex problem. Hence, this study proposes a universal Meta-trajectory Variable Sliding Window (Meta-VSW) method to provide an efficient and high-fidelity solution. In this method, the ship data were connected into the smallest units by the meta-trajectory coding, and combines with variable sliding windows to achieve fast, continuous and accurate recognition of ship behaviors. Taking an inland-water ship and a marine transport ship as examples, the validity of the method was fulfilled and compared with two commonly used algorithms, Affinity Propagation (AP) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). It has the fastest computational speed and can effectively classify the behaviors of massive unknown data from different ships. And it has good performance in capturing behavior boundaries, with the recognition accuracy up to 0.9. Then, the method was applied to analyze the operational effectively and fuel consumption. • An efficient and universal Meta-trajectory Variable Sliding Window (Meta-VSW) method for ship behavior recognition is proposed. • The computational speed and accuracy of the Meta-VSW method are analyzed in comparison with commonly used unsupervised algorithms. • The universality and effectiveness of the Meta-VSW method have been validated through the massive unknown data from two different ships. • The results of ship behavior recognition were applied to the analysis of operational efficiency and fuel consumption. [ABSTRACT FROM AUTHOR]

Some green technologies and operational measures are commonly used in fleet deployment and scheduling, including fuel switching, scrubbers, LNG fuels, and shore power, to reduce sulfur or carbon emissions. In fact, most green technologies only aim to reduce sulfur emissions, which may lead to an increase in carbon emissions and operating costs sometimes. Thus, this study addresses how to use the four green technologies wisely in different application environments to simultaneously achieve the three objectives of reducing sulfur and carbon emissions and operating costs. Accordingly, a multi-objective mixed-integer nonlinear programming model for the heterogeneous fleet deployment and scheduling is formulated, where four green technologies and different ship types with various ages, capacities, costs, fuel consumptions, and speeds are considered. Then, the non-dominated sorting genetic algorithm II (NSGA-II) is designed to obtain the Pareto front of the triple-objective formulation. The numerical experiments based on COSCO Shipping Lines show that: to reduce carbon and sulfur emissions, ships will gradually change from fuel switching and shore power to scrubbers and LNG fuels. Large ships and new ships should first use scrubbers and/or LNG fuels, and then small ships and old ships switch their green technology selection. Our sensitivity analyses show the change rule of costs and emissions with changing different parameters, which can provide a reference for the selection of green technology and the timing of equipment refitting. • An integrated fleet deployment and scheduling problem with heterogeneous ships considered is proposed. • Four green technologies are compared to explore application scenarios. • A novel multi-objective model is proposed to reduce sulfur and carbon emissions simultaneously. • Findings inform engineering guidance for implementing green technologies in fleet scheduling. [ABSTRACT FROM AUTHOR]

As efforts to conserve energy and reduce emissions in the global shipping industry intensify, people are constantly looking for new types of energy to replace traditional fuels. Ammonia is considered a novel energy source due to its potential for carbon-neutral shipping when produced from renewable energy sources and is extremely promising for the shipping industry. In this study, the fuel toxicity risk of ammonia-fueled ships under operating conditions was quantified for the first time. The assessment process adopts the traditional quantitative risk assessment (QRA) framework with accident probability calculations based on Bayesian theory, and the process of partial quantification of consequences also guides the delineation of safety zones. The results of the study suggest that ammonia-powered tugboats are at a higher risk of toxic accidents during operation than accidents at sea, but both are within the acceptable limits of current international maritime safety and environmental standards. Finally, safety measures and risk mitigation strategies are proposed based on the study results. This provides important support in safety risk analysis for the design, production and operation of subsequent ammonia fueled vessels and is of great theoretical and practical importance. In this study, we focus on accident probability and accident consequences. Through HAZOP analysis, we identified potential hazardous scenarios and calculated accident probabilities using Bayesian principles in combination with historical data from LNG ships. For the traditional QRA model, we worked on improving the quantification of toxicity consequences. For the first time, we combined the toxic gas concentration field obtained from computational fluid dynamics calculations with dose-response modeling, and applied it to the hazard assessment work for real operating scenarios of ammonia-fueled tugboats. The research shows that both the personal and societal risks of toxic hazards when operating an ammonia-powered tugboat are within the acceptable range under current international maritime safety and environmental standards. [Display omitted] • Quantitative analysis of toxicity risks when operating ammonia-powered tugboats. • Systematic investigation of hazard scenarios during tugboat operations. • Risk assessment of operational and marine accidents for individuals and society. • Safety measures and risk mitigation strategies for ammonia-powered tugboats. [ABSTRACT FROM AUTHOR]

Due to the high initial costs and the uncertain returns of investment, the efficient green technology in shipping industry, shore power (SP), is still in a developmental dark age, especially its installation at bulk terminals and ships. This study examines the factors influencing the SP installation for bulk terminals and ships, elucidates the impacting mechanisms of one party's installation on the investment returns of the other, and reveals the decisive role of fleet operations in the installation decisions of both parties and corresponding carbon emissions. Employing a dataset representing real-world shipping routes and cargo types, the ship-cargo matching and ships' port-of-calls within a planning horizon are optimized with the objective of maximizing the profit of tramp shipping business by taking SP installation and usage cost into account. Portside SP decision-making model is also conducted to estimate the revenue generated by portside SP usage, and guide portside installation decisions based on the cost-benefit ratio. Finally, recommendations are proposed for ship owner, port operator and government to collaboratively navigate through the dark age of SP development, supported by the further discussion regarding fuel-electricity price gap and port loading efficiency. • Factors influencing the shore power (SP) installation for bulk terminals and ships are listed. • Decisive role of fleet operations in the decisions of both parties are revealed. • Ship-cargo matching and ships' port-of-calls in a horizon are optimized. • Recommendations are proposed for ship owner, port operator to collaboratively navigate through SP dark age. [ABSTRACT FROM AUTHOR]

We propose integrating conversational user interfaces (CUIs) into Maritime Autonomous Surface Ships (MASS) to facilitate direct communication with other ships without human intervention. The aim is to enable other ships and shore stations to inquire about and discuss the autonomous vessel's status, decisions, and future intentions in natural language, offloading VHF radio conversations for MASS operators. Recent advancements in Natural Language Processing (NLP) technology show promise. This paper presents an experimental study conducted in a ship simulator using a functional prototype CUI system. While the CUI system was capable of providing coherent answers to questions, the results indicate that interacting with the system is less trustworthy than interacting with a human ship officer. Key challenges contributing to this included: (1) a missing connection between the CUI and an autonomy system, leading to inaccurate and incorrect answers, (2) speech that lacked intonation and human-like qualities, and (3) a perceived inability of the system to collaborate. The paper discusses several aspects of utilising CUI systems in a maritime shipping environment and concludes by proposing an approach to develop and implement them safely. • There have been significant advancements in Natural Language Processing (NLP). • Conversational User Interface (CUI) allowing MASS to communicate via radio with ships and shore stations, is proposed. • A prototype CUI for autonomous ships (MASS) was developed and tested in a ship simulator. • The CUI was found to be less trustworthy than communicating with a human ship officer. • NLP and CUIs have promising potential in the maritime domain and for MASS. [ABSTRACT FROM AUTHOR]

When ships sail in the sea, ship motion is unstable and stochasticity due to the ever-changing weather and marine conditions. Aiming to address the issue of low prediction accuracy in traditional prediction methods, short term predictions of ship pitch at 1 step, 3 steps, 6 steps, and 12 steps in advance are conducted, and the impact of input-output ratios on the prediction model is studied. The seven types of data of the marine environment and ship motion are taken as model inputs, with pitch as the model output. When predicting 1 step in advance, a prediction model based on LSTM and multi-head attention (LMA) is proposed. This model firstly uses LSTM layer to extract the temporal feature of the input sequence, and then uses the multi-head attention layer to assign larger weights to the key information to enhance prediction accuracy. When predicting 3 steps, 6 steps, and 12 steps in advance, as the input-output ratio increases, the length of the input sequence keeps growing. The model needs to sufficiently capture the local spatial characteristic and long-range dependency between multi-variable inputs, a deep prediction model based on Conv2d and LMA model is proposed. The model firstly uses two-dimensional convolution layer to extract local spatial feature of long sequences, and then through the attention layer, assign larger weights to temporal information and spatial information that are conducive to continuous prediction in order to enhance prediction accuracy. Research on the input-output ratio indicates that, as the number of predicted steps increases, the appropriate ratio continuously decreases, but the length of the input sequence keeps increasing. Finally, four evaluation metrics are used to evaluate the proposed algorithms on real ship data, and the prediction stability and accuracy of the LMA model and the LCMA model are also verified. • A prediction model based on LSTM and multi-head attention (LMA) is proposed to investigate the ship pitch prediction when predicting 1 step in advance. • A deep prediction model based on LSTM-Conv2d and multi-head attention (LCMA) is proposed to investigate the ship pitch prediction when predicting in advance for 3, 6, and 12 steps. • For different prediction lead times, the optimal input-output ratio for both the LMA and LCMA models is investigated. [ABSTRACT FROM AUTHOR]

The current analysis method for temporal-spatial characteristics of carbon emissions from port vessels overlooks the multidimensionality of the carbon emission process. To address this issue, this paper proposes a multidimensional approach for extracting characteristics of carbon emissions from vessels, aiming to enable multidimensional analysis of carbon emission information. Firstly, an inventory for calculating carbon emissions from port vessels was established using AIS data. Secondly, a method for constructing a three-dimensional tensor of carbon emissions from port vessels was proposed. This approach entails discretizing and encoding time, space, vessel type, and operating mode to generate a dataset with carbon emission labels. Following this, a three-dimensional tensor of carbon emissions was created using mapping rules. Thirdly, non-negative tensor factorization (NTF) was employed to decompose the tensor into multiple sub-vectors. The impact of tensor rank on the decomposition results was discussed, and an appropriate tensor rank value was determined, achieving multidimensional feature extraction of vessel carbon emissions. To validate the feasibility of this method, Tianjin Port was taken as the study area, and six major characteristics of vessel carbon emissions are obtained. This study reveals the temporal and spatial distribution of carbon emissions from different vessels under various operating modes. Compared to unidimensional analysis, this method provides more detailed multidimensional information on vessel carbon emissions, thus offering data support and decision-making basis for formulating energy-saving and emission reduction measures for ships and promoting green and sustainable development in the shipping industry. • A spatial-temporal analysis method of carbon emissions from ships in ports is proposed. • A three-dimensional ship carbon emission tensor construction method is developed. • The proposed model is verified through real case analysis and model comparison. [ABSTRACT FROM AUTHOR]

The paper proposes a fleet size and mix vehicle routing model to optimize the river-sea liner shipping service. A mixed integer linear programming model is adopted as a rapid screening tool for strategic fleet planning to determine the type and number of river-sea vessels to deal with the fluctuation of the demand over the year, guaranteeing the efficiency and effectiveness of the shipping service. Moreover, the model provides a better-informed decision on the logistic infrastructure design, demand allocation and fleet size and composition in scenarios of demand uncertainty. Thus, the quantified decision support model increases the service level of the shipping service, reducing the frequent mismatches between fleet capacity and demand. A verification step aiming to test the behaviour of the model solution in a realistic case study is presented to analyse the impact of the demand fluctuation on the fleet composition. In conclusion, river-sea vessels enable door-to-door transport via coastal and inland waterways without transhipment. This approach offers a reliable and cost-effective solution. The findings of the work may enhance the state of the art, expanding the narrow studies on river-sea liner shipping services and demonstrating the advantages of river-sea vessels. • The paper proposes a fleet size and mix vehicle routing model to optimize the river-sea liner shipping service. • A mixed integer linear programming is adopted as a tool for strategic fleet planning i.e. the type and number of vessels. • A decision support model increases the level of the shipping service reducing mismatches between fleet capacity and demand. • A verification step aiming to test the behaviour of the model solution in a realistic case study is presented. • The river-sea transport without transhipment was shown to be a sustainable, reliable, and cost-effective. [ABSTRACT FROM AUTHOR]

The marine navigation environment can become further complex when ships with different autonomy levels are introduced. To ensure navigation safety in such a mixed environment, advanced ship predictor type technologies are essential in aiding ship navigators to attain the highest levels of situation awareness (SA). Consequently, precise ship trajectory prediction, specifically within a short prediction horizon, should be included in such predictors as an indispensable component. This study introduces two methods for ship trajectory prediction on a local scale: the kinematic-based method and the Gate Recurrent Unit (GRU)-Pivot Point (PP)-based method. The first method utilizes kinematic motion models to predict a ship trajectory. In the second method, the GRU is trained to generate the predictions of related ship navigation states. The ship's PP is then extracted from these predicted states, subsequently providing the predicted ship trajectory. Both methods are validated using simulated maneuvering exercises to assess their effectiveness, with a prediction horizon of 90 s. The results show that the kinematic-based method excels in the predictions during ship's stable stages, i.e., steady-state conditions. Meanwhile, the GRU-PP-based method exhibits robust performances in cases when new rudder orders are executed, i.e., transient conditions. It is considered that these applications can provide significant benefits in maritime SA in present and future ship navigation. • Two prediction methods (kinematic-based and GRU-PP-based) are used to predict ship trajectories in a local scale. • The CAA, CMM, CTRA, along with KF-based techniques, are used to provide required ship states. • Kinematic-based predictions excel in stable maneuvers. • GRU-PP-based predictions prove robust with new rudder orders. • Both methods contribute to enhance maritime situation awareness. [ABSTRACT FROM AUTHOR]

To reduce emissions, shipping companies are deploying eco-friendly ships, like those powered by LNG or methanol. Unlike traditional fuels, LNG bunkering is available at a limited number of ports. At the same time, tramp or bulk ships have fewer ports of call on their shipping lines, which may necessitate LNG-fueled ships sailing to alternative bunkering ports for refueling. The selection of bunkering ports impacts the operating costs of shipping lines due to differences in fuel prices and geographical locations. Moreover, it must factor in ship speed limits to meet loading and unloading schedules. To optimize the shipping route, speeds, and bunkering plan for LNG-fueled tramp ships, we develop an arc-based mixed-integer linear programming model with the objective of minimizing total operation costs. Subsequently, we propose a leg-based formulation based on the predetermined port call orders. Finally, case studies are conducted to demonstrate the effectiveness and efficiency of the models. Experimental results indicate that the bunkering mode with alternative bunkering ports effectively reduces total operating costs without altering the total voyage time, particularly with substantial differences in fuel prices between ports. • A novel optimization problem for the LNG-fueled tramp shipping line is introduced. • Route, speed, and bunkering for the tramp shipping line are jointly optimized. • An arc-based and a leg-based mixed-integer programming model are proposed. • Case studies using instances derived from a tramp shipping line are conducted. [ABSTRACT FROM AUTHOR]

Fibre reinforced polymer (FRP) panels are widely employed in ship and marine structures due to their better specific stiffness and strength when compared to metals. Therefore, an accurate estimation of the dynamic behaviour of such panels is necessary from its operational and serviceability point of view. In this study, the influence of exponential window functions and the associated decay rates on the damped dynamic responses of a Glass Fibre Reinforced Plastic (GFRP) plate has been investigated experimentally, using impact hammer testing. In a standard experimental modal testing of a structure, exponential window functions are commonly employed to prevent spectral leakage of the signal and achieve a good signal-to-noise ratio. However, this exercise modifies the Frequency Response Functions (FRFs) and introduces numerical damping into the measurements. A novel technique involving a Finite Element Model Updating using Inverse Eigen-Sensitivity algorithm is employed to estimate the unbiased FRFs and identify the induced numerical damping. The model updating is carried out in two stages: updating stiffness parameters first, followed by updating damping parameters in the second stage. The updating algorithm and the two-stage strategy adopted therein is found to be effective in identifying the induced numerical damping along with the unbiased FRF expressions. • Dynamic properties of a Glass Fibre Reinforced Plate were investigated experimentally. • Influence of exponential windows and related decay rates on damping estimates were observed. • Unbiased damped responses were extracted using finite element model updating. • Stiffness and damping parameters were updated using the Inverse Eigen-sensitivity method. [ABSTRACT FROM AUTHOR]

Solving uncertainty-related tasks such as accident investigation, risk analysis, reliability prediction, and port sustainability analysis has been a key focus in the maritime industry. Over the years, conventional tools such as fault tree analysis, event tree analysis, and statistical-driven methods have been used for accident, risk, and reliability analyses in the maritime industry, which is considered weak. For this reason, the application of the Bayesian network (BN) is attracting growing interest in the maritime industry. A comprehensive review of the past 20 years on the application of BN in the maritime industry is provided. To conduct the review, relevant publications involving 115 journal articles focusing on BN application in the maritime industry were extracted. Based on the extracted studies, a classification framework involving application areas, data sources, operational waters, geographical locations, and model validation was proposed. The proposed framework was used to analyze the literature in detail to reveal the necessary findings and extract possible future research areas. The findings of the research revealed an increasing trend in the application of BN in the maritime industry in diverse areas of risk assessment, human error analysis, reliability estimation, and accident investigation. Furthermore, issues emanating from the systematic review analysis are discussed. • To provide a comprehensive review of BN application in the maritime industry. • To analyze relevant scholarly articles with a focus on BN application in the maritime sector from 2000 to 2022. • To identify various methods used in validating BN models in the maritime industry. • To discuss the critical issues obtained through the systematic review analysis. • To suggest possible areas of interest for future research work. [ABSTRACT FROM AUTHOR]

With the development of intelligent ships, ships with multiple driving modes will exist in maritime traffic scenarios. The traditional methods of ship regulation, which rely on factors such as ship size, speed, and heading to identify navigation risks, are not able to accurately assess the navigation risks between ships of different autonomy levels in the context of mixed maritime traffic. The impact of a ship's autonomy level on navigation risks should also be taken into consideration. Therefore, this paper proposes a key risk ship identification method that considers the autonomy level of ships to assist in ship regulation. The method utilizes the OPTICS algorithm to cluster areas with high ship density and employs an improved Potential Risk Ship Domain (PRSD) approach to determine the Potential Collision Risk (PCR) for different autonomy-level ships, thereby constructing a directed complex network. The centrality characteristics of the network are used to identify key risk ships. Through empirical validation, the proposed method is shown to effectively determine the importance ranking of different autonomy-level ships in maritime mixed traffic scenarios. This method can provide support for regulatory agencies in the supervision and scheduling of ships in mixed scenarios. • A key risk ship identification model in maritime mixed traffic scenarios is proposed. • Improved PRSD to calculate the conflict relationship of ships in mixed scenarios. • Directed complex networks are used to identify critical risk ships in mixed scenarios. [ABSTRACT FROM AUTHOR]

Containership delays affect arrival times of subsequent ports and various shipping routes, putting marine logistics at risk of disruption. Analyzing containership delay propagation risks and understanding the underlying propagation mechanism are critical for optimizing port operations and minimizing disruptions in container shipping networks. To address this challenge, this study applies causality analysis to the container shipping network to investigate the propagation risks of containership delays. A novel dimensionality reduction algorithm, in which the concept of effective transfer entropy is introduced, is employed to reduce errors and quantify the containership delay propagation risk causality. By estimating the effective multivariate transfer entropy, containership delay propagation risk networks are established for three regions: global region, East Asia, and Southeast Asia, where container ports serve as nodes and the connections represent the delay propagation risk relationships among ports. The quantitative analysis results show that each port affects 3 other ports on average and is affected by the delay propagation risks in 3 ports on average. The network density in Southeast Asian region is 0.36, compared with 0.14 and 0.13 in the global and East Asian regions, respectively. This implies that the propagation risks of containership delays will be more intensive in Southeast Asian region. Non-key ports are mainly affected unidirectionally by the delay propagation risks from key ports. To further comprehend containership delay propagation risks, numerical simulations are conducted using the network Susceptible Infected Recovered model. The results indicate that ports with high outdegrees exhibit higher risks of causing delay propagation and a wider range of influence. To improve the stability of ports operations, "infection rate" and "recovery rate" should be reduced and increased, respectively. This study proposes a novel method to construct shipping network and fills the gap in research on delay propagation risks in the field of shipping and provides valuable insights into the underlying mechanisms in containership delay propagation, assisting maritime managers in making informed decisions and implementing effective governance measures to reduce containership delay propagation risks. •This study offers a new method based on causality analysis for shipping network construction. •There is no clear correlation between a port's throughput and its risk level in delay propagation. •Non-key ports tend to receive delay propagation risks from key ports unidirectionally. •The degree in the containership delay propagation risk network measures delay propagation risks. [ABSTRACT FROM AUTHOR]

Wave storms are often responsible for coastal damages, disrupt maritime activities and harbours' operability, and present a risk to human safety. Furthermore, linked to the projected scenario of sea level rising due to climate changes, coastal regions may gain in their vulnerability against wave storms. Understanding the frequency, intensity, and further characteristics of wave storm events is crucial for effective risk management, optimal design and reinforcing of coastal infrastructure, ensuring the safety of maritime operations and increasing community awareness. Thus, this paper presents a long-term characterisation of wave storm events that occurred in the Black Sea between 1979 and 2020. The study is focusing on 18 strategic locations of significant marine activities. The wave storm events at each location were classified based on their intensity. Thus, the inter-annual trend and variability in wave storm intensity, duration and direction were determined. The results showed that Turkish harbours and the Novorossiysk and Sevastopol harbours are the most exposed to catastrophic storm events. Those catastrophic storms are mainly occurring from two different directions. Furthermore, a significant increasing trend in maximum storm wave height was observed at Inebolu and off the Istanbul strait. • The long-term wave storm regime at the most operational harbours was assessed. • Turkish harbours are more exposed to catastrophic storm events. • Catastrophic storms are occurring from two different directions except at Odesa. • Variability in storm duration is more pronounced than other storm characteristics. • A significant increasing trend in SWH _max is noticed off the Istanbul strait. [ABSTRACT FROM AUTHOR]

Ship collision risk prediction is vital for maritime traffic surveillance, which determines if there is a sustainable and efficient development of the shipping industry. In order to solve the problem of ship collision risk prediction, this study proposes a regional ship collision risk prediction model based on the Encoder-decoder LSTM neural network. Firstly, the regional ship collision risk is quantified by aggregation density-based method, and the spatial-temporal collision risk matrix is synthesized by grid processing method. Then, a key aspect of the model involves employing the CNN encoder to capture spatial risk features and compress the collision risk structure scale. The LSTM neural network as the middle layer of the model is adopted to predict the risk with spatial-temporal characteristics. Finally, the CNN decoder processes the prediction results by deconvolution, which restore the original dimension of data. In our study, we conducted numerous experiments using AIS data from Zhoushan Port of Ningbo. The results show that the proposed model has high accuracy in predicting the spatial-temporal collision risk, which the accuracy rate reached 97.9%. The proposed approach of regional ship collision prediction is efficient and accurate, which can provide decision support for intelligent maritime traffic surveillance. • This paper proposes a regional ship collision risk prediction model based on the Encoder-decoder LSTM neural network. • The method considers the spatial-temporal characteristics of ship collision risk. • The combination of CNN based encoder-decoder framework and LSTM parallelizes the input with fewer parameters and faster training speed. [ABSTRACT FROM AUTHOR]

Remote passive detection of vessels within oceanic settings is paramount in bolstering port security and safeguarding coastal and offshore activities. In this context, our study introduces a sophisticated, complex network-based detector tailored for ship detection in maritime environments. Initially, we formulate a weighted transfer network, facilitating the seamless mapping of time series data onto this network structure. To enhance the efficacy of our approach across diverse temporal scales, we introduce the Relative Multiscale Weighted Link Entropy method. Furthermore, our analysis delves into the distributional attributes of the Relative Multiscale Weighted Link Entropy values, contrasting ambient noise data with and without ship-generated noise in the context of the South China Sea. Then, this paper presents a Neyman-Pearson criterion-based relative multiscale weighted link entropy detector for ship signal detection in the marine environment. The results show that the relative multiscale weighted link entropy method, compared with the narrowband energy detection method, has a 3.3 dB SNR gain under the same conditions. It can detect ships 20 km away in a marine environment. In summary, juxtaposed with conventional methods such as narrowband energy detection, our proposed methodology negates the reliance on pre-established target data and demonstrates superior detection performance at diminished SNRs. [ABSTRACT FROM AUTHOR]

Ship recycling is the complex and systematic engineering procedure of disassembling maritime vessels with the primary objective of extracting and recovering valuable materials. The environmentally responsible and safe dismantling of end-of-life ships is currently a significant concern and a formidable undertaking. Turkey stands out as one of the four key nations in global ship recycling, notably centered in the Aliaga region. This study concentrates on conducting a thorough strategic analysis of the ship recycling industry, emphasizing its environmental impact by using the real case of the Aliaga region. The research utilized a combined SWOT-TOWS-ANP triangle method to identify the best possible strategy for the Aliaga ship recycling facility, with a specific focus on the environmental implications. When examining both the environmental aspect and the overall subfactors, based on their respective weighted averages, it can be observed that the most significant sub-factor is "S1 Recycling of ships in conformity with international regulations." The strategy with the highest average weight is the "SO1 Attempting to increase market share in the European ship recycling industry" with 0.15255. From the environmental perspective, "WT2 The aim of highlighting the economic and environmental contributions of the ship recycling industry to seek further incentives by garnering support from the public and government officials" has been the most significant strategy to be followed. The analysis results are expected to guide industry stakeholders in establishing a safe, reliable, cleaner, and finally more sustainable ship recycling industry. • SWOT-TOWS-ANP triangle methodology has been employed in this study. • The status of Aliaga ship recycling facility is presented from an environmental perspective, and strategies are identified. • The most crucial strategy has been determined as focusing on the European ship recycling market. • The key environmental strategy is to convey the adherence to the international standards of the European customers. [ABSTRACT FROM AUTHOR]

In an effort to cope with the global warming environment and oil prices, the development of energy-saving ships is greatly anticipated by the shipping industry. Air lubrication is one of the promising techniques for reducing the frictional resistance of a ship and improving the fuel efficiency. In this study, air layer drag reduction in the frictional resistance under a flat plate was investigated experimentally in the towing tank. The shape of air layers was observed, and the changes of resistance were measured. The results show that air layer drag reduction with bottom cavity is useful in reducing the frictional resistance at specific conditions of air injection. Then, resistance and self-propulsion test of a model ship based on a 95,000 DWT bulk carrier was conducted in the towing tank using method of air layer drag reduction with bottom cavity. Bottom air layer were observed and the expected power savings were estimated, the results indicated that the delivered power can be reach 15% at the full-scale ship without considering the consumption of air injection. • Air layer drag reduction in the frictional resistance under a flat plate was investigated experimentally in the towing tank. • Resistance and self-propulsion test of a model ship was conducted in the towing tank using method of air layer drag reduction with bottom cavity. [ABSTRACT FROM AUTHOR]

Iron Ore Fines (IOF), which is a bulk mineral cargo, exhibits an inherent high level of risk during marine transportation due to its potential to liquefy. With nine marine accidents since 2007 being directly linked to the shifting of IOF cargoes, which resulted in the deaths of twenty-nine seafarers, the danger for shippers is indisputable. This study aims to review the behaviour of IOF during marine transportation, by analysing the factors that lead to geotechnical related failures. It suggests that due to suffusion caused by the dynamic motions of the vessel, the top layer of a cargo of IOF may end up containing a large percentage of fine-grained particles. This would seemingly result in a weak zone near the surface, with the possibility of being fully saturated, and, therefore, increasing the potential for liquefaction or reducing the factor of safety against a slope type failure. The study will also analyse and discuss the reasons behind the past and recent listing and capsizing incidents involving bulk mineral carriers. The final outcome of this study will be the proposal of some possible solutions that may be implemented and recommendations for further studies, in order to reduce the likelihood of cargo shift occurring. • Recent marine accidents attributed to cargo shift have been investigated. • The variance in material properties of mined Iron Ore Fines (IOF) is explored. • Possible failure modes leading to cargo shift have been analysed. • Suffusion has been seen to cause migration of moisture and fine IOF particles. • Potential solutions to prevent cargo shift have been recommended and discussed. [ABSTRACT FROM AUTHOR]

Abstract To solve the auto-berthing control problem for underactuated marine ships subject to unknown dynamics and external disturbances under the circumstance of the restricted waters in port, firstly, an additional control method is adopted to solve the underacruated problem. Secondly, a robust neural network (NN) adaptive approach based on the navigation dynamic deep-rooted information (DRI) is proposed to reconstruct the lumped uncertainties caused by unknown ship dynamics and external disturbances. Meanwhile, the dynamic surface control (DSC) and the minimum learning parameter (MLP) techniques are used to reduce the computational load of the adaptive NN control scheme. Considering the input saturation effects of control actuator (rudder, propeller, etc) and the coupling characteristics of uncertainties, this approach integrates neural network weights, approximation errors and external disturbance term as the composite uncertain parameters, which is estimated online by parameter adaptive technique. Finally, simulations are carried out on an underactuated model ship to verify the effectiveness of the proposed auto-berthing control scheme. [ABSTRACT FROM AUTHOR]

Abstract Of the many factors that influence navigational safety, navigable flow conditions are one of the most important. Therefore, the development of a navigation risk assessment model based on flow conditions is of considerable importance for improving the safety of shipping. In this paper, the influence of flow on shipping is considered. Four determination indices for navigational safety, including water surface slope, backflow, velocity, and depth, and a five-level classification of navigation risk assessment are proposed. To evaluate quantitatively the influence of flow conditions on shipping, a navigation risk assessment model is established using gray relational analysis. In a case study of navigation in the river between the Three Gorges Dam and the Gezhouba Dam, a two-dimensional hydrodynamic model for the study reach is established to simulate four types of flow field (i.e., under Three Gorges Dam discharge of 7,000, 10,000, 20,000, and 30,000 m3/s). Shipping risk maps for specific ship types under different water regimes were drawn in accordance with the simulated results based on the navigation risk assessment model. The research results not only promote effective precise management of navigation in the study reach but they also provide a reference for quantitative research of navigational safety. Highlights • A navigation risk assessment method based on flow conditions was given. • Water surface slope, backflow, velocity, and depth were considered in the method. • Two-dimensional hydrodynamic model simulated real-time flow field. • Shipping risk maps for specific ship types in different flow conditions were obtained. • The method can promote precise management of navigation and navigational safety. [ABSTRACT FROM AUTHOR]

Abstract Manoeuvring performance of a ship in actual sea is significantly different from that in calm water due to wave loads. It is necessary to estimate the ship's manoeuvrabilities in waves at the early design stage for its safe operations. Several theoretical approaches have been attempted to estimate the manoeuvring performance of ships with considerations of wave loads in recent decades, but there are insufficient model test data for the validation of numerical results. In this study, free-running model tests are systematically performed for well-known KVLCC2 tanker. Model tests are carried out in a square basin, regular waves are generated with the variations of directions, lengths, and heights. In particular the wave lengths are selected at around the ship length. The number of propeller revolution is determined for the model speed corresponded to full-scale service speed in calm water, that rps is fixed in all runs. Therefore the loss of approach speed is observed depending on the encountered wave conditions. Encountered wave profiles are estimated by using relative wave heights data measured on the side of deck in real time, the rudder is always deflected at the moments when the wave crest passes on the midship of the model. The timing of rudder deflection has little influence on the low frequency manoeuvring motions of the model ship. Drifting distances of turning trajectories are relatively large when the wave lengths are below the ship length, and relative drifting angles between wave propagation direction and trajectory drifting direction are largest when the wave lengths equal to the ship length. Drifting distances and relative drifting angles increase with increasing wave heights. Although the trajectories at the early stage of turns are varied depending on encountered waves, drifting distance and angles during steady turns are similar if the wave height and length are identical. Based on the present test results, it is appropriate that the trajectory drifting distances and angles are defined as the magnitude and direction of a vector between two positions with the headings of 360° and 720°. Finally, the effects of velocity fluctuations on the trajectory drifts are analyzed in some cases. Highlights • Free-running turning circle tests for a KVLCC2 model ship were performed in multi-directional regular waves. • The effects of regular wave lengths, heights and directions on the turning trajectories are systematically investigated. • Drifting distance, angle are represented by magnitude, direction of a vector between two positions with heading of 360, 720°. • Drifting distances and angles of turning trajectory are sensitively affected by the fluctuations of velocity components. [ABSTRACT FROM AUTHOR]

Abstract Parametric roll is an important stability failure mode in the second generation intact stability criteria, which are currently under development by International Maritime Organization (IMO). The roll restoring arm, which is one of the dominating factors for the prediction of parametric roll, should be calculated preciously. In this paper, firstly, the effects of the radiation and diffraction forces on the roll restoring arm (GZ R&D) under different heeling angles and wave amplitudes are studied based on the body exact method, in which all the hydrodynamic forces are calculated by integrating wave pressure up to the wave surface. Secondly, the effects of radiation and diffraction forces on parametric roll are studied by the direct simulation using a coupled heave-roll-pitch model based on the three dimensional mixed source method. Finally, by taking one passenger ship as an example, the calculation accuracy of the body exact method is compared with the commonly used body linear method. The results show that the effect of radiation and diffraction forces on righting arm variation has nonlinear relationships with heeling angles and wave amplitudes, and the body exact method can provide more accurate prediction results for parametric roll in head waves than the body linear method. Therefore, the nonlinear effects of instantaneous roll angles and wave amplitudes on GZ R&D variation in waves should be considered for the prediction of parametric roll in head waves. [ABSTRACT FROM AUTHOR]

Ports are essential facilities for the functioning of our globalised economy, as well as for the territories they supply. Monitoring port activity is therefore a relevant way of assessing the economic intensity they generate, and enabling regional analyses of their relative weight, their areas of activity and their development trajectories. However, comparative analysis of port activity is hampered by the variable quality and uneven distribution of the data produced by port authorities, which is far from being homogeneous, which makes comparative approaches difficult. In this paper, using vessel positioning data obtained through the Automatic Identification System (AIS), we propose a method for the extraction, quantification and qualification of port calls. We apply this method to a dataset in the Caribbean, covering the whole of 2019. Our method was able to describe the activity of 101 ports (in the sense of port areas) in the Lesser Antilles, by identifying 171,626 calls and their duration, by 5,907 ships of various type, size, age or flag. A comparison with the statistics published by a number of reference ports authorities and by the UNCTAD (United Nations Conference on Trade and Development) shows that the median difference of computed port calls is about 11%, thus validating the proposed approach. By qualifying the type of vessel that passes through port waters, according to its characteristics, this approach based on the number of calls makes it possible to observe the capabilities and the infrastructural quality deployed in the ports in terms of vessels, and, stemming from it, to develop the hypothesis of port vulnerability, that is a perspective for our future work. • A method to extract port calls from AIS data has been developed and implemented. • A careful survey of all quays in the Lesser Antilles allows a great degree of details. • Harbour activity can be characterised by the diversity in the types of vessel calling. • Port call data such as duration or ship age and tonnage enhance traffic understanding. • The volume of computed port calls is validated by comparison against official data. [ABSTRACT FROM AUTHOR]

Automatic Identification System (AIS) plays an essential role in maritime surveillance and the global shipping industry. Thanks to Artificial Intelligence (AI) advances, almost all marine-related application needs can be realized by mining AIS Big Data. Most of the research, however, is still developing an AIS-based AI model for a specific task. When facing complex maritime applications, a large number of AI models are required. How to deploy these single-function AI models has become extremely challenging. Considering the distinctive characteristics of AIS data, this paper develops an AIS-based deep learning model for forecasting, classification, anomaly detection, and imputation in the maritime industry. This paper uses hierarchical blocks to ensure the expressive power and generalization ability, employs signal decomposition methods and processes the decomposed data like image processing. Finally, experiments have been conducted in both publicly available datasets and the dataset established in this paper. The performance of state-of-the-art models has been compared with the proposed model and the results shows that our approach can yield optimal results, including the optimal mean squared error in the forecasting task and imputation task, the accuracy in the anomaly detection task, and the category accuracy in the classification task, on our curated dataset. • We created a labeled dataset on vessel traffic in the upper reaches of the Yangtze River by comparing data through standard screening, trajectory tracing, and manual verification. • To minimize redundancy in AIS sequence information, we employ a whitening extraction method to capture vessel behavior modes. • This paper propose a "Meta" model to cater to different task requirements concerning AIS data. [ABSTRACT FROM AUTHOR]

Ship manoeuvrability of Maritime Autonomous Surface Ships (MASS) revolutionise the maritime industry. However, this paradigm shift necessitates the advancement of manoeuvring control models to meet the complex demands of autonomous navigation. This paper addresses the need for an improved manoeuvring control model for MASS, particularly concerning path planning and tracking in the presence of wave loads. The paper establishes a comprehensive mathematical model for ship manoeuvring, considering forces acting on the ship's hull, propellers, rudders, and wave loads. A time optimisation model using a spatial reformulation approach is introduced. A nonlinear Model Predictive Control (MPC) model is presented for path planning and tracking, with a case study investigating the influence of wave load and comparing two control strategies. 10%–20% of time consumption increases if the wave load exists. This research bridges the gap in existing literature by incorporating wave loads into MPC-based control models for MASS. The findings shed light on the significance of wave loads in ship manoeuvring and provide valuable insights into effective control strategies for autonomous vessels operating in real-world sea conditions. • Introducing wave load to MPC-based path planning and trajectory tracking. • Analysing impact of wave loads to manoeuvring time. • Comparing the pros and cons of two manoeuvring control strategies under wave. [ABSTRACT FROM AUTHOR]

Anchoring is one of the most repeated routine operations concerning many maritime transportation vessel types. The operation poses significant risks due to the nature of the work and could cause damage to the vessel, commodity, or environment. This paper introduces a conceptual framework to analyse the risk of anchoring operations by adopting a rule-based Bayesian Network (BN) under evidential reasoning (ER) modelling. Failure Mode Effects and Criticality Analysis (FMECA) is used to gain detailed insight into the operational hazards of anchoring, while rule-based BN extended ER is capable of dealing with the weaknesses of FMECA by evaluating the importance of risks. In this context, Failure Mode 5.2 (Ship rudder/thruster/steering system failure) with an RPN of 46.42 was determined as the most critical danger for anchoring operations on ships. The research outcomes will provide valuable insight to the maritime industry, in particular ship owners, ship crew, safety managers, and superintendents, to retain a high level of safety at the operational level and minimize anchoring-related accidents onboard ships. • A comprehensive operational risk assessment is conducted for marine vessels. • FMECA is used for evaluating the failures of the anchoring operation. • ER and RBN methods are used to support the outputs of FMECA. [ABSTRACT FROM AUTHOR]

This paper introduces a vessel voyage schedule planning problem for maritime ore transportation arising from a real-world project in Boffa, Guinea. In this problem, delay of vessel arrivals at the loading port often occurs which interrupts the ore loading operation and further impacts the yearly throughput of ore transportation, which dues to variable weather conditions at sea and changing loading/discharging time at ports. Thus, factors regarding the uncertainty of vessel voyages must be considered in order to ensure continuous vessel arrivals and uninterrupted loading operations at the original port, leading to an increase in the overall productivity of the project. And at the same time, since every voyage entails an increase in costs, cost control is necessary to be accounted for, so there is also a need to consider minimizing operating costs. In this study, to address the uncertainty in voyage duration, a chance-constrained model is introduced to obtain sailing schedules as well as the associated costs within a given confidence level. And then, for the multi-objective requirements, we develop a multi-objective planning model and the actual system operating capacity limitation constraints are considered. After that, considering that the multi-objective model cannot be solved by the solver directly, the weighting method is introduced. Afterwards, since the direct solving of the model is rather time-consuming, valid cuts are designed to accelerate it. Finally, considering that the Pareto solutions of multi-objective problems are numerous, a nondominated sorting genetic algorithm II (NSGA-II) is launched with the aim of providing more solutions to the manager in a shorter time. In terms of experiments, each of the aforementioned issues and proposed remedies are verified. The results confirm the effectiveness of the valid cuts and NSGA-II, and demonstrate that the obtained vessel voyage schedule is more robust and outperforms manual decisions. • Introduce a real-world vessel voyage planning problem with uncertain conditions. • Establish a bi-objective mixed integer linear programming model and a chance-constrained sub-model. • Develop a series of valid cuts and a nondominated sorting genetic algorithm II. • Verify the performance of the proposed model and algorithm via computational experiments. • Assist managers in making reliable voyage plans. [ABSTRACT FROM AUTHOR]

Ensuring the safety of ship navigation is essential to support the sustainable development of the shipping industry, and maritime safety analysis has thus received much attention in the field of shipping. Traditionally, the safety evaluation is mostly performed with the help of accident data, which is thus limited by the insufficient amount of accident data and affects the evaluation effect. In order to overcome this limitation, a ship traffic conflict model is established, which consists of three sub-models: multi-risk ship domain model, ship traffic conflict preliminary screening model and ship traffic conflict detection model. The multi-risk ship domain model is the key foundation, combining the traditional ship domain model with the water traffic conflict severity model to fully consider the severity of ship traffic conflict. To validate the proposed model, three ships in the study waters are selected as the own ships, and the severity of traffic conflicts formed with their target ships within a certain conflict time range are analyzed separately. Then, the conflict datasets obtained from the training of the ship traffic conflict model are used to analyze the traffic conflict situation in the heavily trafficked waters near Tianjin Port. Specifically, the spatial distribution characteristics are obtained using the kernel density estimation (KDE) method and the water grid method, and the temporal distribution characteristics are verified with the help of the accident statistics method. The proposed ship conflict model can provide technical support for further research in the field of ship traffic conflict, and the experimental analysis results are of great significance for strengthening maritime traffic supervision. • A ship traffic conflict model combining the traditional ship domain with the water traffic conflict severity was established. • The application value of the obtained ship traffic conflict datasets with different severity levels were clarified. • The severity and distribution of traffic conflicts in the study waters were analyzed from a spatial and temporal perspective. [ABSTRACT FROM AUTHOR]

With the development of the shipping industry, the impact of hydrodynamic interaction between two or more ships on navigational safety has received widespread attention. However, limited research has been conducted on multi-ships moving in waves with forward speed due to the increased complexity of hydrodynamic interactions caused by speed. Therefore, this paper establishes a numerical model of multi-ship hydrodynamics under the wave impact based on the time-domain Rankine source panel method. The hydrodynamic performance and motion characteristics of ships advancing in waves are investigated. An artificial damping layer is applied to free surface boundary conditions to absorb reflected waves. The fourth-order Runge-Kutta method is used for time integration to solve the motion of the ship. The hydrodynamic and motion response of the classical Wigley III ship model are calculated at each wavelength, and the simulation results are compared with other calculations to verify the reasonable reliability of the numerical method employed in this study. On this basis, sensitivity analysis of different parameters is conducted to reveal the influence of various parameters such as speed, wave direction, transverse distance, and water depth on the motion response of the Wigley III ship model in waves. • Developed a potential flow program to deal with the seakeeping problem of the multi-ships moving in waves with forward speed. • Quantify the effect of the hydrodynamic interaction between two ships moving in waves. • Revealed the influence of various parameters on the motion response of the multi-ships moving in waves. [ABSTRACT FROM AUTHOR]

Real-time prediction of tidal level is vital for on-the-spot activities such as marine transportation and ocean surveys. Aiming at the complex characteristics of nonlinearity, time-varying dynamics, and uncertainty generated by celestial bodies' movements and influenced by geographical as well as hydrometeorological factors, an adaptive real-time modular tidal level prediction mechanism is proposed based on empirical mode decomposition (EMD) and Lipschitz quotients method. An adaptive modular tidal level prediction mechanism is proposed by combining the harmonic analysis method with a variable structure neural network. The order of time series decomposition and the prediction input model order of the neural network are automatically determined based on EMD and the Lipschitz quotients method, respectively. The adaptability of the prediction mechanism is further enhanced with the network dimension, hidden units' locations, and connecting parameters of the variable neural network being online adjusted in a sequential learning scheme. While the extraction of harmonic components alleviates the difficulty in prediction, the multi-resolution decomposition of residual series provides further insight into the time-varying tide dynamics caused by environmental disturbances, thus enabling precise predictions for tidal levels. The feasibility and effectiveness of the proposed adaptive modular tidal prediction mechanism are demonstrated based on the real-measured tidal level data. • An adaptive modular tidal prediction mechanism is proposed by combining the harmonic analysis with variable network. • The orders of decomposition and prediction model are adaptively determined by EMD and Lipschitz quotients method. • The adaptive parameter determination alleviates the burden of manual tuning operation. • The processing speed is improved by the sequential learning scheme based on a sliding data window. [ABSTRACT FROM AUTHOR]

This paper presents a review of the state-of-the-art Automatic Identification System (AIS) data-driven studies and applications in effort to control the carbon footprint from maritime transportation, followed by the prospects of decarbonization measures that demand AIS complement. Pertaining critical impact from global anthropogenic emissions, AIS data serves as the primary source of information for the assessment of the ships emission inventories to study their environmental impact against ambient air quality and evoke awareness from the maritime sector. Wide range of information types, transmission coverage and frequency made AIS data outshines other maritime information services in the provision of consistent serial information of ship activities that offer tedious studies of correlations between parameters. As such, it can potentially be further exploited into various approaches of analytical applications, such as Estimated Time of Arrival (ETA) predictions, emission projections, and decision supporting system for ships. Furthermore, AIS data features are flexible for integration with other data sources to achieve a flawless system, such as weather routing and smart inland shipping. Ultimately, the outcomes of these studies aim to contribute piecewise to the fulfilment of ideal concepts – 'green' and 'smart' in maritime sector, by efficient processes and minimum waste of energy. [Display omitted] • Review on state-of-the-art AIS data-driven studies in view of decarbonization. • Overview of the role of AIS data to support decarbonization strategies by IMO. • AIS data enables activity-based emission estimation and favours data visualization. • AIS data facilitates transformation to "green" and "smart" maritime transportation. • Challenges of AIS data handling to fulfil IMO decarbonization goals are discussed. [ABSTRACT FROM AUTHOR]

The complexity and diversity of ship traffic conditions have burdened maritime safety because of the increasing maritime transportation, especially in the waters around busy ports. A shipping route modelling approach around port areas is presented for maritime traffic identification and monitoring in areas characterized by the confluence of routes approaching the port entrance. In this study, a clustering-based approach is adopted, which involves identifying ship trajectories of different motion patterns corresponding to in-port and out-port ship routes based on Principal Component Analysis and K-mean clustering algorithms. Subsequently, the route centrelines are estimated for each ship route using Soft Dynamic Time Wrapping barycentre averaging algorithm from the near centre trajectory defined by Dynamic Time Wrapping. Afterwards, the route boundaries are generated with the optimal distribution of conjunction points at observation lines along the centrelines. Finally, a case study of ship traffic around Leixões port on the Portugal coast indicates that the proposed framework is practical and the ship route model facilitates the prudent selection of shipping routes for vessels, ensuring maritime traffic safety and promoting effective maritime supervision. • A shipping route modelling approach around port areas is presented for maritime surface ship transportation. • The clustering approach involves identifying trajectories of different sub-routes in in-port and out-port datasets. • The route centreline is generated using a Soft Dynamic Time Wrapping barycentre averaging algorithm. • The route boundaries are generated with the optimal distribution of points at observation lines along the centreline. • A case is studied constructing a shipping routes around Leixões port on the Portugal coast. [ABSTRACT FROM AUTHOR]

Abstract Two approaches, namely, the crossing-line and the Monte Carlo methods, for navigation safety assessment in approaching channel based on Automatic Identification System (AIS) are proposed. The crossing-line method evaluated the ship grounding probability using the probability density functions of the ship track parameters from single-crossing-line or multi-crossing-lines. The Monte Carlo method considers the causation of the track parameters. Such a causation was realized using a regression algorithm by which the relationships among the ship track parameters can be established through a crossing-line with the factors corresponding to the previous crossing-lines. With the relationships, Monte Carlo simulation of ship trajectory evaluation was then applied to assess the grounding frequency. The inbound ship behaviors of Kaohsiung Harbor are analyzed to quantitatively assess the grounding risk on entrance and inner traffic lane. The results of the grounding probability acquired from both methods are consistent with each other. The grounding probability of the starboard boundary is higher than the port one. The proposed methods could be further applied to risk assessment and will facilitate navigational safety in congested waterways and ports. Highlights • The crossing-line and the Monte Carlo methods for navigation safety assessment based on AIS data are proposed. • The probability of potential hazard grounding and the probability of grounding in the approaching channel is evaluated. • The ship behaviors of the Kaohsiung Harbour are analyzed to quantitatively assess the grounding risk on entrance and lane. • The causation of the AIS track data is considered in Monte Carlo simulation to assess the grounding frequency. • A two-variate joint distribution models the conditional probability of grounding with lateral position and heading angle. [ABSTRACT FROM AUTHOR]

Abstract The shipping industry has valued wind energy in the last decades due to the global increasingly severe environmental situation. The sail-assisted vessels have employed generous types of sails to exploit wind energy and reduce the energy consumption. To meet the ever-increasing energy saving requirements of sail-assisted vessels, this paper investigates the hard sail design optimization and energy efficiency enhancement problems for sail-assisted vessel. By using parametric section airfoil parametrization (PARSEC) method and particle swarm optimization algorithm, we fulfill the optimization of the wingsail aerodynamic design. The performance of our optimized airfoil has been verified by multi-point theory, computational fluid dynamics (CFD) examination, and energy efficiency design index (EEDI) calculation. The maximum lift-to-drag ratio of the coupled wingsails has increased by 10 % than that of the arc-shaped sail. And the vessel equipped with our coupled wingsails can remarkably reduce EEDI by 18.2 % and 4 % when compared to non-sails vessel, and the vessel equipped with variable-camber sail (VCS), respectively. [ABSTRACT FROM AUTHOR]

Abstract Route planning in maritime transportation is the key to safe, efficient, and smart navigation. We propose a multi-criteria route planning technique for operators to objectively determine the routes according to their intentions. In this study, the navigational traffic risk of a route is quantitatively assessed using a model of a ship. Then, a risk contour map is visualized by structuring the data as absolute danger, hazard factors, and influential factors, which is a framework for route planning. Finally, a multi-criteria route could be modeled by considering the safety, efficiency, convenience, and ability of navigation as the main criteria. The technique assesses each criterion by analyzing the cumulative risk per distance, distance, number of waypoints, and risk gradient of derived routes. In addition, the technique proposes routes by utilizing its algorithm and incorporating contour-based projection and reference points. To verify the proposed technique, we carried out numerical simulations and evaluated actual AIS data. The results show that this technique can not only suggest goal-oriented routes but also assess the used routes. Therefore, the proposed technique can improve the route planning method to be more systematic, which contributes both to smart navigation based on the user's purposes and to future autonomous navigation. [ABSTRACT FROM AUTHOR]

Abstract This study utilizes the life cycle assessment method to compare the environmental impacts of two design concepts of a Panamax bulk carrier: lighter and heavier lightship weights. The lighter solution is designed to maximize the payload by cutting down the lightship weight. On the other hand, the heavier solution increases the thickness of some components of the ship to enlarge corrosion addition margins. This also increases the lightship weight in comparison with the lighter solution. In addition, the heavier solution could cut down the steel replacement consumed for maintenance activities in its life cycle. Regarding the requirements for global shipping transportation and for the ships operating in sulfur emission control areas presented in the International Prevention of Pollution from Ships , heavy fuel oil and low sulfur heavy fuel oil are considered. The results indicate that the heavier solution could reduce up to 4.83% carbon monoxide, 2.62% methane, and 56.64% volatile organic compounds in comparison with the lighter solution over their lifetimes. The heavier solution reduces the environmental impacts on Marine Aquatic Ecotoxicity, Ozone Layer Depletion, and Terrestric Ecotoxicity. Due to lower environmental score, the heavier hull bulk carrier with greater corrosion margins is more beneficial to the environment. [ABSTRACT FROM AUTHOR]

Abstract Clustering analysis is applied extensively in pattern recognition. In marine traffic applications, the clustering results may exhibit a customary route and traffic volume distribution. In order to improve the clustering performance of ship trajectory data, which is characterized by a large data volume and distribution complexity, a method consisting of Douglas-Peucker (DP)-based compression and density-based clustering is proposed. In the first part of the proposed method, the appropriate parameters for the DP algorithm were determined according to the shape changes in the trajectories, which were used to compress the trajectories prior to calculating the dynamic time warping (DTW) distance matrix. In the second part, the density-based spatial clustering of applications with noise (DBSCAN) algorithm was improved in terms of determining the parameters. Based on the statistical characteristics of ship trajectory distribution, the appropriate DBSCAN parameters could be determined adaptively. Evaluation and comparison experiments were conducted based on massive real ship trajectories in the Chinese port of Beilun-Zhoushan. The results demonstrated that, compared to the traditional DTW distance, the proposed similarity measurement exhibits superior performance in terms of both time and quality. Furthermore, the results of the comparison experiment demonstrated that the improved DBSCAN outperforms two existing clustering methods in marine traffic c pattern recognition. [ABSTRACT FROM AUTHOR]

Abstract Risk perception of the situation by an officer on watch is the basis for improving situational awareness, which is crucial in preventing ship collisions in vessel encounter situations. Defining risk perception for a vessel collision situation requires identifying the perceived risk. This paper derives a multiple linear regression model for studying risk perception on a ship in various collision encounter situations. To illustrate the procedure, the collision risk perceived by Korea Coast Guard officers on a patrol ship was obtained and applied to calibrate the regression model. The results demonstrate that the proposed model can be used to define risk perception and to provide a better understanding of the perceived risk in collision encounter situations. The application of some interesting results to the enhancement of the perception of the situation was also discussed.∗ Highlights • Risk perception of a ship's collision encounter situation was established as a topic. • The intensity of the perceived risk to the encountered distance and bearing was identified. • The phase difference between the ship and risk perception domains was found to be 127°. • The results facilitate the modeling of risk perception and visualization of collision encounter situations. [ABSTRACT FROM AUTHOR]