Safety Of Maritime Transportation Research Articles

A probabilistic assessment of ship blackout incident with Fault Tree Analysis into (FTA) Bayesian Network (BN)

Blackouts in maritime activities can cause propulsion loss and dangerous maritime conditions. Bayesian risk analysis is applied to ship blackout incidents in this study to improve understanding and reduce risks. Using Fault Tree Analysis (FTA), a Bayesian Network (BN) model incorporates fuel quality, lubricating oil quality, sensor error, injector error, and mechanical defects to estimate blackout probability. The model analyses how hazards and their interactions affect this situation using probabilistic inference. Sensitivity analysis identifies variables that affect blackout probabilities and prioritises risk mitigation solutions. Based on prior and posterior probabilities, ‘Automatic Voltage Regulator Failure’ (0.03 prior, 0.17 posterior), ‘Rotor Mechanical Fault’ (0.03 prior, 0.15 posterior), and ‘High Cooling Water Temperature’ (0.03 prior, 0.13 posterior) are the top three blackout causes. Other significant variables include ‘Switchboard Line Failure,’ ‘Faulty Fuel Pump,’ ‘Rotor Open Circuit,’ and ‘Temperature Sensor Failure’ in relative amounts. Bayesian risk analysis can identify and minimise marine blackout concerns, giving decision-makers a comprehensive framework for informed decision-making and proactive risk management. This research emphasises blackout accidents’ importance, improving maritime transportation safety and reliability.

STAD: Ship trajectory anomaly detection in ocean with dynamic pattern clustering

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.

Drifting vessel statistics and emergency towing cases

Abstract Recent climate studies forecast violent weather events with impact on the safety of maritime transportation. For example, harsh weather may result in system failures such as blackouts or loss of steering capability. Problems met when performing planned maintenance at sea may lead to adverse events if the weather cause delays or makes it impossible to complete the planned work. A drifting vessel in general is a liability for collisions with offshore structures, groundings, and total loss due to capsizing. The increasing use of ocean structures for energy production at sea (coastal and deepwater windfarms) and the transfer of fish farming structures to more exposed locations increase the probability of collisions between drifting ships and ocean structures. To mitigate risks, several incidents and their consequences have been analysed by national administrations. In some cases, these analyses have recommended the use of dedicated emergency towing resources. The willingness of the national governments to fund such resources is directly related to the time since the last major incident in their territorial waters. This paper reviews some recent cases of emergency towing operations in Norwegian waters and highlights challenges interlinked to setting up an emergency towing connection between a drifting ship and an emergency towing vessel in harsh weather.

Maritime transportation risk assessment: A multilevel node relationship-based fuzzy bayesian network

Maritime transportation safety has encountered unprecedented challenges arising from abrupt changes in various risk attributes in recent years, including natural weather and non-traditional security threats. This study presents a novel two-phase framework for assessing the maritime transportation risk. Firstly, a fuzzy Bayesian network considering multi-level node relationships was constructed to more accurately assess the transportation risk in various sea areas. In the second phase, RF-Shapley was developed to identify crucial risk nodes that exert substantial influence on transportation risk within individual sea areas. The results showed that transportation risk was the lowest in the South Asian and the highest in the Middle East waters. Critical risk nodes were different for each sea area, such as pirate attack node in Southeast Asian waters, extreme weather node in Northeast Asia, changing these nodes would be more helpful in improving transportation safety. Moreover, the contribution of critical risk nodes exhibited variations, such as combating terrorism and piracy were more conducive to reducing transportation risk in Middle Eastern waters, while ship accidents node be low contribution. These results offer valuable insights for evaluating transportation risk in uncertain environments and can serve as a reference for stakeholders to devise tailored policies and action plans.

Parametric study of side collision-induced denting failures on the ultimate strength of a handy-size containership under vertical bending

This study focuses on analysing the collision behaviour and residual ultimate bending strength of a container ship following a collision. Initially, computational models employing nonlinear finite element analysis (NFEA) were used to consider dynamic material parameters like strain rate and crack strain. The accuracy of this numerical approach was verified by comparison with test results. Subsequently, various collision scenarios were analyzed using ABAQUS software, encompassing diverse impact velocities, indenter shapes, and boundary conditions. The aim was to evaluate the reduction in the residual ultimate bending strength of a container ship hull resulting from collisions at both sagging and hogging moments. Finally, empirical equations were formulated to forecast the residual ultimate bending strength based on numerical findings. These equations exhibited excellent accuracy when validated against numerical and experimental data. This methodology and its equations provide valuable tools for assessing the post-collision strength of container ships and enhancing maritime transportation safety.

JUSTIFICATION OF THE CONCEPT OF SAFETY-ORIENTED SHIP OPERATIONS DURING CARGO TRANSPORTATION

Abstract. The article examines topical issues of maritime transportation safety in the context of modern transport production processes. A comprehensive analysis of the key factors affecting the safety of navigation is carried out, and a structured system for classifying these factors is developed. The main categories of safety, including technical, navigational, technological and environmental safety, are considered, and the role of the human factor in ensuring the safety of navigation is determined. Comprehensive research methods were applied, including the analysis of literature sources, statistical data, case studies and expert opinions. The result of the work is a developed system of concepts for ensuring safety in maritime transport, which can contribute to improving safety and reliability in this area. Objective. The purpose of the article is to conduct a comprehensive analysis of shipping safety issues in the context of modern transport production processes. The paper aims to thoroughly consider the key factors affecting the safety of maritime transportation and to provide a structured system for classifying these factors. Additionally, the article identifies the main safety categories and considers the role of the human factor in ensuring all aspects of shipping safety. As a result, a general approach to shipping safety is formulated, which takes into account all the necessary aspects and methods of ensuring safety in the process of maritime transportation. Research methods. This article uses a comprehensive research method, including the analysis of literary sources and scientific publications, the analysis of statistical data on incidents in maritime shipping, and also uses case studies and expert assessments. Additionally, the method of modeling situations that could potentially affect shipping safety was used. All these methods were used for a comprehensive review and classification of factors affecting safety and the development of a system of concepts for ensuring safety in maritime transportation. Results. As a result of the study, a comprehensive system of concepts for ensuring safety in maritime transportation was developed. It includes four main types of safety: technical, navigational, technological and environmental. Each of these aspects of safety has been analyzed in detail and classified depending on the stages of the shipping production process. Scientific novelty. The novelty of this article lies in the development of an approach to comprehensive analysis and security in maritime transportation, which can contribute to improving safety and reliability in this industry. Practical significance. The developed comprehensive system of concepts for ensuring safety during maritime transportation provides specific recommendations and methods for ensuring safety at various stages of ship operation, including cargo transportation and other ship operations.

A Systematic Literature Review of Maritime Transportation Safety Management

Maritime transportation plays a critical role in global trade, and studies on maritime transportation safety management are of great significance to the sustainable development of the maritime industry. Consequently, there has been an increasing trend recently in studies on maritime transportation safety management, especially in terms of safety risk analysis and emergency management. Therefore, the general idea of this article is to provide a detailed literature review of maritime transportation safety management based on 186 articles in the Web of Science (WOS) database published from 2011 to 2022. The purposes of this article are as follows: (1) to provide a statistics-based description and conduct a network-based bibliometric analysis on the basis of the collected articles; (2) to summarize the methodologies/technologies employed in maritime transportation safety management spatiotemporally; and (3) to propose four potential research perspectives in terms of maritime transportation safety management. Based on the findings and insights obtained from the bibliometric and systematic review, the development of a resilient maritime transportation system could be facilitated by means of data- or intelligence-driven technologies, such as scenario representation, digital twinning, and data simulation. In addition, the issues facing intelligent maritime shipping greatly challenge the current maritime safety management system due to the co-existence of intelligent and non-intelligent maritime operation.

Human Error Analysis and Fatality Prediction in Maritime Accidents

The main objective of this paper is to underscore the significance of human error as a dominant cause of maritime accidents. The research is based on a comprehensive analysis of 247 maritime accidents, with the aim being to identify human failures occurring during onboard and port activities, as well as during the supervision process. The first step of the analysis was facilitating the Human Factor Analysis and Classification System (HFACS) as an advanced analytical tool for the identification and categorisation of human factors. Based on coding process, the most critical areas of human error are identified, based on the process of risk evaluation and assessment. Furthermore, a prediction model was developed for predicting the probability of fatality in a maritime accident. This model was constructed using logistic regression, considering the predominant causal factors and their interplay. Lastly, a set of preventive measures aimed at enhancing the efficiency and safety of maritime transport is provided.

Design and Manufacture Ballast Management System Model for Reduce Ship Rolling Motion

The safety of maritime transportation is a critical aspect that must be addressed to ensure the well-being of ships and their crew. Frequent ship accidents highlight the need for improvements in the maritime transportation system. One of the causes of ship accidents is ship instability, leading to a loss of balance and even sinking. Ship stability is influenced by both internal and external factors, including human negligence in observing and addressing ship instability. This research aims to design and create a management ballast system model that can be operated automatically as a solution to reduce ship rolling motion. This system enables the ship to maintain balance using automatic side ballast tank management by utilizing two wing tanks on either side of the ship. The ballast management system will be equipped with an accurate ship roll angle detector, the Initial Measurement Unit sensor, a microcontroller, and a series of actuators, including relays as voltage control switches for the pump motor. This research involves simulation and testing/experiments at various angles, namely 5, 10, and 15 degrees. Simulations are conducted under conditions with and without the ballast management system, which is then confirmed through experiments under the same conditions. The expected outcome of this research is that the created ballast management system can be used to reduce ship rolling.

Ensuring the safety of maritime transportation of drilling fluids by platform supply-class vessel

The technology of drilling fluids transportation on the offshore Platform Supply-class Vessel has been considered. It has been established that density stratification of drilling fluid by depth of a cargo tank in the interval of its transportation time of 6–48 h is 4.03–46.29%. The research was carried out in the onboard mud system of a specialized Platform Supply Vessel 5750 tons deadweight offshore vessel. The objective was to improve the drilling fluid transportation system by developing technology to ensure that the density of drilling fluid is maintained at a constant value along the height of the tank while they are being transported by Platform Supply Vessel-class vessels. Using of an additional X-circulation of the drilling fluid in the volume of adjacent cargo tanks has been proposed as such method. Samples for drilling fluid density were taken at different points of the tank.

A Decision-Focused Learning Framework for Vessel Selection Problem

Maritime transportation safety is pivotal in international trade, with port state control (PSC) inspections being crucial to vessel safety. However, port authorities need to identify substandard vessels effectively because of resource constraints and high costs. Therefore, we propose robust predictive models and optimization strategies for vessel selection, using the random forest (RF) algorithm. We first use a traditional RF model serving as a benchmark, denoted as model M0. Then, we construct model M1 by refining the RF algorithm with a batch-processing method, thereby providing a better measure of the relative relationship between the predicted deficiency counts within a batch of ships. Then, we propose model M2, incorporating a decision-focused learning (DFL) framework into the tree construction process, enhancing the decision performance of the algorithm. In addition, we propose a variant model of M2, denoted as M2-0, considering the worst-case scenario when designing the decision loss function. By conducting experiments with data from the port of Hong Kong, we demonstrate that models M1 and M2 offer superior decision-making performance compared to model M0, and model M2 outperforms model M2-0 in both decision performance and stability. We further verify the robustness of these models by testing them under various instance scales. Overall, our study enhances the PSC inspection efficiency, ultimately bolstering maritime transportation safety.

A Novel Deep Learning Network with Deformable Convolution and Attention Mechanisms for Complex Scenes Ship Detection in SAR Images

Synthetic aperture radar (SAR) can detect objects in various climate and weather conditions. Therefore, SAR images are widely used for maritime object detection in applications such as maritime transportation safety and fishery law enforcement. However, nearshore ship targets in SAR images are often affected by background clutter, resulting in a low detection rate, high false alarm rate, and high missed detection rate, especially for small-scale ship targets. To address this problem, in this paper, we propose a novel deep learning network with deformable convolution and attention mechanisms to improve the Feature Pyramid Network (FPN) model for nearshore ship target detection in SAR images with complex backgrounds. The proposed model uses a deformable convolutional neural network in the feature extraction network to adapt the convolution position to the target sampling point, enhancing the feature extraction ability of the target, and improving the detection rate of the ship target against the complex background. Moreover, this model uses a channel attention mechanism to capture the feature dependencies between different channel graphs in the feature extraction network and reduce the false detection rate. The designed experiments on a public SAR image ship dataset show that our model achieves 87.9% detection accuracy for complex scenes and 95.1% detection accuracy for small-scale ship targets. A quantitative comparison of the proposed model with several classical and recently developed deep learning models on the same SAR images dataset demonstrated the superior performance of the proposed method over other models.

Monte Carlo Simulation Approach to Shipping Accidents Consequences Assessment

The purpose of this study is to present and apply an innovative technique to model environmental consequences of shipping accidents in relations to events initiating those accidents. The Monte Carlo simulation technique is used to model shipping accidents and chemical release consequences within the world’s sea and ocean waters. The model was created based on the previously designed novel general probabilistic approach to critical infrastructure accident consequences, including three models: the process of initiating events generated by a critical infrastructure accident, the process of environmental threats coming from released chemicals that are a result of initiating events, and the process of environmental degradation stemming from environmental threats. It is a new approach that has never been proposed and applied before. The Monte Carlo simulation method is used under the assumption of the semi-Markov model of these three processes. A procedure for the realization and generation of this process and evaluation of its characteristics is proposed and applied in the preparation of the C# program. Using this program, the processes’ characteristics are predicted for a specific sea area. Namely, for the considered processes, the limit values of transient probabilities between the states and the mean values of total sojourn times at the particular states for the fixed time are determined. The results obtained can be used practically by maritime practitioners involved in making decisions related to the safety of maritime transport and to mitigation actions concerned with maritime accidents.

Spatio-temporal characteristics of global maritime ship casualties

Maritime transportation safety of ships is important for international trade. Analyzing the spatio-temporal characteristics of global maritime ship casualties is the basis for maritime ship traffic planning, risk assessment and prevention. Based on the global maritime ship casualty data from 2000 to 2019, the number of the ship casualties were count by detailed event type, year, and month. Kernel density estimation of four periods were carried out. Sea areas around the English Channel, and the Aegean Sea were the hotspots from 2000 to 2019. The Korea Strait, Gulf of Saint Lawrence, Gulf of Alaska, Malacca Strait, and sea areas near Shanghai were also the hotspots in some of the four periods from 2000 to 2019.

Ship detection based on multi-scale weighted fusion

Ship surveillance plays an important role in ensuring the safety of maritime transportation and navigation. Due to the influence of factors such as waves and special weather, the existing detection methods still cannot balance the accuracy, speed and the parameters of the model in the changeable and complex marine environment. To solve this problem, this paper proposes an improved real-time method based on YOLOv5, which has few parameters and achieves high detection accuracy with little memory and computation cost. Collaborative Attention (CA) mechanism is added to the network structure, which enables the model to more accurately locate and identify target regions. We also design a Spatial Pyramid Pooling module (SPP) and a weighted pyramid network called Bidirectional Feature Pyramid Network (BiFPN) based on the characteristics of the ships to better fuse feature information. Transformer encoder is introduced to capture long-distance dependencies and preserve global and local features to the greatest extent. Furthermore, the ability of our proposed structure to localize objects at each stage is improved through integrating the output of multiple modules. The experimental results show that, the comprehensive performance of this method is better than the existing technology in ship detection on different evaluation criteria.

Generating live commentary for marine traffic scenarios based on multi-model learning

The Internet of Things (IoT), which allows for seamless communication and interaction with an array of smart devices, has rapidly become an indispensable part of our daily lives. Maritime transportation is one of the primary modes of transportation that heavily relies on Internet of Things (IoT) technologies to obtain crucial information such as atmospheric, oceanographic, navigation, and state data. By utilizing these IoT technologies, marine traffic scenarios can be constructed to obtain a comprehensive understanding of the traffic flow. Having a thorough understanding of marine traffic scenarios is crucial for improving the efficiency and safety of maritime transportation. Due to the time lag and data incompleteness inherent in marine traffic broadcasting and manual marine monitoring systems, it can be challenging to rely on them entirely. Meanwhile, the required resource to analyze marine traffic scenarios can be enormous. To address these challenges,we propose an approach based on multi-model learning for the automatic interpretation of marine traffic scenarios and developed an innovative dataset of multi-model marine traffic scenarios. Specifically, we have aligned maritime meteorological data with existing marine video data to create a comprehensive dataset. This dataset includes both raw data and manually created professional annotations to address the lack of benchmark datasets for multi-modal marine traffic scenarios. In addition to the dataset, we have developed a text generation model based on a multi-modal Transformer architecture. This model extracts the characteristics of multi-scale objects from visual marine frames by a parallel convolution mechanism, converts meteorological indexes into post-numeric codes, and combines the coding results of each mode through a multi-head self-attention mechanism to form fine-grained interpretation text. To train our text generation model for marine traffic scenarios, the threshold cross-entropy loss function is used to compensate the class imbalance. Our experimental results demonstrate the effectiveness of our approach, showing that our model can efficiently generate accurate and informative descriptions of maritime activity.

Ensuring safety of maritime transport: current trends and conceptual generalizations of its international legal mechanism

The article deals with topical issues of ensuring security of maritime transport and preventing relevant threats based on legal measures. The scientific literature in the field of maritime law and legal means (norms, methods) of ensuring safety in maritime transport is analyzed. The variability and complexity of the nature of maritime threats and various measures to respond to them require an understanding of the policies, regulations, national civil and criminal legislation of the participating States and international public law, and therefore actualize the issues of conceptualization of international legal regulation of maritime transport security in this study. The subject of the study is the provision of safety in maritime transport through the knowledge of current trends and the substantiation of the conceptual provisions of the international legal mechanism as such. Systematic, comparative legal and dialectical approaches have been applied to generalize the essence and features of international legal regulation of safety in maritime transport. The main research methods used are diverse variations of analysis and synthesis of its results, analogy and comparison, generalization and concretization, functional cognition and deduction, abstraction, historical and formal legal methods. The main scientific result of the study is that the problem of ensuring safety in maritime transport has been studied from a legal and organizational point of view. The paper examines the formation of the legal mechanism for ensuring international security in maritime transport, defines the concept of the legal mechanism for ensuring international security in maritime transport, reveals the characteristics of the main threats to international security in maritime transport, justifies the right to ensure security in maritime transport as an institute of international maritime law, presents the results of the analysis of the existing areas of security in maritime transport, put forward proposals to improve international legislation in the field of maritime transport safety. The scientific results obtained as a result of the research can be used in educational activities when teaching the basics of international law, as well as in scientific research procedures when learning about the functioning of modern international law and its individual institutions.

A Hybrid Rule-Based and Data-Driven Approach to Illegal Transshipment Identification with Interpretable Behavior Features

Illegal transshipment of maritime ships is usually closely related to illegal activities such as smuggling, human trafficking, piracy plunder, and illegal fishing. Intelligent identification of illegal transshipment has become an important technical means to ensure the safety of maritime transport. However, due to different geographical environments, legal policies and regulatory requirements in each sea area, there are differences in the movement characteristics and geographical distribution of illegal transshipment behavior in different time and space. Moreover, in areas with dense traffic flow, normal navigation behavior can easily be identified as illegal transshipment, resulting in a high rate of misidentification. This paper proposes a hybrid rule-based and data-driven approach to solve the problem of missing identification in fixed threshold methods and introduces a traffic density feature to reduce the misidentification rate in dense traffic areas. The method is both interpretable and adaptable through unsupervised clustering to get suitable threshold distribution combination for regulatory sea areas. The evaluation results in two different sea areas show that the proposed method is applicable. Compared with other widely used identification methods, this method identifies more illegal transshipment events, which are highly suspicious, and gives warning much earlier. The proposed method can even filter out misidentification events from compared methods' results, which account for more than half of the total number.

Vessel Trajectory Prediction in Maritime Transportation: Current Approaches and Beyond

The growing availability of maritime IoT traffic data and continuous expansion of the maritime traffic volume, serving as the driving fuel, propel the latest Artificial Intelligence (AI) studies in the maritime domain. Among the most recent advancements, vessel trajectory prediction is one of the most essential topics for assuring maritime transportation safety, intelligence, and efficiency. This paper presents an up-to-date review of existing approaches, including state-of-the-art deep learning, for vessel trajectory prediction. We provide a detailed explanation of data sources and methodologies used in the vessel trajectory prediction studies, highlight a discussion regarding the auxiliary techniques, complexity analysis, benchmarking, performance evaluation, and performance improvement for vessel trajectory prediction research, and finally summarize the current challenges and future research directions in this field.

Maritime traffic clustering to capture high-risk multi-ship encounters in complex waters

Maritime traffic situational awareness is fundamental to the safety of maritime transportation. The state-of-the-art research primarily attaches importance to collision risk estimation and evaluation between/among ships but encounters the challenges of capturing the high-risk traffic clusters in complex waters. This paper develops a systematic traffic clustering approach to enhance traffic pattern interpretability and proactively discover high-risk multi-ship encounter scenarios, in which both the conflict connectivity and spatial compactness of encounter ships are considered. Specifically, a novel hybrid clustering approach that integrates a composite distance measure, a constrained Shared Nearest Neighbour clustering, and a fine-tuning strategy is developed to segment maritime traffic into multiple conflict-connected and spatially compact clusters. Meanwhile, a hierarchical bi-objective optimization algorithm is introduced to search for optimal clustering solutions. Through maritime traffic data obtained from the Ningbo-Zhoushan Port, a thorough methodology performance evaluation is carried out through application demonstration and validation. Experiment results reveal that the new approach: 1) can effectively capture the high-risk/density traffic clusters; 2) is robust with respect to various traffic scenarios; and 3) can be extended to assist in collision risk management. It therefore offers new insights into enhancing maritime traffic surveillance capabilities and eases the design of risk management strategy.