Search Results (30)

The backbone of maritime transportation has always been the successful execution of ship operations. However, the human factor has proven to be a weak point in the system. To reduce and mitigate it, a regulatory framework and consequently a safety system for ship barriers were created and implemented with this goal in mind. The expected result of these measures was the creation of a resilient maritime transport system. Nevertheless, the available statistics show that most of the reported accidents and incidents occurred during ship operation, with the human factor as the main cause. Therefore, it is useful to investigate whether the regulatory framework can influence the safety system of ship barriers. Therefore, the objectives of the study are as follows: (a) to investigate and determine the regulatory safety requirements and the elements related to the ship barrier system, and (b) to investigate the influence of the regulatory safety requirements on the elements related to the ship barrier system. From the data obtained and the analyses performed, seven factors emerged. Four of them were related to the regulatory requirements and three to the shipboard barrier system, a basis for the presented models. Several important findings were obtained that have theoretical and practical implications and further highlight the importance and potential undesirable side effects of the provisions of the current regulatory framework. Full article

Background: The transportation of petroleum products via multimodal logistics systems is a complex process subject to operational inefficiencies and elevated risk exposure. The efficient and resilient transportation of petroleum products increasingly depends on multimodal logistics systems, where operational risks and process inefficiencies can significantly impact safety and performance. This study addresses the research question of how an integrated risk-based and workflow-driven approach can enhance the management of oil products logistics in complex port environments. Methods: A dual methodological framework was applied at the Port of Midia, Romania, combining a probabilistic risk assessment model, quantifying incident probability, infrastructure vulnerability, and exposure, with dynamic business process modeling (BPM) using specialized software. The workflow simulation replicated real-world multimodal oil operations across maritime, rail, road, and inland waterway segments. Results: The analysis identified human error, technical malfunctions, and environmental hazards as key risk factors, with an aggregated major incident probability of 2.39%. BPM simulation highlighted critical bottlenecks in customs processing, inland waterway lock transit, and road tanker dispatch. Process optimizations based on simulation insights achieved a 25% reduction in operational delays. Conclusions: Integrating risk assessment with dynamic workflow modeling provides an effective methodology for improving the resilience, efficiency, and regulatory compliance of multimodal oil logistics operations. This approach offers practical guidance for port operators and contributes to advancing risk-informed logistics management in the petroleum supply chain. Full article

The growth of maritime transportation has been accompanied by a gradual increase in accident rates, drawing greater attention to the critical issue of man-overboard incidents and drowning. Traditional maritime search-and-rescue (SAR) methods are often constrained by limited efficiency and high operational costs. Over the past few years, drones have demonstrated significant promise in improving the effectiveness of search-and-rescue operations. This is largely due to their exceptional ability to move freely and their capacity for wide-area monitoring. This study proposes an enhanced SR-DETR algorithm aimed at improving the detection of individuals who have fallen overboard. Specifically, the conventional multi-head self-attention (MHSA) mechanism is replaced with Efficient Additive Attention (EAA), which facilitates more efficient feature interaction while substantially reducing computational complexity. Moreover, we introduce a new feature aggregation module called the Cross-Stage Partial Parallel Atrous Feature Pyramid Network (CPAFPN). By refining spatial attention mechanisms, the module significantly boosts cross-scale target recognition capabilities in the model, especially offering advantages for detecting smaller objects. To improve localization precision, we develop a novel loss function for bounding box regression, named Focaler-GIoU, which performs particularly well when handling densely packed and small-scale objects. The proposed approach is validated through experiments and achieves an mAP of 86.5%, which surpasses the baseline RT-DETR model’s performance of 83.2%. These outcomes highlight the practicality and reliability of our method in detecting individuals overboard, contributing to more precise and resource-efficient solutions for real-time maritime rescue efforts. Full article

With the increasing digitalization of maritime transportation, the demand for structured and interoperable data has grown. While the S-100 framework developed by the International Hydrographic Organization (IHO) provides a foundation for standardizing maritime information, a data model for representing marine casualties has not yet been developed. As a result, past incident data—such as collisions or groundings—remain fragmented in unstructured formats and are excluded from electronic navigational systems, limiting their use in safety analysis and route planning. To address this gap, this paper proposes a data model for structuring and visualizing marine casualty information within the S-100 standard. The model was designed by defining an application schema, constructing a machine-readable feature catalogue, and developing a portrayal catalogue and custom symbology for integration into Electronic Navigational Charts (ENCs). A case study using actual casualty records was conducted to examine whether the model satisfies the structural and portrayal requirements of the S-100 framework. The proposed model enables previously unstructured casualty data to be standardized and spatially integrated into digital chart systems. This approach allows accident information to be used alongside other S-100-based data models, contributing to risk-aware route planning and future applications in smart ship operations and maritime safety services. Full article

Water transportation is a critical component of the overall transportation system. However, the gradual increase in traffic density has led to a corresponding rise in accident occurrences. This study proposes a quantitative framework for analyzing the evolutionary paths of maritime traffic accident risks by integrating complex network theory and link prediction methods. First, 371 maritime accident investigation reports were analyzed to identify the underlying risk factors associated with such incidents. A risk evolution network model was then constructed, within which the importance of each risk factor node was evaluated. Subsequently, several node similarity indices based on node importance were proposed. The performance of these indices was compared, and the optimal indicator was selected. This indicator was then integrated into the risk evolution network model to assess the interdependence between risk factors and accident types, ultimately identifying the most probable evolution paths from various risk factors to specific accident outcomes. The results show that the risk evolution path shows obvious characteristics: “lookout negligence” is highly correlated with collision accidents; “improper route selection” plays a critical role in the risk evolution of grounding and stranding incidents; “improper on-duty” is closely linked to sinking accidents; and “illegal operation” show a strong association with fire and explosion events. Additionally, the average risk evolution paths for collisions, groundings, and sinking accidents are relatively short, suggesting higher frequencies of occurrence for these accident types. This research provides crucial insights for managing water transportation systems and offers practical guidance for accident prevention and mitigation. Full article

Maritime safety is a critical concern for the transport sector and remains a key challenge for the international shipping industry. Recognizing that maritime accidents pose significant risks to both safety and operational efficiency, this study explores the application of big data analysis techniques to understand the factors influencing maritime transport accidents (MTA). Specifically, using extensive datasets derived from vessel performance measurements, environmental conditions, and accident reports, it seeks to identify the key intrinsic and extrinsic factors contributing to maritime accidents. The research examines more than 90 thousand incidents for the period 2014–2022. Leveraging big data analytics and advanced statistical techniques, the findings reveal significant correlations between vessel size, speed, and specific environmental factors. Furthermore, the study highlights the potential of big data analytics in enhancing predictive modeling, real-time risk assessment, and decision-making processes for maritime traffic management. The integration of big data with intelligent transportation systems (ITSs) can optimize safety strategies, improve accident prevention mechanisms, and enhance the resilience of ocean-going transportation systems. By bridging the gap between big data applications and maritime safety research, this work contributes to the literature by emphasizing the importance of examining both intrinsic and extrinsic factors in predicting maritime accident risks. Additionally, it underscores the transformative role of big data in shaping safer and more efficient waterway transportation systems. Full article

This study presents a comprehensive, data-driven analysis of the causes and risks associated with container loss during maritime transport, utilizing incident data from 2011 to 2023. By employing advanced statistical analysis, machine-learning techniques, and data preprocessing, the study identifies key factors influencing container loss, including vessel size, incident locations, and primary causes. A predictive model based on decision trees was developed to assess the severity of container loss incidents, while K-means clustering was used to classify incident zones. Adverse weather conditions were found to be the predominant cause, accounting for 57.14% of incidents. The study reveals that larger vessels, despite experiencing fewer incidents, face more severe losses, whereas smaller vessels are more prone to frequent but less severe losses. The decision-tree model demonstrated high accuracy in predicting low-risk incidents but showed limitations in moderate- and high-risk scenarios. The findings underscore the importance of understanding the correlation between vessel parameters and incident outcomes to enhance risk management strategies. The study also highlights the potential for improving predictive capabilities by incorporating environmental data. These insights provide a robust framework for ship owners and maritime authorities to anticipate and mitigate risks, emphasizing the need for continuous monitoring and enhanced safety measures in maritime operations. Full article

Shipping activities continue to experience growth across a multitude of industrial sectors within the Arctic, hence there are risks in terms of severity and likelihood of accidents. The Arctic region is inherently dangerous to transportation and human existence due to its extreme climate and environmental conditions, and hence the complexities associated with emergency situations within the maritime domain are amplified when operating within the Arctic and North-Atlantic (ANA). The definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region are significant enablers in providing a set of critical and sustainable tools for Search and Rescue (SAR), Oil Spill Response (OSR), and emergency management practitioners. Therefore, in this paper we aim to identify and characterise high-priority potential seaborne disasters and catastrophic incidents in the ANA region such as cruise ship accidents, oil leaks, radiological leaks, and fishing boat groundings. These were compiled as an outcome of a set of workshops carried out as part of the ARCSAR, EU Horizon 2020 funded project, and from analysis of the literature. We also provide root cause analysis techniques, tools for strategic decision-making, and means of mitigation. We demonstrate how such tools can be used by applying some of them to a selective case study and drawing lessons learned from the application of root cause analysis, which can help emergency response organisations with preparedness work and hence more efficient response. In doing so, we provide a set of tools that can be used for strategic and operational learning. Such approaches can help standardise the definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region in both prospective and retrospective analysis. Full article

This study examines the significant impact of Houthi insurgent activities on maritime traffic within the strategic Red Sea and Suez Canal routes, essential conduits for global trade. It explores the correlation between regional instability, exemplified by Houthi actions from 19 November 2023 to 5 February 2024, and changes in maritime traffic patterns and operational efficiency. This study seeks to answer a critical question in transport geography: how does regional instability, exemplified by Houthi insurgent activities, affect the maritime traffic patterns and operational efficiency of the Red Sea and Suez Canal? Using descriptive statistics, qualitative analysis, and geospatial methods, this research highlights recent trends in maritime traffic and incidents, revealing spatial and geopolitical challenges in this crucial trade route. The findings indicate a notable decline in maritime activity in the Gulf of Aden and Suez Canal due to security concerns from Houthi attacks, prompting a significant shift to alternative routes, particularly around the Cape of Good Hope. This shift underscores the broader implications of regional instability on global trade and the importance of maintaining an uninterrupted maritime flow. This study also emphasizes the economic ramifications, such as increased operational costs and freight rates due to longer transit times and enhanced security measures. This research concludes with a call for improved maritime security protocols and international cooperation to protect these strategic maritime pathways. It contributes to the discourse on transport geography by quantifying the direct impacts of regional conflicts on maritime logistics and proposing strategies for future resilience, highlighting the interconnected nature of global trade and security and the need for collective action against evolving geopolitical challenges. Full article

Maritime transport has adapted to recent political and economic shifts by addressing stringent pollution reduction requirements, redrawing transport routes for safety, reducing onboard technical incidents, managing data security risks and transitioning to autonomous vessels. This paper presents a novel approach to predictive maintenance in the maritime industry, leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to enhance fault detection and maintenance planning for naval systems. Traditional maintenance strategies, such as corrective and preventive maintenance, are increasingly ineffective in meeting the high safety and efficiency standards required by maritime operations. The proposed model integrates AI-driven methods to process operational data from shipboard systems, enabling more accurate fault diagnosis and early identification of system failures. By analyzing historical operational data, ML algorithms identify patterns and estimate the functional states, helping prevent unplanned failures and costly downtime. This approach is critical in environments where technical failures are a leading cause of incidents, as demonstrated by the high rate of machinery-related accidents in maritime operations. Our study highlights the growing importance of AI and ML in predictive maintenance and offers a practical tool for improving operational safety and efficiency in the naval industry. The paper discusses the development of a fault detection approach, evaluates its performance on real shipboard data-through tests on a seawater cooling system from an oil tanker and concludes with insights into the broader implications of AI-driven maintenance in the maritime sector. Full article

This paper investigates the factors influencing the probability of fatality in various types of maritime accidents, including grounding, capsizing, sinking, man overboard incidents, and fatal falls, with a focus on several contributing factors—alcohol consumption, meteorological conditions, and visibility. Through comprehensive analysis, the alcohol consumption was examined in order to show how it impairs judgment and physical abilities, significantly increasing the risk of fatal outcomes in these accidents. The paper explores the interplay between alcohol consumption and other contributing factors, such as time of day (daytime/night) and weather conditions, providing a comprehensive understanding of how these variables collectively influence fatality rates in EU maritime transportation. The findings underscore the critical need for stringent alcohol regulations and enhanced safety protocols to mitigate the heightened risks associated with alcohol-impaired maritime operations. Full article

Marine transportation accounts for approximately 90% of the total trade managed in international logistics and plays a vital role in many companies’ supply chains. However, en-route factors like weather conditions or piracy incidents often delay scheduled arrivals at destination ports, leading to downstream inefficiencies. Due to the maritime industry’s digital transformation, smart ports and vessels generate vast amounts of data, creating an opportunity to use the latest technologies, like machine and deep learning (ML/DL), to support terminals in their operations. This study proposes a data-driven solution for accurately predicting vessel arrival times using ML/DL techniques, including Deep Neural Networks, K-Nearest Neighbors, Decision Trees, Random Forest, and Extreme Gradient Boosting. This study collects real-world AIS data in the Eastern Mediterranean Sea from a network of public and private AIS base stations. The most relevant features are selected for training and evaluating the six ML/DL models. A comprehensive comparison is also performed against the estimated arrival time provided by shipping agents, a simple calculation-based approach, and four other ML/DL models proposed recently in the literature. The evaluation has revealed that Random Forest achieves the highest performance with an MAE of 99.9 min, closely followed by XGBoost, having an MAE of 105.0 min. Full article

This paper presents the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). This solution is based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be applied as the first line of the response to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or in the open sea during transport activities in a fast, efficient, and low-cost way. The paper describes the work done in the development of a team of autonomous vehicles able to carry as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), and the development of a multi-robot framework for efficient oil spill mitigation. Field tests have been performed in Portugal and Spain’s harbors, with a simulated oil spill, and the coordinate oil spill task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV) STORK has been validated. Full article

As a bridge for international trade, maritime transportation security is crucial to the global economy. Southeast Asian waters have become a high-incidence area of global piracy attacks due to geographic location and complex security situations, posing a great threat to the development of the Maritime Silk Road. In this study, the factors affecting the risk of pirate attacks are analyzed in depth by using the Global Ship Piracy Attacks Report from the IMO Global Integrated Shipping Information System (GISIS) database (i.e., 2013–2022) in conjunction with a Bayesian Network (BN) model, and the Expectation Maximization algorithm is used to train the model parameters. The results show that piracy behaviors and the ship’s risk are the key factors affecting the risk of pirate attacks, and suggestions are made to reduce the risk of pirate attacks. This study develops a theoretical basis for preventing and controlling the risk of pirate attacks on ships, which helps maintain the safety of ship operations. Full article

In the midst of a rapidly evolving economic landscape, the global demand for oil is steadily escalating. This increased demand has fueled marine extraction and maritime transportation of oil, resulting in a consequential and uneven surge in maritime oil spills. Characterized by their abrupt onset, rapid pollution dissemination, prolonged harm, and challenges in short-term containment, oil spill accidents pose significant economic and environmental threats. Consequently, it is imperative to adopt effective and reliable methods for timely detection of oil spills to minimize the damage inflicted by such incidents. Leveraging the YOLO deep learning network, this paper introduces a methodology for the automated detection of oil spill targets. The experimental data pre-processing incorporated denoise, grayscale modification, and contrast boost. Subsequently, realistic radar oil spill images were employed as extensive training samples in the YOLOv8 network model. The trained detection model demonstrated rapid and precise identification of valid oil spill regions. Ultimately, the oil films within the identified spill regions were extracted utilizing the simulated annealing particle swarm optimization (SA-PSO) algorithm. The proposed method for offshore oil spill survey presented here can offer immediate and valid data support for regular patrols and emergency reaction efforts. Full article