Search Results (144)

Maritime transportation is the backbone of global trade, with ports acting as pivotal nodes for the efficient and resilient movement of goods in international supply chains. However, most existing studies lack a systematic and integrated framework for assessing port connectivity. To address this gap, this study develops an integrated Bayesian Network (BN) modeling approach that, for the first time, simultaneously incorporates international connectivity, port competitiveness, and hinterland connectivity within a unified probabilistic framework. Drawing on empirical data from 26 major coastal countries in Asia, the model quantifies the multi-layered and interdependent determinants of port connectivity. The results demonstrate that port competitiveness and hinterland connectivity are the dominant drivers, while the impact of international shipping links is comparatively limited in the current Asian context. Sensitivity analysis further highlights the critical roles of rail transport development and trade facilitation in enhancing port connectivity. The proposed BN framework supports comprehensive scenario analysis under uncertainty and offers targeted, practical policy recommendations for port authorities and regional planners. By systematically capturing the interactions among maritime, port, and inland factors, this study advances both the theoretical understanding and practical management of port connectivity. Full article

Maritime transportation plays a critical role in global containerized cargo logistics, with seaports serving as key nodes in this system. Ports are responsible for container loading and unloading, along with inspection, storage, and timely delivery to the destination, all of which heavily depend on the performance of the container handling equipment (CHE). Inefficient maintenance strategies and unplanned maintenance of the port equipment can lead to operational disruptions, including unexpected delays and long waiting times in the supply chain. Therefore, the maritime industry must adopt intelligent maintenance strategies at the port to optimize operational efficiency and resource utilization. Towards this end, this study presents a machine learning (ML)-based approach for predicting faults in CHE to improve equipment reliability and overall port performance. Firstly, a statistical model was developed to check the status and health of the hydraulic system, as it is crucial for the operation of the machines. Then, several ML models were developed, including artificial neural networks (ANNs), decision trees (DTs), random forest (RF), Extreme Gradient Boosting (XGBoost), and Gaussian Naive Bayes (GNB) to predict inverter over-temperature faults due to fan failures, clogged filters, and other related issues. From the tested models, the ANNs achieved the highest performance in predicting the specific faults with a 98.7% accuracy and 98.0% F1-score. 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

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

Ship-speed prediction is an emerging research area in marine traffic safety and other related fields, occupying an important position with respect to these areas. At present, the effectiveness of techniques used in in time-series forecasting methods in ship-speed prediction is poor, and there are accumulated errors in long-term forecasting, which is limited in its processing of ship-speed information combined with multi-feature data input. To overcome this difficulty and further optimize the accuracy of ship-speed prediction, this research proposes a new deep learning framework to predict ship speed by combining GANs (Generative Adversarial Networks) and LSTM (Long Short-Term Memory). First, the algorithm takes an LSTM network as the generating network and uses the LSTM to mine the spatiotemporal correlation between nodes. Secondly, the complementary characteristics linked between the generative network and the discriminant network are used to eliminate the cumulative error of a single neural network in the long-term prediction process and improve the prediction accuracy of the network in ship-speed determination. To conclude, the Generator–LSTM model advanced here is used for ship-speed prediction and compared with other models, utilizing identical AIS (automatic identification system) ship-speed information in the same scene. The findings indicate that the model demonstrates high accuracy in the typical error measurement index, which means that the model can reliably better predict the ship speed. The results of the study will assist maritime traffic participants in better taking precautions to prevent collisions and improve maritime traffic safety. Full article

The electrical installation of a ship includes the generation, transport and distribution of the generated electrical energy to the electrical consumers on board. In recent years, there have been many attempts to replace traditional auxiliary generators with renewable energy sources, in particular solar panels, as this is a highly developed technology on land. Accordingly, this paper analyzes the different energy requirements on board a merchant vessel and carries out a feasibility analysis. The feasibility analysis considers technical, economic and legal aspects. Sustainable aspects are analyzed too, due to their importance nowadays. It is verified that the use of solar panels is only technically feasible for a small part of the ship’s total consumption, as the area required by the panels to cover the total demand would exceed the available area of the ship. Therefore, the possibility of installing solar panels for the fire detection system only was analyzed. This is a technically and legally feasible solution, but not an economically viable one. However, from a sustainability point of view, which takes into account economic, social and environmental aspects, this proposal is appropriate. This study concludes that, while solar panels are not a viable solution for covering all energy needs on merchant ships, they can be used for specific systems such as the fire detection network or similar small consumers, albeit with economic limitations. These findings provide valuable insights for future research and practical implementations of renewable energy solutions in the maritime sector. Full article

High-density cities have obvious characteristics of compact urban spatial form and intensive land use in terms of spatial environment, and have always been a topic of academic focus. As a typical coastal historical district, the Macau Peninsula pier district (mainly the Macau Inner Harbour) has a high building density and a low average street width, forming a vertical coastline development model that directly converses with the ocean. This area is adjacent to Macau’s World Heritage Site and directly related to the Marine trade functions. The distribution pattern of cultural heritage linked by the ocean has strengthened Macau’s unique positioning as a node city on the Maritime Silk Road. This text is based on the theory of urban development, integrates spatial syntax and POI analysis techniques, and combines the theories of waterfront regeneration, high-density urban form and post-industrial urbanism to integrate and deepen the theoretical framework, and conduct a systematic study on the urban spatial characteristics of the coastal area of the Macau Peninsula. This study found that (1) Catering and shopping facilities present a dual agglomeration mechanism of “tourism-driven + commercial core”, with Avenida de Almeida Ribeiro as the main axis and radiating to the Ruins of St. Paul’s and Praça de Ponte e Horta, respectively. Historical blocks and tourist hotspots clearly guide the spatial center of gravity. (2) Residential and life service facilities are highly coupled, reflecting the spatial logic of “work-residence integration-service coordination”. The distribution of life service facilities basically overlaps with the high-density residential area, forming an obvious “living circle + community unit” structure with clear spatial boundaries. (3) Commercial and transportation facilities form a “functional axis belt” organizational structure along the main road, with the Rua das Lorchas—Rua do Almirante Sérgio axis as the skeleton, constructing a “functional transmission chain”. (4) The spatial system of the Macau Peninsula pier district has transformed from a single center to a multi-node, network-linked structure. Its internal spatial differentiation is not only constrained by traditional land use functions but is also driven by complex factors such as tourism economy, residential migration, historical protection, and infrastructure accessibility. (5) Through the analysis of space syntax, it is found that the core integration of the Macau Peninsula pier district is concentrated near Pier 16 and the northern area. The two main roads have good accessibility for motor vehicle travel, and the northern area of the Macau Peninsula pier district has good accessibility for long and short-distance walking. Full article

With the expansion of global trade, China’s commodity futures market has become increasingly intertwined with regional maritime logistics. The Ningbo Containerized Freight Index (NCFI), as a key regional indicator, reflects freight rate fluctuations and logistics efficiency in real time. However, limited research has explored how commodity futures data can enhance NCFI forecasting accuracy. This study aims to bridge that gap by proposing a hybrid deep learning model that combines recurrent neural networks (RNNs) and gated recurrent units (GRUs) to predict NCFI trends. A comprehensive dataset comprising 28,830 daily observations from March 2017 to August 2022 is constructed, incorporating the futures prices of key commodities (e.g., rebar, copper, gold, and soybeans) and market indices, alongside Clarksons containership earnings. The data undergo standardized preprocessing, feature selection via Pearson correlation analysis, and temporal partitioning into training (80%) and testing (20%) sets. The model is evaluated using multiple metrics—mean absolute Error (MAE), mean squared error (MSE), root mean square error (RMSE), and R²—on both sets. The results show that the RNN–GRU model outperforms standalone RNN and GRU architectures, achieving an R² of 0.9518 on the test set with low MAE and RMSE values. These findings confirm that integrating cross-market financial indicators with deep sequential modeling enhances the interpretability and accuracy of regional freight forecasting. This study contributes to sustainable shipping strategies and provides decision-making tools for logistics firms, port operators, and policymakers seeking to improve resilience and data-driven planning in maritime transport. Full article

The rapid expansion of international maritime transportation has led to rising greenhouse gas emissions, exacerbating climate change and environmental sustainability concerns. According to the International Maritime Organization, carbon dioxide (CO₂) emissions from vessels are projected to increase by over 17% by 2050. Traditional emission estimation methods are prone to inaccuracies due to uncertainties in emission factors, and inconsistencies in fuel consumption data. This study proposes deep learning-based CO₂ emission prediction models leveraging engine operation data. Unlike previous approaches that primarily relied on fuel consumption, this model incorporates multiple parameters capturing the relationship between combustion characteristics and emissions to enhance predictive accuracy. We developed and evaluated individual models—convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN)—as well as hybrid model (TCN–LSTM). The hybrid model achieved the highest predictive performance, with a coefficient of determination of 0.9726, outperforming other models across multiple quantitative metrics. These findings demonstrate the potential of deep learning for vessel emission assessment, providing a scientific basis for carbon management strategies and policy development in the international shipping industry. This study thus holds major academic and industrial value, advancing the field of deep learning-based emission prediction and extending its applicability to diverse operational scenarios. Full article

Current Satellite-Based Augmentation Systems (SBASs) improve the positioning accuracy and integrity of GPS satellites and provide safe civil aviation navigation services for procedures from en-route to LPV-200 precision approach over specific regions. SBAS systems, such as WAAS, EGNOS, GAGAN, and MSAS, already operate. The development of operational SBAS systems is in transition due to the extension of L1 SBAS services to new regions and the improvements expected by the introduction of dual frequency multi-constellation (DFMC) services, which allow the use of more core constellations such as Galileo and the use of ionosphere-free L1/L5 signal combination. The UKSBAS Testbed is a demonstration and feasibility project in the framework of ESA’s Navigation Innovation Support Programme (NAVISP), which is sponsored by the UK’s HMG with the participation of the Department for Transport and the UK Space Agency. UKSBAS Testbed’s main objective is to deliver a new L1 SBAS signal in space (SIS) from May 2022 in the UK region using Viasat’s Inmarsat-3F5 geostationary (GEO) satellite and Goonhilly Earth Station as signal uplink over PRN 158, as well as L1 SBAS and DFMC SBAS services through the Internet. SBAS messages are generated by GMV’s magicSBAS software and fed with data from the Ordnance Survey’s station network. This paper provides an assessment of the performance achieved by the UKSBAS Testbed during the last two years of operations at the SIS and user level, including a number of experimentation campaigns performed in the aviation and maritime domains, comprising ground tests at airports, flight tests on aircraft and sea trials on a vessel. This assessment includes, among others, service availability (e.g., APV-I, LPV-200), protection levels (PL), and position errors (PE) statistics over the service area and in a network of receivers. Full article

Accurate and real-time ship detection is crucial for intelligent waterborne transportation systems. However, detecting ships across various scales remains challenging due to category diversity, shape similarity, and complex environmental interference. In this work, we propose EGM-YOLOv8, a lightweight and enhanced model for real-time ship detection. We integrate the Efficient Channel Attention (ECA) module to improve feature extraction and employ a lightweight Generalized Efficient Layer Aggregation Network (GELAN) combined with Path Aggregation Network (PANet) for efficient multi-scale feature fusion. Additionally, we introduce MPDIoU, a minimum-distance-based loss function, to enhance localization accuracy. Compared to YOLOv8, EGM-YOLOv8 reduces the number of parameters by 13.57%, reduces the computational complexity by 11.05%, and improves the recall rate by 1.13%, demonstrating its effectiveness in maritime environments. The model is well-suited for deployment on resource-constrained devices, balancing precision and efficiency for real-time applications. Full article

Autonomous vessels are becoming paramount to ocean transportation, while they also face complex risks in dynamic marine environments. Machine learning plays a crucial role in enhancing maritime safety by leveraging its data analysis and predictive capabilities. However, there has been no review grounded in bibliometric analysis in this field. To explore the research evolution and knowledge frontier in the field of maritime safety for autonomous shipping, a bibliometric analysis was conducted using 719 publications from the Web of Science database, covering the period from 2000 up to May 2024. This study utilized VOSviewer, alongside traditional literature analysis methods, to construct a knowledge network map and perform cluster analysis, thereby identifying research hotspots, evolution trends, and emerging knowledge frontiers. The findings reveal a robust cooperative network among journals, researchers, research institutions, and countries or regions, underscoring the interdisciplinary nature of this research domain. Through the review, we found that maritime safety machine learning methods are evolving toward a systematic and comprehensive direction, and the integration with AI and human interaction may be the next bellwether. Future research will concentrate on three main areas: evolving safety objectives towards proactive management and autonomous coordination, developing advanced safety technologies, such as bio-inspired sensors, quantum machine learning, and self-healing systems, and enhancing decision-making with machine learning algorithms such as generative adversarial networks (GANs), hierarchical reinforcement learning (HRL), and federated learning. By visualizing collaborative networks, analyzing evolutionary trends, and identifying research hotspots, this study lays a groundwork for pioneering advancements and sets a visionary angle for the future of safety in autonomous shipping. Moreover, it also facilitates partnerships between industry and academia, making for concerted efforts in the domain of USVs. Full article

Oceanic fronts delineate the boundaries between distinct water masses within the ocean, typically marked by shifts in weather patterns and the generation of oceanic circulation. These fronts are identified in research on intelligent oceanic front detection primarily by their significant temperature gradients. The refined identification of oceanic fronts is of great significance to maritime material transportation and ecological environment protection. In view of the weak edge nature of oceanic fronts and the misdetection or missed detection of oceanic fronts by some deep learning methods, this paper proposes an oceanic front detection method based on the U-Net model that integrates Edge-Attention-Module and the Feature Pyramid Network Module (FPN-Module). We conduct detailed statistical analysis and change rate calculation of the oceanic front, and batch process to obtain preliminary high-quality annotation data, which improves efficiency and saves time. Then, we perform manual corrections to correct missed detections or false detections to ensure the accuracy of annotations. Approximately 4800 days of daily average sea temperature fusion data from CMEMS (Copernicus Marine Environment Monitoring Service) are used for analysis, and an Encoder-Edge-FPN-Decoder Network (EEFD-Net) structure is established to enhance the model’s accuracy in detecting the edges of oceanic fronts. Experimental results demonstrate that the improved model’s front identification capability is in strong agreement with fronts segmented and annotated using the threshold method, with IoU and weighted Dice scores reaching 98.81% and 95.56%, respectively. The model can accurately locate the position of oceanic fronts, with superior detection of weak fronts compared to other network models, capturing smaller fronts more precisely and exhibiting stronger connectivity. Full article

This study examines the impact of monitoring, reporting, and verification (MRV) system indicators on the costs associated with the emissions trading system (ETS) of the maritime sector in the European Union. Since maritime transport has recently been incorporated into the ETS, it becomes essential to understand how different operational and environmental factors affect the economic burden of shipping companies and port competitiveness. To this end, a model based on Bayesian networks is used to analyse the interdependencies between key variables, facilitating the identification of the most influential factors in the determination of the costs of the ETS. The results show that fuel efficiency and CO₂ emissions in port are decisive in the configuration of costs. In particular, it was identified that emissions during the stay in port have a greater weight than expected, which suggests that strategies such as the use of electrical connections in port (cold ironing) may be key to mitigating costs. Likewise, navigation patterns and traffic regionalisation show a strong correlation with ETS exposure, which could lead to adjustments in maritime routes. This probabilistic model offers a valuable tool for strategic decision-making in the maritime sector, benefiting shipping companies, port operators, and policymakers. However, future research could integrate new technologies and regulatory scenarios to improve the accuracy of the analysis and anticipate changes in the ETS cost structure. Full article

Ships are the main carriers of maritime transportation. Real-time object detection of ships through remote sensing satellites is of great significance in ocean rescue, maritime traffic, border management, etc. In remote sensing ship detection, the complexity and diversity of ship shapes, along with scenarios involving ship aggregation, often lead to false negatives and false positives. The diversity of ship shapes can cause detection algorithms to fail in accurately identifying different types of ships. In cases where ships are clustered together, the detection algorithm may mistakenly classify multiple ships as a single target or miss ships that are partially obscured. These factors can affect the accuracy and robustness of the detection, increasing the challenges in remote sensing ship detection. In view of this, we propose a remote sensing ship detection method, E-WFF Net, based on YOLOv8s. Specifically, we introduced a data enhancement method based on elliptical rotating boxes, which increases the sample diversity in the network training stage. We also designed a dynamic attention mechanism feature fusion module (DAT) to make the network pay more attention to ship characteristics. In order to improve the speed of network inference, we designed a residual weighted feature fusion method; by adding a feature extraction branch while simplifying the network layers, the inference speed of the network was accelerated. We evaluated our method on the HRSC2016 and DIOR datasets, and the results show some improvements compared to YOLOv8 and YOLOv10, especially on the HRSC2016 dataset. The results show that our method E-WFF Net achieves a detection accuracy of 96.1% on the HRSC2016 dataset, which is a 1% improvement over YOLOv8s and a 1.1% improvement over YOLOv10n. The detection speed is 175.90 FPS, which is a 3.2% improvement over YOLOv8 and a 9.9% improvement over YOLOv10n. Full article