Search Results (859)

Given the central importance of maritime logistics to global trade, accurate and efficient vessel motion forecasting is essential for strengthening supply chain resilience and improving operational efficiency. However, traditional physical and statistical models often fail to effectively capture the multivariate, noisy, and strongly coupled nature of maritime dynamics. In this manuscript, we adapt the DSformer architecture for ship motion forecasting, leveraging its dual sampling and dual-attention design to address the multi-scale and cross-variable dependencies inherent in maritime data. Across three real-world datasets, the adapted DSformer reduces prediction error by 23% and training time by 70% compared with 13 state-of-the-art (SOTA) baselines. Moreover, we identify a consistent relationship between sampling strategies and sea states, where dense sampling performs best under stable conditions, whereas moderately sparse sampling with multi-head attention improves robustness under turbulent environments. These results apply the algorithm’s new capabilities to the daily management of maritime logistics. By adapting the architecture to real-world operational settings and optimizing its key parameters, the approach enables efficient, real-time vessel forecasting and decision support across global supply chains. Full article

In response to the International Maritime Organization’s strengthened regulations on carbon emissions, the introduction of novel eco-friendly technologies for ship operators has become necessary. In this context, various energy saving devices such as wind-assisted propulsion systems (e.g., wing/rotor sails), propeller-rudder efficiency enhancers (e.g., pre-swirl stators or ducted propellers), and the gate rudder system have been proposed. Among various energy-saving technologies, the air lubrication system has been widely investigated as an effective means of reducing hull frictional resistance through air injection beneath the hull. The performance of air lubrication systems can be evaluated through experimental testing or computational fluid dynamics (CFD) simulations. However, accurately simulating air lubrication systems in CFD remains challenging. Therefore, this study aims to quantitatively evaluate the influence of numerical parameters on the CFD implementation of air lubrication systems. To evaluate these influences, CFD simulations employing the unsteady Reynolds-averaged Navier–Stokes (URANS) method were conducted to investigate air layer formation and sweep angle on a flat plate. The numerical predictions were systematically compared with experimental results by varying key numerical parameters. These quantitative estimations of the effects of numerical variables are expected to serve as a useful benchmark for CFD simulations of air lubrication systems. Full article

With the gradual rise of battery-powered ships, the high-power charging demand during berthing is poised to exacerbate the peak-to-valley difference in the port grid, possibly leading to grid congestion and logistical disruption. To address this challenge, this paper proposes a bi-level coordinated scheduling scheme across both logistical operations and energy flow dispatch. Initially, by developing a refined model for the dynamic power characteristics of quay crane (QC) clusters, the surplus power capacity that can be stably released through an orderly QC operational delay is quantified. Subsequently, a hybrid AC/DC ship–shore interconnection architecture based on a smart interlinking unit (SIU) is proposed to utilize the QC peak-shaving capacity and satisfy the increasing shore power demand. In light of these, at the logistics level a coordinated scheduling of berths, QCs, and ships charging is performed with the objective of minimizing port berthing operational costs. At the energy flow level, the coordinated delay in QC clusters’ operations and SIU-enabled power dispatching are implemented for charging power support. The case studies demonstrate that, compared with the conventional independent operational mode, the proposed coordinated scheduling scheme enhances the shore power supply capability by utilizing the QC peak-shaving capability effectively. Moreover, as well as satisfying the charging demands of electric ships, the proposed scheme significantly reduces the turnaround time of ships and achieves a 39.29% reduction in port berthing operational costs. Full article

The shipbuilding industry relies heavily on welding, which accounts for approximately 70% of the overall production process. However, the recent decline in skilled workers, together with rising labor costs, has accelerated the automation of shipbuilding operations. In particular, the welding activities are concentrated in the double-bottom region of ships, where collaborative robots are increasingly introduced to alleviate workforce shortages. Because these robots must directly recognize U-shaped weld joints, this study proposes an image-based classification system capable of automatically identifying and classifying such joints. In double-bottom structures, U-shaped weld joints can be categorized into 176 types according to combinations of collar plate type, slot, watertight feature, and girder. To distinguish these types, deep learning-based image recognition is employed. To construct a large-scale training dataset, 3D Computer-Aided Design (CAD) models were automatically generated using Open Cascade and subsequently rendered to produce synthetic images. Furthermore, to improve classification performance, the input images were split into left, right, upper, and lower regions for both training and inference. The class definitions for each region were simplified based on the presence or absence of key features. Consequently, the classification accuracy was significantly improved compared with an approach using non-split images. Full article

Wind-assisted propulsion using Flettner rotors has gained attention as the shipping sector faces stricter decarbonization regulations. This study compares conventional Fixed Speed Control with Adaptive Speed Control for a 100 m coastal vessel. The proposed Adaptive Speed Control selectively activates the rotor based on relative wind conditions and adjusts rotor speed according to the surge-direction projection of Magnus force. A simulation framework based on the MMG maneuvering model evaluates path-following performance, fuel consumption, and annual performance indicators. Results show that Adaptive Speed Control achieves 18.84% reduction in fuel consumption, corresponding to annual savings of 212.02 tons of fuel, USD 190,823 in OPEX, and 679.76 tons of CO₂ emissions. Selective rotor operation reduces the Fatigue Damage Index by approximately 89%, resulting in 84.48% reduction in annual maintenance costs. Unwanted lateral forces and yaw disturbances are mitigated, improving path-following and maneuvering stability. These findings demonstrate that situationally aware Adaptive Speed Control improves energy efficiency and operational characteristics of Flettner-rotor-assisted propulsion systems while maintaining maneuvering performance, providing practical guidance for wind-assisted ship operation under realistic coastal conditions. Full article

AI technologies are increasingly applied to optimize operations in both port and urban logistics systems, yet integration across the full maritime city chain remains limited. The objective of this study is to assess, using a simulation-based experiment, the impact of an AI-orchestrated control policy on the performance of port–city logistics relative to a baseline scheduler. The study proposes an AI-orchestrated approach that connects autonomous ships, smart ports, central warehouses, and multimodal urban networks via a shared cloud control layer. This approach is designed to enable real-time, cross-domain coordination using federated sensing and adaptive control policies. To evaluate its impact, a simulation-based experiment was conducted comparing a traditional scheduler with an AI-orchestrated policy across 20 paired runs under identical conditions. The orchestrator dynamically coordinated container dispatching, vehicle assignment, and gate operations based on capacity-aware logic. Results show that the AI policy substantially reduced the total completion time, lowered truck idle time and estimated emissions, and improved system throughput and predictability without modifying physical resources. These findings support the expectation that integrated, data-driven decision-making can significantly enhance logistics performance and sustainability in port–city contexts. The study provides a replicable pathway from conceptual architecture to quantifiable evidence and lays the groundwork for future extensions involving learning controllers, richer environmental modeling, and real-world deployment in digitally connected logistics corridors. Full article

The Estimated Time of Arrival (ETA) of vessels is a vital operational indicator for voyage planning, fleet deployment, and resource allocation. However, most existing studies focus on short-distance liner services with fixed routes, while ETA prediction for long-distance tramp bulk carriers remains insufficiently accurate, often resulting in operational inefficiencies and charter party disputes. To fill this gap, this study proposes a data-driven stacking ensemble learning framework that integrates Light Gradient-Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) as base learners, combined with a Linear Regression meta-learner. This framework is specifically tailored to the unique complexities of tramp shipping, advancing beyond traditional single-model approaches by incorporating systematic feature engineering and model fusion. The study also introduces the construction of a comprehensive multi-dimensional AIS feature system, incorporating baseline, temporal, speed-related, course-related, static, and historical behavioral features, thereby enabling more nuanced and accurate ETA prediction. Using AIS trajectory data from bulk carrier voyages between Weipa (Australia) and Qingdao (China) in 2023, the framework leverages multi-feature fusion to enhance predictive performance. The results demonstrate that the stacking model achieves the highest accuracy, reducing the Mean Absolute Error (MAE) to 3.30 h—a 74.7% improvement over the historical averaging benchmark and an 11.3% reduction compared with the best individual model, XGBoost. Extensive performance evaluation and interpretability analysis confirm that the stacking ensemble provides stability and robustness. Feature importance analysis reveals that vessel speed, course stability, and remaining distance are the primary drivers of ETA prediction. Additionally, meta-learner weighting analysis shows that LightGBM offers a stable baseline, while systematic deviations in XGBoost predictions act as effective error-correction signals, highlighting the complementary strengths captured by the ensemble. The findings provide operational insights for maritime logistics and port management, offering significant benefits for port scheduling and maritime logistics management. Full article

Reducing CO₂ emissions from global shipping remains a critical challenge in the pursuit of sustainable international trade. Though the technical and operational (T/O) measures and alternative fuel (AF) solutions have shown promise, the global maritime sector continues to face strategic and structural hurdles. This thematic narrative review revisits the fundamentals and explores the roles of T/O measures and Alternative fuel options in reducing CO₂ emissions in international shipping, with a focus on the maritime energy transition. The study reveals that maximizing the benefits of T/O measures, alongside establishing a balanced energy transition matrix encompassing clean energy sources, can foster an environment conducive to future sustainability performance and substantial CO₂ emission reductions. More specifically, combining operational efficiency improvements with scalable, future-focused, infrastructure-ready alternative fuels can yield significant emission reductions. The paper also introduces a conceptual model to guide the maritime energy transition, outlining a phased pathway that leverages innovation, policy, and system-level design. These insights contribute to shaping a resilient roadmap for decarbonizing international shipping by enhancing the sector’s sustainability performance. Full article

Ship detection serves as a core foundational task for marine environmental perception. However, in real marine scenarios, dense vessel traffic often causes severe target occlusion while multi-scale targets, asymmetric vessel geometries, and harsh conditions (e.g., haze, low illumination) further degrade image quality. These factors pose significant challenges to vision-based ship detection methods. To address these issues, we propose ${\mathrm{M}}^2\mathrm{S}\text{-}\mathrm{YOLOv}8$, an improved framework based on YOLOv8, which integrates three key enhancements: First, a Multi-Scale Asymmetry-aware Parallelized Patch-wise Attention (MSA-PPA) module is designed in the backbone to strengthen the perception of multi-scale and geometrically asymmetric vessel targets. Second, a Deformable Convolutional Upsampling (DCNUpsample) operator is introduced in the Neck network to enable adaptive feature fusion with high computational efficiency. Third, a Wasserstein-Distance-Based Weighted Normalized CIoU (WA-CIoU) loss function is developed to alleviate gradient imbalance in small-target regression, thereby improving localization stability. Experimental results on the Unmanned Vessel Zhoushan Perception Dataset (UZPD) and the open-source Singapore Maritime Dataset (SMD) demonstrate that ${\mathrm{M}}^2\mathrm{S}\text{-}\mathrm{YOLOv}8$ achieves a balanced performance between lightweight design and real-time inference, showcasing strong potential for reliable deployment on edge devices of unmanned marine platforms. Full article

Accurate estimation of ship emissions is essential for the effective enforcement of emission control policies in inland waterways. However, existing “bottom-up” models face significant challenges owing to severe data scarcity for inland ships, particularly regarding ship static parameters. This study proposes a novel data fusion and machine learning framework to address this issue. The methodology integrates real-time SO₂ and CO₂ pollutant concentrations on the Nanjing Dashengguan Yangtze River Bridge, Automatic Identification System (AIS) data, and meteorological information. To address the scarcity of design data for inland ships, web scraping was used to extract basic parameters, which were then used to train five machine learning models. Among them, the XGBoost model demonstrated superior performance in predicting the main engine rated power. A refined activity-based emission model combines these predicted parameters, ship operational profiles, and specific emission factors to calculate real-time emission source strengths. Furthermore, the model was validated against field measurements by comparing the calculated and measured emission source strengths from ships, demonstrating high predictive accuracy with R² values of 0.980 for SO₂ and 0.977 for CO₂, and MAPE below 13%. This framework provides a reliable and scalable approach for real-time emission monitoring and supports regulatory enforcement in inland waterways. Full article

Wind-assisted ship propulsion (WASP) has gained considerable interest as a means of reducing fuel consumption and Greenhouse Gas (GHG) emissions, with further benefits when combined with weather-optimized routing. This study employs and extends a National Technical University of Athens (NTUA) weather-routing optimization tool to more realistically assess WASP performance through integrated modeling. The original tool minimized fuel consumption using forecasted weather data and a physics-based performance model. A previous extension to account for the WASP effect introduced a 1-Degree Of Freedom (DOF) model that accounted only for longitudinal hydrodynamic and aerodynamic forces, estimating the reduced main-engine power required to maintain speed in given conditions. The current study incorporates a 3-DOF model that includes side forces and yaw moments, capturing resulting drift and rudder deflection effects. A Kamsarmax bulk carrier equipped with suction sails served as the case study. Initial simulations across various operating and weather conditions compared the two models. The 1-DOF model predicted fuel-saving potential up to 26% for the tested apparent wind speed and the range of possible headings, whereas the 3-DOF model indicated that transverse effects reduce WASP benefits by 2–7%. Differences in Main Engine (ME) power estimates between the two models reached up to 7% Maximum Continuous Rating (MCR) depending on the speed of wind. The study then applied both models within a weather-routing optimization framework to assess whether the optimal routes produced by each model differ and to quantify performance losses. It was found that the revised optimal route derived from the 3-DOF model improved total Fuel Oil Consumption (FOC) savings by 1.25% compared with the route optimized using the 1-DOF model when both were evaluated with the 3-DOF model. Full article

Modern port operations face increasing challenges from rapidly changing weather and environmental conditions, requiring accurate short-term forecasting to support safe and efficient maritime activities. This study presents a sensor fusion-based machine learning framework for real-time multi-target nowcasting of wind gust speed, sustained wind speed, and wind direction using heterogeneous data collected at the Port of Livorno from February to November 2025. Using an IoT architecture compliant with the oneM2M standard and deployed at the Port of Livorno, CNIT integrated heterogeneous data from environmental sensors (meteorological stations, anemometers) and vessel-mounted LiDAR systems through feature-level fusion to enhance situational awareness, with gust speed treated as the primary safety-critical variable due to its substantial impact on berthing and crane operations. In addition, a comparative performance analysis of Random Forest, XGBoost, LSTM, Temporal Convolutional Network, Ensemble Neural Network, Transformer models, and a Kalman filter was performed. The results show that XGBoost consistently achieved the highest accuracy across all targets, with near-perfect performance in both single-split testing (R² ≈ 0.999) and five-fold cross-validation (mean R² = 0.9976). Ensemble models exhibited greater robustness than deep learning approaches. The proposed multi-target fusion framework demonstrates strong potential for real-time deployment in Maritime Autonomous Surface Ship (MASS) systems and port decision-support platforms, enabling safer manoeuvring and operational continuity under rapidly varying environmental conditions. Full article

This study presents a human-navigable ship-handling support system that employs artificial intelligence (AI) for survey line tracking. AI was developed using the Deep Deterministic Policy Gradient (DDPG), a type of deep reinforcement learning (DRL), and was evaluated through experiments conducted with a research vessel. The experiments revealed several issues inherent to DRL that required improvement. The first issue was the asymmetry observed in the policy learned through the DDPG. To address this, a learning approach that utilizes symmetric training data and symmetry-constrained actor and critic neural networks was proposed. The second issue was excessive steering during tracking maneuvers. To mitigate this, an objective function for actor learning that incorporates a cost term to suppress the magnitude of actions was proposed. The third issue was the frequent oscillation of actions. To resolve this, improved conditioning for action policy smoothing was introduced in the objective function to smooth actions appropriate to the situation. A subsequent experiment at sea was conducted to evaluate the improved AI-based ship-handling support system. As a result, precise path tracking performance with minimal operator discomfort and smooth control actions was achieved through manual ship handling guided by AI-generated instructions under actual sea conditions. Full article

This study provides a comprehensive analysis of the impact of different marine fuels such as heavy fuel oil (HFO), methane, methanol, ammonia, or hydrogen, on energy efficiency and pollutant emissions in maritime transport, using a combined application of the Energy Efficiency Design Index (EEDI), Energy Efficiency Operational Indicator (EEOI), and Carbon Intensity Indicator (CII). The results show that methane offers the most balanced alternative, reducing CO₂ by more than 30% and improving energy efficiency, while methanol provides an intermediate performance, eliminating sulfur and partially reducing emissions. Ammonia and hydrogen eliminate CO₂ but generate NO_x (nitrogen oxides) emissions that require mitigation, demonstrating that their environmental impact is not negligible. Unlike previous studies that focus on a single fuel or only on CO₂, this work considers multiple pollutants, including SO_x (sulfur oxides), H₂O, and N₂, and evaluates the economic cost of emissions under the European Union Emissions Trading System (EU ETS). Using a representative model ship, the study highlights regulatory gaps and limitations within current standards, emphasizing the need for a global system for monitoring and enforcing emissions rules to ensure a truly sustainable and decarbonized maritime sector. This integrated approach, combining energy efficiency, emissions, and economic evaluation, provides novel insights for the scientific community, regulators, and maritime operators, distinguishing itself from previous multicriteria studies by simultaneously addressing operational performance, environmental impact, and regulatory gaps such as unaccounted NO_x emissions. Full article

This study investigates the sailing performance and maneuverability of a fully wind-powered ship equipped with two rigid wing sails and a rudder, using Computational Fluid Dynamics (CFD). Unlike some conventional approaches that separately analyze above-water and underwater forces, this research employs a comprehensive CFD model to predict ship motion and performance under various wind directions and sail angles, from a stationary state to steady sailing. The accuracy of the CFD method is confirmed through comparison with experimental drift test data. Although the simulated drift data showed some discrepancies from the observed data due to the difficulty of accurately modeling the wind field in the simulation, the results indicate that the CFD method can effectively reproduce the ship motions observed in the experiments. Simulations reveal that the previously proposed L-shaped and T-shaped sail arrangements, which were designed to maximize thrust without considering maneuvering effects, remain effective even when ship motion is included. However, the results also show that conventional sail arrangements can achieve higher steady-state speeds due to reduced leeway-related resistance, while the L-shaped and T-shaped arrangements yield distinct steady-state leeway (drift) characteristics under heading control. These findings suggest that dynamically adjusting sail arrangements according to operational requirements may help manage the ship’s trajectory (lateral offset) and mitigate maneuvering difficulties, contributing to the practical application of fully wind-powered ships. The study provides quantitative insights into the relationship between sail arrangement, acceleration, and leeway/drift behavior, supporting the design of next-generation wind-powered ships. Full article