Search Results (518)

Accurate vessel traffic flow prediction provides an important data basis for intelligent shipping management, including maritime traffic monitoring, navigational risk awareness, waterway organization, and emission-related assessment. Although recent studies have advanced spatiotemporal, graph-based, and hybrid forecasting methods, improving the predictive ability of a conventional ConvLSTM backbone without introducing substantially more complex model structures remains underexplored in grid-based waterway scenarios. This study proposes a lightweight CA-ConvLSTM framework for grid-level vessel inflow and outflow prediction. AIS-derived flow data and MERRA-2 meteorological variables are rasterized onto a common spatial grid and fused at an early stage. A residual dilated convolution module with dilation rates of 1, 2, and 4 is used to extract multi-scale spatial dependencies, and a channel attention mechanism is applied before ConvLSTM-based temporal prediction to adaptively reweight the fused flow-meteorological feature channels. Experiments using AIS and MERRA-2 data from the northern Bohai Strait waterway show that the proposed framework improves baseline ConvLSTM performance. Compared with ConvLSTM, CA-ConvLSTM reduces MSE and MAE by 24.93% and 12.55% for outflow prediction, and by 24.80% and 12.82% for inflow prediction. These results suggest that spatially aligned meteorological fusion, multi-scale spatial feature extraction, and channel-wise feature weighting can effectively enhance ConvLSTM-based grid-level vessel traffic flow prediction without relying on complex model fusion or heavy graph-based architectures. Full article

Ship exhaust emissions have become an increasingly prominent global atmospheric environmental issue, triggering a series of ecological disturbances and adverse public health consequences. However, comprehensive analyses of the research progress and evolution trends in this field remain scarce. This study systematically retrieved 1346 scholarly publications in the ship exhaust emissions field for the period 2011–2025 from the Web of Science Core Collection and carried out a bibliometric analysis encompassing publication outputs, contributing countries/regions, and keyword characteristics. The findings reveal a sustained and robust growth trajectory in global research output, with annual publications increasing nearly fivefold over the 15-year study period. Notably, academic interest in this field has increased significantly since 2020 due to the implementation of the global sulfur cap regulation. Core thematic clusters (mean silhouette S = 0.7205) in this field include source apportionment, numerical modeling analysis, atmospheric criteria pollutants, and technological emission reduction strategies. The geographical distribution of research output shows a significant positive correlation with the importance of regional maritime economies. China, the United States, and Germany are the leading contributors in terms of publication outputs, while frequent research collaborations have been observed among European countries. Since 2021, the emergence of Automatic Identification System data as a keyword with high burst strength (intensity = 3.60) marks a paradigm shift toward a “big data-enabled refined management” framework. Concurrently, the sustained burst activity of keywords including nitrogen oxides, volatile organic compounds, and traffic-related emissions from 2023 to 2025 indicates rapidly growing scholarly attention to secondary aerosol precursors from shipping, and the critical need for coordinated multi-pollutant control strategies. Future research directions for ship exhaust emissions are expected to transition from fundamental characterization research to big data-driven monitoring and estimation methods, as well as advanced emission reduction technologies. The bibliometric insights derived from this study provide a valuable reference framework for subsequent in-depth studies on ship exhaust emissions. Full article

In recent years, with the continuous development of the global economy, maritime transportation has become an essential mode of transportation for both domestic and international trade, making it an urgent task for countries around the world to improve the intelligence level of maritime traffic management. As a key technology in real-time vessel monitoring, traffic hazard early warning, and traffic flow estimation, vessel trajectory prediction has been widely applied across both civil and commercial sectors. To address the problems with existing trajectory prediction methods, such as difficulty in establishing real-time vessel kinematic models, limited understanding of vessel navigation strategies, and poor adaptability for long-term prediction, this paper proposes a deep reinforcement learning-based vessel trajectory prediction method. Firstly, the trajectory prediction problem is formulated as a Markov Decision Process (MDP), thereby transforming the prediction problem into an optimal policy-solving problem of the MDP. Secondly, given the complexity of the trajectory prediction problem, a convolutional neural network (CNN) is adopted to parameterize the policy network, and a CNN-PPO deep reinforcement learning method is used to learn the target vessel’s navigation strategy from historical trajectories. Comparative experimental results show that the proposed algorithm is not only suitable for predicting the target’s position at a specific future moment but also offers clear advantages in trajectory prediction across multiple future moments. Full article

Accurate segmentation of ship targets in high-resolution remote sensing images is crucial for maritime monitoring, traffic management and naval security. However, existing methods struggle to simultaneously address extreme scale variations in ships and severe complex background interference, leading to unsatisfactory accuracy and generalization in scenarios with shoreline occlusion and ocean wave noise. To tackle this challenge, we first construct a large-scale, high-quality multi-scale ship dataset containing 69,407 professionally annotated samples. Then, we propose ShipMS-BSNet, a multi-scale feature fusion network based on nnU-Net. At the encoder, the Multi-Scale Receptive Field Enhancement (MSRF) module captures multi-scale contextual information, while the Background Suppression Channel Attention (BSCA) module suppresses invalid background responses via learnable negative bias. At the decoder, dynamic upsampling restores spatial details, and a final Multi-Scale Refinement (MSR) module optimizes target boundaries. Extensive experiments on our self-built dataset and the public HRSC2016 dataset show that our method outperforms mainstream approaches. On the self-built dataset, it achieves 0.879 precision, 0.875 Recall, 0.868 F1-score and 0.761 IoU, validating its strong robustness for multi-scale ship segmentation in complex marine environments. Full article

Vessel traffic service systems in remote or newly established maritime regions face significant operational limitations due to the scarcity of historical AIS data, which undermines the reliability of trajectory-based situational awareness and collision risk assessment. Existing deep learning models, predominantly validated on data-rich major shipping corridors, suffer severe performance degradation under cross-domain deployment, rendering them impractical for vessel traffic management in underserved waters. To bridge this operational gap, this study proposes a Boundary-Aware Distillation and LoRA-Based Transfer (BD-LT) framework that enables reliable trajectory prediction with as few as 132 target-domain trajectories. The framework integrates HDBSCAN-based geographic-semantic domain partitioning, a Time-Aware Transformer with Time2Vec encoding for irregular AIS sampling, hybrid knowledge distillation with error-boundary gating for selective cross-domain transfer, and LoRA-based parameter-efficient adaptation to mitigate overfitting. Validated on NOAA full-scale AIS measurements, the framework reduces the 60 min Final Displacement Error by 35.2% relative to the no-framework baseline, consistently outperforming state-of-the-art models across all prediction horizons, with statistical reliability confirmed via bootstrap resampling. These results demonstrate the practical feasibility of deploying data-driven trajectory prediction in maritime regions where conventional approaches have historically been inapplicable, with direct implications for collision avoidance decision support and port approach traffic management. Full article

This perspective paper develops a system-level characterization of the transportation metaverse as a persistent, policy-aware digital environment integrating digital twins, real-time data, advanced analytics, and human–machine interaction into a unified operational framework. The study presents a cross-modal review of metaverse applications in road, rail, maritime, and aviation systems, identifying common opportunities, limitations, and research challenges. It further proposes a structured metaverse-based framework for smart roads as a reference case. The framework demonstrates how persistent virtualization, parallel future scenarios, embedded governance constraints, and human-in-the-loop decision support can improve uncertainty-aware planning, management, and operations. The paper positions the metaverse not as a deployable technology, but as an emerging paradigm for transportation governance. The study provides an architectural vision and research agenda for developing more resilient, transparent, and adaptive transportation systems. Potential applications include smart road management, multimodal traffic coordination, real-time operational control, infrastructure resilience planning, and decision support for policymakers under uncertain conditions. Full article

Accurate ship trajectory prediction is vital for intelligent maritime traffic management, yet conventional hybrid models often fail to balance local feature extraction, long-term dependency capture, and flexible feature weighting when processing AIS data. This paper proposes an MSC-BiLSTM-ATTENTION model that integrates trajectory clustering and an adaptive attention mechanism into a unified framework. Its fundamental advance over existing incremental hybrid architectures is twofold. First, a K-means clustering step groups trajectories with similar motion patterns before model training, effectively reducing the impact of data heterogeneity on prediction accuracy. Second, the deep learning backbone synergizes multi-scale convolution (MSC)—which captures local features at multiple temporal granularities via parallel kernels—with a bidirectional LSTM (BiLSTM) for forward–backward dependency learning, and an adaptive self-attention mechanism that dynamically optimizes feature weights to amplify critical navigation information. Extensive experiments on AIS data from the Gulf of Mexico and the U.S. Atlantic Coast, covering four seasons, benchmark the model against attention-enhanced architectures including Transformer, CNN-BiLSTM-ATTENTION, and DenseNet-BiGRU-ATTENTION across two distinct regions. The proposed model achieves significant improvements in predicting longitude, latitude, speed over ground, and course over ground, reducing MAE by over 76.9% and RMSE by over 65.3% compared with the strongest baseline. Ablation studies confirm that the synergy of all three modules is essential. The results demonstrate the model’s effectiveness and its practical value for intelligent maritime supervision, navigation risk warning, and waterborne traffic management. Full article

The Automatic Identification System (AIS) generates massive volumes of real-world ship trajectory data, providing a critical foundation for maritime ship-type classification. However, existing methods often struggle to simultaneously capture long-range temporal dependencies, maintain computational efficiency, and ensure model interpretability, making accurate multi-class classification challenging in real-world maritime environments. To address these limitations, this study proposes a robust and efficient hybrid framework that integrates a Feature Transformer module for deep temporal feature extraction with a LightGBM model for ensemble classification. The multi-head self-attention within the Feature Transformer captures long-range dependencies in preprocessed AIS sequences to generate compact 64-dimensional trajectory fingerprints. These deep representations are concatenated with 103 carefully designed kinematic, geometric, statistical, frequency-domain, and segment-level features and fed into a LightGBM classifier for final ship-type identification. We evaluate the framework on a real-world AIS dataset of 2196 trajectories collected between 2019 and 2023, covering 14 ship types under a natural long-tail distribution. Across five random seeds, the proposed hybrid model achieves 78.06% ± 1.15% accuracy (95% CI) and 74.09% ± 1.82% Macro-F1 (95% CI), significantly outperforming Transformer-only (65.09% accuracy) and LightGBM-only (66.85%) baselines, with paired statistical tests confirming the improvement (McNemar χ² = 172.07, p < 10⁻³⁹ vs. Transformer; χ² = 92.24, p < 10⁻²¹ vs. LightGBM). The hybrid model offers ultra-fast inference at 0.051 ms per trajectory on GPU at batch size 128 (≈19,500 samples/s), and provides instance-level interpretability via SHapley Additive exPlanations (SHAP) analysis. These properties make the framework practical for near-real-time maritime traffic monitoring and decision support. Full article

To optimize the freight distribution structure of ports and reduce carbon emissions from freight transportation, this paper develops a bi-level programming model for freight traffic shifting between roadway and waterway networks that incorporates carbon emissions. First, a complex freight network based on the roadway–water transport system is constructed, comprising roadway networks, inland waterway networks, maritime networks, and transshipment nodes. A traffic impedance model is then formulated within this complex network framework, integrating the roadway BPR function, the M/M/1 queuing model for lock passage time on inland waterways, and the M/M/c queuing model for port cargo handling into the impedance function. This allows micro-level congestion effects to be combined with macro-level traffic assignment. Next, a bi-level programming model for freight traffic shifting in the roadway–water network system is established, with carbon emissions incorporated. The NSGA-II algorithm is employed to determine the optimal carbon subsidy level, based on which the traffic distribution in the complex freight network is analyzed. Finally, the proposed model is applied to the roadway–waterway bimodal network in the Hangzhou Bay port area of Cixi. The results indicate that without subsidies, the waterway transport share is only 1.74%. The optimal subsidy efficiency frontier is identified at CNY 350,000/day, where the waterway share increases to 22.7% and carbon emissions decrease by 33.27 tons/day. The subsidy strategy evolves through three stages: first, prioritizing maritime shipping; second, jointly promoting inland and maritime shipping; and finally, shifting focus to infrastructure investment once subsidies reach saturation. This study offers a quantitative analytical tool for designing differentiated carbon subsidy policies to facilitate the road-to-waterway modal shift under fiscal constraints. Full article

Ship collision risk is a critical issue in maritime traffic safety regulation, as it directly affects the safety, efficiency, and sustainability of maritime transportation. It depends not only on the current encounter geometry among ships, but is also closely related to the ship trajectory distribution structure and the traffic state. Existing studies have mostly identified collision risk based on collision avoidance parameters. Although such methods can characterize explicit collision risks, they remain insufficient in identifying the additional risks induced by trajectory densification, uncovering the potential risks reflected by frequent trajectory intersection and change, and representing the structural collision risks of regional traffic. To address these limitations, this study proposes a trajectory analysis-based regional collision risk model within the framework of the radial distribution function. First, the mapping relationships between collision risk and three aspects, namely trajectory density, trajectory conflict, and trajectory abruptness, are established, which are respectively characterized by trajectory density and aggregation, trajectory intersections and time differences, and trajectory alterations and fluctuations. Then, the ship traffic system is transformed into a particle system, and two-dimensional radial distribution feature planes for the above three aspects are constructed to identify the risk level of a region from different dimensions. Finally, a three-dimensional fusion space is further developed to achieve a comprehensive quantification of collision risk in a specified water area. Experiments were conducted using one week of daytime and nighttime Automatic Identification System (AIS) data from the Bohai Strait. The proposed model showed a strong temporal correlation with AIS record-based high-risk patterns (R = 0.866, p = 0.01), and, compared with a regional collision risk model based on traditional collision avoidance parameters, exhibited 20–50% higher sensitivity in identifying additional and potential risks caused by dense, intersecting, and abrupt trajectory patterns. The proposed model can provide methodological support for maritime authorities in collision risk monitoring of key waters, precise allocation of regulatory resources, and proactive safety regulation, thereby contributing to safer and more sustainable maritime transportation. Full article

With the development of sustainable inland waterway transportation, maritime safety has become a matter of wide concern. Causation analysis of ship accidents is known as an important prerequisite for achieving waterborne transportation safety. A novel AcciMap–graph convolutional network (GCN)-based causation analysis model has been proposed for maritime traffic accidents in the main Yangtze River waterway. Mutual information, a grey wolf optimizer (GWO), a dropout layer and weakly supervised learning are introduced to obtain causal chains and the causal importance scores of accident causes. The results indicate that, compared with baseline models (e.g., GIN, GAT, APPNP, and GraphSAGE), the AcciMap–GCN model achieved the lowest values for all performance metrics, with an MAE of 0.0820, an RMSE of 0.1694, and a training convergence epoch of 74. The model robustness was comprehensively investigated through ablation experiments. Based on the causal importance scores of accident causes, maritime safety administration strategies are systematically proposed. The present study provides a novel perspective for analyzing the causal relationships of maritime accidents and shares useful insights into sustainable waterway transportation. Full article

This study examines the probabilistic patterns associated with casualty severity in collisions between commercial and fishing vessels in China’s coastal waters. Using 137 official accident investigation reports from 2013 to 2022, a structured dataset capturing vessel characteristics, environmental conditions, and human liability factors was constructed. A Tree-Augmented Bayesian Network (TAN-BN) was developed to model the probabilistic interactions among these variables and to identify the key drivers of casualty severity. Sensitivity analysis based on mutual information indicates that fishing vessel length is the most influential factor affecting casualty outcomes (MI = 0.322), followed by time of occurrence, wind speed, visibility, and season. Scenario analysis using MPE indicates that severe casualty scenarios are associated with adverse temporal and environmental conditions such as nighttime, poor visibility, and open-water environments, while liability-specific analysis further shows that collisions attributed primarily to commercial vessel errors are most likely to result in 4–10 casualties. The results highlight the structural vulnerability of small fishing vessels and the critical role of environmental exposure in heterogeneous vessel encounters. This study provides an interpretable probabilistic framework for examining casualty severity patterns in maritime collisions and offers risk-informed insights for improving sustainable maritime safety management in mixed-traffic coastal waters. Full article

The Gulf of Mexico and the Caribbean Sea are key regions of the western Atlantic, where sea-state conditions are critical for coastal safety and offshore operations. This study analyzes wave climate trends (1981–2022) using WAVEWATCH III simulations validated against buoy observations. The Mann–Kendall test and Theil–Sen estimator were employed to quantify trends in significant wave height (Hs), energy period (Te), and wave power (P), while correlation analysis was performed to explore teleconnections with the Oceanic Niño Index (ONI), Atlantic Multidecadal Oscillation (AMO), and North Atlantic Oscillation (NAO). The results reveal basin-wide increases in mean Hs and P, characterized by pronounced spatial and seasonal heterogeneity. The most robust positive trends occur during winter and spring; in summer and fall, the weaker or negative tendencies, particularly in Te, suggest an intensification of seasonal contrasts rather than uniform change. Teleconnection analysis demonstrates that, among the climate indices considered in this study, ENSO is the primary driver of interannual wave variability in the Caribbean, particularly modulating wave power through remotely generated swell. While the NAO exerts regionally dependent control associated with storm-track modulation, the AMO plays a secondary role, affecting swell-dominated sectors. In contrast, the Gulf of Mexico shows limited sensitivity to large-scale climate modes, with wave variability largely governed by local wind–sea processes. These findings highlight the contrasting wave dynamics between these two basins, providing critical insights for coastal hazard assessments, maritime traffic along major shipping routes, oil spill management, and regional wave energy planning. Full article

Mediterranean meadows of Posidonia oceanica are chronically exposed to complex mixtures of environmental contaminants, including metals and trace elements derived from coastal urbanization, maritime traffic, and industrial activities. This study aimed to assess metal-related toxicity in P. oceanica by integrating multi-element burden analysis with a panel of oxidative stress biomarkers. Concentrations of a wide suite of elements were quantified in samples of internal (juvenile), intermediate, and external (adult) leaves, reflecting the ontogenetic structure of the plant. Oxidative responses were evaluated using five biomarkers [i.e., hydrogen peroxide (H₂O₂), lipid peroxidation (TBARS), superoxide dismutase (SOD), glutathione S-transferase (GST), and catalase (CAT)] measured on each leaf compartment. Biomarker data were standardized and integrated into a merged Stress Index summarizing overall physiological toxicity. Associations between individual elements, the sum of all measured elements (ΣallElements), the Stress Index, and single biomarkers were explored using Pearson correlation analysis. Juvenile leaves exhibited the highest Stress Index values, elevated H₂O₂ and TBARS, and marked activation of SOD and GST, indicating early oxidative toxicity. Intermediate leaves showed a trend toward increased CAT activity, not reaching statistical significance, along with minimal damage, suggesting effective detoxification, whereas adult leaves accumulated higher levels of Fe, Ni, and Pb, but displayed moderate stress responses. Overall, leaf-class structure strongly modulated both exposure and toxicological response. The integration of ΣAllElements with multi-biomarker indices provides a robust framework for diagnosing metal-related toxicity in P. oceanica under realistic multi-element exposure scenarios. Full article

With the rapid growth of the global shipping industry and the increasing availability of Automatic Identification System (AIS) data, accurate vessel trajectory prediction has become crucial for ensuring navigational safety and optimizing maritime traffic management. This paper presents a systematic review of recent advances in deep learning-based methods for vessel trajectory prediction. We provide a comprehensive analysis of mainstream models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, Sequence-to-Sequence (Seq2Seq) models, and the Transformer architecture. Their performance is compared in terms of spatio-temporal data processing capability, prediction accuracy, and computational efficiency. Furthermore, this review examines practical applications of these methods in scenarios such as collision avoidance and route optimization. Despite notable progress, several challenges remain, including data quality issues, real-time prediction capability, and model interpretability. Future research directions may focus on multi-source data fusion and the development of lightweight model designs to further improve prediction performance. This survey aims to serve as a valuable reference for researchers and contribute to ongoing innovation in vessel trajectory prediction technology. Full article