Search Results (128)

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

Ballast water has become a significant vector for the global spread of non-indigenous aquatic species. These species may cause severe ecological disruption and economic losses when introduced into new environments. Traditional monitoring techniques often lack the sensitivity and efficiency required for early monitoring, hindering timely and effective management. In this study, we used environmental DNA (eDNA) technology to assess fish diversity and identify non-indigenous fish species in ballast water samples collected from 14 international vessels entering Dongjiakou Port, China. Genetic evidence of five non-indigenous fish species was monitored, including two recognized invasive species (Lates calcarifer and Anguilla anguilla). Among all groups, samples from Group B (V2, V3, V6, V8) exhibited the highest diversity of non-indigenous species, suggesting regional differences in species composition that may reflect source port biodiversity. These findings highlight the utility of eDNA-based monitoring not only for early detection of potentially non-indigenous taxa but also for capturing biogeographic patterns associated with global maritime traffic. By demonstrating the effectiveness of this approach at an international port, this study contributes a scientific foundation for both local biodiversity conservation and broader ecological surveillance, offering valuable insights for the ongoing development of ballast water management strategies worldwide. 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

The long-term sustainability of seaports depends on various operational factors, including infrastructure efficiency, digital innovation, environmental management, and regulatory compliance, among which maritime pilotage plays a crucial role in ensuring safe navigation and minimizing environmental, economic, and social risks. This research employed the PRISMA-ScR framework to evaluate the environmental, economic, and social impacts of pilotage on the sustainability of seaports. The findings demonstrate efficient navigation and spill avoidance, which reduce emissions, safeguard marine biodiversity, and maintain water quality. Economically, it reduces delays, optimizes operational expenses, and increases port competitiveness by increasing maritime traffic. Moreover, pilotage improves navigational safety, local professional skill development, and community interactions via ecological conservation and operational efficiency. It also indicates how environmental initiatives benefit the economy, increase port competitiveness, and promote job security and community happiness. The results also emphasize the significance of pilotage in sustainable seaport operations by quantifying pollution reductions, cost savings, and safety. The result also suggests that successful pilotage enhances ports’ viability and responsibility in global shipping networks while addressing environmental, economic, and social concerns. Full article

SAR ship detection is of great significance in marine safety, fisheries management, and maritime traffic. At present, many deep learning-based ship detection methods have improved the detection accuracy but also increased the complexity and computational cost. To address the issue, a lightweight prior feature fusion network (LPFFNet) is proposed to better improve the performance of SAR ship detection. A perception lightweight backbone network (PLBNet) is designed to reduce model complexity, and a multi-channel feature enhancement module (MFEM) is introduced to enhance the SAR ship localization capability. Moreover, a channel prior feature fusion network (CPFFNet) is designed to enhance the perception ability of ships of different sizes. Meanwhile, the residual channel focused attention module (RCFA) and the multi-kernel adaptive pooling local attention network (MKAP-LAN) are integrated to improve feature extraction capability. In addition, the enhanced ghost convolution (EGConv) is used to generate more reliable gradient information. And finally, the detection performance is improved by focusing on difficult samples through a smooth weighted focus loss function (SWF Loss). The experimental results have verified the effectiveness of the proposed model. Full article

Synthetic aperture radar (SAR) ship detection holds significant application value in maritime monitoring, marine traffic management, and safety maintenance. Despite remarkable advances in deep-learning-based detection methods, performance remains constrained by the vast size differences between ships, limited feature information of small targets, and complex environmental interference in SAR imagery. Although many studies have separately tackled small target identification and multi-scale detection in SAR imagery, integrated approaches that jointly address both challenges within a unified framework for SAR ship detection are still relatively scarce. This study presents MC-ASFF-ShipYOLO (Monte Carlo Attention—Adaptively Spatial Feature Fusion—ShipYOLO), a novel framework addressing both small target recognition and multi-scale ship detection challenges. Two key innovations distinguish our approach: (1) We introduce a Monte Carlo Attention (MCAttn) module into the backbone network that employs random sampling pooling operations to generate attention maps for feature map weighting, enhancing focus on small targets and improving their detection performance. (2) We add Adaptively Spatial Feature Fusion (ASFF) modules to the detection head that adaptively learn spatial fusion weights across feature layers and perform dynamic feature fusion, ensuring consistent ship representations across scales and mitigating feature conflicts, thereby enhancing multi-scale detection capability. Experiments are conducted on a newly constructed dataset combining HRSID and SSDD. Ablation experiment results demonstrate that, compared to the baseline, MC-ASFF-ShipYOLO achieves improvements of 1.39% in precision, 2.63% in recall, 2.28% in ${AP}_{50}$, and 3.04% in $AP$, indicating a significant enhancement in overall detection performance. Furthermore, comparative experiments show that our method outperforms mainstream models. Even under high-confidence thresholds, MC-ASFF-ShipYOLO is capable of predicting more high-quality detection boxes, offering a valuable solution for advancing SAR ship detection technology. Full article

Efficient ship scheduling in inland waterways is critical for maritime transportation safety and economic viability. However, traditional scheduling methods, primarily based on First Come First Served (FCFS) principles, often produce suboptimal results due to their inability to account for complex spatial–temporal dependencies, directional asymmetries, and varying ship characteristics. This paper introduces SSRL (Ship Scheduling through Reinforcement Learning), a novel framework that addresses these limitations by integrating three complementary components: (1) a Q-learning framework that discovers optimal scheduling policies through environmental interaction rather than predefined rules; (2) a clustering mechanism that reduces the high-dimensional state space by grouping similar ship states; and (3) a sliding window approach that decomposes the scheduling problem into manageable subproblems, enabling real-time decision-making. We evaluated SSRL through extensive experiments using both simulated scenarios and real-world data from the Xiaziliang Restricted Waterway in China. Results demonstrate that SSRL reduces total ship waiting time by 90.6% compared with TSRS, 48.4% compared with FAHP-ES, and 32.6% compared with OSS-SW, with an average reduction of 57.2% across these baseline methods. SSRL maintains superior performance across varying traffic densities and uncertainty conditions, with the optimal information window length of 13–14 ships providing the best balance between solution quality and computational efficiency. Beyond performance improvements, SSRL offers significant practical advantages: it requires minimal computation for online implementation, adapts to dynamic maritime environments without manual reconfiguration, and can potentially be extended to other complex transportation scheduling domains. Full article

In response to the increasing frequency of maritime traffic accidents along China’s coast, this study develops an accident-cause analysis framework that integrates an optimized Bidirectional Encoder Representations from Transformers (BERT) with a Bidirectional Long Short-Term Memory network (BiLSTM), combined with the Apriori association rule algorithm. Systematic performance comparisons demonstrate that the BERT + BiLSTM architecture achieves superior unstructured-text-processing capability, attaining 89.8% accuracy in accident-cause classification. The hybrid framework enables comprehensive investigation of complex interactions among human factors, vessel characteristics, environmental conditions, and management practices through multidimensional analysis of accident reports. Our findings identify improper operations, fatigue-related issues, illegal modifications, and inadequate management practices as primary high-risk factors while revealing that multi-factor interaction patterns significantly influence accident severity. Compared with traditional single-factor analysis methods, the proposed framework shows marked improvements in Natural Language Processing (NLP) efficiency, classification precision, and systematic interpretation of cross-factor correlations. This integrated approach provides maritime authorities with scientific evidence to develop targeted accident prevention strategies and optimize safety management systems, thereby enhancing maritime safety governance along China’s coastline. Full article

Conventional multi-object tracking approaches frequently exhibit performance degradation in marine radar (MR) imagery due to complex environmental challenges. To overcome these limitations, this paper proposes ShipMOT, an innovative multi-object tracking framework specifically engineered for robust maritime target tracking. The novel architecture features three principal innovations: (1) A dedicated CNN-based ship detector optimized for radar imaging characteristics; (2) A novel Nonlinear State Augmentation (NSA) filter that mathematically models ship motion patterns through nonlinear state space augmentation, achieving a 41.2% increase in trajectory prediction accuracy compared to conventional linear models; (3) A dual-criteria Bounding Box Similarity Index (BBSI) that integrates geometric shape correlation and centroid alignment metrics, demonstrating a 26.7% improvement in tracking stability under congested scenarios. For a comprehensive evaluation, a specialized benchmark dataset (Radar-Track) is constructed, containing 4816 annotated radar images with scenario diversity metrics, including non-uniform motion patterns (12.7% of total instances), high-density clusters (>15 objects/frame), and multi-node trajectory intersections. Experimental results demonstrate ShipMOT’s superior performance with state-of-the-art metrics of 79.01% HOTA and 88.58% MOTA, while maintaining real-time processing at 32.36 fps. Comparative analyses reveal significant advantages: 34.1% fewer ID switches than IoU-based methods and 28.9% lower positional drift compared to Kalman filter implementations. These advancements establish ShipMOT as a transformative solution for intelligent maritime surveillance systems, with demonstrated potential in ship traffic management and collision avoidance systems. Full article

The maritime transport industry contributes around 3% to worldwide CO₂ emissions, with 2023 emissions projected to be approximately 58 billion tons. Consequently, to attain decarbonization objectives, the implementation of effective reduction measures in maritime operations, especially at seaports as significant contributors, is essential. On the other hand, seaport operations are categorized into two main areas: land logistics, encompassing cargo handling, storage, customs processing, and inland transportation, and maritime logistics, which includes vessel traffic management, berth allocation, cargo loading and unloading, and fuel and maintenance services. While land logistics’ decarbonization has been extensively studied, maritime logistics operations, accounting for about 60% of port CO₂ emissions, remain underexplored. Their progress relies on regulations, cleaner fuels, and digital solutions; yet high costs and slow adoption pose significant challenges. As a result, this study employed PRISMA-ScR methodology to select relevant research resources and validate global reports from international organizations, enhancing transparency and providing practitioners and experts with a comprehensive analysis of seaport maritime emissions, as well as decarbonization initiatives. This study analyzes the future trajectory of the initiative based on current data, evaluating its potential benefits and systematically reviewing recent literature. It explores decarbonization strategies in maritime operations, emphasizing regulations, cleaner fuels, and digital solutions while highlighting challenges such as high costs and slow adoption. Key issues examined include maritime border delineation, infrastructure constraints, technological advancements, regulatory barriers, and the opportunities that decarbonized seaports offer to ports and their surrounding regions. Full article

Digitalization is transforming the maritime sector, and the amount and variety of data generated is increasing rapidly. Effective data utilization is crucial for data-driven services such as for highly automated maritime systems and efficient traffic coordination. However, these applications depend on heterogeneous, distributed data sources managed by different actors, making secure and sovereign information sharing difficult. This paper investigates how maritime data can be exchanged reliably and securely without jeopardizing data sovereignty. Based on the existing literature, we identify the main challenges and current research gap in sharing maritime information, emphasizing the importance of data availability. From this, we derive requirements for a secure and sovereign infrastructure for data exchange. To address these challenges, we propose a fully decentralized architecture for the maritime sector based on the concept of a data space. Our approach integrates protocols to improve data availability while minimizing data volume, considering maritime constraints such as volatile connectivity, low bandwidth and existing standards. We evaluate our architecture through a maritime traffic management case study and demonstrate its ability to enable secure and sovereign exchange of heterogeneous data. The results confirm that our solution reliably supports distributed data collection and enables data-driven, value-added services, which in turn will improve the safety and efficiency of the maritime domain in the near future. Full article

The Bohai Sea is the only semi-enclosed inland sea in China. With active marine economic activities, it faces a persistently high risk of oil spill accidents. This study assesses the occurrence risk and pollution risk of oil spills by considering factors such as sea state, the location of oil platform, and shipping route density in the Bohai Sea. The results show that the central part of the Bohai Sea, the southern Liaodong Peninsula, and the Bohai Strait area have a relatively high occurrence risk of oil spills due to busy maritime traffic and harsh sea conditions. In contrast, some areas in the northern, western, and southern parts of the Bohai Sea have a relatively low occurrence risk of oil spills because of weak maritime activity intensity and relatively calm sea state. In terms of the oil pollution risk, its distribution in the Bohai Sea shows significant seasonal characteristics, which are mainly comprehensively affected by multiple dynamic factors such as circulation, monsoon, and seawater exchange. Based on the oil pollution risk distribution, seasonally targeted strategies are proposed, which can provide a scientific basis for oil spill prevention and emergency management in the Bohai Sea, and help relevant departments formulate targeted prevention and control strategies. Full article

With the rapid growth in maritime traffic, navigational safety has become a pressing concern. Some vessels deliberately manipulate their type information to evade regulatory oversight, either to circumvent legal sanctions or engage in illicit activities. Such practices not only undermine the accuracy of maritime supervision but also pose significant risks to maritime traffic management and safety. Therefore, accurately identifying vessel types is essential for effective maritime traffic regulation, combating maritime crimes, and ensuring safe maritime transportation. However, the existing methods fail to fully exploit the long-term sequential dependencies and intricate mobility patterns embedded in vessel trajectory data, leading to suboptimal identification accuracy and reliability. To address these limitations, we propose MESTR, a Multi-Task Enhanced Ship-Type Recognition model based on Automatic Identification System (AIS) data. MESTR leverages a Transformer-based deep learning framework with a motion-pattern-aware trajectory segment masking strategy. By jointly optimizing two learning tasks—trajectory segment masking prediction and ship-type prediction—MESTR effectively captures deep spatiotemporal features of various vessel types. This approach enables the accurate classification of six common vessel categories: tug, sailing, fishing, passenger, tanker, and cargo. Experimental evaluations on real-world maritime datasets demonstrate the effectiveness of MESTR, achieving an average accuracy improvement of 12.04% over the existing methods. Full article

This research explores the role of innovation in fostering sustainability within the Spanish Port System, emphasizing its implications for transport infrastructure. It examines the intersection of innovation and sustainability, addressing key challenges such as maritime traffic growth, energy efficiency, waste management, and community integration. It identifies opportunities for technological advancements, collaborative initiatives, and circular economy strategies that contribute to the sustainable development of port infrastructure. The findings highlight the necessity of implementing innovative solutions to enhance operational efficiency, mitigate environmental impact, and strengthen stakeholder engagement. The application of advanced technologies and cooperative frameworks among port stakeholders emerges as a critical driver for achieving sustainability objectives within maritime transport systems. 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