Search Results (1,971)

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

This article addresses the operational challenges facing maritime vessels in the context of decarbonization, with a focus on developing staged recommendations for the integration of power limitation systems and alternative fuels. The systematisation of existing decarbonization problems in the maritime sector and the establishment of their interrelationships constitute the framework for developing coherent decarbonization strategies for the industry. The analysis of alternative fuels identifies the key factors that drive fuel selection in practice. The analysis of contemporary energy consumption regulation technologies has shown that power limitation systems operating through controllable pitch propellers (CPP), integrated with electronic remote-control systems, provide the highest flexibility in managing propulsion characteristics without altering engine rotational speed. The comparative analysis of the engine power limitation (EPL) and shaft power limitation (SHaPoLi) systems has confirmed that SHaPoLi offers a greater potential for reducing fuel consumption and carbon dioxide (CO₂) emissions; however, it comes at higher capital expenditure at the implementation stage. Pairing power limitation with alternative fuels shows that deep cuts in the sector’s carbon footprint are within reach. The economic analysis of power limitation system deployment has revealed the potential for achieving considerable operational cost savings, with a balanced consideration of capital investments and operational benefits. Future research should target the optimisation of EPL and SHaPoLi systems and their integration with other energy-saving technologies. Transitioning to alternative fuels in parallel offers the greatest cumulative reduction in the sector’s carbon footprint. Full article

A real-time optimized model predictive control–equivalent consumption minimization strategy (MPC-ECMS) is proposed for the energy management of shipboard gas turbine–photovoltaic hybrid energy storage (GT-PV-HESS) power systems. Different from conventional MPC-ECMS methods that only adopt single-level SOC-based feedback regulation, the strategy aims to overcome the limitations of conventional methods, including the poor adaptability of rule-based strategies and the lack of foresight in traditional ECMS, which cannot achieve simultaneous improvements in fuel economy, generation efficiency, and battery lifespan while maintaining system stability under dynamic operating conditions. The proposed strategy integrates the forward-looking optimization ability of MPC and the real-time decision-making advantage of ECMS. MPC is used to predict short-term load and photovoltaic power and identify operating modes, and a two-level equivalent factor adjustment mechanism is designed based on predicted conditions and battery state of charge (SOC). The optimized factor is applied in ECMS to achieve optimal power allocation between the gas turbine and battery under system constraints, while the supercapacitor implements power secondary correction to suppress bus voltage fluctuations caused by gas turbine operation. The architectural novelty lies in the two-level coordination mechanism and the marine-oriented hybrid energy storage cooperation. Simulation studies are conducted on the MATLAB/Simulink R2021b platform, and the results validate that it yields superior performance to the rule-based control and traditional ECMS under typical ship operating conditions. It increases gas turbine efficiency to 15.62% (0.47% and 6.24% higher than the two conventional methods). Over the 120 s simulation period, the proposed strategy reduces total fuel consumption to 1.049 kg, which is lower than 1.054 kg for the rule-based strategy and 1.192 kg for conventional ECMS. The battery SOC fluctuation is restricted to only 3.89%. The maximum DC bus voltage fluctuation rate is controlled within 3.28%, which meets the stability requirements of shipboard DC microgrids. The proposed strategy achieves a comprehensive and superior balance among fuel economy, power generation efficiency, and battery life while ensuring stable system operation under all working conditions. This two-level MPC-ECMS framework provides a high-performance and practically feasible energy management solution for shipboard hybrid power systems. Full article

This study investigates the hydrodynamic performance of the KVLCC2 tanker in deep and shallow water using computational fluid dynamics (CFD) simulations, focusing on resistance and self-propulsion with both ducted and non-ducted propellers. The Reynolds-averaged Navier–Stokes (RANS) equations, coupled with the SST k-ω turbulence model, are solved using STAR-CCM+ to analyze ship resistance, open-water propeller characteristics, and self-propulsion factors. Validation against experimental data confirms the numerical accuracy, with uncertainties below acceptable thresholds. In deep water, the body force propeller and body force ducted propeller methods are validated against the discretized propeller approach, yielding errors under 5%. The ducted propeller enhances open-water efficiency but results in higher thrust deduction and lower wake fractions, leading to reduced hull and overall propulsive efficiencies compared to the non-ducted case. In shallow water, as the depth-to-draft ratio (H/T) decreases to 1.5, added resistance, sinkage, and trim increase sharply due to blockage effects. The ducted configuration mitigates these penalties, achieving a 20.8% power reduction at H/T = 1.5. Added self-propulsion factors reveal that the duct improves hull efficiency and offsets shallow-water losses, enhancing propulsive efficiency. Flow field analysis shows accelerated stern wakes and asymmetric structures in shallow water, with the body force methods providing consistent predictions despite minor discrepancies in extreme conditions. This research highlights the efficacy of ducted propellers in shallow water and the reliability of body force methods for efficient simulations, offering insights for ship design in restricted depths. Full article

Marine oil spills pose critical challenges to environmental sustainability and socioeconomic stability. Taking four pivotal cases as the entry point, this study uses comparative case analysis, semi-structured stakeholder interviews, policy analysis and international gap comparison to systematically analyze the evolution of marine oil spill governance in the Taiwan region of China over two decades, aiming to identify systemic gaps and propose actionable reforms. By integrating and explicitly detailing these multiple methodologies, this research not only identifies but also systematically examines the Taiwan region of China’s unique challenges as a non-UN-member entity navigating international conventions like the international convention for the prevention of pollution from ships, 1973, as modified by the protocol of 1978 relating thereto (MARPOL 73/78). Key findings reveal persistent issues in decision-support tools, fragmented inter-agency coordination, and legal inadequacies in compensation mechanisms. The study’s novelty lies in its rigorous synthesis of localized case-driven insights compared with global best practices, proposing a concrete, phased model for a unified task force and context-aware, data-driven contingency plans to enhance real-time response efficiency. It further advocates for pragmatic steps to align the Taiwan region of China’s Marine Pollution Control Act with international standards while critically addressing the transboundary collaboration barriers imposed by its political status, exploring potential pathways through sub-national and regional partnerships. Notably, the 2023 Angel Container case underscores the urgency of modernizing enforcement capacities and integrating advanced technologies. By bridging gaps in governance, legal accountability, and practical international engagement, this research not only advances the Taiwan region of China’s preparedness but also offers a nuanced and adaptable blueprint for coastal regions facing similar geopolitical and environmental constraints. Its recommendations hold significant implications for global marine pollution management, emphasizing the interplay of policy innovation, technological adoption, and pragmatic cross-jurisdictional cooperation. Full article

As regional industrial hubs face structural decline, the “Regional industrial resurrection (RIR)” model emerges as a critical solution to bridge systemic deficits in capital, talent, and infrastructure via public Open Labs. This study evaluates the Busan maritime Open Lab’s performance (2021–2022), focusing on representative SMEs strategically selected for their active infrastructure utilization and their capacity to provide verifiable quantitative, qualitative, and economic evidence. Technologically, the intervention facilitated significant advancements, including drastic improvements in operational efficiency for ship reverse engineering, the acquisition of the Minister of Oceans and Fisheries award, enhanced capabilities in maritime structure and water engineering, and the successful commercialization of immersive AR, VR, and DT contents. Economically, these firms realized over 3.3 million USD in consolidated revenue, achieving a robust +66.4% growth rate based on a constant exchange rate to isolate macroeconomic volatility. Socially, the model generated 11.5 verified new jobs, while qualitative surveys confirmed a 100% satisfaction rate for expert-led technical advisory, validating the Lab’s role as an essential innovation intermediary. Finally, by comparing this framework with global intermediary models, the study proposes tailored regional expansion strategies and highlights the necessity of future quasi-experimental research to objectively isolate long-term causal impacts. Full article

Low-visibility environments induced by sea fog severely constrain the navigational efficiency and safety in narrow waterways, where traditional radar and Automatic Identification Systems (AIS) frequently encounter challenges such as perception blind spots and information lag. To address this critical issue, this study proposes a UAV-based perception and decision-making methodology for main navigational routes in fog, integrating physical priors with unmanned aerial vehicle (UAV) vision. Firstly, a joint physical dehazing and fog-domain adaptive detection network is constructed. This network addresses the overcomes the interference of non-uniform fog through feature-level enhancement, generating a spatio-temporally continuous visibility field and ship probability grids under a bird’s-eye view (BEV). Subsequently, a quantified “Sailability Score” model is established, providing a scientific basis for the dynamic diversion, speed limitation, and safe distance maintenance of main navigational routes. Simulation-based verifications using real-world fog navigation scenarios in the Qiongzhou Strait, coupled with a joint analysis of Vessel Traffic Service (VTS) and AIS data, suggest that at the critical visibility threshold (≤500 m), the proposed method improves the recall rate of long-distance small target detection by approximately 16.2% and reduces the visibility estimation error by 19.3%. Furthermore, the consistency between the proposed Sailability Score and the actual VTS navigation restriction windows reaches 82.1%, exhibiting a conservative preference for safety (i.e., risk preference ratio $\gamma >1$ ). Additionally, by introducing a temporal anti-jitter mechanism (parameterized by a smoothing window $\Delta t$), the proposed method extends the navigable time window of the main routes by approximately 12.4% while ensuring navigational safety. The simulation results indicate the framework’s potential perception capabilities and engineering applicability, providing reliable technical support for smart shipping and intelligent VTS systems. Full article

With the melting of Arctic sea ice and extended navigable windows, polar navigation has gained prominent commercial and strategic value but faces challenges like strong ice reflection, high target texture similarity, and large obstacle scale variation. Aiming at scarce polar-specific datasets, poor adaptability of general algorithms, and disconnection between identification and navigation decisions, this study constructed a technical system integrating “dataset construction–algorithm improvement–system development”. A purpose-built polar dataset with 1342 images (covering drift ice, iceberg, ice channel, and ship) was built via web crawling, video frame extraction, and data augmentation. A dual-path optimization scheme for lightweight YOLO models was proposed: the YUV + CLAHE module suppresses strong reflection, and the IceTextureAttention module enhances discriminability of similar targets, with SCConv optimizing computational efficiency. A visual intelligent system embedded with a Polar Code-based risk assessment module was developed to output three-level risks and navigation suggestions. Experimental results show the optimized YOLOv8n + YUV + CLAHE model achieves an overall mAP@0.5 of 0.858 and a recall rate of 0.821. The system runs stably on shipborne equipment with an average image processing latency of 85 ms and a practical detection accuracy of 84.3%, effectively reducing crew workload and improving polar navigation safety. 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

Accurate and rapid identification of microalgae in ship ballast water is critical for preventing the spread of invasive aquatic species and ensuring ecological security. However, traditional manual microscopic examination is labor-intensive and limited by challenges such as high intra-class morphological variability, frequent cell aggregation, and inter-class similarity among microalgae. This study proposes YOLO11-DBalgae, a specialized end-to-end object detection framework designed for fine-grained microalgae recognition in complex aquatic environments. Two key architectural innovations are introduced into the YOLO11n baseline—a Detail-enhanced Vanishing-prevention Block (DVB), which processes input features through a VoVGSCSP cross-stage aggregation module followed by parallel Conv and DSConv paths, preserving fine-grained boundary signals of morphologically diverse algal cells during repeated downsampling, and a Bidirectional Feature Pyramid Network (BiFPN), which employs learnable cross-scale weighting to optimize multi-scale feature fusion across the extreme size range of co-occurring microalgal targets. Experimental results demonstrate that YOLO11-DBalgae achieves an mAP@0.5 of 97.3%, representing an improvement of 7.0 percentage points over the baseline YOLO11n model. The model sustains an inference speed of 240 FPS with 2.83 M parameters, maintaining a lightweight and deployment-viable profile. Qualitative analysis via per-class precision–recall curves, detection visualization, and Grad-CAM attention maps confirms the model’s robustness in recovering near-invisible weak-feature targets, minimizing false detections within dense cell clusters, and accurately distinguishing morphologically convergent species. The proposed framework provides a practical and deployable solution for automated microalgae monitoring, offering maritime regulatory bodies an efficient and reliable tool for ballast water management. Full article

This paper studies an integrated planning problem in tramp shipping operations, with a particular focus on the bunker–cargo trade-off in maritime logistics. In practice, bunker load and cargo capacity are mutually restrictive: carrying more bunker reduces available payload, while cargo load affects fuel consumption and subsequent bunker demand, jointly shaping operating profitability. To capture this interdependency and overcome the limitations of fragmented decision-making, we jointly optimize cargo selection, vessel scheduling, routing, and bunkering decisions. The problem is formulated as a profit-maximizing mixed-integer linear programming (MILP) model. To solve large-scale instances efficiently, we adopt Dantzig–Wolfe decomposition and develop a column generation algorithm. For the pricing subproblem, we design a customized dynamic programming-based labeling algorithm for the resource-constrained shortest path problem, enhanced by dominance rules and acceleration strategies. Computational experiments show that the proposed approach outperforms alternative algorithms in both solution quality and computational efficiency, especially for large-scale problems. Ablation experiments further quantify the value of key components, including fuel discretization and deterministic acceleration strategies. Scenario analyses under different fuel prices, freight rates, bunkering policies, and fleet structures illustrate how integrated optimization can balance bunker–cargo trade-offs and provide practical decision support for tramp shipping operators. Full article

The AI-supported digitisation of bills of lading has become an important requirement for the maritime transport industry, because it accelerates maritime shipping operations and helps avoid the drawbacks of paper bills of lading. This importance prompted the UAE legislator to introduce a legal provision in the new Maritime Law expressly permitting the use of electronic bills of lading. Despite the significance of this legislative step, this study demonstrates that it suffers from regulatory shortcomings; accordingly, the study aims to bridge the legal gap arising from the deficiency and ambiguity that characterise the rules governing the use of electronic bills of lading. This research fills a gap in the legal literature, as the digitisation of bills of lading under the new UAE Maritime Law has not been deeply explored. It also examines the role of artificial intelligence as an auxiliary instrument in enhancing the efficiency and reliability of this digital transformation. The research adopts an inductive and analytical approach to the provisions of the Maritime Law and related legislation to extract the general legal principles governing dealings in electronic bills of lading. The study shows that the digitisation of maritime bills of lading raises several legal issues resulting from their subjection to more than one legal regime, which may lead to legislative conflict and divergence in judicial approaches. The study concludes that the effective use of electronic bills of lading requires issuance of implementing regulations that explicitly clarify the conditions for their issuance, recognising their possession and electronic negotiability. Full article

The continuous advancement of autonomy and intelligence in marine shipping has made the safe and efficient navigation of unmanned surface vehicles in complex waters a major research focus. As a key link of the autonomous decision-making system for unmanned surface vehicles (USVs), local path planning needs to achieve real-time collision avoidance and motion optimization under dynamic obstacles, multiple rule constraints, and strong environmental uncertainty. In recent years, reinforcement learning has gradually become an important technical route for local path planning of USVs by virtue of its autonomous decision-making ability in high-dimensional continuous state space and adaptability to complex nonlinear problems. Combined with the evolution of the algorithm paradigm and its functional positioning in different water scenarios, this paper systematically reviews the relevant literature by examining the evolution of algorithmic paradigms; focuses on summarizing deep Q-network (DQN), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradient (TD3), along with the collaborative architectures integrated with traditional planning methods such as A* and Rapidly-exploring Random Tree (RRT); and summarizes the performance characteristics, advantages, and limitations of various methods in typical scenarios. The review shows that the main bottlenecks of current research include insufficient reward mechanism design, low sample utilization efficiency, difficulty in transferring from simulation to real ships, and insufficient safety and trustworthiness verification. This paper looks forward to the future development trends from the two directions of data fusion and security enhancement in order to provide reference for related research. Full article

This study analyzes the econometric correlation between resale prices and CO₂ emissions of 832 bulk carriers sold from 2018 to 2025. It uses a cross-sectional hedonic pricing model to look at how environmental performance affects the value of sub-types of dry bulk vessels (Capesize, Panamax, Supramax, and Handysize) and age groups (0–5, 6–10, 11–15, and 16+). The findings show that emission efficiency has a statistically significant and negative effect on second-hand prices for all models. Results indicate that higher emission intensity (higher technical efficiency values) reduces vessel values. The magnitude of this effect varies by ship type and age group. Based on the Technical Efficiency Indicator (TEI), refers to Energy Efficiency Existing Ship Index (EEXI) or Energy Efficiency Design Index (EEDI) coefficients, the Supramax segment appears to be the most price-sensitive, followed by Panamax, Capesize, and Handysize. Age has a consistently negative and significant effect on prices, while vessel size positively affects asset values. Further analysis shows that TEI levels increase with vessel age, whereas they decrease with larger vessel size and more recent measurement years. These results are consistent with tightening regulatory pressures under the International Maritime Organization (IMO) frameworks. The economic implications of IMO’s environmental regulations on carbon intensity indicate that compliance with regulation standards creates a measurable price differential in the second-hand ship market. These findings have important implications for shipowners’ investment strategies, regulatory policy design, and the decarbonization path of the maritime sector. This study contributes to the growing research on environmental economics in maritime transport by providing empirical evidence on how carbon regulations translate into tangible asset value impacts. Full article

$Background$: In the grain logistics context, pre-processing operations such as reception, pre-cleaning, drying, storage, and shipping are performed at farm, collecting, intermediate, sub-terminal, and terminal storage units to preserve quality, reduce losses, and add value in the products. However, high transportation costs and limited static storage capacity reduce the selling prices. The objective of this article is to maximize profit associated with pre-processing, storage, and transportation along the grain flow in Brazil. $Methods$: A generic post-harvest logistics network is represented as a graph connecting producers, multi-level storage units, agribusiness facilities, and ports. A multi-period, multi-level mathematical model is applied in a case study framework explored in three scenarios, covering pre-cleaning, drying, storage, and transportation costs from production areas to commercialization nodes. $Results$: In all three scenarios, road transport resulted in transportation costs ranging from approximately US$ 49 million to US$ 492 million, mainly over long distances. $Conclusions$: The location and static capacity of collecting and intermediate storage units strongly influenced transport, storage use, CO₂ emissions, and post-harvest efficiency. Also, the flow concentration increased heavy-vehicle traffic, reducing overall logistics performance. Full article