Search Results (127)

Inland waterways, characterized by their complex, narrow paths, see significantly higher traffic volumes compared to maritime routes, increasing the regulatory demands on traffic management. Predictive modeling of ship traffic flows, utilizing real AIS historical data, enhances route and docking planning for ships and port managers. This approach boosts transportation efficiency and safety in inland waterway navigation. Nevertheless, AIS data are flawed, marred by noise, disjointed paths, anomalies, and inconsistent timing between points. This study introduces a data processing technique to refine AIS data, encompassing segmentation, outlier elimination, missing point interpolation, and uniform interval resampling, aiming to enhance trajectory analysis reliability. Utilizing this refined data processing approach on ship trajectory data yields independent, complete motion profiles with uniform timing. Leveraging the Transformer model, denoted TRFM, this research integrates processed AIS data from the Yangtze River to create a predictive dataset, validating the efficacy of our prediction methodology. A comparative analysis with advanced models such as LSTM and its variants demonstrates TRFM’s superior accuracy, showcasing lower errors in multiple metrics. TRFM’s alignment with actual trajectories underscores its potential for enhancing navigational planning. This validation not only underscores the method’s precision in forecasting ship movements but also its utility in risk management and decision-making, contributing significantly to the advancement in maritime traffic safety and efficiency. Full article

The analysis of large amounts of vessel trajectory data can facilitate more complex traffic management and route planning, thereby reducing the risk of accidents. The application of deep learning methods in vessel trajectory prediction is becoming more and more widespread; however, due to the complexity of the marine environment, including the influence of geographical environmental factors, weather factors, and real-time traffic conditions, predicting trajectories in less constrained maritime areas is more challenging than in path network conditions. Ship trajectory prediction methods based on kinematic formulas work well in ideal conditions but struggle with real-world complexities. Machine learning methods avoid kinematic formulas but fail to fully leverage complex data due to their simple structure. Deep learning methods, which do not require preset formulas, still face challenges in achieving high-precision and long-term predictions, particularly with complex ship movements and heterogeneous data. This study introduces an innovative model based on the transformer structure to predict the trajectory of a vessel. First, by processing the raw AIS (Automatic Identification System) data, we provide the model with a more efficient input format and data that are both more representative and concise. Secondly, we combine convolutional layers with the transformer structure, using convolutional neural networks to extract local spatiotemporal features in sequences. The encoder and decoder structure of the traditional transformer structure is retained by us. The attention mechanism is used to extract the global spatiotemporal features of sequences. Finally, the model is trained and tested using publicly available AIS data. The prediction results on the field data show that the model can predict trajectories including straight lines and turns under the field data of complex terrain, and in terms of prediction accuracy, our model can reduce the mean squared error by at least $6\times {10}^{-4}$ compared with the baseline model. Full article

This article starts by providing an updated literature review and the EU legislative framework concerning reducing carbon emissions in the maritime industry as part of the European Green Deal (EGD). It specifically examines the EU Emission Trading System (ETS) tax regime. This document then analyses the current factors influencing ships’ decisions to avoid stopping at hub ports and going to neighbouring Mediterranean countries, such as North Africa and Turkey. In the discussion section, this study presents various suggestions for updating EU laws or expediting the collection and analysis of data to prompt the Commission to take appropriate actions to prevent unfair competition between EU and non-EU ports. This study focuses on identifying the most effective solutions within the EU legislative framework to address the need for the Commission to take legitimate action to prevent ships from bypassing EU hub ports. These solutions can be further developed alongside initiatives at the International Maritime Organization (IMO), and certain provisions can be adjusted at the EU level. The IMO’s call for a carbon fee on bunkering exacerbates the existing challenges. Preventive measures must be implemented to control the diversion of shipping traffic from EU hub ports, ensure fair treatment of EU ports involved in transhipment, and prevent carbon leakage. Moreover, the recent Houthi attacks in the Red Sea have significantly increased shipping costs on the route around the Cape of Good Hope to Europe, necessitating increased allowances for traffic to and from Europe. Full article

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä’s Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations. Full article

Long kept in the British Library, a liturgical manuscript from the port of Haicheng, Fujian, holds details of the rich system of beliefs that Chinese sailors held. Originally untitled, the text by the shelfmark OR12693/18 is usually referred to as “Libation Ritual (for Ship Safety)” ([An Chuan] Zhuoxian Ke [(安船)酌献科]). Formerly, it was given scholarly attention mostly due to its addended lists of maritime placenames, which follows Qing-era sea routes across China’s coasts and to the South China Sea. Further inquiry into the manuscript’s terminology, deity names, and maritime knowledge confirms its deep relation to sailors’ lore. By tracing this text into a wide range of sources, this paper demonstrates how manuscript OR12693/18 reflects a cohesive maritime system of beliefs and knowledge. Manifested within the prayer are a hierarchical pantheon, ritual practices, and a perceived sacred seascape. Moreover, it is evident that the manuscript belonged to a tradition of sailing ritual masters who were regular members of the crew onboard junks. As such, this paper offers an analysis of a religious-professional tradition with trans-local aspects, shedding new light on seafaring in pre-modern China. Full article

The melting of Arctic ice has facilitated the successful navigation of merchant ships through the Arctic route, often requiring icebreakers for assistance. To reduce the risk of accidents between merchant vessels and icebreakers stemming from human errors during operations, this paper introduces an enhanced human reliability assessment approach. This method utilizes the Dynamic Bayesian Network (DBN) model, integrated with the information, decision, and action in crew context (IDAC) framework. First, a qualitative analysis of crew maneuvering behavior in scenarios involving a collision with the preceding vessel during icebreaker assistance is conducted using the IDAC model. Second, the D–S evidence theory and cloud models are integrated to process multi-source subjective data. Finally, the human error probability of crew members is quantified using the DBN. The research results indicate that during convoy operations, the maximum probability that the officer on watch (OOW) chooses an incorrect deceleration strategy is $8.259\times {10}^{-2}$ and the collision probability is $4.129\times {10}^{-3}$. Furthermore, this study also found that the factors of Team Effectiveness and Knowledge/Abilities during convoy operations have the greatest impact on collision occurrence. This research provides important guidance and recommendations for the safe navigation of merchant ships in the Arctic waters. By reducing human errors and adopting appropriate preventive measures, the risk of collisions between merchant ships and icebreakers can be significantly decreased. Full article

Due to COVID-19, the shipping market has faced uncertainty, and the possibility of changes in port routes has increased. The purpose of this study was to analyze the network of container liner shipping routes between Asia and Europe. In particular, this research focused on a global risky situation—the COVID-19 pandemic. The data examined encompassed Asia–Europe route schedules from January 2018 to October 2021, which exhibited significant fluctuations due to the COVID-19 pandemic originating in 2019. To access this problem, utilizing concepts of centrality from social network analysis (SNA), namely degree centrality and betweenness centrality, this analysis incorporated route capacity as a weighted factor. The findings revealed that the port of Rotterdam held the highest degree of centrality in 2018, 2019, and 2021, while Shanghai claimed the highest degree of centrality in 2020. Singapore exhibited the highest betweenness centrality. Asian ports wielded greater influence during the COVID-19 pandemic compared to European ports. Furthermore, Singapore emerged as a pivotal mediator in the Asia–Europe routes, playing a significant role within the global supply chain. Results showed that the port could be put into an unstable situation. Therefore, the managers of port and shipping companies should be ready to minimize risk. From an academic perspective, it is difficult to integrate and analyze container liner schedules as they are monthly updated. This study therefore analyzed continuous schedules to examine dynamic changes in schedules. By adopting SNA, we presented changes in connectivity over multiple periods. This study addressed questions stakeholders may have had about route changes during the global crisis, contributing to sustainable container transportation. This study provides a general understanding of Asia–Europe container scheduling for decision makers. Using market schedules, this research analyzed the connections, and evaluated and compared each port. Full article

The sun’s activity role in climate change has become a topic of debate. According to data from the IPCC, the global average temperature has shown an increasing trend since 1850, with an average increase of 0.06 °C/decade. Our analysis of summer temperature records from five weather stations in northern Fennoscandia (65°–70.4° N) revealed an increasing trend, with a range of 0.09 °C/decade to 0.15 °C/decade. However, due to the short duration of instrumental records, it is not possible to accurately assess and predict climate changes on centennial and millennial timescales. In this study, we used the Finnish super-long (~7600 years) tree-ring chronology to create a climate prediction for the 21st century. We applied a method that combines a long short-term memory (LSTM) neural network with the continuous wavelet transform and wavelet filtering in order to make climate change predictions. This approach revealed a significant decrease in tree-ring growth over the near term (2063–2073). The predicted decrease in tree-ring growth (and regional temperature) is thought to be a result of a new grand solar minimum, which may lead to Little Ice Age-like climatic conditions. This result is significant for understanding current climate processes and assessing potential environmental and socio-economic risks on a global and regional level, including in the area of the Arctic shipping routes. Full article

With the development of satellite altimetry technology and the application of new altimetry satellites, the accuracy and resolution of altimeter-derived gravity field models have improved over the last decades. Nowadays, they are close enough to shipborne gravimetry. In this paper, multi-source shipborne gravity data in the South China Sea were taken to evaluate the accuracies of two high-precision altimeter-derived marine gravity field models (SS V30.1, DTU17). In these shipborne gravity data, there are dozens of routes’ ship gravimetry data, obtained from the National Geophysical Data Center (NGDC); data were tracked from a marine survey with a commercial marine gravimeter (type KSS31M), and data were tracked from a marine gravimetry campaign that was conducted with a newly developed platform gravimeter (type JMG) in the South China Sea in September 2020. After various data filtering, processing, and calibrations, the shipborne gravity data were validated with crossover points analysis. Then, the processed shipborne data were employed to evaluate the accuracy of the altimeter-derived marine gravity field models. During this procedure, the quality of JMG shipborne gravity data was compared with the results of KSS31M and NGDC data. Analysis and evaluation results show that the crossover points verification accuracies of KSS31M and JMG are 0.70 mGal and 1.61 mGal, which are much better than the accuracy of NGDC, which is larger than 8.0 mGal. In the area where the bathymetry changes slowly, the root mean square error values between altimetry gravity models and KSS31M data are respectively 3.28 mGal and 4.54 mGal, and those of the JMG data are respectively 2.94 mGal and 2.60 mGal. According to the above results, we can conclude that the JMG has the same 1–2 mGal accuracy level as KSS31M and can meet the measurement requirements of marine gravity. Full article

Liner shipping accounts for over $80\%$ of the global transportation volume, making substantial contributions to world trade and economic development. To advance global economic integration further, it is essential to link the flows of global liner shipping routes with the complex system of international trade, thereby supporting liner shipping as an effective framework for analyzing international trade and geopolitical trends. Traditional methods based on first-order global liner shipping networks, operating at a single scale, lack sufficient descriptive power for multi-variable sequential interactions and data representation accuracy among nodes. This paper proposes an effective methodology termed “Multi-Scale Higher-Order Dependencies (MSHOD)” that adeptly reveals the complexity of higher-order interactions among multi-scale nodes within the global liner shipping network. The key step of this method is to construct high-order dependency networks through multi-scale attributes. Based on the critical role of high-order interactions, a method for key node identification has been proposed. Experiments demonstrate that, compared to other methods, MSHOD can more effectively identify multi-scale nodes with regional dependencies. These nodes and their generated higher-order interactions could have transformative impacts on the network’s flow and stability. Therefore, by integrating multi-scale analysis methods to mine high-order interactions and identify key nodes with regional dependencies, this approach provides robust insights for assessing policy implementation effects, preventing unforeseen incidents, and revealing regional dual-circulation economic models, thereby contributing to strategies for global, stable development. Full article

The continuous improvement in the seaworthiness of Arctic shipping routes has caused an urgent international demand for meteorological and sea ice information. In view of the diversity of Arctic meteorological and sea ice information websites and the uneven service levels of the websites, and to assist Arctic navigation ships in selecting timely, stable, and reliable meteorological and sea ice information, this paper summarizes the websites providing Arctic meteorological and sea ice information. Constructing an evaluation indicator system for the service level of the Arctic meteorological and sea ice information websites from the two dimensions of data quality and browsing experience, this system integrates the cloud model, the Dempster–Shafer (D-S) evidence theory, and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to construct a corresponding service-level evaluation and decision optimization process of Arctic meteorological and sea ice information websites. Finally, through case analysis, the feasibility of this research method is demonstrated. Full article

Port congestion significantly impacts the reliability of container ship schedules. However, the existing research often treats vessel time in port as a random variable, failing to systematically consider the complex impact of port congestion on ship schedules. This study addresses the issue of container ship schedule design under port congestion. Vessel waiting times in ports are predicted and quantified by queueing theory, along with information on vessel schedules, cargo handling volumes, and available port operating time windows. We propose a mechanism for selecting container handling efficiencies for arriving vessels, thereby determining their in-port handling times. By jointly considering the uncertainty of vessel waiting and handling times in port, we establish a mixed-integer nonlinear programming model aimed at minimizing the total cost of liner transportation services. We linearize the model and solve it using CPLEX, ultimately devising a robust ship schedule. A simulation analysis is conducted on a real liner shipping route from Asia to the Mediterranean, revealing that extreme weather events, geopolitical conflicts, and other factors can lead to severe congestion at certain ports, necessitating timely adjustments to vessel schedules by shipping companies. Moreover, such events can impact the marine fuel market, prompting shipping companies to adopt strategies such as increasing vessel numbers and reducing vessel speeds in response to high fuel prices. Additionally, the container handling efficiency selection mechanism based on information sharing enables shipping companies to flexibly design liner schedules, balancing the economic costs and service reliability of container liner transportation. Full article

In recent years, carbon emission reduction in the shipping sector has increasingly garnered scholarly attention. This study delves into the pathways for carbon emission reduction in shipping across the Yangtze River, emphasizing fuel alternatives. It initiates by introducing a novel ship carbon emission calculation methodology predicated on voyage data, followed by the development of a predictive model for ship carbon emissions tailored to specific voyages. Then, emission reduction scenarios for various voyage categories are designed and exemplary alternative fuels selected to assess their potential for emission mitigation. Subsequently, scenario analysis is employed to scrutinize the CO₂ emission trajectories under diverse conditions, pinpointing the most efficacious route for carbon emission abatement for inland vessels. Finally, the proposed method is applied to the middle and lower reaches of the Yangtze River. The results indicate that accelerating the adoption of alternative fuels for long-distance cargo ships would greatly accelerate the development of environmentally friendly shipping. Under a scenario prioritizing zero-carbon growth, emissions from inland vessels are anticipated to reach their zenith by 2040. These findings can provide theoretical guidance for emission reductions in inland shipping and effectively promote the green and sustainable development of the shipping sector. Full article

To gain a sustained competitive advantage, organizations such as UPS, Fedex, Amazon, etc., began to seek for industry 5.0 innovative autonomous delivery options for the last mile. Autonomous unmanned aerial vehicles are a promising alternative for the logistics industry. The fact that drones are propelled by green renewable energy source fits the companies’ need to become sustainable, replacing their fuel truck fleets, especially for traveling to remote rural locations to deliver small packages, but a major obstacle is the necessity for charging stations which is well documented in the literature. Therefore, the current research embarks on devising a novel yet practical piece of technology adopting the simplicity approach of direct flights to destinations. The analysis showcases the application for a network of warehouses and hospitals in Israel while controlling costs. Given the products in the case study are medical, direct flight has the potential to save lives when every moment counts. Hydrogen cell technology allows long-range flying without refueling, and it is both vibration-free which is essential for sensitive medical equipment and environmentally friendly in terms of air pollution and silence in urban areas. Importantly, hydrogen cells are lighter, with higher energy density than batteries, which makes them ideal for drone usage to reduce weight, maintain a longer life, and enable faster charging, all of which minimize downtime. Also, hydrogen sourcing is low-cost and unlimited compared to lithium-ion material which needs to be mined. The case study investigates an Israeli entrepreneurial company, Gadfin, which builds a vertical takeoff-and-landing-type of drone with folded wings that enable higher speed for the delivery of refrigerated medical cargo, blood, organs for transplant, and more to hospitals in partnership with the Israeli medical logistic conglomerate, SAREL. An analysis of shipping optimization (concerning the number and type of drone) is conducted using a mixed-integer linear programming technique based on various types of constraints such as traveling distance, parcel weight, the amount of flight controllers and daily number of flights allowed in order to not overcrowd the airspace. Importantly, the discussion assesses the ecosystem’s variety of risks and commensurate safety mechanisms for advancing a newly shaped landscape of drones in an Israeli tight airspace to establish a network of national routes for drone traffic. The conclusion of this research cautions limitations to overcome as the utilization of drones expand and offers future research avenues. Full article

The growing adoption of electric propulsion systems in Maritime Autonomous Surface Ships (MASSs) necessitates advancements in shipboard electronics for safe, efficient, and reliable operation. These advancements are crucial for tasks such as real-time sensor data processing, control algorithms for autonomous navigation, and robust decision-making capabilities. This study investigates research trends in MASSs, using bibliographic analysis to identify policy and future research directions in this evolving field. We analyze 3363 MASS-related articles from the Web of Science database, employing co-occurrence word analysis and latent Dirichlet allocation (LDA) topic modeling. The findings reveal a rapidly growing field dominated by image recognition research. Keywords such as “datum”, “image”, and “detection” suggest a focus on collecting and analyzing marine data, particularly with deep learning for synthetic aperture radar imagery. LDA confirms this, with “image analysis and classification research” as the leading topic. The study also identifies national and organizational leaders in MASS research. However, research on Arctic routes lags behind that on other areas. This work provides valuable insights for policymakers and researchers, promoting a deeper understanding of MASSs and informing future policy and research agendas regarding the integration of electric propulsion systems within the maritime industry. Full article