Search Results (136)

Clustering algorithms are widely used in statistical data analysis as a form of unsupervised machine learning, playing a crucial role in big data mining research for Maritime Intelligent Transportation Systems. While numerous studies have explored methods for optimizing ship trajectory clustering, such as narrowing dynamic time windows to prevent errors in time warp calculations or employing the Mahalanobis distance, these methods enhance DBSCAN (Density-Based Spatial Clustering of Applications with Noise) by leveraging trajectory similarity features for clustering. In recent years, machine learning research has rapidly accumulated, and multiple studies have shown that HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) outperforms DBSCAN in achieving accurate and efficient clustering results due to its hierarchical density-based clustering processing technique, particularly in big data mining. This study focuses on the area near Taichung Port in central Taiwan, a crucial maritime shipping route where ship trajectories naturally exhibit a complex and intertwined distribution. Using ship coordinates and heading, the experiment normalized and transformed them into three-dimensional spatial features, employing the HDBSCAN algorithm to obtain optimal clustering results. These results provided a more nuanced analysis compared to human visual observation. This study also utilized O notation and execution time to represent the performance of various methods, with the literature review indicating that HDBSCAN has the same time complexity as DBSCAN but outperforms K-means and other methods. This research involved approximately 293,000 real historical data points and further employed the Silhouette Coefficient and Davies–Bouldin Index to objectively analyze the clustering results. The experiment generated eight clusters with a noise ratio of 12.7%, and the evaluation results consistently demonstrate that HDBSCAN outperforms other methods for big data analysis of ship trajectory clustering. Full article

Maritime regulators are closely monitoring the progression of green shipping, and liner companies are seeking strategies to meet tough ship emission rules. To reduce the operating cost while conforming to the increasingly strict environmental regulations, the study first constructs a mixed-integer nonlinear optimization model. Subsequently, the nonlinear parts in the objective function and constraints are transformed into linear forms. Thereafter, the model is applied to the Asia–Europe route of the CMA CGM Shipping Company to find the planned speeds and bunkering strategies for container liners sailing in expanded emission control areas (ECAs) that will be implemented in the future. Finally, a sensitivity analysis is performed to examine the influence of bunker tank capacity and fuel price difference on the operating cost, carbon dioxide emission, bunkering strategy and planned sailing speed. The study contributes to determining the optimal tank capacity and developing bunkering strategies at different fuel price differences. With stricter policies, operators must strategically choose refueling ports, adjust refueling amounts, and optimize planned sailing speeds based on ship and route data. The proposed approach provides a solution to the contradiction between compliance with environmental regulations and cost-effectiveness of shipping companies and is of great significance for promoting the sustainable development of the waterway transportation industry. Full article

Ports and their affiliated shipping routes are fundamental to the maritime logistics system, crucial for global trade. However, they face risks from natural disasters and human-induced crises. Enhancing port resilience, the ability to quickly recover and maintain operations during disruptions is vital for a robust maritime network. This study focuses on enhancing port resilience by improving the shipping route network, using an innovative link-prediction-based approach. Initially, a multi-dimensional resilience analysis is conducted to identify key low-resilience and bottleneck ports, guiding targeted network optimizations. Then, a novel link prediction algorithm is applied to find potential new shipping connections, significantly enhancing network efficiency, robustness, and port resilience. The optimized network effectively improves the connectivity of critical low-resilience ports with central hub ports and bottleneck ports with surrounding ones. Route diversification mitigates risks and strengthens overall resilience. Key low-resilience ports and bottleneck ports are reduced by an average of 20% and 25%. Finally, practical strategies are proposed. Low-resilience ports should establish direct connections with major hubs, and regional sub-networks can offer support. For bottleneck ports, additional secondary and short distance links should be added to transform them into more integrated hubs, enhancing the network’s robustness. These strategies improve the network’s operational capacity during crises, ensuring efficient cargo flow. Full article

Despite the growing body of research on fuel alternatives for reducing carbon emissions in maritime shipping, there remains a lack of comprehensive cost–benefit analyses from the perspective of shipowners considering both retrofit and new construction options across multiple shipping routes. This paper carries out the optimization of carbon emission reduction investment schemes for replacement fuel ships from the perspective of the shipowners, with low-carbon fuel ships (LNG-fueled and methanol-fueled) and zero-carbon fuel ships (ammonia-fueled and hydrogen-fueled) as feasible options for shipowners to choose. Shipowners are advised to consider fuel retrofit options carefully, with methanol as a promising low-carbon fuel on certain routes and LNG for achieving both cost-effectiveness and compliance with upcoming zero-carbon regulations. The considered influencing factors include sailing distances, fuel prices, and container freight rates. A cost–benefit analysis model is proposed to conduct quantitative comparative analyses. The feasibility of various fuel options reflects both economic conditions and regulatory environments influencing operational costs and potential future carbon pricing. Under baseline conditions, our analysis reveals: For route 1, the NPV of retrofitting ships to use methanol yields the highest return among low-carbon options; for route 2, all replacement fuel options result in negative NPVs, indicating no investment value; and for route 3, retrofit options for LNG and new constructions for methanol are feasible, with LNG offering the shortest payback period. Full article

This paper explores how optimizing vessel speeds can help reduce carbon emissions in the maritime industry. Focusing on liner shipping routes between China and Europe, it examines how carbon pricing mechanisms, including carbon taxes and emissions trading under the European Union Emissions Trading Scheme (EU ETS), impact operational costs and emissions reduction. With the use of advanced optimization methods, such as the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and the Technique for Order of Preference by Similarity to an Ideal Solution (TOPSIS), this research explores the balance between adjusting vessel speeds and minimizing emissions. The findings show that shipping companies on the China–Europe route can reduce the financial strain of carbon pricing by carefully managing speeds and voyage times. This study compares two scenarios of carbon tax policy and carbon trading rights in terms of voyage costs and carbon emissions. The results of this comparison based on the given parameters indicate a reduction of 1124 tons of carbon emissions with the carbon tax policy scenario, while the carbon trading rights scenario allows for more voyages yearly (5.24 vs. 5.30). This demonstrates one policy being more economical, while the other is also more environmentally efficient. These insights support the development of strategies that align environmental goals with economic priorities, paving the way for more sustainable maritime operations. The study introduces its objectives and reviews relevant literature by presenting a detailed methodology, incorporating emissions modeling with clearly defined parameters. The analysis presents results that undergo sensitivity testing and limitations using MATLAB (R2022a version). The study concludes by discussing policy implications and recommendations for future research and practical advancement Full article

The dry bulk shipping network is an important carrier of global bulk commodity flow. To better understand the structural characteristics and future development trends of the global dry bulk shipping network (GDBSN), this study proposes a framework for characteristics analysis and link prediction based on complex network theory. The study integrates large-scale heterogeneous data, including automatic identification system data and port geographic information, to construct the GDBSN. The findings reveal that the network exhibits small-world properties, with the Port of Singapore identified as the most influential node. Link prediction results indicate that many potential new shipping routes exist within regions or between neighboring countries, exhibiting clear regional clustering characteristics. The added links mainly influence the local structure, with minimal impact on the overall network topology. This study provides valuable insights for shipping companies in route planning and for port authorities in developing strategic plans. Full article

Container terminals play a crucial role in global logistics and trade, with gate-in operations significantly impacting overall terminal efficiency and cargo turnover speed. This paper analyzes a series of problems caused by the randomness of the arrival of export containers at the container yard, including wastage of yard space, excessive waiting time for external trucks, and conflicts with other production operations. To address these issues, a method based on a decomposed ensemble framework is proposed to predict short-term container quantities for gate-in operations at container terminal gates. The experiment compares the autoregressive integrated moving average (ARIMA) algorithm, the prophet algorithm, and the Long Short-Term Memory (LSTM) algorithm, with results indicating the clear advantage of Long Short-Term Memory in decomposed time series modeling. The introduction of this method is expected to enhance the accuracy and flexibility of terminal production planning, optimizing resource utilization. Contributions of this paper include the proposal of predictive models, a shipping route-based decomposed-ensemble framework, and confirmation of the superiority of Long Short-Term Memory in prediction through comparative analysis. These contributions are expected to improve terminal operational efficiency, reduce resource wastage, and better adapt to the highly stochastic gate-in operation environment. Full article

In this study, a ship-route prediction model based on a long short-term memory network using port-to-port trajectory data is presented. Data from a traditional automatic identification system—often used for ship-route prediction—are limited by uneven sampling intervals and noise. To address these issues, equal-interval data collected every 10 s from a target ship, which is a liner container vessel, were employed. Our study focuses on predicting the entire trajectory between the Gunsan and Busan ports. The root mean square error (RMSE), mean absolute error (MAE), and average distance $\overline{d}$ between two trajectories were used as the key evaluation metrics. The analysis yielded excellent predictive performance, with the values RMSE = 0.000999, MAE = 0.000672, and $\overline{d}$ = 0.101 km. This study thus provides a foundation for predicting complete port-to-port routes and offers practical insights for managing vessel operations. Accurate route prediction contributes to reducing port congestion, improving fuel efficiency, and lowering carbon emissions. Full article

Existing route-clustering methods often fail to identify abnormal sections or similarities between routes, mainly when working with large or long datasets. While sub-route clustering can detect regional patterns, it struggles to accurately capture the overall route structure. The present study proposes a new ship route-clustering method that enhances computational efficiency and noise recognition while addressing these limitations. We refined Automatic Identification System data via four data-cleaning processes and applied a statistical distance measurement to assess ship trajectory similarity. Dimensionality reduction was then used to facilitate clustering. The clustering of ship route similarities is non-parametric and can be applied to datasets not separated based on density to find clusters of various densities. Density-Based Spatial Clustering of Applications (DBSCA) applies to many research fields; using the DBSCA with Noise (DBSCAN) algorithm, we propose an improved DBSCAN algorithm that automatically determines the parameters Epsilon and MinPts. In this study, as a core ship route-clustering process, we propose a sub-route clustering process by setting the distance and density of data points to clear standards for re-analysis and completion. The proposed approach demonstrates markedly enhanced clustering performance, offering a more sophisticated and efficient basis for ship route decision-making. Full article

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