Maritime Transport Research最新文献

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Solving the container premarshalling problem with an auxiliary bay 用辅助舱解决集装箱预编组问题

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-06-01 DOI: 10.1016/j.martra.2025.100134

Celia Jiménez-Piqueras , Kevin Tierney

The relocation of containers is essential at port terminals to increase operational efficiency during container retrieval from the yard. When a container must be retrieved, any container placed on top of it must be moved to another stack, delaying the retrieval process. The container premarshalling problem (CPMP) aims to tackle this issue by finding a sequence of minimal container relocations to achieve a bay arrangement where no container needs to be moved during retrieval. The classical formulation of this problem assumes that all premarshalling relocations occur within the bay being arranged. However, this study demonstrates that practical applications of premarshalling can benefit from more efficient use of available resources. We introduce a novel problem variant that allows the use of an auxiliary bay as additional space for relocating containers during the arrangement process. We present constraint programming solution methods for this variant that reveal a significant reduction in premarshalling relocations when an auxiliary bay is used. The results demonstrate that bays where high occupancy rates prevent premarshalling can be successfully arranged with an auxiliary bay. Additionally, we propose two alternative formulations allowing different rates of relocations between bays, offering adaptability to varying port terminal requirements.

集装箱的重新安置在港口码头是必不可少的，以提高从堆场取回集装箱的操作效率。当必须检索容器时，必须将放置在其顶部的任何容器移动到另一个堆栈，从而延迟检索过程。容器预编组问题（CPMP）旨在通过找到一系列最小的容器重新定位来解决这个问题，以实现在检索期间不需要移动容器的海湾安排。该问题的经典公式假设所有的预编组重新定位都发生在被安排的海湾内。然而，这项研究表明，预编组的实际应用可以从更有效地利用可用资源中受益。我们引入了一个新的问题变体，允许使用辅助舱作为在安排过程中重新安置集装箱的额外空间。我们提出了这种变体的约束规划解决方法，揭示了当使用辅助舱时，预编组重定位的显着减少。结果表明，在高入住率不利于预编组的情况下，可以成功地利用辅助海湾进行预编组。此外，我们提出了两种可供选择的配方，允许在海湾之间进行不同的重新定位，以适应不同的港口码头要求。

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引用次数: 0

High-accuracy prediction of vessels’ estimated time of arrival in seaports: A hybrid machine learning approach 船舶到达海港时间的高精度预测：一种混合机器学习方法

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-04-26 DOI: 10.1016/j.martra.2025.100133

Sunny Md. Saber , Kya Zaw Thowai , Muhammad Asifur Rahman , Md. Mehedi Hassan , A.B.M. Mainul Bari , Asif Raihan

Optimizing the Estimated Time of Arrival (ETA) for seaport-bound vessels is crucial to maritime operations since inaccurate ETA predictions can have a ripple effect, causing vessel schedule disruptions, congestion, and decreased port operational effectiveness. To address these challenges and fill substantial deficiencies in existing prediction models, we have introduced a novel hybrid tree-based stacking machine learning framework integrating Extra Trees, AutoGluon Tabular, and LightGBM, with Random Forest Regressor (RFR) as the meta-learner. Utilizing Automatic Identification System (AIS) data from vessels in the Baltic Sea, our model significantly improves ETA predictions, achieving a mean absolute percentage error (MAPE) of 0.25 %. Compared to existing machine learning algorithms, our stacking model exhibits superior prediction performance. Our study's feature importance analysis highlights the crucial role of variables like speed, distance, course, and vessel type in ETA forecasts. Cross-validation further confirms the robustness of our ensemble model. In conclusion, this study improves predictive analytics in marine logistics by giving useful information about real-time ETA estimates. This helps port authorities make the best use of their resources, reduces vessel idle time and congestion, and increases overall efficiency and sustainability. This way, this study can significantly contribute towards attaining operational excellence and provide a strong foundation for future predictive models, advancing smart port management and maritime logistics.

优化海港船舶的预计到达时间（ETA）对海上运营至关重要，因为不准确的ETA预测可能会产生连锁反应，导致船舶时间表中断、拥堵和港口运营效率下降。为了解决这些挑战并填补现有预测模型的实质性不足，我们引入了一种新的混合基于树的堆叠机器学习框架，该框架集成了Extra Trees、AutoGluon Tabular和LightGBM，并以随机森林回归器（RFR）作为元学习器。利用波罗的海船只的自动识别系统（AIS）数据，我们的模型显着提高了ETA预测，实现了0.25%的平均绝对百分比误差（MAPE）。与现有的机器学习算法相比，我们的叠加模型具有更好的预测性能。我们研究的特征重要性分析强调了速度、距离、航线和船舶类型等变量在ETA预测中的关键作用。交叉验证进一步证实了我们的集成模型的稳健性。总之，本研究通过提供有关实时ETA估计的有用信息，改进了海洋物流的预测分析。这有助于港口当局充分利用其资源，减少船舶闲置时间和拥堵，并提高整体效率和可持续性。通过这种方式，本研究可以为实现卓越运营做出重大贡献，并为未来的预测模型、推进智能港口管理和海上物流提供坚实的基础。

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引用次数: 0

Cost–benefit analysis and design optimization of wind propulsion systems for a Tanker retrofit case 某油轮改造案例中风力推进系统的成本效益分析与设计优化

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-04-19 DOI: 10.1016/j.martra.2025.100132

M. Reche-Vilanova , H.B. Bingham , M. Fluck , D. Morris , H.N. Psaraftis

This study introduces WindWise, a cost–benefit analysis and design optimization tool for Wind Propulsion Systems (WPS) in sustainable shipping. By integrating route simulations, ship constraints, and fuel pricing scenarios, WindWise determines the optimal WPS configuration to maximize fuel savings and minimize payback periods. A retrofit case study of an oil tanker evaluates two WPS classes—DynaRigs and Rotor Sails—across multiple operational and economic conditions. Results reveal that optimal configurations vary based on constraints: in an unconstrained scenario, larger, well-spaced installations minimize aerodynamic losses, whereas realistic constraints shift the preference towards smaller, distributed setups to mitigate cargo loss and air draft penalties. Rotor Sails offer lower upfront costs and shorter payback periods for modest savings targets and for side-wind routes, while DynaRigs emerge as the more viable solution for higher emissions reductions and long-term profitability. Optimization of WPS configurations proves crucial, with non-optimized configurations exhibiting payback periods over 150% higher than optimized ones. Although payback period remains an important metric, considering both payback and net present value provides a more comprehensive assessment of WPS financial viability, with Rotor Sails generally offering faster payback but DynaRigs delivering higher long-term profitability across most scenarios.

本研究介绍了风力推进系统（WPS）在可持续航运中的成本效益分析和设计优化工具 WindWise。通过整合航线模拟、船舶约束条件和燃料定价方案，WindWise 可以确定最佳的 WPS 配置，以最大限度地节省燃料并缩短投资回收期。通过对一艘油轮的改造案例研究，评估了两类 WPS--DynaRigs 和 Rotor Sails--在多种运营和经济条件下的性能。研究结果表明，最佳配置会因限制条件的不同而有所变化：在无限制条件的情况下，较大、间距合理的装置可最大限度地减少空气动力损失，而在现实限制条件下，则更倾向于较小、分布式的装置，以减少货物损失和吃水损失。旋翼风帆的前期成本较低，投资回收期较短，可实现适度的节能目标和侧风航线，而 DynaRigs 则是更可行的解决方案，可实现更高的减排量和长期盈利能力。事实证明，优化 WPS 配置至关重要，非优化配置的投资回收期比优化配置高出 150% 以上。尽管投资回收期仍是一个重要指标，但同时考虑投资回收期和净现值可以更全面地评估 WPS 的财务可行性，一般来说，旋翼风帆的投资回收期更快，但在大多数情况下，DynaRigs 的长期盈利能力更高。

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引用次数: 0

Predicting the destination port of fishing vessels utilizing transformers 利用变压器预测渔船目的港

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-03-17 DOI: 10.1016/j.martra.2025.100131

Andreas Berntsen Løvland, Helge Fredriksen, John Markus Bjørndalen

Vast databases on historical ship traffic are currently freely available in the form of AIS (Automatic Identification System) messages dating back to as early as 2002. This provides a rich source for training deep learning models for predicting various behaviors of vessels, which in this context is motivated by resource management of fisheries. In this paper, we explore the possibility for combining a transformer model’s powerful capabilities for long-term path prediction with added logic to infer probable destination harbors for fishing vessels. An additional baseline model is also developed for comparison, based on historically preferred harbors for the vessels. With AIS data from the Troms and Finnmark region of Norway, the prediction accuracy of the trained model is found to be highly dependent on the number of past tracked positions of the vessel. We foresee that a new and more precise model will need to incorporate not only dynamic AIS data, but static information about harbors and vessel types during training and inference.

关于历史船舶交通的庞大数据库目前以AIS（自动识别系统）信息的形式免费提供，最早可追溯到2002年。这为训练用于预测船舶各种行为的深度学习模型提供了丰富的资源，在这种情况下，这些模型是由渔业资源管理驱动的。在本文中，我们探索了将变压器模型强大的长期路径预测能力与附加逻辑相结合的可能性，以推断渔船可能的目的地港口。还根据历史上船舶的首选港口开发了一个额外的基线模型进行比较。使用来自挪威Troms和Finnmark地区的AIS数据，发现训练模型的预测精度高度依赖于船只过去跟踪位置的数量。我们预计，在训练和推理过程中，一个新的、更精确的模型不仅需要包含动态AIS数据，还需要包含有关港口和船舶类型的静态信息。

引用次数: 0

Measuring the impact of port congestion on containership freight rates 衡量港口拥堵对集装箱船运价的影响

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-01-21 DOI: 10.1016/j.martra.2025.100130

Nektarios A. Michail , Konstantinos D. Melas

We examine the impact of port congestion on containership freight rates. Our overall results show that port congestion has a positive and significant effect on containership freight rates. The most important region is Asia, where a 1 % increase in port congestion has a >1 % effect on shipping freight rates. This suggests that the region, being the world's largest manufacturing area and an integral part of the supply chain, has much more importance than previously considered. As such, the results highlight the importance of the manufacturing region in supply chains and are also in line with the derived demand system in shipping. As per the results, a return to the pre-pandemic congestion levels in Asia would lead to at least a 25 % decline in containership freight costs.

我们研究港口拥挤对集装箱船运价的影响。我们的总体结果表明，港口拥堵对集装箱船运价有显著的正向影响。最重要的地区是亚洲，在那里，港口拥堵每增加1%，对航运运费的影响就会增加1%。这表明，作为世界上最大的制造业地区和供应链不可分割的一部分，该地区的重要性远比之前认为的要大得多。因此，结果突出了制造业区域在供应链中的重要性，并且也符合航运的衍生需求系统。根据研究结果，如果亚洲恢复到大流行前的拥堵水平，集装箱船货运成本将至少下降25%。

引用次数: 0

A Bayesian network model integrating data and expert insights for fishing ship risk assessment 基于贝叶斯网络的渔船风险评估

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-01-12 DOI: 10.1016/j.martra.2024.100128

Sang-A Park , Deuk-Jin Park , Jeong-Bin Yim , Hyung-ju Kim

Marine accidents can result in severe economic losses and casualties, underscoring the critical need for effective risk assessment.. In this study, quantitative marine accident reports from Korea that objectively describe accident variables were collected and classified to analyze marine accidents of fishing ships To analyze the causes of accidents involving different types of fishing ships, a survey with subject matter experts (SMEs) was conducted. A fishing ship accident Bayesian network (FABN) scenario was then developed by integrating fishing ship accident data with SME insights. The FABN was comprehensively modeled based on the scenario, with marine accidents being modeled based on causal variables each marine accident. Changes in the output value of the FABN were verified via a sensitivity analysis, and the independence and statistical significance of the model were confirmed using a statistical analysis of the collected data. FABN allows for the immediate assessment of the probability of marine accidents related to fishing ships by utilizing network structures, and provides the advantage of structurally assessing ship accident risks

海上事故可能造成严重的经济损失和人员伤亡，因此迫切需要进行有效的风险评估。本研究收集了客观描述事故变量的韩国定量海上事故报告，并对其进行分类，以分析渔船的海上事故。为了分析不同类型渔船的事故原因，对主题专家（sme）进行了调查。然后，将渔船事故数据与中小企业的见解相结合，开发了渔船事故贝叶斯网络（FABN）场景。FABN基于情景进行了全面建模，其中海上事故基于每个海上事故的因果变量进行了建模。通过敏感性分析验证了FABN输出值的变化，并通过收集数据的统计分析证实了模型的独立性和统计显著性。FABN允许利用网络结构对与渔船有关的海上事故概率进行即时评估，并提供结构评估船舶事故风险的优势

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引用次数: 0

Comparative and critical analysis of data sources used for ship traffic spatial pattern analysis in Canada and across the global Arctic 对加拿大和全球北极地区用于船舶交通空间格局分析的数据源进行比较和批判性分析

IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-01-11 DOI: 10.1016/j.martra.2025.100129

Adrian Nicoll , Jackie Dawson , Jérôme Marty , Michael Sawada , Luke Copland

This study presents a comprehensive comparative analysis of three primary datasets commonly employed to evaluate shipping patterns in Arctic waters: 1) Northern Canada Vessel Traffic Zone (NORDREG), 2) satellite-based Automatic Identification System (S-AIS) from a private provider, and 3) the Arctic Ship Traffic Database (ASTD). Covering the years 2011 to 2022, the analysis assesses spatial and temporal metrics for each dataset while employing robust data cleaning techniques to address signal manipulation and detection gaps. Findings reveal that S-AIS and NORDREG excel in detecting vessel traffic in Canadian waters, including the Northwest Passage (NWP), while ASTD demonstrates strong performance in regions with dense terrestrial AIS coverage, such as Norway and Iceland. However, ASTD is less effective along critical shipping routes, including the NWP and the Northern Sea Route (NSR), where S-AIS provides broader coverage. Both datasets indicate an upward trend in AIS-based traffic throughout the Arctic. The results underscore the value of fusing S-AIS and ASTD datasets to provide a more complete and accurate understanding of Arctic shipping patterns. This research offers critical insights for policymakers and researchers selecting ship traffic data for regional and global Arctic analyses, maritime safety, and environmental decision-making.

本研究对通常用于评估北极水域航运模式的三个主要数据集进行了全面的比较分析：1)加拿大北部船舶交通区（NORDREG）， 2)私人供应商提供的基于卫星的自动识别系统（S-AIS），以及3)北极船舶交通数据库（ASTD）。该分析涵盖2011年至2022年，评估了每个数据集的空间和时间指标，同时采用强大的数据清理技术来解决信号操纵和检测差距。研究结果表明，S-AIS和NORDREG在加拿大水域（包括西北航道（NWP））的船舶交通检测方面表现出色，而ASTD在挪威和冰岛等陆地AIS覆盖密集的地区表现出色。然而，ASTD在关键航线上的效果较差，包括NWP和北海航线（NSR），其中S-AIS提供了更广泛的覆盖范围。两个数据集都表明，整个北极地区基于人工智能系统的流量呈上升趋势。这些结果强调了融合S-AIS和ASTD数据集的价值，以提供对北极航运模式更完整和准确的了解。这项研究为政策制定者和研究人员选择船舶交通数据进行区域和全球北极分析、海上安全和环境决策提供了重要见解。

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IF 3.9 Q2 TRANSPORTATION

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引用次数: 0

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