Maritime Transport Research最新文献

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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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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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Maritime Transport Research

Pub Date : 2025-01-01

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IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-01-01

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IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

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IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

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IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

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IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-01-01

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IF 3.9 Q2 TRANSPORTATION

Maritime Transport Research

Pub Date : 2025-01-01

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Maritime Transport Research

Pub Date : 2025-01-01

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