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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Dynamic risk assessment for methanol ship-to-ship bunkering operations</title>
      <link>https://trid.trb.org/View/2728693</link>
      <description><![CDATA[Methanol is a promising alternative marine fuel that can contribute to greenhouse gas emission reductions. Despite its significant environmental potential, the inherent toxicity and high flammability pose substantial safety challenges during ship-to-ship (STS) bunkering operations. Effective risk management is essential to enhance operational safety in methanol bunkering and promote its adoption. To address this, a Dynamic Bayesian Network (DBN) combining physical and statistical models is proposed for methanol STS bunkering. This approach improves dynamic probabilistic assessment of hazardous consequence by integrating weather, operational, and port conditions that change over time. It builds a fault tree from investigation reports and methanol properties, and maps it to a DBN with time-dependent properties. Conditional probability tables are derived from weighted scoring, fluid mechanics, and diffusion models. The method was applied to three sites in Hong Kong to assess dynamic probabilities of hazardous consequences. The analysis identified Cheung Chau South Anchorage as the most suitable location for methanol STS bunkering due to its lower mean threat probabilities during the bunkering process across toxic dispersion, vapor cloud explosion, and flash/pool fire. Sensitivity analysis identified key influencing factors, revealing that although methanol and oil fuel share common key factors, they also exhibit significant differences. Methanol requires particular attention to bunkering pressure, bunkering rate, and corrosion degree. The validated and adaptable model offers actionable guidance for ports and regulators to enhance methanol STS bunkering safety. It can also be extended to other ports for dynamic probabilistic safety assessment of methanol STS bunkering.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:36:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2728693</guid>
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    <item>
      <title>An experimental study on passive roll reduction for LNG bunkering vessels using a Baffled Sloshing Tank</title>
      <link>https://trid.trb.org/View/2754046</link>
      <description><![CDATA[This study proposes a passive Baffled Sloshing Tank (BST) device installed inside tanks to reduce roll response of LNG bunkering vessels. Small to medium-sized bunkering vessels with Type C tanks experience significant roll amplification due to internal sloshing, as the ratio of fluid mass to vessel size is relatively large compared to conventional LNG carriers. To address this operational challenge, a novel BST concept was developed featuring a longitudinal swash bulkhead with a controlled opening that divides the tank into two compartments, enabling phase-lagged fluid transfer between compartments to generate passive damping. Comprehensive model tests were conducted at 1:100 Froude scaling covering filling ratios from 20% to 80% under both regular and irregular wave conditions. As an independent cross-check on the physical validity of the experimental setup, a numerical framework combining frequency-domain and time-domain analyses with a quasi-static free-surface correction was used to reproduce the baseline (without BST) roll response. The parametric studies identified BST-3 with 3.77% opening ratio as the best performing configuration, achieving 47.7% average RAO-area remaining ratio in regular waves across all filling ratios. Under irregular wave conditions with significant wave height of 4.5 cm and mean periods of 0.95 and 1.15 s, BST-3 reduced maximum roll angles to 46-56% of baseline values for filling ratios 20-60%, with standard deviation reductions of approximately 50%. Performance was enhanced under longer wave periods due to increased time for phase-lag development between tank motion and fluid response. At high filling ratios (80%), effectiveness was moderate due to reduced sloshing behavior with limited free surface mobility. To preliminarily examine possible transferability to another tank geometry, subject to further investigation, the BST-3 configuration was applied to membrane-type tanks, showing comparable performance levels with 44-47% maximum roll reduction and 38-40% standard deviation reduction under irregular waves. These results suggest that the BST concept may be transferable to another tank geometry, subject to further geometry-specific validation, while showing comparable model-scale performance in the tested membrane-tank configuration. The spectral analysis confirmed dramatic suppression of resonance peaks with energy redistribution away from critical frequencies for both Type C and membrane tanks. This study experimentally demonstrates that a simple longitudinal bulkhead with the selected model-scale BST configuration can achieve substantial roll reduction, consistent with a passive phase-lag damping mechanism. The BST device offers a cost-effective solution for enhancing stability and operational safety of LNG bunkering vessels without active control systems, complex structural modifications, or additional energy consumption, making it practical for retrofit applications and new vessel designs.]]></description>
      <pubDate>Wed, 16 Sep 2026 16:15:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2754046</guid>
    </item>
    <item>
      <title>A bi-objective tramp ship routing and scheduling problem to accelerate shipping sustainability transition</title>
      <link>https://trid.trb.org/View/2704271</link>
      <description><![CDATA[The transition to sustainable shipping entails a combination of short-term and long-term measures to minimize environmental impact. In this work, we study a bi-objective 𝘛𝘳𝘢𝘮𝘱 𝘚𝘩𝘪𝘱 𝘙𝘰𝘶𝘵𝘪𝘯𝘨 𝘢𝘯𝘥 𝘚𝘤𝘩𝘦𝘥𝘶𝘭𝘪𝘯𝘨 𝘗𝘳𝘰𝘣𝘭𝘦𝘮 (TSRSP) in which we plan routes for a fleet of tramp shipping vessels using a feature-rich model to mitigate emissions in the short term. The two objective functions are the maximization of profit and the minimization of g𝘨𝘳𝘦𝘦𝘯𝘩𝘰𝘶𝘴𝘦 𝘨𝘢𝘴 (GHG) emissions. This feature-rich model can handle speed optimization, chartering costs, and bunker planning. This tactical/operational model has many similar solutions, only differing slightly in operational choices, resulting in marginally different profits and emissions. This way, emissions can be effectively reduced by optimizing operations using an emission-aware model without waiting for technological advancement. Its single-objective counterpart is efficiently solved using column generation, where the columns represent the possible routes of a vessel, while the master problem controls route selection. New columns are generated using dynamic programming on a time-space graph. The bi-objective model is solved iteratively using an enhanced ϵ-constraint method. Lagrangian relaxation is used to improve the solutions bounds by leveraging the constrain matrix integrality properties, and a dicotomic search procedure is used to accelerate the search. The algorithm determines the Pareto front, which describes the optimal compromise between profit and emission. We compute the Pareto front for 10 different historical scenarios between 2021 and 2023, showing that emissions can be reduced on average by more than 10% with less than a 2% profit decrease and by 20% with a 5% profit decrease. Then, we compute the Pareto front with alternative fuels-engine combinations, obtaining average emissions reduction of over 40% with less than a 5% profit decrease. We integrate these results with insights to explain the positive results. Finally, we compare the results with GHG tax levies, showing that these solutions are strongly dominated by the Pareto fronts we compute.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704271</guid>
    </item>
    <item>
      <title>Net-zero GHG emission in shipping networks: Role of alternative fuel bunkering hubs</title>
      <link>https://trid.trb.org/View/2725260</link>
      <description><![CDATA[The study analyzes the role of ports as alternative fuel bunkering hubs in achieving net-zero GHG emissions in container trades. The multi-dimensional analysis provides a realistic assessment that encompasses vessel size, number of vessels, and service frequency in addition to individual vessel movements. The study covers deployed containership capacity across all major trade routes, regions, and vessel classes. Leveraging data-driven intrinsic structures, GMM clustering is used to identify cluster centroids as candidate ports for alternative fuel bunkering. The research shows that a select number of ports can exert considerable impact on decarbonization, particularly on long-haul and inter-region routes. Under the modeled hub-coverage logic, four key bunkering locations can eliminate 77.1% of CO₂ emissions in the life-cycle scenario, rising to 87.3% when additional strategically located ports are included. These results indicate that deep decarbonization in container shipping is likely to be led by a small set of high-leverage hubs on the major east–west corridors. In addition, the dispersed nature of feeder and intra-region trades calls for a more distributed bunkering approach, with electrification as a feasible strategy for emission reduction.]]></description>
      <pubDate>Fri, 31 Jul 2026 15:16:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2725260</guid>
    </item>
    <item>
      <title>Compatibility Analysis of Hydrogen Infrastructure and Ship Bunkering Needs</title>
      <link>https://trid.trb.org/View/2579441</link>
      <description><![CDATA[The EU set ambiguous targets of reducing GHG emissions by at least 55% by 2030 compared to 1990 levels and becoming carbon neutral (net zero) by 2050. There are a number of technical solutions to meet those regulations. As there is no relevant battery electric option for decarbonizing the deep-sea shipping sector, the synthetic fuels, ammonia, hydrogen and biofuels are the most realistic low-carbon alternatives. Hydrogen is particularly important and rapidly developing pillar of the energy transition, as hydrogen produced from renewable energy sources emits zero CO2. The crucial factor limiting applying hydrogen as a ship fuel might be lack of bunkering infrastructure in the ports. Due to high uncertainty of demand, investments into hydrogen bunkering infrastructure are still risky and therefore vague. The aim of research is to examine hydrogen as a ship fuel demand scenarios and evaluate capacities of various hydrogen bunkering infrastructure configurations. To achieve this aim hydrogen bunkering capacities mathematical model was designed, and case study for Klaipeda Seaport was developed. The results of the research show that operational aspects are critically important, determining technical configuration of bunkering system. These results might be used for further research involving hydrogen fuelled ship prototypes.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579441</guid>
    </item>
    <item>
      <title>Connectedness and portfolio diversification among major bunker fuel market: The role of crude oil and geopolitical risk</title>
      <link>https://trid.trb.org/View/2681456</link>
      <description><![CDATA[This study examines the time-varying connectedness among major bunker markets, crude oil futures, and geopolitical risk using the dynamic connectedness approach based on the TVP-VAR framework. The results show that the total connectedness index (TCI) is predominantly high and largely driven by short-term spillovers during periods of economic crises and global disruptions. The net spillover effect shows that the global bunkering system is highly responsive to short-term geopolitical risk, whereas oil price shocks exert influence only in the long run. Regional analysis highlights notable asymmetries: American ports are more sensitive to geopolitical risk, whereas Asian ports are more resilient. Conversely, Asian ports are more susceptible to oil price volatility, while European ports demonstrate relative stability in response to oil market fluctuations. The net spillover result identifies Rotterdam as the dominant transmitter across both horizons, followed by Singapore, while Los Angeles consistently reacts to external market shocks. Finally, the portfolio allocation analysis reveals that the dynamic portfolio weights under the MCP and MCoP strategies are broadly comparable, yet clearly differentiated from those obtained under the MVP framework. Notably, Japan and Hong Kong consistently receive the highest weight allocations across the optimal portfolios. These result offers valuable guidance for investors, portfolio managers, and policymakers in formulating effective portfolio strategies and managing risk preferences.]]></description>
      <pubDate>Thu, 25 Jun 2026 09:40:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681456</guid>
    </item>
    <item>
      <title>Inland electric ship battery swapping station selection and swapped battery quantity optimisation</title>
      <link>https://trid.trb.org/View/2675877</link>
      <description><![CDATA[In response to escalating environmental concerns and tightening regulations, battery power has emerged as a promising solution for short- and medium-distance inland waterway freight transportation. Recently, inland electric ships have increasingly adopted the battery swapping mode. This paper develops an optimisation-based approach to identify the most cost-effective battery swapping strategy, taking into account energy management and voyage scheduling. The proposed approach fills a critical gap by providing flexibility in selecting battery swapping stations and determining the quantities of batteries to be swapped. A case study of an inland electric container ship sailing on the Yangtze River is presented, demonstrating that the proposed approach can achieve cost savings of approximately 5%. Furthermore, this paper explores the impact of voyage-related and battery-related factors on battery swapping strategies. The proposed approach also demonstrates adaptability to scenarios involving battery grouping strategies. These findings could offer valuable insights for ship operators, station operators, and policymakers.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675877</guid>
    </item>
    <item>
      <title>Simultaneous Operations During LNG Bunkering at LNG-powered Vessels</title>
      <link>https://trid.trb.org/View/2655571</link>
      <description><![CDATA[The usage of liquefied natural gas (LNG) as fuel for maritime vessels is widely viewed as a transitional option to meet the stringent EU and IMO regulations in the context of long-term decarbonization. Usage of LNG as a transitional fuel is associated with the goal of reducing or completely eliminating CO2 production (related to the emissions from fossil fuel use such as oil, hard coal, and lignite) in the shipping industry. Bunkering operations can be done by trucks, bunker barges and specialize ports. In case of bunkering by trucks or barges, vessel can provide at the same time other operations, such as cargo operations or ballasting. This article considers some key issues associated with Simultaneous Operations (SIMOPS) Procedure Guide. A typical case study of bunkering LNG-powered vessel "Containerships Nord" by barge in Rotterdam is used to demonstrate and verify the proposed SIMOPS Procedure. The results indicate that any SIMOPS activity undertaken by LNG Bunker vessel, LNG receiving vessel and Container Terminal shall be reviewed and scheduled to minimize any potential interference or safety impact.]]></description>
      <pubDate>Thu, 28 May 2026 16:16:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655571</guid>
    </item>
    <item>
      <title>Evaluating the Safety of Port-to-Ship Liquefied Hydrogen Bunkering through Fault Tree Analysis and Fuzzy Bayesian Networks</title>
      <link>https://trid.trb.org/View/2698309</link>
      <description><![CDATA[As marine fuels transition toward zero carbon dioxide emissions, hydrogen emerges as a promising alternative due to its near-zero emissions and high energy density. However, hydrogen’s high flammability presents a significant risk of fire-related accidents onboard ships. Among the various operations involving hydrogen in the maritime sector, bunkering is one of the most critical. This process carries risks such as leakage, pressure failures, and human errors, which can lead to severe consequences. This study analyzes the safety of the liquefied hydrogen bunkering process. Fault Tree Analysis (FTA) was used to identify failures within the bunkering system, employing a top-down approach that traces failures from the top event to the basic events. Fuzzy Set Theory was applied to quantify risk by aggregating linguistic variables from five experts to estimate the probabilities of basic events. Bayesian Networks (BN) were then used to establish causal relationships between factors and identify the minimum cut sets leading to the top event of bunkering failure. Rate of Variation (RoV) analysis of the posterior probabilities of basic events revealed that human error, particularly due to task execution problems and human-machine interface issues, is the most significant factor influencing bunkering failures. The findings highlight critical areas that require attention to enhance the safety of liquefied hydrogen bunkering operations in the maritime sector, especially for relevant stakeholders.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698309</guid>
    </item>
    <item>
      <title>Charting a Course for Sustainable Port Operations: Insights on Green Ammonia Production and Bunkering</title>
      <link>https://trid.trb.org/View/2579676</link>
      <description><![CDATA[Maritime transport, pivotal to global trade, faces challenges in reducing greenhouse gas emissions. The International Maritime Organization aims to mitigate emissions through strategies like green marine fuels, with ammonia (NH3) emerging as a promising option. However, implementing ammonia bunkering (AB) in ports raises health, safety, security, and emergency (HSSE) concerns. This study addresses HSSE assessments and economic impact by examining AB’s implications for port sustainability, focusing on managing HSSE aspects to promote sustainable port practices and operational efficiency. The findings underscore the need for robust risk management, emergency preparedness, and collaboration to ensure safe and secure port operations amid the transition to cleaner energy sources. Findings from a case study of a planned green ammonia plant in Paldiski, Estonia, highlight potential economic and operational benefits, including driving sustainable growth, supporting AB’s port operations, and facilitating the maritime industry’s transition to cleaner energy through significant investment in port infrastructure. The research is based on empirical data from several projects on NH3 use in the maritime sector.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579676</guid>
    </item>
    <item>
      <title>A Risk-Based Modelling Approach to Predict Human Error-Induced Oil Pollution Accident in Bunker Transferring Operation</title>
      <link>https://trid.trb.org/View/2691603</link>
      <description><![CDATA[Risk of oil pollution during bunker transfer operation remains a significant threat to marine ecosystems and safety as human error being a major contributing factor. This article proposes probabilistic risk modelling framework integrating Dempster-Shafer evidence theory (D-S), Cognitive Reliability and Error Analysis Method (CREAM) and Fault Tree Analysis (FTA) to quantify human error-induced bunker pollution accident in maritime transport. Since DS-CREAM approach is applied to capture epistemic uncertainty and quantification of human error probability incorporating evidence-based reasoning and context-dependent performance shaping factors (PSFs), FT performs probabilistic risk modelling for oil pollution accident. The findings show that occurrence probability of TE (Oil pollution accident risk on-board bunker transfer operation) is found 1.566E-01. Beside its robust theoretical background, the findings of the research will contribute safety and marine environment researchers, professionals, ship crew and superintendents for minimising oil pollution risk and enhancing operational safety during ship to ship bunker transfer operation. The proposed modelling will also provide a robust decision-support tool for risk management and oil pollution prevention in maritime bunker transfer operations.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691603</guid>
    </item>
    <item>
      <title>Distributionally robust optimization of sailing speed, bunkering, and fuel switching for dual-fuel liner services</title>
      <link>https://trid.trb.org/View/2656126</link>
      <description><![CDATA[To reduce CO₂ and SO₂ emissions, shipping companies have started deploying LNG or methanol dual-fuel ships on liner services. Unlike traditional container ships, these dual-fuel ships can use multiple types of fuels during a voyage, allowing them to comply with emission regulations while reducing operational costs through fuel switching and speed optimization. Given the significant fluctuations in bunker prices across different ports, decisions regarding fuel switching, refueling, and sailing speeds must account for price uncertainty. We develop a distributionally robust chance-constrained programming model based on the Wasserstein uncertainty set to minimize operating costs under this uncertainty. We divide each port-to-port sailing leg into sub-legs, considering regional emission requirements or canal segments. This segmentation enables the optimization of fuel usage proportions, sailing speeds, and refueling strategies for each sub-leg. The model is then reformulated as a tractable mixed-integer second-order conic programming model. We validate the model using real-world data from COSCO Shipping. Numerical experiments demonstrate that the model can identify optimal solutions for real-scale instances within practical computational time. Furthermore, the robust solutions significantly outperform those obtained using the traditional sample average approximation method. Our results suggest that the joint optimization of fuel management and sailing speeds for dual-fuel ships can effectively reduce operating costs without increasing emissions.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656126</guid>
    </item>
    <item>
      <title>A review of services management, application of alternative fuels and operational risk assessment for ship bunkering technology</title>
      <link>https://trid.trb.org/View/2637913</link>
      <description><![CDATA[Ship bunkering is an essential part of maritime transportation. Given the increasing interest in adopting alternative marine fuels, there is a pressing need for comprehensive studies to discuss alternative fuel bunkering. This study conducts a systematic literature review on ship bunkering from perspectives of services management, alternative fuel bunkering and operational risk assessment. For services management, most literature focuses on liner shipping bunkering demand, covering bunker amount determination, consumption and management. Several key factors, including bunker price, bunker quality, supply waiting time, geographical advantage, port tariffs, and restrictive environmental regulations, predominantly influence the selection of bunkering ports. Research into alternative fuel bunkering has seen strong growth in the past six years, with liquefied natural gas emerging as the most studied topic, followed by ammonia and hydrogen. The dominant focus of research on bunkering operational risk is bunker releases. The key factors influencing the consequences of such releases are typically categorised into five groups: fuel type, bunkering supply parameters, release conditions, and meteorological and geographical factors. A concept of sustainable, safe and smart bunkering is proposed for future research. This study has wide-ranging implications for academia and industry, enabling a better understanding of how ship bunkering operates with emerging bunker fuels.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:55:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2637913</guid>
    </item>
    <item>
      <title>Joint bunker fuel and freight revenue management in liner shipping: A decision-focused learning approach</title>
      <link>https://trid.trb.org/View/2626024</link>
      <description><![CDATA[The volatility of bunker fuel prices significantly impacts the maritime industry due to increasing regulatory complexities and market uncertainties. This study focuses on addressing these challenges by identifying bunker price prediction as a critical area of impact for improving operational efficiency and decision robustness. A comprehensive optimization model has been proposed to determine optimal ship speed and bunkering strategies both within and outside Emission Control Areas (ECAs). The traditional two-stage method involves training predictive models for bunker price, with the predictions being used as input parameters to solve the optimization problem. However, the loss function in this two-stage method does not consider the effect of predictions on the downstream decision-making problem. Therefore, this study adopts an integrated framework, Decision-Focused Learning (DFL) that unifies prediction and optimization processes into a cohesive approach. Unlike traditional sequential approaches, this method embeds the predictive model directly within the optimization process. It is comparatively more capable of handling data-driven optimization problems than traditional two-stage methods. In this study, computational experiments show that how DFL outperforms traditional methods, achieving 1.29% lower Normalized Regret, which translates to predictions resulting in approximately $22,730 higher profit for liner services. These findings offer valuable managerial insights into improving efficiency and sustainability in liner shipping operations by making better decisions under real world uncertainties.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2626024</guid>
    </item>
    <item>
      <title>Maritime service network design with oil-fueled and LNG-fueled mixed vessel fleet under uncertainty: Overview and future research directions</title>
      <link>https://trid.trb.org/View/2612940</link>
      <description><![CDATA[The adoption of Liquefied Natural Gas (LNG) as an alternative fuel for vessels is a critical path to realizing the strategy of shipping decarbonization. Against the backdrop of the increasing promotion of LNG-fueled vessels, the maritime fleet of the shipping company will comprise a mixed fleet of conventional oil-fueled and LNG-fueled vessels in the mid- and long-term. Due to unpredictable trade fluctuations (e.g., the financial crisis), war, global public health emergencies (e.g., the COVID-19 pandemic), and the inherent characteristics of LNG evaporation, the maritime service network design for a mixed vessel fleet typically faces high uncertainty. This paper aims to review the literature on maritime service network design for mixed vessel fleet under uncertainty, including the problems of LNG fleet renewal, LNG bunkering station location, LNG maritime inventory routing, and the collaborative optimization of bunkering location and vessel deployment. The results remark that the following research gaps have not been well addressed: (1) The traditional fleet renewal problem has not considered the impact of government involvement on the fleet renewal decisions, and they have not measured the effects of deferring or implementing earlier of the renewal decision on shipping companies’ revenues; (2) Few studies have addressed the LNG bunkering location and vessel routing problem under uncertainty; (3) There is a lack of research on the dynamic inventory-routing problem, and the existing model formulation and solving techniques are not suitable for large-scale instances; (4) The existing collaborative optimization problems for the location-inventory and vessel deployment are mainly concentrated on the homogeneous vehicle fleet or vessel fleet, and they have not introduced uncertainty into the optimization problems. Finally, we discuss the future directions that we believe are especially important for addressing the aforementioned research gaps. Our research outcomes can help expand the theoretical research of maritime service network design and offer essential research directions for promoting the sustainable development of the port and maritime industries.]]></description>
      <pubDate>Wed, 14 Jan 2026 17:40:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612940</guid>
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