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    <title>Transport Research International Documentation (TRID)</title>
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    <language>en-us</language>
    <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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Crude Oil Pipeline Corrosion Defect Localization and Remaining Life Assessment Based on Graph Neural Networks</title>
      <link>https://trid.trb.org/View/2711497</link>
      <description><![CDATA[To address the issues of insufficient accuracy in locating corrosion defects and low reliability in remaining life assessment of crude oil pipelines, a systematic analysis method based on graph neural networks is proposed. The pipeline system is abstracted as a graph structure, with pipe segments as nodes and spatial and physical relationships as edges. A bi-branch graph neural network model is constructed to process pipeline topology and multi-dimensional time-series monitoring data respectively, achieving accurate location of corrosion defects. Furthermore, by combining multi-scale temporal convolutional networks and graph attention mechanisms, the Paris corrosion extension model is introduced as a physical constraint to complete the dynamic assessment of pipeline remaining life. Experiments based on measured data from the DN600 pipeline in Shengli Oilfield show an average defect location error of 0.265 meters and an average absolute error in remaining life prediction of 0.072 years, providing reliable data-driven support for pipeline integrity management.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711497</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>Volatility spillover effects of crude oil futures on tanker freight rates: new evidence from a time-frequency perspective</title>
      <link>https://trid.trb.org/View/2675879</link>
      <description><![CDATA[In this study, we investigate the volatility spillover impact of oil prices on tanker freight rates, by calculating the monthly volatilities of eleven representative tanker freight indices, West Texas Intermediate (WTI), and Brent futures price indices to measure the oil and tanker shipping markets risk, respectively. In particular, the high-frequency component of the tanker freight rate volatility is disentangled by adopting the ensemble empirical mode decomposition (EEMD) method and Fine-to-coarse algorithm. Besides, the time-varying parameter vector autoregressive (TVP-VAR) model is employed to analyse the impact of crude oil futures prices on the high-frequency component of tanker freight rates at the volatility level. Our results reveal that crude oil price volatility affects tanker freight rate volatility on different routes to varying degrees, and crude oil price volatility has a positive spillover effect on tanker freight rate volatility on short-haul routes and small-sized ships located on routes.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675879</guid>
    </item>
    <item>
      <title>The risk spillover between the crude oil market and the tanker market under the influence of the carbon market</title>
      <link>https://trid.trb.org/View/2604739</link>
      <description><![CDATA[The low-carbon development of the shipping and crude oil markets is crucial for achieving the EU’s energy and climate goals. Based on the background of the role of the global carbon market, this paper investigates the risk linkage issue between the crude oil and oil tanker markets. Through the Vine-copula-CoVaR model, this paper reveals the cross-market dynamic risk spillover mechanism and measures the risk spillover effects. The results show that: (1) With the extension of the time scale, the impact of carbon market fluctuations on the crude oil and tanker markets is changing, and the degree of long-term risk spillover is greater than that of short-term risk spillover. (2) Carbon market regulation restructures the traditional relationship between the crude oil and tanker markets. (3) Under time–frequency conditions, the CoVaR values of both the tanker and crude oil markets are systematically higher than the VaR values, which means that the risks from the carbon market will amplify the extreme risks of tankers and crude oil systems. (4) The risk spillover relationship during market crisis is stronger than that during stable period. (5) As the time scale extends, the correlation between the carbon market and the oil tanker market gradually increases, and there is a strong intrinsic linkage mechanism between the carbon market and the crude oil market.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604739</guid>
    </item>
    <item>
      <title>The Impact of Fluctuations in Global Energy Prices on Maritime Transportation: A Frequency-Based Approach</title>
      <link>https://trid.trb.org/View/2698314</link>
      <description><![CDATA[This study examines the volatility spillovers between oil prices (WTI, Brent, Dubai) and shipping indices (BDI, BCTI, BDTI) from January 02, 2015, to July 31, 2024. Using the methods of Diebold and Yilmaz (2012) and Baruník and Krehlík (2018), the volatility relationships between energy and shipping markets are analysed in directional and frequency terms. The findings show that oil prices have a strong volatility effect on shipping indices, which is particularly pronounced in the long run. The long-term impact of Brent oil prices on shipping indices suggests that shocks in energy markets can have lasting effects on global transportation costs. Moreover, global events, such as the COVID-19 pandemic and the Russia-Ukraine war, have further amplified the spillover of energy market volatility to shipping indices. The study results emphasise the need for firms operating in energy and shipping markets to develop stronger risk management strategies against volatility. Future research should examine the effects of larger data sets and macroeconomic factors on these relationships. © 2026, Faculty of Maritime Studies. All rights reserved.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698314</guid>
    </item>
    <item>
      <title>Differentiated carbon pricing mechanism for crude oil shipping: balancing efficiency and equity</title>
      <link>https://trid.trb.org/View/2697514</link>
      <description><![CDATA[Accelerating maritime decarbonization requires Market-Based Measures (MBMs) to bridge the fossil-green cost gap. Prevailing uniform carbon pricing, however, creates an efficiency-equity dilemma: it offers aggregate cost-effectiveness yet disproportionately burdens developing economies. To enable differentiated treatment within a globally uniform framework, we propose the Port Carbon Intensity Index (PCII) mechanism. Utilizing ports as the nexus of global trade, the PCII links route-specific emissions to economic value-added. Using an integrated optimization framework parameterized by global Automatic Identification System (AIS) data from 2020 for crude oil tankers, we quantify trade-offs between uniform pricing and the PCII. Results show that the PCII mechanism robustly promotes regional equity by shifting compliance costs toward developed regions. Furthermore, the comparative cost-effectiveness of the two mechanisms is highly sensitive to ballast voyage allocation methodologies, and technological cost reductions alone cannot resolve long-term distributional imbalances. These findings provide important insights for designing equitable and effective maritime decarbonization policies.]]></description>
      <pubDate>Tue, 05 May 2026 09:26:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697514</guid>
    </item>
    <item>
      <title>Dynamic linkage and extreme risk spillover between international crude oil futures and clean product tanker markets</title>
      <link>https://trid.trb.org/View/2688647</link>
      <description><![CDATA[In this study, a combination of quantile regression and spillover index methods is employed to delve into extreme volatility spillovers between crude oil futures (WTI and Brent) and clean product tanker markets. The findings reveal that volatility connectedness at the conditional mean and median levels remains relatively modest, while volatility spillovers are notably stronger at the lower and upper quantile estimates. Specifically, during periods of extremely bullish markets, robust correlations emerge between WTI and Brent futures and the clean product tanker markets. The product tanker market is found to play a significant role in influencing fluctuations in the prices of crude oil futures during these periods, highlighting a dynamic interplay between these markets. Furthermore, this study uncovers that the volatility spillover across markets is time-varying and susceptible to major contingencies. This nuanced understanding of extreme volatility spillovers provides valuable insights for market participants, allowing for a more informed approach to risk management and strategic decision-making in response to fluctuations in crude oil futures and clean product tanker markets.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:48:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688647</guid>
    </item>
    <item>
      <title>Experimental study on yield–slip characteristics during shutdown–restart of deepwater high-wax oil–water pipelines</title>
      <link>https://trid.trb.org/View/2688310</link>
      <description><![CDATA[Deepwater shutdowns in high-wax oil–water pipelines can form wax-gelled segments with very high yield strength, making conventional yield-stress models unreliable for restart-pressure prediction. Thermally regulated loop experiments together with rheometer measurements were conducted to quantify post-shutdown oil–water distribution, internal water-content characteristics of the gelled segment, and restart-pressure dynamics. Owing to coupled oil–water emulsification and gravitational stratification, the gel exhibits a pronounced non-uniform water-content gradient, and its effective length is ∼1.45 × the theoretical pure-oil gel length. Increasing water content and gel length both markedly raise restart pressure. Restart is governed by two concurrent mechanisms: bulk yielding of the crude oil and wall slip. Wall slip dominates well below the pour point, whereas bulk yielding prevails as temperature approaches the pour point, with a distinct critical temperature separating the two regimes. Based on these observations, we propose a restart-pressure model that couples yielding and slip while accounting for internal water-content gradients, enabling improved prediction accuracy. The results provide guidance for the design and safe operation of deepwater high-wax oil–water transportation pipelines.]]></description>
      <pubDate>Tue, 07 Apr 2026 15:37:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688310</guid>
    </item>
    <item>
      <title>Analyses of the mechanical characteristics of long-distance crude oil pipeline support assembly on “L”-type pipeline</title>
      <link>https://trid.trb.org/View/2627187</link>
      <description><![CDATA[Trenched crude oil pipelines rely on anchor blocks and pipe supports for stability, however mechanical interaction remains unclear, limiting design safety. This study employs fluid-solid-thermal coupling methods to establish a three-dimensional finite element calculation model of an “L”-type crude oil pipeline based on an equivalent model of the end-side displacement of the anchor block and pipe section. The mechanical response patterns of anchor blocks and pipe supports to the isolated action of “L"-type pipes were investigated separately. After evaluating the optimal design position of pipe supports to balance internal forces within the pipe, we further investigated the synergistic optimisation mechanism of anchor blocks and pipe supports on the mechanical characteristics of the pipe. Results indicate that the anchor block provides limited protection to the structural safety of pipeline. Under 300 mm end-side displacement imposed by anchor block, the maximum deformation of elbow decreases by 2.31 mm. However, increasing the end-side displacement from 500 mm to 600 mm reduces the maximum deformation by only 0.05 %. A symmetrical distribution of pipe supports delivers the optimal solution, lowering the maximum stress of the pipeline to 78.049 MPa, the 25.55 % reduction. Additionally, the combined use of anchor blocks and pipe supports demonstrates a synergistic optimisation effect on pipeline safety, reducing elbow deformation by approximately 85.14 % (to 33.121 mm). These findings provide a theoretical basis for optimising the working conditions of pipeline support assemblies in long-distance crude oil pipeline networks.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:01:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627187</guid>
    </item>
    <item>
      <title>Oil pipeline leaks: modelling and design of innovative plant solutions based on free floating sensor systems</title>
      <link>https://trid.trb.org/View/2648554</link>
      <description><![CDATA[The paper addresses the problem of leaks in pipelines used to transport crude oil or refined petroleum products over long distances in petroleum industrial plants. The article refers to a pipeline monitoring system based on the latest generation sensors which, flowing inside the pipes together with the transported fluid, are able to record the acoustic emissions generated by any leaks and locate them. The paper proposes an optimization model to size a monitoring service of this type for a given pipeline in order to guarantee specific performance such as monitoring frequency, timely data acquisition, and sensor battery/memory limits while minimizing costs. The model outputs the minimum number of sensors required and the sequence of insertion and withdraw points for each sensor. All withdraw points are points where the data recorded in the sensor in the previous route is read. Some of the withdraw points may be charging points, if the sensor battery is not enough to cover the next trip. Relocation trips, required to move a sensor from the withdraw point to the next insertion point and performed by an operator were also taken into account. The proposed algorithms were tested on a network of examples and the results obtained confirmed that the optimization model is able to minimize the number of deployed devices under realistic constraints, demonstrating the feasibility and scalability of the optimization methodology.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648554</guid>
    </item>
    <item>
      <title>Research on VOCs Composition Emissions in the Oil Transport Link Based on Multi-source Detection Data</title>
      <link>https://trid.trb.org/View/2643391</link>
      <description><![CDATA[Volatile Organic Compounds (VOCs) generated in the oil transportation process are important precursors for secondary organic aerosols (SOA) and photochemical smog. These emissions have become one of the key environmental constraints in China’s 14th Five-Year Plan. Due to the diversity of oil products, VOC composition varies significantly among different types of oil, such as crude oil and refined oil, making it a critical consideration in the development of pollution control policies and treatment processes for the transportation sector. This study employs gas chromatography with a hydrogen flame ionization detector and mass spectrometry to analyze VOCs emitted from 31 types of crude oil and refined oil samples under simulated transportation and storage conditions. By utilizing multi-source detection and mass spectrometry overlay, along with area normalization spectral analysis, we provide a more accurate breakdown of VOC components from crude oil, asphalt mixtures, gasoline, diesel, aviation kerosene, and naphtha. Special attention is given to the olefin and aromatic hydrocarbon components, which contribute significantly to ozone formation. The research results can provide important basis for the research and design selection of VOCs treatment technology and equipment in the transportation process.]]></description>
      <pubDate>Tue, 20 Jan 2026 10:11:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643391</guid>
    </item>
    <item>
      <title>Unraveling structural shifts and resilience patterns of global and intercontinental crude oil maritime transportation networks under geopolitical conflicts: A multiplex network model</title>
      <link>https://trid.trb.org/View/2630656</link>
      <description><![CDATA[In the context of geopolitical conflicts, the varying operational tasks and economic drivers lead to distinct route and port selection strategies for laden and ballast crude oil tankers, reflecting functional and structural heterogeneity within the crude oil maritime transportation network (COMTN). This paper proposes a multiplex network model with laden and ballast layers to characterize the COMTN. Taking the Russia–Ukraine conflict as a case study, we examine changes in the global and four intercontinental COMTNs between 2021 and 2023. Specifically, we characterize the network structure using multiple empirical metrics, and characterize resilience by two dimensions of robustness and adaptability. We construct a redistribution model to characterize the dynamic adjustment of COMTN. Furthermore, we design a deliberate attack scenario based on the duplex collective influence (DCI) score of ports and four historical disruption scenarios to assess network resilience. Results show that the shift in global transport center of transport flows and regional reconfiguration alter the hierarchical importance of ports in the laden/ballast layer and significantly affect the average path cost (APC). After the conflict, resilience increases in the Middle East–East Asia (ME–EA) and Middle East–Europe (ME–EU) corridors but decreases in the North America–Europe (NA–EU) and North America–East Asia (NA–EA) corridors. These findings offer valuable insights for stakeholders and policymakers seeking to better understand the complex transport relationships in COMTN and enhance network resilience.]]></description>
      <pubDate>Mon, 22 Dec 2025 17:03:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630656</guid>
    </item>
    <item>
      <title>Evaluation of global crude oil shipping network based on complex network theories</title>
      <link>https://trid.trb.org/View/2589803</link>
      <description><![CDATA[This study analyses the global crude oil shipping network and determines the important and weaker points within the network in a specific time span. The study also provides a novel longitudinal perspective on the structural dynamics and flexibility of the global crude oil shipping network by constructing year-by-year maritime transport networks based on Automatic Identification System (AIS) data spanning from 2011 to 2023. The results identified a dual-dimensional shift in the core architecture of the global oil shipping network and reveal a spatial transition in nodal centrality from traditional European ports—such as Rotterdam and Antwerpen—toward emerging hubs in the Asia-Pacific region, most notably Singapore East Anchorage, Al Fujayrah, and Juaymah Terminal. In response to the decline of oil price, it is seen that the oil shipping and trading become less active, uncertain and volatile in the market price after 2014 than prior. This work aims to leave a great perspective to the investors and the policy makers to understand the market movement in the distribution of crude oil industry during and after crisis and to act efficiently within available alternatives.]]></description>
      <pubDate>Mon, 29 Sep 2025 08:35:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2589803</guid>
    </item>
    <item>
      <title>Enhancing energy security through multi-scale network analysis: robustness in global crude oil shipping–trade networks</title>
      <link>https://trid.trb.org/View/2587697</link>
      <description><![CDATA[The stable operation of the global crude oil supply chain is vital to energy security, and the close connection between shipping and trade networks critically determines the system’s ability to respond to unexpected disruptions. However, systematic quantitative assessments of robustness mechanisms across their multi-scale structures remain limited. This study aims to establish a multi-scale crude oil shipping–trade network (MCOSTN), then systematically assess its robustness under realistic disruption scenarios, identify critical nodes and propose measures to enhance network resilience. A multi-scale nested analytical framework is developed, constructing a port-level shipping network and a country-level trade network through AIS data mining techniques. Based on this, robustness of the MCOSTN is precisely evaluated using node load penalty factors, simulating flow redistribution and cascading failures following node disruptions. Results indicate that robustness assessments under different attack strategies demonstrate that flow-based attacks cause the most severe disruptions to network performance, whereas node centrality indicators exhibit relatively limited impacts. Dynamic node failure simulations further confirm that simultaneous failures of critical chokepoints can severely impair global crude oil transport functionality. Additionally, sensitivity analyses of load penalties underscore the critical role high-load nodes play in network performance degradation and cascading effects post-disruption. Conclusions emphasize the importance of enhancing physical network redundancy and trade network flexibility, recommending improved transportation capacities at secondary nodes, optimization of global strategic reserves, and strengthened security measures for critical nodes to significantly enhance the overall resilience of the global crude oil supply chain.]]></description>
      <pubDate>Fri, 26 Sep 2025 13:39:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587697</guid>
    </item>
    <item>
      <title>Rapid Prediction Models for Oil Temperature and Surrounding Environment Temperature Fields in Buried Hot Crude Oil Pipelines</title>
      <link>https://trid.trb.org/View/2552267</link>
      <description><![CDATA[Wax-rich or high-viscosity crude oil requires heating for long-distance transportation in buried pipelines. Monitoring the temperature distributions and variations of both oil and surrounding environment is crucial to ensuring safety and economic efficiency. This study proposes two models for rapidly predicting the steady-state oil temperature and surrounding environment temperature fields based on the Fourier neural operator (FNO) network and U-shaped network (UNet), respectively. These models leverage numerical results as training data, incorporating boundary conditions and environment grid coordinates at the pipeline cross section as inputs to predict the temperature distributions of both oil and the surrounding environment along the buried pipeline. With optimized hyperparameters, the models achieve accurate and efficient predictions. The FNO and UNet models had average RMS errors (RMSEs) in environment temperature field prediction of 2.68×10⁻³ and 5.49×10⁻³ at the pipeline cross section, respectively. For oil temperature predictions, the FNO model had an average relative error of 1.49×10⁻⁴, compared with 2.55×10⁻⁴ for the UNet model, with average absolute error values of 5.32×10⁻³ and 7.24×10⁻³, respectively. Moreover, both models exhibited strong generalization, with an average RMSE in the environment temperature field prediction of less than 3.5×10⁻² and an average relative error in oil temperature predictions of less than 2.1×10⁻³ across different data sets. Comparatively, the FNO and UNet model had slightly higher prediction accuracy than the UNet model. In terms of computational efficiency for a 100-km pipeline, these models offer improvements of at least 116.25× over the numerical simulation method, with a maximum improvement of 1,775.03× as the number of simultaneously predicted pipeline cross sections increases.]]></description>
      <pubDate>Wed, 24 Sep 2025 15:24:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2552267</guid>
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