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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <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>A Method for Predicting Traction Load of Electrified Railways Considering Spatiotemporal Correlation Characteristics</title>
      <link>https://trid.trb.org/View/2735120</link>
      <description><![CDATA[In the prediction of traction loads for electrified railways, conventional forecasting methods often focus exclusively on temporal correlations within historical data from individual substations. However, traction loads are profoundly affected by train schedules and exhibit substantial spatial interdependence across different substations. To address this limitation, this study proposes a hybrid model integrating a graph convolutional network (GCN) and a bidirectional long short-term memory (BiLSTM) network, which comprehensively incorporates both spatial and temporal dependencies to significantly improve ultrashort-term prediction accuracy. The proposed framework operates in several stages. First, spatial correlations among regional substations are captured using a GCN. To mitigate the risk of including spurious connections—often referred to as “pseudo-adjacency” relationships—the adjacency matrix is refined using Pearson correlation coefficients, thereby strengthening the model’s representation of meaningful spatial interactions. The spatial features extracted by the GCN at consecutive time steps are then organized into a temporal sequence and input into the BiLSTM module. To further enhance temporal modeling, an attention mechanism is incorporated to adaptively weigh the importance of hidden states, enabling the model to focus on the most relevant temporal information. This integrated approach results in a notable improvement in the accuracy of traction load power forecasting. Results from case studies demonstrate that the proposed model, with appropriately configured spatiotemporal parameters, achieves superior prediction accuracy. This finding underscores the necessity of incorporating spatiotemporal characteristics for traction load forecasting.]]></description>
      <pubDate>Tue, 29 Sep 2026 15:49:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735120</guid>
    </item>
    <item>
      <title>Extending the numerical ship maneuvering models in irregular long-crested waves to low-speed applications</title>
      <link>https://trid.trb.org/View/2776710</link>
      <description><![CDATA[This paper extends a time-domain, 6-DOF direct superposition numerical framework for ship maneuvering in waves to the less-explored low-speed regime. The model couples a Maneuvering Modelling Group (MMG) calm-water formulation with a blended non-linear seakeeping model. Validated against experimental data for the KVLCC2 tanker, the framework is tested through turning-circle simulations in irregular long-crested seas and low-speed course-keeping simulations across various wave headings. The results demonstrate that the direct superposition approach provides reliable predictions for control purposes in waves, even under reduced maneuvering capabilities. Moreover, the study reveals that once the calm-water model is calibrated, the framework maintains a consistent level of accuracy across different speed regimes, proven here for low-speed conditions.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2776710</guid>
    </item>
    <item>
      <title>An Information-Sharing Current Harmonics Suppression Method Considering Small Time Scale for PMSM Based on Interactive Cascaded Quasi-Resonant Extended State Observer</title>
      <link>https://trid.trb.org/View/2734891</link>
      <description><![CDATA[Steady-state current harmonics suppression has been widely researched. In contrast, dynamic current harmonics suppression receives little attention and is often ignored because the dynamic process is short and occurs on a small time scale. However, it affects the dynamic characteristics of the permanent magnet synchronous motor (PMSM) in electric vehicles. In order to address this issue, an information-sharing current harmonics suppression method considering a small time scale for PMSM based on an interactive cascaded quasi-resonant extended state observer (QR-ICESO) is proposed. First, the inefficient observation of typical quasi-resonant cascade extended state observer (QR-CESO) is analyzed. The disturbances in different frequency bands are observed separately. The bandwidth design of ESO is small to avoid observing the disturbances in the medium-to-high frequency band, and the observation speed is constrained. It leads to inefficient observation. Second, an interactive characteristic is introduced to change the one-way information transmission. The current harmonics estimation information is shared interactively in real time between the quasi-resonant extended state observer (QR-ESO) and the cascaded ESO. Thus, the estimation speed and estimation accuracy of current harmonics are enhanced. Therefore, the dynamic current harmonics suppression effect is improved on a small time scale. Finally, the validity and effectiveness of the proposed method are verified on a 17.26-kW PMSM platform.]]></description>
      <pubDate>Fri, 25 Sep 2026 10:51:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2734891</guid>
    </item>
    <item>
      <title>Time domain characteristic analysis of bell plate hydraulic mount</title>
      <link>https://trid.trb.org/View/2719758</link>
      <description><![CDATA[To mitigate the high-frequency hardening phenomenon observed in hydraulic mount systems, this investigation centres on bell plate hydraulic mounts. A comprehensive lumped parameter model is devised, facilitating the derivation of analytical expressions in both the frequency and time domains specific to bell plate hydraulic mounts. Moreover, a temporal analysis of hydraulic mounts is conducted, encompassing variables such as the pressure within the upper chamber of the bell plate, as well as the flow rates within the inertia and decoupling membrane channels. Lastly, the study examines the temporal evolution patterns of high and low-frequency bell plate hydraulic mounts. Findings reveal that the inertia channel contributes significantly to damping and stiffness at lower frequencies, with the decoupling membrane exerting minimal influence on low-frequency dynamic characteristics. Conversely, at higher frequencies, the disruptive effects of bell plate disturbances and the decoupler membrane synergistically mitigate the high-frequency hardening phenomenon encountered in hydraulic mounts.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:56:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2719758</guid>
    </item>
    <item>
      <title>A Predictive–Prescriptive Analytics Framework for Route Planning via Discrete Time–SpaceGraph and ST-GCN-GRU Model</title>
      <link>https://trid.trb.org/View/2727444</link>
      <description><![CDATA[Route planning in dynamic traffic environments requires decisions that anticipate future traffic evolution rather than rely on time-invariant costs. This study proposes a predictive–prescriptive analytics framework for time-dependent route planning that integrates short-term traffic speed forecasting with optimization on a first-in–first-out–consistent discrete time–space network. The predictive module employs a spatiotemporal graph convolutional network integrated with gated recurrent units (ST-GCN-GRU) to forecast multihorizon link speeds from historical observations, while the prescriptive module embeds these forecasts into a discrete time–space representation that transforms the time-dependent shortest-path problem into a polynomially solvable form. The framework is evaluated in two complementary ways. First, a real-world end-to-end experiment on an OpenStreetMap-derived Nanjing subnetwork tests whether prediction-informed routing improves downstream decisions under a unified replay protocol. Second, controlled experiments on the Sioux Falls benchmark are used for mechanism exploration, examining when the value of future-aware routing becomes more pronounced. Results show that the proposed framework improves traffic forecasting accuracy, supports near-oracle routing performance in realistic settings, and clarifies how trip length and temporal variability influence routing gains.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:56:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727444</guid>
    </item>
    <item>
      <title>Source/sink-centric mobility cube analytic framework to discover multi-level urban mobility patterns</title>
      <link>https://trid.trb.org/View/2710221</link>
      <description><![CDATA[A central challenge in hub-centered mobility analytics is to bridge short-term trajectory variability with long-term structural regularity around source/sink areas (SSAs). Existing mobility mining methods can describe flow volumes or frequent routes, but provide limited insight into how localized SSA interactions generate stable flow structures and how those flow structures are reshaped by branching, merging, and disturbance. To address this gap, a source/sink-centric analytic framework is presented based on a Source/Sink-Centric Mobility Space–Time Cube (SSM-Cube), where each source/sink area defines a functional reference system that organizes hub-triggered (or hub-terminating) episodes and encodes localized interactions via operator-grounded triplets. On this representation, a progressive, generative pipeline (SSC-PGA) links micro-level states to meso-level radial/circling paths through constrained path growth, while logging branching/merging/disturbance interactions online to construct macro-level path-interaction networks. Empirical evaluations using taxi GPS data from Xiamen and Wuhan demonstrate the proposed framework can recover that the extracted SSA-centered structures are more spatially extensive and temporally persistent than baseline models (e.g., TRACLUS, AOFC, and CTM). On a representative SSA, the framework achieves the largest spatial coverage (0.157), complete temporal coverage (1.000), and a larger radial propagation radius (5.28 km), while still covering 37.5% of trips. External validation further shows that disturbance hotspots align with POI-dense and road-dense areas and exhibit lower mean speed, higher speed variability, and higher stop-and-go ratio, supporting hub management and resilience-oriented mobility diagnostics.]]></description>
      <pubDate>Thu, 27 Aug 2026 10:04:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2710221</guid>
    </item>
    <item>
      <title>Dynamic response of a submerged floating tunnel under coupled wave−current−vehicle loads</title>
      <link>https://trid.trb.org/View/2729049</link>
      <description><![CDATA[The dynamic response of submerged floating tunnels (SFTs) under combined wave, current, and moving vehicle loads involves complex nonlinear fluid–structure–vehicle interactions. This paper develops a three-dimensional nonlinear time-domain framework for a tether-supported multi-span SFT. The modal properties, spatiotemporal response patterns, spectral characteristics, stress fluctuations, and local phase-space dynamic behavior of the SFT are systematically investigated under separate and coupled excitations. The results indicate that discrete flexible tether layouts create dense low-order modal clusters, especially for cross-flow bending modes. Moving vehicle loads produce distinct spatiotemporal phase lags between traffic forces and structural displacements, and eccentric loads trigger global torsion and slight secondary in-line displacement. As steady current velocity increases, the cross-flow VIV region expands spatially, while in-line responses gather around the tunnel mid-span. Under pure wave action, cross-flow responses depend on local wave kinematics, while in-line vibration is dominated by global frequency matching and mode amplification. Under combined wave–current–vehicle excitations, the SFT response deviates from the linear superposition of individual excitation responses. The coupled response exhibits nonlinear multi-source modulation.]]></description>
      <pubDate>Tue, 18 Aug 2026 14:11:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2729049</guid>
    </item>
    <item>
      <title>Synergistic Control Strategy for Three-Phase Single-Stage Isolated AC-DC Converters</title>
      <link>https://trid.trb.org/View/2735069</link>
      <description><![CDATA[Three-phase single-stage isolated DAB converters eliminate bulky DC-link capacitors, making them attractive for electric vehicle (EV) and energy storage systems (ESSs) applications. However, compared with conventional current-source converters (CSCs) and DAB converters, the grid-periodic steady-state condition together with the use of bidirectional switches introduces high current stress and commutation challenges, further complicating the control. This article proposes a synergistic control strategy that coordinates multiple degrees of freedom (DOFs) to enhance commutation reliability while reducing current stress. To support this framework, a time-domain analytical model is developed for single-cycle half-wave symmetric modulation, providing precise linkage between modulation DOFs and performance metrics such as current stress and power. Based on this model, a hybrid PSO-SLSQP optimization method is applied to jointly tune the DOFs, with ZVS conditions imposed as soft constraints. Both simulation and experimental results on a 2.5 kW prototype validate the proposed method.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2735069</guid>
    </item>
    <item>
      <title>Time-variant reliability of natural gas pipelines considering trivariate spatial correlation of corrosion defects using limited in-line inspection data</title>
      <link>https://trid.trb.org/View/2704323</link>
      <description><![CDATA[The performance of corroding natural gas pipelines evolves dynamically across both temporal and spatial dimensions, yet in practice, integrity assessment is often constrained by limited and sparsely sampled in-line inspection (ILI) data. Such data scarcity introduces significant epistemic uncertainty and challenges the accurate characterization of corrosion morphology, which exhibits inherently multidimensional and stochastic growth in longitudinal, circumferential, and radial directions. In this work, a probabilistic framework is developed to simulate natural corrosion morphology using limited ILI. Correlated random field theory is employed to model the spatially distributed corrosion features, enabling discrete random sampling across three dimensions while explicitly capturing spatial correlations within the trivariate degradation process. A gamma process-based integral growth model is further proposed for the direct statistical estimation of unknown parameters, allowing continuous updating as new ILI data become available and thereby progressively reducing epistemic uncertainty. To quantify the time evolution of failure behavior, the PHI2 approach is adopted for time-variant reliability. The results demonstrate that the spatial variability of randomly distributed defects plays a critical role in governing pipeline performance and the evolution of failure paths, particularly under data-limited conditions. Numerical examples validate the robustness and applicability of the proposed framework, highlighting its capability to support reliability-informed integrity management of corroding natural gas pipelines when inspection data are scarce.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2704323</guid>
    </item>
    <item>
      <title>Bayesian spatio-temporal modeling of Arctic maritime incidents with integrated nested Laplace approximation</title>
      <link>https://trid.trb.org/View/2702232</link>
      <description><![CDATA[Rapid environmental change and increased vessel activity have reshaped the risk landscape of Arctic maritime transportation. Understanding how different types of maritime incidents are distributed across space and evolve over time is essential for effective safety management in this highly heterogeneous environment. This study develops a Bayesian spatio-temporal framework to examine the relative propensity of Arctic maritime incident types from 2005 to 2017. Latent spatial and spatio-temporal dependence is represented through an INLA–SPDE approach, enabling efficient inference of temporally correlated spatial risk surfaces from sparse and irregularly distributed incident data. Model comparison indicates that incorporating spatio-temporal dependence improves interpretability and robustness. The preferred spatio-temporal AR(1) model reveals pronounced non-stationarity in incident-type composition, with shifting hotspots, regional sign reversals, and distinct temporal persistence. Results further indicate that vessel characteristics, route exposure, and seasonal conditions systematically influence incident-type composition after controlling for spatio-temporal effects. With mechanical failure as the reference incident type, collision-related incidents exhibit more diffuse, corridor-oriented patterns, whereas loss-of-control–related incidents display larger-amplitude spatio-temporal random effects, stronger localization and temporal sensitivity. This study advances Arctic maritime risk research toward explicit spatio-temporal mechanism inference and supports adaptive, region-specific safety management.]]></description>
      <pubDate>Fri, 07 Aug 2026 09:21:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702232</guid>
    </item>
    <item>
      <title>Effects of steady flow and side hull layouts on motions and added resistance of a trimaran sailing in waves</title>
      <link>https://trid.trb.org/View/2737476</link>
      <description><![CDATA[As a high-performance ship, the trimaran holds significant potential in military and civilian fields. This study proposes a novel numerical method based on the Rankine panel method for predicting the time domain motion and wave loads of trimarans navigating in waves. This approach incorporates a more accurate steady flow model for trimarans and meticulously derives the boundary value problem for the unsteady disturbance potential, while retaining the steady terms related to steady flow throughout the process. This approach ensures a comprehensive consideration of the impacts of speed effect and ship wave. The method was innovatively validated using the trimaran hull form, and its accuracy and reliability were confirmed through comparisons with experimental data. Then, the influence of steady flow on time domain motions and added resistance of trimarans was thoroughly investigated. Comparative analysis of different steady flow models demonstrates that the steady flow model for trimaran proposed provides high precision and advantages in predicting the time domain motions of high-speed trimarans in waves. Furthermore, a systematic investigation into the effects of ship speed and side hull layouts on trimaran's motion and added resistance in waves has been conducted, offering crucial technical support for trimarans' seakeeping optimal solution selection.]]></description>
      <pubDate>Thu, 06 Aug 2026 09:08:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737476</guid>
    </item>
    <item>
      <title>Time-domain buffeting analysis for a long-span suspension bridge considering directional discrepancies of non-stationary wind fluctuating components</title>
      <link>https://trid.trb.org/View/2684535</link>
      <description><![CDATA[Conventional non-stationary buffeting analyses for long-span bridges frequently overlook aerodynamic admittance function (AAF) effects or rely on empirical/equivalent models, neglecting directional discrepancies between longitudinal (u-) and vertical (w-) turbulent components by assuming equal u- and w- components of AAF (E-AAF). This simplification potentially inducing catastrophic miscalculations. To distinguish the effects of non-stationary turbulence components (u- and w-) on buffeting responses, this study employs both the E-AAF and the decoupled AAF (D-AAF, includes the longitudinal component u-AAF and the vertical component w-AAF) to reconstruct non-stationary wind fields, generating buffeting forces that reflect the distinct contributions of u- / w- turbulence components. Subsequent time-domain buffeting analyses for a long-span suspension bridge demonstrate three critical findings. First, analysis under consistent conditions demonstrates that neglecting self-excited forces can overestimate displacement responses by up to 51.41%. Second, with all other conditions equal, non-stationary winds produce RMS displacements 45% higher than stationary winds due to dynamic energy amplification; Third, the error in the E-AAF-based response relative to the D-AAF solution shows a pronounced dependence on the angle of attack (AoA); the minimal displacement error (-2.63%) occurs at AoA= 0°, whereas systematic overestimation emerges under non-zero AoAs (2°–8°), with displacement errors peaking (AoA=4°) at 128%, velocity errors reaching 117.86%, and acceleration errors attaining 68.89%. This research contributes to enhancing the accuracy for buffeting response prediction in long-span bridges subjected to non-stationary winds.]]></description>
      <pubDate>Wed, 01 Jul 2026 09:36:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684535</guid>
    </item>
    <item>
      <title>A deep-learning-based approach for temporal transportation network dismantling</title>
      <link>https://trid.trb.org/View/2681955</link>
      <description><![CDATA[Accurately assessing the importance of individual nodes at different time for maintaining the efficiency of temporal transportation networks is an important challenging problem. It is for the first time that this issue has been formulated as a temporal network dismantling (ND) problem, which aims to identify the minimal set of nodes with time information whose disruption can rapidly degrade temporal network efficiency. This paper proposes a deep-learning-based network dismantling framework for its solution. Specifically, a directed acyclic graph representation is used to capture the continuous topological changes in a temporal network. Then, our framework employs multi-head graph attention networks to learn latent higher-order features by aggregating different temporal features of nodes and their neighbors, and incorporates the shortcut connections technique to mitigate the vanishing gradient problem. Extensive experiments on synthetic and real-world networks demonstrate that the proposed approach outperforms baselines and exhibits significant robustness. Furthermore, an early warning signal indicator is adopted to quantify the dismantling risk of temporal networks. Our approach offers a practical tool for identifying critical nodes in temporal transportation networks, thereby assisting stakeholders in enhancing robustness of transportation services.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:13:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681955</guid>
    </item>
    <item>
      <title>Time-domain prediction of pure loss of stability (PLS) in waves based on the maneuvering-seakeeping coupling method</title>
      <link>https://trid.trb.org/View/2710716</link>
      <description><![CDATA[Pure loss of stability (PLS) is a critical capsizing mechanism for ships operating in waves, so its reliable prediction in the time domain is of great importance for ensuring navigation safety. In this study, a six-degree-of-freedom (6-DOF) mathematical model based on a seakeeping–maneuvering coupling framework is developed to simulate PLS in waves. The proposed numerical approach is first validated through comparisons with results of free-running model test for the ONR Tumblehome hull. On this basis, a series of numerical investigations is conducted to examine the influence of different modeling assumptions and motion components. The results demonstrate that inclusion of the hull-induced and rudder-induced roll moments are essential for reproducing capsizing events associated with PLS. Neglecting surge, sway, and yaw motions suppresses the occurrence of the instability, whereas omission of heave and pitch leads to a noticeable overestimation of the roll response. These findings confirm that PLS arises from strongly coupled and nonlinear ship–wave interactions, and indicate that the proposed 6-DOF mathematical model is capable of capturing the key physical mechanisms governing this phenomenon in waves.]]></description>
      <pubDate>Mon, 08 Jun 2026 08:37:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2710716</guid>
    </item>
    <item>
      <title>Numerical simulation of time-domain harbour response from an incident pulse</title>
      <link>https://trid.trb.org/View/2701727</link>
      <description><![CDATA[We consider here the response in the time domain to a harbour due to an incident wave pulse. The motion is simulated in the time domain. The harbour is assumed to be of constant depth and the waves of small amplitude, allowing the frequency domain problem to be reduced to the Helmholtz equation. This is solved by the eigenfunction matching technique. The time-domain solution is then computed for incident wave pulses of arbitrary spectral density using the single-frequency solutions. Rectangular harbour geometries with one or more entrances are considered. Numerical results are presented and discussed to illustrate how different incident wave shapes and water depths affect resonance within the harbour. The theory applies to any wave forcing, but the focus of our simulations is on incident waves due to long-wave forcing, such as that arising from storm surges or tsunamis.]]></description>
      <pubDate>Thu, 21 May 2026 09:10:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701727</guid>
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