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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" />
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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>
    </image>
    <item>
      <title>Integrated Algorithm for Multi-Source Data Conversion of Rail Transit Digital Model Based on Bim+Gis</title>
      <link>https://trid.trb.org/View/2666437</link>
      <description><![CDATA[Data formats, data structures and coordinate systems differ across data sources, hindering data conversion and integration. Therefore, a multi-source data conversion and integration algorithm of rail transit digital model based on BIM+GIS is proposed. Based on the B/S architecture of the Cesium open-source map engine, an integrated framework for rail transit digital multi-source data conversion, with BIM+GIS technology at its core, has been developed. In the data layer, tilt photogrammetry technology is employed to collect topographic data of rail transit, vector data is gathered through a tilt model and BIM data is generated in response to the demands of rail transit construction. The GIS model is constructed based on terrain data and vector data, while the BIM model is established using BIM data. The business logic layer handles and publishes multi-source data through BIM servers, GIS servers and other information databases. The transformation integration unit uses a spatial semantic integration algorithm to integrate data transformation from the BIM model into the GIS model, thereby achieving complete transformation and integration of BIM and GIS multi-source data in geometry, semantics and accuracy. Finally, the outcomes of multi-source data conversion and integration are presented to users via the presentation layer. Experiments show that the algorithm can effectively collect the terrain data of rail transit and establish a BIM model and GIS model. We transformed multisource data of an integrated rail transit digital model to improve its comprehensiveness, accuracy and reliability.]]></description>
      <pubDate>Tue, 26 May 2026 09:41:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666437</guid>
    </item>
    <item>
      <title>Performance of vector-valued fragility for coastal bridge under earthquake and tsunami hazards</title>
      <link>https://trid.trb.org/View/2661966</link>
      <description><![CDATA[Extreme hazards such as earthquake and ensuing tsunamis can pose significant threats to offshore infrastructures, among which bridges are particularly vulnerable due to their locations. Accurate assessment of bridge performance under such events is crucial to enhance structural safety. In this study, the fragility method was employed to evaluate bridge capability against combined hazard effects, with three variables introduced to capture multi-hazard intensity. The vector-valued method was used to quantify bivariate tsunami intensities, with different fragility functions compared in their fitting capability. A new fragility form was proposed for earthquake-tsunami scenarios, with the system-level fragility also examined via multiple bridge components. A case study was conducted to compare the effectiveness of various functions to isolated bridges. The component-level fragility shows an inconsistent development with increasing seismic magnitudes but consistent trends with tsunami intensity. The comparison analysis implies the highest fitness of log-sum model, while the proposed method yields consistent outcomes despite the unified factor. System-level fragility results indicate that isolated bridges have notable vulnerability due to multi-component contributions. Further, the expected damage ratio was assessed and shows notable sensitivity to spectral acceleration and relative wave height, as opposed to the limited influences from water depths. This study provides preliminary guidance for estimating the seismic-tsunami fragility of isolated bridges using complex intensity sets.]]></description>
      <pubDate>Fri, 01 May 2026 14:33:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2661966</guid>
    </item>
    <item>
      <title>Evaluation of SVR and Random Forest Models for Accurate Prediction of Electronic Fuel Injector Behavior in Common-Rail Systems</title>
      <link>https://trid.trb.org/View/2665622</link>
      <description><![CDATA[In this study, machine learning algorithms were applied to predict the main injection quantity in a high-pressure common rail system on a diesel engine test bench. The input parameters included engine load, fuel pressure, injection speed, and pulse time. Two models were selected for comparison: Support Vector Regression (SVR) and Random Forest (RF). The results showed that on the training dataset, the RF model outperformed SVR, with RMSE and MAE values of 0.027362 and 0.017628 respectively, significantly lower than those of SVR (RMSE = 0.051563, MAE = 0.027733). Additionally, RF achieved a higher coefficient of determination R² (0.995759 vs. 0.984939), indicating better learning of the relationships among variables. However, on the test dataset, SVR demonstrated superior predictive accuracy, achieving RMSE = 0.050097, MAE = 0.027673, and R² = 0.983550, while RF showed higher RMSE (0.060355), greater MAE (0.040485), and lower R² (0.976123). These results indicate that SVR has better generalization capability and is less prone to overfitting than RF. To assess the contribution of each input parameter, SHAP (SHapley Additive exPlanations) analysis was employed. The results revealed that injection speed, pulse duration, and fuel pressure had the most significant impact on the injection quantity. Meanwhile, engine load had a relatively lower influence but still played an important role under certain operating conditions. These analyses not only provide an intuitive understanding of model sensitivity but also help identify key factors to prioritize in control strategies. This study lays a foundation for the development of optimized control systems aimed at accurately and effectively reducing engine emissions in the future.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:57:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665622</guid>
    </item>
    <item>
      <title>Optimal vector-valued IMs for probabilistic seismic demand modeling of V-shaped pier bridges under coupled horizontal and vertical ground motions</title>
      <link>https://trid.trb.org/View/2612470</link>
      <description><![CDATA[Seismic responses of V-shaped pier bridges have been found significantly sensitive to vertical ground motions due to the unique V-shaped piers. Thus, reliable probabilistic seismic demand modelling (PSDM) of these bridges hinges on the selection of appropriate vector-valued intensity measures (i.e.,IMs) that characterize both horizontal and vertical ground motions (GMs). A total of 33 scalar IMs, covering acceleration, velocity, displacement, and time-related parameters, were paired to generate 1089 horizontal and vertical IM candidates. Planar PSDMs were then constructed for key engineering demand parameters and ranked to identify optimal IMs, via a proposed novel metric called independence, which quantifies the correlation of IM components in IMs to minimize redundancy in GM characterization, together with the traditional metrics of efficiency, practicality, proficiency, sufficiency, and relative sufficiency. Evaluation results indicate that abutment responses were governed primarily by horizontal velocity-related IMs, whereas the seismic demands of bearing and pier required IMs that couple horizontal structure-specific spectral measures with vertical time-dependent parameters. Among these bridge components, the optimal IMs were [Sd-20, VAratio], [Sd-10, SD5–75], and [Sd-20, SD5–95] for the bearing, left pier leg, and right pier leg, respectively. These findings provide practical guidance for performance-based seismic assessment of V-shaped bridges subjected to horizonal and vertical seismic excitations.]]></description>
      <pubDate>Fri, 19 Dec 2025 10:19:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612470</guid>
    </item>
    <item>
      <title>On the Benefits of Torque Vectoring for Automated Collision Avoidance at the Limits of Handling</title>
      <link>https://trid.trb.org/View/2606498</link>
      <description><![CDATA[This paper presents a novel approach integrating motion replanning, path tracking and vehicle stability for collision avoidance using nonlinear Model Predictive Contouring Control. Employing torque vectoring capabilities, the proposed controller is able to stabilise the vehicle in evasive manoeuvres at the limit of handling. A nonlinear double-track vehicle model, together with an extended Fiala tyre model, is used to capture the nonlinear coupled longitudinal and lateral dynamics. The optimised control inputs are the steering angle and the four longitudinal wheel forces to minimise the tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergency manoeuvres. These optimised longitudinal forces generate an additional direct yaw moment, enhancing the vehicle's lateral agility and aiding in obstacle avoidance and stability maintenance. The longitudinal tyre forces are constrained using the tyre friction cycle. The proposed controller has been tested on rapid prototyping hardware to prove real-time capability. In a high-fidelity simulation environment validated with experimental data, the authors' proposed approach successfully avoids obstacles and maintains vehicle stability. It outperforms two baseline controllers: one without torque vectoring and another one without collision avoidance prioritisation. Furthermore, the authors demonstrate the robustness of the proposed approach to vehicle parameter variations, road friction, perception, and localisation errors. The influence of each variation is statistically assessed to evaluate its impact on the performance, providing guidelines for future controller design.]]></description>
      <pubDate>Wed, 08 Oct 2025 09:58:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606498</guid>
    </item>
    <item>
      <title>Minimal Zero-Sequence Circulating Current Modulation Strategy Based on Equivalent Vector Redundancy for Two Paralleled Inverters</title>
      <link>https://trid.trb.org/View/2559301</link>
      <description><![CDATA[Due to the difference of common-mode voltage (CMV), the zero-sequence circulating current (ZSCC) becomes a major issue in two paralleled voltage source inverters (VSIs) with a common dc bus. To address this challenge, this article proposes a minimal ZSCC modulation strategy based on the vector redundancy of paralleled system, which theoretically eliminates the excitation source of ZSCC. Given the redundancy of equivalent vectors, the vectors with zero CMV difference are developed to synthesize the command voltage. Each 60° sector of space vector modulation (SVM) is further segmented into four subsectors. Optimal vector sequences in these subsectors are designed according to the rules of preventing line-to-line voltage reversal and minimizing switching actions, and the implementation of the proposed method is thoroughly investigated, including the determination and duty cycle calculation of subsectors. Compared with the existing works, the proposed method demonstrates superior performance in suppressing ZSCC. The effectiveness of the proposed method is validated on a test platform fed by two paralleled two-level VSIs.]]></description>
      <pubDate>Fri, 29 Aug 2025 10:05:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559301</guid>
    </item>
    <item>
      <title>Machine learning based pavement performance prediction for data-driven decision of asphalt pavement overlay</title>
      <link>https://trid.trb.org/View/2548705</link>
      <description><![CDATA[This study is to develop pavement performance models using support vector regression and ensemble machine learning methods for selection of pavement overlay strategy. Predictive models of pavement distresses were developed based on the Long-Term Pavement Performance (LTPP) data and compared using support vector machine, random forest regression, gradient boosting machine, and stacking ensemble. Gradient boosting machine was found to be a more effective method to establish predictive models of rut depth and International Roughness Index. Stacking ensemble and random forest regression would provide reliable prediction of alligator cracking. The models developed based on the clusters of climate and traffic parameters were found to be more effective. Based on the developed performance models, the effects of asphalt overlays on pavement distresses and service lives were investigated. When the overlay with recycled asphalt concrete (AC) was applied, the propagation of alligator cracking was faster compared to the overlay with virgin asphalt mixture. Milling before overlay tended to slow the increase of IRI but fasten the development of rut depth.]]></description>
      <pubDate>Fri, 23 May 2025 15:34:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548705</guid>
    </item>
    <item>
      <title>A Mixed-Precision Transformer Accelerator With Vector Tiling Systolic Array for License Plate Recognition in Unconstrained Scenarios</title>
      <link>https://trid.trb.org/View/2487951</link>
      <description><![CDATA[Power efficiency for license plate recognition (LPR) under unconstrained scenarios is a crucial factor in many edge-based real-world applications, e.g., autonomous vehicles whose power budget is limited. While a bulk of prior works have explored LPR approaches for unconstrained situations on CPU and GPU servers, these methods result in huge power dissipation, and are ineffective in challenging scenes. In this work, the authors present a mixed-precision (MP) Transformer hardware architecture to meet the requirements of power efficiency and satisfactory LPR accuracy in unconstrained scenarios, dedicated to implementing power-efficient edge accelerators for difficult LPR tasks. Firstly, MP-LPR Transformer model is proposed, where novel mixed-precision quantization and non-linear approximation techniques are tailored for low bit-width inference. Secondly, vector tiling systolic array Transformer architecture (VTSATA) is proposed with unique vector tiling systolic array (VTSA) and vector ALU (VALU) designs, where VTSA is used to accelerate matrix multiplications, and VALU is used to process non-linear operations. Finally, a FPGA accelerator prototype based on their approach is developed. Experimental results demonstrate that their hardware platform can reduce power consumption more than  $20\times $  compared to GPU platforms, and for challenge subset on CCPD dataset, their method can further improve nearly 1% accuracy with respect to the state-of-the-art performance.]]></description>
      <pubDate>Thu, 08 May 2025 09:56:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2487951</guid>
    </item>
    <item>
      <title>Gantry2Vec: Embedding Heterogeneous Gantry Information into Vectors for Enhanced Traffic Flow Prediction</title>
      <link>https://trid.trb.org/View/2526278</link>
      <description><![CDATA[Highway gantry sensors record real-time traffic data and uniquely demonstrate spatiotemporal and behavioral heterogeneity for traffic prediction. This study combines the modeling capabilities of representation learning and traffic domain knowledge to embed gantry information to vectors, termed Gantry2Vec. Three representations are defined: temporal embedding and spatial embedding at the macro level and behavioral embedding at the micro level. As the representation of behavior information for highway traffic flow prediction is underutilized in the current literature, the authors incorporated these macro–micro representations and subsequently proposed a gantry-aware framework for downstream traffic flow prediction. Herein, customized feature ablation experiments using the proposed Gantry2Vec have been conducted for validating the performance of model. The authors also provide insights into the embedding performance on distinguishing traffic flow patterns of heterogeneous gantries on a real-world highway data set. Experimental results demonstrated that the proposed model outperforms other baselines.]]></description>
      <pubDate>Mon, 28 Apr 2025 08:50:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2526278</guid>
    </item>
    <item>
      <title>Macroeconomic determinants of maritime transport development – VAR models for the Polish economy</title>
      <link>https://trid.trb.org/View/2489249</link>
      <description><![CDATA[World trade in goods is largely based on maritime transport. Virtually all groups of goods are transported by sea, from general cargo to heavy machinery, equipment, raw materials, etc. The conducted research is focused both on the impact of maritime transport on the economy and the impact of the economy on maritime transport. Mutual interactions are often emphasized. Hence, the aim of the research conducted in this paper was to determine the impact of fuel prices, economic growth and inflation on the volume of transshipments in Polish seaports. The research was based on GUS data for the years 2000-2023. The vector autoregressive (VAR) models were chosen as the analysis method. The research was conducted for the levels and increments of variables. Although the problems posed were formulated as one-sided, the research tool used allows to verify the two-sided dependencies. This research is relevant by offering a comprehensive examination of how intelligent data analysis can enhance the understanding of complex economic and logistical interactions. The findings provide actionable insights for policymakers and industry stakeholders, suggesting strategies to optimize maritime transport operations and economic planning based on the identified interactions. By integrating advanced modeling techniques with real data, this study contributes to the development of smarter, more efficient information systems in the context of maritime transport and economic analysis.]]></description>
      <pubDate>Tue, 28 Jan 2025 14:52:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2489249</guid>
    </item>
    <item>
      <title>Numerical study on the thrust vector of a propeller-rudder unit in the bollard pull condition</title>
      <link>https://trid.trb.org/View/2442656</link>
      <description><![CDATA[In this paper, in order to enhance the DP capability of a four-screw shallow-drafted ship with large moulded breadth and block coefficient, the thrust vector for the inner-sided propeller-rudder unit in the bollard pull condition is studied by RANS-based CFD method. To account for the blockage effect of the hull on the propeller-rudder unit, numerical simulations of hull-propeller-rudder interaction are carried out. To capture accurately the propeller-induced violent free-surface flow in the stern area, an adaptive refined mesh method is adopted. Validation study shows good agreement between the CFD and EFD results in terms of the open-water propeller characteristics, the thrust vector angle, and the free-surface flow in the stern area. Based on the numerical study, a reliable relation between the thrust vector angle and the rudder angle is established. Detailed investigations into the thrust vector angle, the hull-propeller-rudder interaction, the propeller and rudder hydrodynamic forces, and the propeller-induced free-surface flow show that as long as the requirement of the theoretical critical Reynolds number is met, the thrust vector characteristics are almost unaffected by the propeller shaft speed. Thus, the thrust vector angle obtained by the CFD method at model scale can be used for full-scale ship without further corrections.]]></description>
      <pubDate>Tue, 22 Oct 2024 17:13:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2442656</guid>
    </item>
    <item>
      <title>A Simplified Multivector-Based Model Predictive Current Control for PMSM With Enhanced Performance</title>
      <link>https://trid.trb.org/View/2403894</link>
      <description><![CDATA[To address the challenges of poor steady-state performance and large amount calculation of conventional model predictive current control (MPCC), a simplified multivector-based MPCC with enhanced performance is proposed in this article. First, an effective voltage vectors (VVs) selection method based on current error is proposed, which can directly determine the optimal VVs without cost function enumeration. Compared with the standard method, the number of vectors that need to be evaluated is reduced from 7 to 1. Meanwhile, the duty cycle of each VV is calculated based on the current error to realize error-free control. In addition, to alleviate the deleterious effect of dead time on the control performance, an improved MPCC scheme with optimal utilization of dead time is proposed. The key is that the dead time existing in MPCC is regarded as a dead-time VV (DVV), which can be rationally utilized to improve the control performance. Both theoretical analysis and experimental results are given to verify the effectiveness of the proposed MPCC schemes.]]></description>
      <pubDate>Fri, 20 Sep 2024 16:24:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2403894</guid>
    </item>
    <item>
      <title>Multimode Model Predictive Control for PMSM Drive System</title>
      <link>https://trid.trb.org/View/2402170</link>
      <description><![CDATA[In order to improve the control performance of the motor model predictive control (MPC) system, a multimode hybrid-vector MPC strategy is proposed in this article, which is developed from the conventional double-vector MPC strategy. The proposed method extends the last voltage vector of the previous control period to the present control period according to the relationship between the last voltage vector of the previous control period and the first voltage vector of the present control period. Then, two voltage vectors or three voltage vectors are included in each control period, which forms the concept of the hybrid vector. Since a voltage vector is inherited from the previous control period, the proposed method can achieve better steady-state control performance at a lower switching frequency, considering the two adjacent control periods as a whole. Finally, comparative experiments demonstrate that the proposed method has better steady-state control performance at the same switching frequency.]]></description>
      <pubDate>Wed, 28 Aug 2024 13:22:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2402170</guid>
    </item>
    <item>
      <title>Vector Control Optimization of Traction Motors Based on Online Parameter Identification</title>
      <link>https://trid.trb.org/View/1975765</link>
      <description><![CDATA[In order to further improve the accuracy of parameter identification under the fluctuation of traction motor speed (torque) and improve the speed control performance of the motor, the vector control strategy of traction motor is optimized. An online identification model of motor parameters based on recursive least squares (RLS) and model reference adaptive method (MRAS) is proposed. The motor stator and rotor parameters identified by RLS are input into MRAS on the basis of the rotor flux observation model. A proportional-integral adaptive law by use of Popov’s hyperstability theory is designed to identify the rotor resistance. Through the above optimization, the vector control strategy is optimized to realize effective control of speed regulation characteristics of traction motors in different speed intervals and under different working conditions. Consequently, the effectiveness of the proposed model and control strategy is realized and verified by simulation.]]></description>
      <pubDate>Fri, 23 Aug 2024 15:26:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/1975765</guid>
    </item>
    <item>
      <title>Hybrid Vector Model Predictive Control for Open-Winding PMSM Drives</title>
      <link>https://trid.trb.org/View/2395008</link>
      <description><![CDATA[To improve the control performance of open-winding permanent magnet synchronous motor (OW-PMSM) and suppress zero-sequence current (ZSC), this article proposes a hybrid vector model predictive control (MPC) for OW-PMSM drives. The fast-voltage-vector selection strategy is used to select one optimal nonzero voltage vector (VV) to act on inverter1 (INV1) for each control period, and then, inverter2 (INV2) is controlled with a novel four-segment-mode-vector (FSMV) control strategy to operate the dual inverter separately. In this strategy, the two zero VVs, u'₀ and u'₇, are applied at the starting and ending of each control period alternately, while two adjacent nonzero VVs, u'ₓ and u'y, with minimum switching times, are applied in the middle of the control period to generate PWM pulses with a switching frequency fixed at half of the control frequency. Furthermore, the durations of two zero VVs (u'₀ and u'₇) are optimized to suppress the ZSC based on the zero-axis current deadbeat (DB) control principle. Finally, the experimental findings suggest that the proposed strategy ensures the low-switching frequency of INV1 and the fixed switching frequency of INV2 to obtain good control performance.]]></description>
      <pubDate>Thu, 22 Aug 2024 15:09:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2395008</guid>
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