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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>
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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>Traffic Organization Design of Intersections Based on Holographic Portrait</title>
      <link>https://trid.trb.org/View/2613190</link>
      <description><![CDATA[With the increasing number of motor vehicles and travel demand, reasonable intersection traffic organization design is of practical significance for alleviating urban traffic congestion. First, the intersection holographic data is obtained by the Yolov5 + Deepsort algorithm and other methods, and then the holographic features of the intersection are extracted from the holographic data by cluster analysis, and then a holographic portrait of the intersection is constructed. Finally, an intersection in Xi’an is taken as an example to construct a holographic portrait for traffic organization design, and VISSIM is used for simulation evaluation. The results show that the service level of the intersection is improved from E to D, and the average delay and average queue length are reduced by 15% and 33%, respectively, indicating that the optimization scheme proposed based on the holographic portrait of the intersection is effective in improving the traffic organization operation status of the intersection.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613190</guid>
    </item>
    <item>
      <title>State Design in Reinforcement Learning-Based Traffic Signal Control Using Similarity Metrics</title>
      <link>https://trid.trb.org/View/2613160</link>
      <description><![CDATA[In traffic signal control tasks based on reinforcement learning, the design of observation forms presents a challenging issue. Simple observation forms may result in information loss, while complex observation forms can lead to the curse of dimensionality. This paper addresses this challenge by employing bisimulation metrics to calculate the similarity between heterogeneous observations, thereby decomposing the entire state space and achieving a more compact observation form. By leveraging the limited fitting capability of shallow neural networks, we compensate for the discrepancies caused by the observation forms. Moreover, simulation software acts as a data source for models; however, the bounded resources of these simulations impede the comprehensive exploration of the entire state space. To tackle these challenges, a novel simulation platform named LiikeSim has been developed. It is designed to enhance simulation speeds, enabling more extensive exploration of the state space within the constraints of limited simulation resources.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613160</guid>
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    <item>
      <title>Comparison Analysis of Traffic Control Methods Based on Multi-Processing Technology</title>
      <link>https://trid.trb.org/View/2613063</link>
      <description><![CDATA[With the increasing pace of urbanization and rising vehicle ownership, traffic congestion has emerged as a significant challenge. This paper proposes a multi-process-based traffic simulation and control strategy selection approach aimed at enhancing computational efficiency. The proposed method constructs a simulation platform utilizing SHP-format map data, simulates traffic incidents, and evaluates different control strategies. By employing multi-process technology to partition data processing and control measure selection into parallel subtasks, the approach significantly improves the efficiency of identifying optimal traffic control measures. Simulation results validate the efficacy of the proposed method in enhancing overall simulation performance.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613063</guid>
    </item>
    <item>
      <title>Vehicle Trajectory Prediction and Simulation Based on Graph Neural Network Considering Multi-Vehicle Interaction</title>
      <link>https://trid.trb.org/View/2613001</link>
      <description><![CDATA[Aiming at the validity and applicability of the prediction model, a trajectory prediction method based on a graph neural network and an encoder-decoder architecture is proposed in this paper. In this method, the graph neural network is used to obtain the interactive information of the dynamic interaction between vehicles in the traffic scene, and then the decoder is used to divide the processed features into multiple time steps to generate the trajectory. In addition, in order to ensure the rationality and safety of the predicted trajectory, a punishment mechanism is introduced to optimize the accuracy and feasibility of the predicted trajectory. Trajectory data and environmental information are collected and evaluated online by constructing environmental maps and using online simulation software (SUMO, Carla). The experimental results show that this method has strong trajectory prediction ability and can provide an effective solution for collision prevention and path planning in automatic driving.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613001</guid>
    </item>
    <item>
      <title>Unjamming transition insights into rutting behavior of asphalt pavements: Digital modeling and laboratory experiments</title>
      <link>https://trid.trb.org/View/2639990</link>
      <description><![CDATA[While conventional laboratory tests are effective in characterizing rutting formation at the macroscopic level, they offer limited insight into the internal micro-mechanical behavior within the mixture, which governs the rutting formation. Numerical simulations using the discrete element method have emerged as a powerful tool to overcome this limitation. Herein, the discrete element models of the two asphalt mixtures with different gradations were developed using stochastic algorithms to observe particle flow during deformation. Laboratory rutting tests have confirmed the accuracy of both models in characterizing rutting deformation. The area fraction beneath the loading region in both mixture models indicate that the region remained persistently in a state of shear jamming transition, albeit experiencing a brief unjamming transition intermittently. The unjamming transition occurring in regions flanked by load serves as the primary mechanism driving lateral flow. Shear stress is identified as the primary driving factor behind the unjamming transition during rutting deformation. Aggregate complete both shear jamming and unjamming transitions through displacement and rotation, thereby promoting the reorganization into strong force chains.]]></description>
      <pubDate>Thu, 12 Feb 2026 08:53:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639990</guid>
    </item>
    <item>
      <title>Slope Stability Analysis and Risk Assessment for Transportation Infrastructure Using GIS and Simulation</title>
      <link>https://trid.trb.org/View/2562088</link>
      <description><![CDATA[Slope instability is a significant threat to transportation infrastructure, particularly in regions prone to natural hazards. This research aims to develop a comprehensive GIS-based methodology to identify and assess slope instability risks along highways. By integrating high-resolution DEMs, geological maps, hydrological data, and historical landslide inventories, potential slope failure zones can be delineated. Numerical simulations, such as limit equilibrium analysis and finite element modeling, will be used to quantify slope stability under various conditions. This research will enable the prioritization of high-risk sections for targeted mitigation measures, such as slope stabilization techniques and early warning systems. By enhancing the resilience of transportation infrastructure, this research contributes to mitigating the socio-economic impacts of slope failures.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562088</guid>
    </item>
    <item>
      <title>The Research Landscape of Data-Driven Simulation in Transport and Logistics</title>
      <link>https://trid.trb.org/View/2648139</link>
      <description><![CDATA[Data-driven simulation is a fairly new trend in the development of computer modeling technologies and related applications in the field of transport and logistics. The main applications cover urban mobility and traffic management, harbor and maritime logistics, airport and airside operations management, and warehouse and supply chain management. Despite growing interest and developments, the field of data-driven simulation in transportation and logistics remains fragmented across sub-disciplines and applications. The aim of this paper is to review the evolution, current status and future trends of research in this field through a comprehensive analysis of the relevant literature and recent technological advances. The role of artificial intelligence in advancing data-driven simulation is discussed, in particular the use of digital twins of real logistics systems to operate in real time and fill gaps in incomplete data sets.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2648139</guid>
    </item>
    <item>
      <title>Further development of small infant body shape models for assessment of child seats and harness fit</title>
      <link>https://trid.trb.org/View/2598652</link>
      <description><![CDATA[Small infants are not a prominent group in traffic injury data. However, on a daily basis neonatal nurses struggle to help parents fit their small infants, ready to be discharged from the hospital, in infant car seats that are not suited for them. The fit of the harness is critical for protection in a crash situation and current regulations in UN R129 are not sufficient to ensure a good harness fit for very small infants. In addition, prematurely born infants are especially sensitive to sitting posture, where unsuitable postures may cause heart and breathing problems during normal car travel. In many parts of the world, the care of premature infants is moving towards earlier discharge from hospital and regular check-ups, which means that many of these infants will increase their car travel exposure. Today, there is a lack of tools that can be used by car seat manufacturers to assess the fit of infant car seats. Our previous project resulted in three body shape models of premature infants based on anthropometric measurements for the 5th, 50th, and 95th percentiles in supine position. Only the 5th percentile body shape model was imported to a CAD software and enhanced with joints for the shoulders, elbows, hips and knees. This enabled repositioning of the model into a sitting posture, to assess the fit of infant car seat designs with CAD. The 5th percentile CAD-model was evaluated at CYBEX, identifying several needs for improvement. Therefore, the purpose of this project was to further develop tools that represent the prematurely born infants' body shapes, that can be used by car seat manufacturers to assess harness and seat fit early in the production process.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:19:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598652</guid>
    </item>
    <item>
      <title>Analysis of intelligent and connected vehicles driving system modeling</title>
      <link>https://trid.trb.org/View/2539867</link>
      <description><![CDATA[In response to the limitations of traditional offline static simulation modeling technology in accurately addressing the intricacies and complexities of intelligent and connected vehicles (ICVs) driving processes, this paper introduces the concept of an ICV driving system (ICVDS) model based on digital twin technology. Firstly, the paper delves into the theory of ICVDS digital twin modeling, covering aspects such as model elements and the operational mechanism of the model. The ICVDS, which relies on digital twin (DT) technology, is designed in accordance with the characteristics of ICVs, their technical requirements, and the architecture of the DT system. Subsequently, the paper explores four key areas: the modeling of driving elements, the modeling of the driving process, simulation modeling of the driving process, and a summary of modeling technology. The section on modeling driving elements primarily elucidates the methodology for creating twin models and illustrates how these models describe the system’s functionality in controlling the subject. The segment on modeling the driving process elucidates the approach to real-time data-driven modeling. The part on driving process simulation modeling explains the methodology for establishing simulation models and demonstrates how they predict the future state of the subject. Lastly, the paper introduces the construction of autonomous driving test scenarios based on ICVDS.]]></description>
      <pubDate>Tue, 27 May 2025 09:33:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539867</guid>
    </item>
    <item>
      <title>BIM-lean integration for construction scheduling of road intersections</title>
      <link>https://trid.trb.org/View/2554215</link>
      <description><![CDATA[Road intersections are critical components of urban infrastructure networks, ensuring safe and efficient traffic flow. However, their construction frequently experiences delays and cost overruns, often due to inadequate schedule planning. To mitigate these issues, the integration of Building Information Modeling (BIM) and Lean Construction has emerged as a promising strategy. Despite the recognized benefits, their combined application in road infrastructure projects remains limited. This paper proposes a planning framework for planning road intersection construction schedules based on integrating BIM and Lean Construction. The framework was developed using Design Science Research (DSR), through iterative design, development, and evaluation stages. Validation was conducted using a case study involving an at-grade and a grade-separated intersection. Results demonstrate that incorporating BIM and Lean facilitates improved scheduling by integrating digital simulations of construction and traffic management. The proposed framework helps planners make more accurate, efficient, and data-driven scheduling decisions in digital simulations of complex infrastructure.]]></description>
      <pubDate>Tue, 27 May 2025 09:33:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2554215</guid>
    </item>
    <item>
      <title>Transformer-based time-series GAN for data augmentation in bridge digital twins</title>
      <link>https://trid.trb.org/View/2540126</link>
      <description><![CDATA[Recent advancements in AI-based Digital Twins (DTs) have substantially influenced bridge monitoring and maintenance, especially through Deep Learning (DL) for sensor-based damage detection. However, the effectiveness of DL models is constrained by the extensive training data they require, which is often costly and time-consuming to collect in bridge infrastructure contexts. To address this data scarcity, this paper proposes a data augmentation strategy employing a transformer-based time-series Wasserstein generative adversarial network with gradient penalty (TTS-WGAN-GP) to generate synthetic acceleration data. The synthetic data's fidelity is validated through similarity metrics and frequency domain analysis, showing close alignment with real acceleration signals for damage detection. Results demonstrate that this method achieves high-quality synthetic data with superior computational efficiency compared to existing approaches, improving dataset balancing and potentially enhancing the performance of data-driven models in DTs. This approach reduces dependence on extensive data collection, supporting reliable bridge health monitoring applications.]]></description>
      <pubDate>Fri, 25 Apr 2025 16:11:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2540126</guid>
    </item>
    <item>
      <title>Development of a Boundary-Driven FEM-SMU Framework for Enhanced Stiffened Plate Performance Prediction of Ship Structures as an Essential Part for Digital Twin</title>
      <link>https://trid.trb.org/View/2534688</link>
      <description><![CDATA[The digital twin refers to a virtual representation of a physical object, system, or process using digital techniques like sensors and simulation models. The successful structural digital twin of ship structures is expected to provide designers with high-frequency feedback and reliable predictive results, thereby enhancing safety assessments of the structures. However, there are still many gaps in existing techniques in achieving these expectations. One prominent problem is that existing boundary methods in numerical analyses cannot accurately describe the boundary constraint effects on the objective structures. This would inevitably cause significant errors in the predictions from finite element analysis-based surrogate models. This study aims to improve existing finite element analysis-based surrogate model strategies by proposing a precise method for describing boundary constraints based on elastic boundaries. A boundary-driven model-updating approach was subsequently proposed together with an enhanced framework for establishing integrated surrogate models. Finally, an experimental case study was conducted to validate the proposed approach. This study can be regarded as an essential foundation and complement to future structural digital twins of ship structures.]]></description>
      <pubDate>Mon, 21 Apr 2025 12:04:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534688</guid>
    </item>
    <item>
      <title>A real-time lifecycle analysis model with digital twin and novel control method for enhancing the environmental performance of electric/hybrid propulsion ships</title>
      <link>https://trid.trb.org/View/2537282</link>
      <description><![CDATA[This paper presents a novel investigation into battery-hybrid electric propulsion systems using alternative fuels such as hydrogen and ammonia, integrating optimal load control mechanisms with real-time lifecycle assessment models. Through a digital twin framework, this study evaluates six vessel types under diverse propulsion configurations, incorporating real-time data and fuel lifecycle inventories to create a dynamic and adaptive assessment system. Digital twin technology is used to simulate and monitor ship power outputs, enabling comprehensive lifecycle analyses and identifying key operational and environmental performance metrics. Case studies demonstrate substantial lifecycle environmental benefits of renewable fuels like hydrogen, ammonia, and electricity, highlighting their potential to achieve significant greenhouse gas (GHG) reductions. The findings underscore the critical importance of fuel-specific strategies, regional energy conditions, and advanced load optimization techniques in maximizing emission reductions. Real-time monitoring and digital twin integration are shown to enhance decision-making, fuel efficiency, and emissions control by providing actionable insights and adaptive control mechanisms. These innovations redefine conventional approaches to lifecycle assessment and offer practical pathways for achieving cleaner, more efficient, and sustainable maritime operations, providing actionable guidance for ship operators, policymakers, and researchers dedicated to advancing a low-carbon maritime industry.]]></description>
      <pubDate>Fri, 18 Apr 2025 12:25:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2537282</guid>
    </item>
    <item>
      <title>Digital Twin-Based Identification of Aerodynamic Admittance Functions of a Long-Span Bridge</title>
      <link>https://trid.trb.org/View/2530232</link>
      <description><![CDATA[In consideration of uncertainties involved in the aerodynamic admittance function (AAF) identification conducted in wind tunnels and structural health monitoring (SHM) systems installed in long-span bridges, a digital twin-based AAF identification method using field measurement data collected by a SHM system is proposed in this study. Firstly, a theoretical model for buffeting analysis of a long-span bridge under low wind speed conditions is introduced. Based on the measured wind speed, displacement, and acceleration data, the design document-based finite element model of the bridge is updated and the coherence functions, aerodynamic force coefficients, and damping ratios of the bridge are identified. Subsequently, based on the digital twin concept, the parameters in the AAFs of the bridge are identified using a genetic algorithm and the digital twin is established. The effects of wind turbulence on AAFs as well as the statistics of AAFs parameters are further investigated. The feasibility and accuracy of the digital twin are validated through a case study of a real long-span suspension bridge. The comparisons between the simulating results of the digital twin and the field measured data verify the efficacy of the proposed method in identifying AAFs and predicting the buffeting responses of the bridge.]]></description>
      <pubDate>Fri, 11 Apr 2025 16:46:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2530232</guid>
    </item>
    <item>
      <title>Modeling heterogeneous spatiotemporal pavement data for condition prediction and preventive maintenance in digital twin-enabled highway management</title>
      <link>https://trid.trb.org/View/2526861</link>
      <description><![CDATA[Pavement preventive maintenance is one of the most fundamental use cases when deploying digital twins (DTs) for highway infrastructure management. To achieve this, it is essential to accurately predict the pavement conditions in future years. This paper developed a Spatial-Temporal Graph Attention network (STGAT) that can effectively capitalize on both spatial and temporal dependencies while addressing inherent heterogeneity in pavement data for more accurate condition predictions. On top of this, a structured assessment procedure was introduced to determine the need for preventive maintenance on road sections based on the STGAT predictions. A case study on the highway network in the United Kingdom was conducted to evaluate the method's performance. The results showed that the proposed method can achieve superior accuracy for pavement condition prediction and subsequent preventive maintenance assessment compared to existing methods, thus signifying its potential to improve the effectiveness of DTs for highway infrastructure management.]]></description>
      <pubDate>Wed, 09 Apr 2025 09:50:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2526861</guid>
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