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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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Advancing inclusive urban mobility: A multifaceted evaluation framework for wheelchair-friendly environments</title>
      <link>https://trid.trb.org/View/2713109</link>
      <description><![CDATA[The increasing prevalence of mobility impairments with the accelerated aging of the global population poses new challenges to the accessible design of urban environments. The importance of wheelchairs as a key aid in supporting the independence and mobility of older adults and people with disabilities is becoming significantly pronounced. Although urban environments have made some progress in accessible design, wheelchair users still face many travel inconveniences. The lack of a comprehensive evaluation framework to measure the wheelchair friendliness of cities limits our ability to effectively optimize urban environments. To address this issue, this study proposed a multifaceted evaluation framework to comprehensively assess wheelchair-friendly mobility along outdoor street segments. In addition to basic wheelchair accessibility factors, the framework incorporates road bumpiness, slope conditions, destination proximity, and streetscape environmental features to capture a more complete representation of the challenges faced by wheelchair users. We developed an Internet of Things (IoT)-based real-time monitoring system utilizing an inertial measurement unit (IMU) and GPS integrated on an STM32 microcontroller to monitor road bumpiness encountered during wheelchair travel. Meanwhile, the streetscape environmental factors were extracted through semantic segmentation of street view images, and slope information was derived from digital elevation model (DEM) data. These factors were then combined with the subjective perceptions of wheelchair users and subsequently weighted and evaluated using the analytic hierarchy process (AHP). The results show that our evaluation framework can effectively quantify and assess wheelchair-friendly mobility. This evaluation framework offers a new perspective to understand the experience and deep needs of wheelchair users. It also provides urban planners with a practical tool for recommending wheelchair-friendly routes, while also reinforcing the concept of social inclusiveness by promoting the active participation of wheelchair users in the development of sustainable cities.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:38:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713109</guid>
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    <item>
      <title>LiDAR-Based Road Height Profile Estimation and Bump Detection for Preview Suspension</title>
      <link>https://trid.trb.org/View/2659113</link>
      <description><![CDATA[This paper proposes a Light Detection And Ranging (LiDAR) based system for estimating road surface height profiles and detecting bumps and dips for preview suspension systems. The proposed system includes a method for ground extraction to remove obstacles and applies Adaptive Smoothing method to generate a continuous and accurate road height profile. Additionally, a bump-dip detection technique is introduced to identify road irregularities directly from the generated profile. We conducted a quantitative analysis of the system's performance through simulations and proving ground experiments, considering various LiDAR installation configurations and bump-dip environments. The results verify the accuracy and robustness of the proposed algorithm, and real-world vehicle experiments further confirm the reliability of the road height profile estimation.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659113</guid>
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    <item>
      <title>Analysis of Handling Stability under Combined Steering and Road Irregularity Inputs</title>
      <link>https://trid.trb.org/View/2658354</link>
      <description><![CDATA[Most analyses of handling stability have been carried out on flat roads without road surface irregularities, and ride comfort analyses have been carried out on straight-line driving without steering input. In this study, the handling stability has been examined under the combined effects of steering inputs and road surface irregularity inputs. In this report, a five-degree-of-freedom motion model has been constructed to account for road surface irregularities, and the effects of suspension vertical friction have been investigated.]]></description>
      <pubDate>Mon, 13 Apr 2026 09:40:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658354</guid>
    </item>
    <item>
      <title>Utilizing Drone Technology for Pavement Surface Condition Evaluation at Truck Weigh Stations: A Case Study from the State of Virginia</title>
      <link>https://trid.trb.org/View/2675891</link>
      <description><![CDATA[Pavement management systems (PMS) are essential for optimizing maintenance budgets and scheduling effective treatments for pavement networks. Traditionally, PMS rely on manual pavement distress data collection, a process that is both costly and time-consuming. This study explores the use of unmanned aircraft systems (UAS), commonly known as drones, as an alternative for collecting pavement surface distress data. The study focused on 13 truck weigh stations managed by the Virginia Department of Transportation and the Virginia Department of Motor Vehicles in the U.S. A drone was utilized to capture detailed images of pavement sections, which were then analyzed using an artificial intelligence model to generate pavement surface evaluation and rating (PASER) scores. These drone-derived PASER scores were compared with those obtained through traditional manual inspections. The results demonstrate that the PASER values from drone imagery and manual surveys align closely, with statistical analyses showing no significant differences overall. However, discrepancies were noted for Portland cement concrete sections, where the drone technology analysis method missed certain distresses such as joint seal damage. This limitation highlights the need for improvements in drone imaging or additional technologies to fully capture and analyze all distress types. Moreover, challenges such as weather dependency, regulatory constraints, and site conditions must be addressed to optimize drone use in pavement management.]]></description>
      <pubDate>Mon, 02 Mar 2026 13:29:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2675891</guid>
    </item>
    <item>
      <title>Functional-Observer Implementation for Road-Profile Prediction in Quarter-Vehicle Model</title>
      <link>https://trid.trb.org/View/2666765</link>
      <description><![CDATA[Ensuring safe driving requires continuous monitoring of both vehicle dynamics and external conditions such as road irregularities, which often act as unknown disturbances. Accurately estimating these unmeasured states and inputs is critical for advanced driver assistance systems (ADAS) and vehicle stability control. This study proposes a novel functional observer that simultaneously reconstructs unknown road disturbances and estimates unmeasured vehicle-state variables in real time using only standard onboard sensor measurements. The observer design is grounded in Lyapunov stability theory, with estimation conditions expressed as linear matrix inequalities (LMIs), whose solution guarantees robust convergence and stability. Validation is conducted through numerical simulations of a quarter-car vertical dynamics model under two scenarios. Results demonstrate that the proposed observer achieves accurate and reliable state estimation, outperforming conventional approaches such as the Kalman filter and full-order Luenberger observer, particularly in the presence of unknown inputs.]]></description>
      <pubDate>Tue, 10 Feb 2026 09:11:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666765</guid>
    </item>
    <item>
      <title>A laboratory experiment on warping behaviour of large-scale concrete specimens influenced by moisture conditions</title>
      <link>https://trid.trb.org/View/2643691</link>
      <description><![CDATA[The upward warping of concrete pavement slabs caused by differential drying shrinkage can deteriorate the smoothness of road surfaces and cause cracks, negatively affecting user comfort and safety. Therefore, in this study, a laboratory experiment was conducted to investigate the effects of differential drying shrinkage on the warping behaviour of jointed concrete pavement slabs. Large-scale concrete specimens were fabricated to measure the longitudinal strain and the vertical and longitudinal displacements according to the distance from the fixed point and the age of the specimens. The experimental results revealed significant displacement and strain differences for the specimen with the bottom submerged in water owing to differential drying shrinkage. The upward displacement and difference in the contraction strain of the specimen with a submerged bottom increased sharply toward the free end. Additionally, the overall behaviour of the specimens was analysed to estimate the early age behaviour of jointed concrete pavement slabs according to moisture conditions. To validate the experimental results, the curvatures of the specimens were calculated, and the resulting vertical displacements were compared with the measured displacements. Further research is required to mitigate the differential drying shrinkage, which is a key factor in the warping of jointed concrete pavements.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643691</guid>
    </item>
    <item>
      <title>RSM-based optimisation of unmanned aerial vehicles (UAV) photogrammetry parameters for pavement distress assessment</title>
      <link>https://trid.trb.org/View/2643668</link>
      <description><![CDATA[Unmanned aerial vehicles (UAVs) enable pavement managers to inspect larger areas more efficiently and frequently than traditional ground-based methods. However, the success of UAV-based pavement distress assessment depends heavily on photogrammetric flight parameters that influence image quality and, consequently, detection accuracy. Despite advances in automated distress extraction, limited attention has been given to understanding how flight parameters affect data quality. This study addresses that gap by evaluating the influence of UAV flight parameters on surface distress detection, focusing on crack detection, the most challenging distress to be identified from vision data. A D-optimal experimental design was employed to define an efficient parameter space using a limited number of flights over two-lane highways. Response Surface Methodology (RSM) was applied to model the relationships between flight parameters (altitude, side, and front overlap) and image quality, quantified as contrast-to-noise ratio (CNR). The resulting regression model achieved an R² of 0.78, demonstrating a reasonable fit. Using a desirability-based optimisation approach, the study identified parameter combinations that enhance image quality, providing a transferable framework for optimizing UAV-based collection of pavement distress data.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:02:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643668</guid>
    </item>
    <item>
      <title>Study on the dynamic response characteristics of subgrade soil in runways with stepped fill-and-cut foundations under aircraft taxiing loads</title>
      <link>https://trid.trb.org/View/2643662</link>
      <description><![CDATA[Analyzing the response of the runway under aircraft taxiing loads can offer technical support for runway construction and maintenance. In this study, a 4-DOF runway taxiing model considering taxiing lift is established, and the time-domain pavement roughness based on white noise is introduced, and the taxiing load caused by pavement excitation is calculated by state space method. The dynamic response of the runway under taxiing loads is investigated through finite element secondary development. When compared to a relatively homogeneous subgrade, the peak values and the absolute values of the valleys of three-directional normal stresses near the fill-cut interface in inhomogeneous subgrades increase as a result of the superposition and reflection of surface waves. And the time-history curve trend of longitudinal normal stress and YZ shear stress of subgrade soil has changed. Because of the time-delay effect between aircraft lift and stress wave propagation, the timing of peak vertical stresses at different depths of the subgrade is related to the moving speed of aircraft, it shows obvious speed effect. The stress path in the subgrade soil broadens as pavement roughness increases, and it initially expands and then contracts with increasing speed. The distribution position, shape and size of stress path in the coordinate system have significant changes with the structural form of subgrade, the position and depth of measuring points.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:02:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643662</guid>
    </item>
    <item>
      <title>Segmentation method for pavement cracks with few samples based on a class activation map</title>
      <link>https://trid.trb.org/View/2643657</link>
      <description><![CDATA[This paper proposes a pavement crack segmentation model framework for few samples. Modified by inserting Grad-CAM, the YOLO-CAM model is proposed to obtain the class activation map (CAM) to mark the locations of cracks. By analysing the gray distribution characteristics of cracks in the CAM, the pseudo-labels by using the adaptive threshold segmentation method are used to train U2-Net. The trained U2-net is used to optimise the pseudolabels to retrain U2-net with better quality. In addition, this research adds the Dice loss function on the basis of BceLoss to adjust U2-net. Finally, three groups of open source datasets are selected to compare and verify the feasibility and universality of the framework. The results show that the framework reduces the labeling work and effectively narrows the performance gap caused by few samples.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:02:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643657</guid>
    </item>
    <item>
      <title>Advanced surface texture assessment of roller compacted concrete pavement mixtures by an in-house optical surface profiler</title>
      <link>https://trid.trb.org/View/2643647</link>
      <description><![CDATA[Roller-compacted concrete (RCC) pavements have gained increasing attention, yet the literature still lacks quantitative insight into their surface texture and functional implications. This study evaluates RCC surface texture and skid resistance using both the Superpave gyratory compactor (SGC) and a roller compactor, and—under identical gradations and compaction protocols—compares these results directly with hot-mix asphalt (HMA). Surface topography was measured using an in-house three-dimensional (3D) optical profiler, and texture metrics were computed from parameters defined in ISO 25178-2; macrotexture was benchmarked with the sand patch test. Results show that SGC-compacted RCC does not replicate the field texture produced by roller compaction, so SGC data are used only for comparative trend evaluation. Roller-compacted RCC demonstrated greater macrotexture but lower wet British Pendulum Number (BPN) than HMA, consistent with the distinct functions of texture scales. RCC maintained coarser, less uniform microtexture, whereas HMA exhibited finer, sharper asperities and higher BPN. Strong agreement between 3D-derived macrotexture and mean texture depth (MTD) supports the measurement approach. The findings indicate that RCC can provide beneficial macrotexture for drainage but may require supplemental surface texturing to enhance microtexture for higher-speed applications.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643647</guid>
    </item>
    <item>
      <title>Measuring asphalt pavement surface texture using a multi-view stereo based approach</title>
      <link>https://trid.trb.org/View/2643631</link>
      <description><![CDATA[Surface texture information is pivotal to driving safety and asphalt pavement condition evaluation. Conventional surveys rely on costly laser-based equipment, limiting large-scale application. This study proposes an efficient algorithm to reconstruct 3D asphalt pavement surfaces from multi-view images. It integrates SuperPoint with SuperGlue for feature extraction and matching, outperforming the traditional Scale-Invariant Feature Transform (SIFT) and Nearest-Neighbor Mutual (NN-Mutual) methods. A comprehensive validation framework compared algorithm performance, laser-scanned and Sand Patch Test measurements, and texture parameters. Results show SuperPoint detects more features on regular shapes, corners, and edges, while SIFT is more sensitive to pixel gradients. SuperGlue excels in complex textures matching, whereas NN-Mutual suits simpler point pairs. Image-based reconstructions achieved mean texture depth (MTD) within ±10% of laser measurements, with skewness and kurtosis differences within ± 0.1 and 0.3, respectively, demonstrating high fidelity. Consistent Kullback-Leibler divergence values using laser-scanned data as reference confirm that reconstructed surfaces preserve the distributional characteristics of real pavement. Overall, the proposed approach offers a cost-effective, accurate, and robust alternative for practical pavement texture evaluation, with strong potential for automated road inspection and intelligent construction applications.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643631</guid>
    </item>
    <item>
      <title>Estimation of wheel-rail dynamic load in vicinity of track local defects using on-board train acceleration measurements</title>
      <link>https://trid.trb.org/View/2612948</link>
      <description><![CDATA[This research addresses the limitations of the current wheel-rail dynamic load (WRDL) measurement techniques by considering the effect of track local defects. A new model is developed based on measurement of train accelerations passing over five common track local defects. The defects are step up, step down, corrugation, negative pulse joint and pulse joint. A train is instrumented on the bogie frame, axle and car body. On-board accelerations are then measured. Kalman filter was used to obtain WRDL from recorded accelerations. The effectiveness of the new model was shown, comparing its results with corresponding values of multi-body dynamic models (MBD) currently used in WRDL calculations. Results showed that the new model is able to consider the effect of track local defects in WRDL calculations. As the new method does not require direct access to the track, it is faster and easy to use compared with existing approaches.]]></description>
      <pubDate>Wed, 19 Nov 2025 17:09:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612948</guid>
    </item>
    <item>
      <title>Application of Optical Satellite Data in Pavement Management: Assessing Its Value in Partially Observable Stochastic Environments</title>
      <link>https://trid.trb.org/View/2556944</link>
      <description><![CDATA[This study evaluated integrating multispectral satellite imagery and machine learning algorithms in monitoring and managing pavement conditions. The research used XGBoost, an advanced machine learning technique, to classify pavement conditions into three categories—good, fair, and poor—based on International Roughness Index (IRI) data using multispectral images from 2018 to 2023. A partially observable Markov decision process (POMDP) framework was applied to optimize maintenance and monitoring decisions, considering the inherent uncertainties in pavement condition assessments. The study area included of four Interstate Highways for which satellite imagery was analyzed. The findings showed that the XGBoost model achieved a classification accuracy of 69%, demonstrating substantial potential for satellite data in pavement condition assessment despite challenges posed by resolution and mixed pixel issues. The POMDP model indicated that incorporating satellite monitoring can reduce the life-cycle costs of pavements by approximately 0.1%. This research highlights the potentials of combining remote sensing, machine learning, and decision analysis techniques in enhancing pavement maintenance strategies, paving the way for more-efficient infrastructure management.]]></description>
      <pubDate>Fri, 19 Sep 2025 08:58:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2556944</guid>
    </item>
    <item>
      <title>Efficient Pavement Damage Detection Algorithm Based on Lightweight Feature Enhancement Network</title>
      <link>https://trid.trb.org/View/2582848</link>
      <description><![CDATA[Efficient detection of pavement damage in complex natural environments is crucial for intelligent maintenance of transportation infrastructure. To address challenges related to structural complexity, resource utilization, and detection accuracy, this paper presents a novel pavement damage detection algorithm based on a lightweight feature enhancement network. The model integrates the ghost module into YOLOv5s to improve feature extraction efficiency and creates a more lightweight backbone network. SimAM is then employed to enhance the ability to localize and perceive the damage target. Additionally, the position loss function is replaced with Wise-IoUv3, optimizing gradient distribution to further improve detection accuracy. Experimental results demonstrate that the proposed model achieves a size of only 8.03 M, average detection frames per second of 144, and a mean average precision (mAP) of 84.1%, outperforming YOLOv5s by 4.4%. It also reduces parameters and GFLOPs by 41.4% and 44.3%, respectively, while increasing detection speed by 37.9%. Compared to YOLOv7-tiny, the model improves accuracy by 6.7%, mAP by 3.4%, recall by 0.2%, and F1 score by 0.5%. Additionally, the average precision for four damage types—longitudinal cracks, transverse cracks, alligator cracks, and potholes—improves by 3.2%, 2.7%, 6.5%, and 1.0%, respectively. These findings confirm that the proposed algorithm not only meets real-time detection requirements but also provides lightweight and high-precision pavement damage detection, enabling broader applications in intelligent transportation monitoring systems.]]></description>
      <pubDate>Fri, 19 Sep 2025 08:58:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582848</guid>
    </item>
    <item>
      <title>Hybrid deep learning model for predicting failure properties of asphalt binder from fracture surface images</title>
      <link>https://trid.trb.org/View/2569683</link>
      <description><![CDATA[Cracking impacts asphalt concrete durability primarily due to cohesive asphalt binder failures. The poker chip test has recently been introduced to better characterize the cracking potential of asphalt binders by fracturing a specimen in a realistic stress state to a thin binder film. However, broader adoption faces challenges due to high instrumentation costs for measuring load and displacement. This paper presents and validates a deep learning model that predicts ductility and tensile strength from posttest images of fractured binder surfaces, with potential extensions to simplified instrumentation. The hybrid model, named PCNet, integrates a custom lightweight convolutional neural network (CNN) developed to capture local features (e.g., edges, boundaries, contours) within fracture cavities with a Swin Transformer that models global contextual dependencies. A bidirectional cross-attention fusion module is designed to facilitate mutual information exchange between CNN and transformer branches. The fused features are then processed by a fully connected network (FCN) to predict indices derived from the test. The proposed model demonstrates high predictive accuracy across a range of binders and test configurations, achieving an $ R^2$ of 0.966 and a mean absolute percentage error (MAPE) of 12.95% in predicting ductility, while also attaining an $ R^2$ of 0.947 and a MAPE of 9.15% for strength, outperforming standalone models. Monte Carlo Dropout is also incorporated in the FCN to quantify prediction confidence. This cost-effective methodology provides insights into fracture propagation in soft viscoelastic media and contributes to the field of experimental mechanics. With further data collection, the model holds potential for broader implementation, directly linking fracture surface images and mixture or field-scale cracking behavior.]]></description>
      <pubDate>Wed, 30 Jul 2025 16:21:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2569683</guid>
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