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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>
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    <item>
      <title>Off-System Public Roads Annual Average Daily Traffic (AADT) Estimation and Validation Tools: Literature Review and Research Report</title>
      <link>https://trid.trb.org/View/2693724</link>
      <description><![CDATA[This report describes a process for estimating AADT on non-Federal-Aid public roads (collectively called “off-system” roads) in Idaho. This will provide additional data for ITD and regional and local transportation agencies to conduct analysis, and it will meet new federal requirements for estimating AADT on all public roads. The report includes a review of federal guidance, documentation of ITD’s current AADT estimation process, an overview of spatial interpolation via Kriging, a user guide for the Rural AADT Estimation toolbox in Esri’s ArcGIS Pro software environment, and end materials including works cited, a list of independent variables considered, and the Python code used to run the toolbox.]]></description>
      <pubDate>Fri, 17 Apr 2026 10:41:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693724</guid>
    </item>
    <item>
      <title>Determining the Accuracy of a Digital Terrain Model Based on Image Data Obtained from an Unmanned Aerial Vehicle</title>
      <link>https://trid.trb.org/View/2665625</link>
      <description><![CDATA[This article presents and describes the results of research on determining the accuracy of a Digital Terrain Model (DTM) developed based on image data obtained from an Unmanned Aerial Vehicle (UAV). The Digital Terrain Model was created using image data acquired by an Unmanned Aerial Vehicle, specifically the fixed-wing with electric propulsion, flying at an altitude of 300 meters. The image data were collected during a photogrammetric survey conducted over a mountainous area in 2021. The final elevation values of the Digital Terrain Model were recorded in a GRID format with a spatial resolution of 5 meters. The article also includes a comparison of the DTM elevations with results obtained from the satellite GPS RTK technique. Based on this, an accuracy of elevation determination for different vertical profiles ranged from 0.19 m to 0.24 m was obtained. Moreover, the study also involves the development of a DTM from data acquired by the Unmanned Aerial Vehicle at an altitude of 150 meters. In this case, the accuracy of determining the elevations of the DTM for different vertical profiles ranged from 0.10 m to 0.16 m. The results of the research are very interesting for the application of UAV technology in aerial photogrammetry, particularly in inaccessible areas, especially mountainous regions.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:57:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665625</guid>
    </item>
    <item>
      <title>MNT-TNN: spatiotemporal traffic data imputation via compact multimode nonlinear transform-based tensor nuclear norm</title>
      <link>https://trid.trb.org/View/2606968</link>
      <description><![CDATA[Imputation of random or non-random missing data is a long-standing research topic and a crucial application for Intelligent Transportation Systems (ITS). However, with the advent of modern communication technologies such as Global Satellite Navigation Systems (GNSS), traffic data collection has introduced new challenges in random missing value imputation and increasing demands for spatiotemporal dependency modelings. To address these issues, we propose a novel spatiotemporal traffic imputation method based on a Multimode Nonlinear Transformed Tensor Nuclear Norm (MNT-TNN), which can effectively capture the intrinsic multimode spatiotemporal correlations and low-rankness of the traffic tensor, represented as location × location × time. To solve the nonconvex optimization problem, we design a proximal alternating minimization (PAM) algorithm with theoretical convergence guarantees. We also suggest an Augmented Transform-based Tensor Nuclear Norm Families (ATTNNs) framework to enhance the imputation results of TTNN techniques, especially at very high miss rates. Extensive experiments on real datasets demonstrate that our proposed MNT-TNN and ATTNNs can outperform the compared state-of-the-art imputation methods, completing the benchmark of random missing traffic value imputation.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2606968</guid>
    </item>
    <item>
      <title>Hybrid a Node-Based Smoothed Radial Point Interpolation Method and Artificial Neural Networks for Stability Analysis of Dual Square Tunnels at Different Depths</title>
      <link>https://trid.trb.org/View/2628350</link>
      <description><![CDATA[This study developed a hybrid methodology integrating the node-based smoothed radial point interpolation method (NS-RPIM) with artificial neural networks (ANN) to determine the ultimate load of dual square tunnels in cohesive-frictional soils at varying depths. NS-RPIM performed effectively in upper bound limit analysis by eliminating mesh dependency and enhancing accuracy through smoothed strain fields, while allowing for flexible node distribution in complex tunnel geometries. Its integration with second-order cone programming ensures precise computation of critical surcharge loads with improved computational efficiency. ANN complements NS-RPIM providing reliable predictions of stability numbers N = σₛ/c, and its ability to model nonlinear soil-tunnel interactions and adapt to diverse geotechnical conditions. An ANN trained on 2587 NS-RPIM-generated data samples achieves exceptional predictive accuracy (R² ≈ 0.9967, RMSE ≈ 1.3482), enabling instantaneous stability predictions compared to NS-RPIM. The hybrid framework is validated against numerical simulations demonstrating superior performance in capturing the effects of tunnel depth H/B, the horizontal spacing ratio S/B, and the vertical spacing ratio L/B, soil properties γB/c and internal friction angle φ. The hybrid NS-RPIM and ANN approach is a powerful tool for geotechnical engineers addressing the stability of dual square tunnels under complex loading conditions.]]></description>
      <pubDate>Wed, 26 Nov 2025 14:13:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628350</guid>
    </item>
    <item>
      <title>A Stacking-Based Ensemble Learning Model for Intelligent Ship Trajectory Interpolation</title>
      <link>https://trid.trb.org/View/2592932</link>
      <description><![CDATA[Incomplete ship trajectories caused by irregular Automatic Identification System (AIS) updates pose a critical challenge to reliable modeling of ship behaviors, which underpins the understanding and management of complex maritime traffic systems. Ship trajectory interpolation has therefore become essential for reconstructing missing segments and ensuring data continuity. However, most existing methods adopt an individual interpolation model regardless of varying ship behaviors, which limits their adaptability and may degrade the accuracy of reconstructed trajectories in diverse traffic scenarios. This study presents a Stacking Trajectory Interpolation Model (STIM) that enables adaptive and behavior-aware selection of the most suitable interpolation algorithm for accurate and robust ship trajectory data reconstruction. Specifically, a Markov-based feature extraction approach is first designed to divide trajectories into segments reflective of behavioral patterns, providing informative inputs to support the model’s learning process. Five widely adopted interpolation methods (linear, polynomial, cubic spline, cubic Hermite, and kinematic interpolation) are then implemented in base learners for initial predictions. The meta-learner empowered by a Transformer-based dual-branch multi-classifier subsequently learns the latent relationship between segment features and interpolation performance, enabling the model to support reliable trajectory reconstruction under diverse behavioral patterns. Experimental results using AIS data from Ningbo-Zhoushan Port demonstrate that STIM has strong adaptability and scalability in handling diverse trajectory characteristics, generating more accurate interpolated trajectories compared to conventional methods. Additionally, the impact of different trajectory features on interpolation results is discussed, which further validates the strength of STIM in providing valuable insights for optimizing future trajectory-based application solutions.]]></description>
      <pubDate>Tue, 21 Oct 2025 16:10:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592932</guid>
    </item>
    <item>
      <title>A Novel Approach to PEMFCs Degradation Prediction: An Enhanced Refinement of the Gray Verhulst Model</title>
      <link>https://trid.trb.org/View/2559226</link>
      <description><![CDATA[In pursuing superior operational efficiency of proton exchange membrane fuel cells (PEMFCs), the precise prediction of lifespan becomes a prerequisite. To address the issues of traditional static indicators such as voltage and power, this article propounds the relative voltage loss rate (RVLR) as an innovative degradation indicator in dynamic operation contexts. An advanced refinement of the gray Verhulst model (GVM) is proposed for the prediction of PEMFCs degradation. First, to deal with the volatility of PEMFCs’ voltage leading to the roughness in PEMFs’ degradation indicators, a novel exponential hyperbolic sine function transformation is applied to enhance the smoothness of the PEMFCs’ indicator sequences. In addition, the approach uses Newton-Cotes and Newton interpolation techniques to reduce the original model’s background value error. Empirical validation is provided through three case studies involving aging tests from the YK-S20, RG-FCTS-15, and G20 test stations. The proposed method demonstrates substantial accuracy and robustness, evidenced by RMSE values of 0.0031, 0.0017, and 0.0012, R² values of 0.9851, 0.9984, and 0.9939, and MAE values of 0.0019, 0.0010, and 0.0007 across the three datasets, respectively. These results indicate the method’s promise for accurate degradation prognostication.]]></description>
      <pubDate>Wed, 06 Aug 2025 15:02:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559226</guid>
    </item>
    <item>
      <title>Urban Microcirculation Traffic Network Planning Method Based on Fast Search Random Tree Algorithm</title>
      <link>https://trid.trb.org/View/2534124</link>
      <description><![CDATA[Unbalanced urban development causes complex and diverse urban traffic conditions, which complicates microcirculation traffic network planning. To address this, a method based on fast search random tree algorithm is proposed. An urban microcirculation traffic network is constructed using directed graphs, and road network interference intensity and capacity are calculated. The interpolation collision detection method is used to determine the shortest path while considering constraint conditions. By incorporating target gravity into the RRT algorithm, a growth guidance function is obtained, optimising the planned path and completing urban microcirculation traffic network planning. Experimental results demonstrate accurate shortest path calculation with up to 11% delay reduction compared to existing methods. Energy consumption during planning is lower than 10 k𝖩, ensuring fair resource distribution within the urban microcirculation transportation network. These advantages highlight the practicality and effectiveness of this research method.]]></description>
      <pubDate>Wed, 30 Apr 2025 16:59:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534124</guid>
    </item>
    <item>
      <title>Towards a Transitional Weather Scene Recognition Approach for Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2389669</link>
      <description><![CDATA[Driving in adverse weather conditions is a key challenge for autonomous vehicles (AV). Typical scene perception models perform poorly in rainy, foggy, snowy, and cloudy conditions. In addition, the authors observe transition states between extremes (cloudy to rainy, rainy to sunny, etc.) in nature with variations in adversity. It is crucial to define and understand these transition states in order to develop robust AV perception models. Existing research works on classification focused on identifying extreme weather conditions. However, there is a lack of emphasis on the transition between these extreme weather scenes. Hence, this paper proposes an approach to define and understand six intermediate weather transition states: sunny to rainy, rainy to sunny, and others. Firstly, they propose a way to interpolate the intermediate weather transition data using a variational autoencoder and extract its spatial features using VGG. Further, they model the temporal distribution of these spatial features using a gated recurrent unit to classify the corresponding transition state. Also, they introduce a large-scale dataset called the AIWD6: Adverse Intermediate Weather Driving dataset, generated for three different time intervals. Experimental results on the AIWD6 dataset demonstrate that their model efficiently generates weather transition conditions for AV technology. Also, the spatio-temporal deep neural network can effectively classify the adverse weather transition states for different time intervals.]]></description>
      <pubDate>Mon, 30 Sep 2024 18:17:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2389669</guid>
    </item>
    <item>
      <title>High-Fidelity and Curvature-Continuous Path Smoothing With Quadratic Bézier Curve</title>
      <link>https://trid.trb.org/View/2389802</link>
      <description><![CDATA[G₂-continuous trajectories are crucial for vehicles under non-holonomic dynamic constraints to run smoothly. In this paper, the authors present a path smoothing method based on piece-wise quadratic Bézier curves, which can fit a set of sequential 2D collision-free path points generated by a higher-level path planner. The generated trajectories are G₂-continuous except at the inflection points, and points of local curvature maxima appear exactly at path points and nowhere else. The local curvature control ensures that the trajectories keep high fidelity with the original paths, thus maintaining key properties of the original paths, such as free collision and gentle turns. Furthermore, the authors develop a joint optimization method for both fidelity and continuity, which has an efficient analytical solution for each iteration. Extensive experiments in both simulated and real scenarios validate that 1) the smoothed trajectories can deviate less than 10 cm from the original paths, 2) the average curvature differences at junctures are under 1e-4 (1/m), 3) the smoothing time reduced by about 50% versus high-order bézier methods, showing superior performance of the proposed method in terms of fidelity, continuity, efficiency, and practicability.]]></description>
      <pubDate>Sun, 30 Jun 2024 16:02:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2389802</guid>
    </item>
    <item>
      <title>Processing driving simulator data before statistical analysis by means of interpolation and an integral formula</title>
      <link>https://trid.trb.org/View/2344981</link>
      <description><![CDATA[Driving simulator data can be sampled in function of distance (equally spaced) or time (with constant frequency). Consequently, the sampling data might have problems in the envisaged type of analysis (i.e. point location based analysis vs. zonal-based analysis). These issues are illustrated by means of five driving simulator datasets. The nearest sampled parameter value in the direct vicinity of the specific point is a very good proxy for the driving parameter value at the point of interest along the road. The analysis of driving parameters in zones requires a different approach. In summary, the interpolation technique is preferred over using raw sampled data to calculate mean parameter values. We introduce an equivalent time integral formula to compute the mean value of a driving parameter with respect to distance. Based on this paper, we demonstrate that it is very important to mention the data processing approach in driving simulator methodology.]]></description>
      <pubDate>Tue, 12 Mar 2024 09:32:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2344981</guid>
    </item>
    <item>
      <title>Reconstruction Method for Multi-Vehicle Trajectories on Arterials Driven by Multi-Source Data</title>
      <link>https://trid.trb.org/View/2237759</link>
      <description><![CDATA[Vehicle trajectories contain enriched spatial and temporal traffic information. In this study, the vehicle trajectory data were obtained after the data-fusion process of multi-source heterogeneous data on arterials. Both the piecewise cubic Hermite interpolation algorithm and cubic spline interpolation algorithm were used to reconstruct the single vehicle trajectories. A cross-validation method was applied in the comparison for obtaining the optimal model. Based on the reconstructed vehicle trajectories, an interpolation method was used to predict the unrecorded multi-vehicle trajectories by interpolating the time of unknown vehicles. The results show that the piecewise cubic Hermite interpolation can achieve better performance in reconstructing the single-vehicle trajectory and it is effective in predicting the missing trajectories. This study supports the spatial-temporal analysis of vehicle trajectories, traffic-state estimation, and transportation optimization.]]></description>
      <pubDate>Wed, 31 Jan 2024 09:16:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2237759</guid>
    </item>
    <item>
      <title>Assessment of Different Spatial Interpolation Techniques for Generating Synthetic Soil Boring Data</title>
      <link>https://trid.trb.org/View/2283416</link>
      <description><![CDATA[Subsurface soil conditions usually involve special site variability that cannot be ignored for design and analysis. Therefore, the effect of site variability on associated soil properties should be assessed using gathered field data, such as soil boring data collected from discrete locations. In this study, six spatial interpolation techniques, the ordinary kriging (OK), simple kriging (SK), universal kriging (UK), inverse distance weight (IDW), spline, and natural neighbor (NaN) were evaluated to assess the best prediction strategy for considering site variability. The efficacy of these methods was tested at four soil boring sites. Boring profiles were generated using the different techniques at specified locations for each site, and the created data were compared with the measured soil boring profiles. For each location, the best-fit line of measured versus predicted undrained shear strength (Su) or standard penetration test (SPT) number, mean bias factor (?), coefficient of effectiveness (COE), root mean square error (RMSE), and coefficient of variation (COV), were calculated and used to assess the various interpolation methods. The findings of this study demonstrated the ability of these spatial interpolations to produce precise soil boring data. The slope of best-fit line of measured/generated Su and SPT ranged from 0.89 to 0.99. The best-performing interpolation methods (in order) are: IDW, OK/UK, and SK methods. The results show that the COVs between the measured and synthetic soil boring data at the selected points are significantly lower than the COVs between the measured soil boring profiles for the entire site.]]></description>
      <pubDate>Mon, 30 Oct 2023 16:33:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2283416</guid>
    </item>
    <item>
      <title>Real-Time Point Cloud Object Detection via Voxel-Point Geometry Abstraction</title>
      <link>https://trid.trb.org/View/2189972</link>
      <description><![CDATA[Recent advances in 3D object detection typically learn voxel-based or point-based representations on point clouds. Point-based methods preserve precise point positions but incur high computational load, whereas voxel-based methods rasterize unordered points into voxel grids efficiently but give rise to an accuracy bottleneck. To take advantage of voxel- and point-based representations, we develop an effective and efficient 3D object detector via a novel voxel-point geometry abstraction scheme. The authors' motivation is to use coarse voxel representation to accelerate proposal generation while using precise point representation to facilitate proposal refinement. For voxel representation learning, they propose a context enrichment module with a novel 3D sparse interpolation layer to augment raw points with multi-scale context. They further develop a point-based RoI pooling module with explicit position augmentation for proposal refinement. Extensive experiments on the widely used KITTI Dataset and the latest Waymo Open Dataset show that the proposed algorithm outperforms state-of-the-art point-voxel-based methods while running at 24 FPS on the TITAN XP GPU.]]></description>
      <pubDate>Tue, 17 Oct 2023 13:42:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2189972</guid>
    </item>
    <item>
      <title>Improved BSO for ship path planning with grounding alert using interpolated Delaunay triangulation</title>
      <link>https://trid.trb.org/View/2231019</link>
      <description><![CDATA[An improved depth-contour points generation algorithm under navigation safety constraints based on possible shallowest sounding and interpolation is proposed. An improved brain storm optimisation (BSO) for ship path planning with grounding alert using interpolated Delaunay triangulation is designed. The initial clustering centre selection method is improved to overcome the sensitivity of the initial value generated by the k-means algorithm. The clustering algorithm of the random walking centroid is used to improve the efficiency, stability, randomness, and diversity of the iterative process and prevents convergence to the local optimal solution. The tide height with grounding alert is considered in path planning to ensure that real-time water depth is deeper than the safe water depth threshold. The curve interpolation method is used for points protruding into shallow water, while the linear interpolation method is used for points protruding into deep water.]]></description>
      <pubDate>Mon, 28 Aug 2023 09:19:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2231019</guid>
    </item>
    <item>
      <title>Off-System Public Roads Annual Average Daily Traffic (AADT) Estimation Study: Final Report</title>
      <link>https://trid.trb.org/View/2228997</link>
      <description><![CDATA[This report describes a process to estimate AADT on non-Federal-Aid public roads (collectively called “off-system” roads) in Idaho. This will provide additional data for ITD and regional and local transportation agencies to conduct analysis, and it will meet new federal requirements for estimating AADT on all public roads. The report includes a review of AADT estimation approaches in research and in practice, documentation of ITD’s current AADT estimation process, and summaries of federal guidance. It also includes an inventory of potential data sources. The project team proposed three methods (related to regression, travel demand models, and geospatial interpolation), and the technical advisory committee (TAC) selected the geospatial interpolation approach based on its flexibility, understandability, moderate technical requirements, and accuracy. Therefore, this report also details how to implement the geospatial interpolation method for estimating off-system AADT. Some methodological decisions remain to be made based on the data (such as the specific geospatial interpolation technique and variables to include), so the report describes the decisions to be made and how to approach them. Finally, the report provides an implementation and validation plan with next steps, implementation roles, schedule, and statistics for use in model validation. This process can be implemented using tools in Esri ArcGIS and Python.]]></description>
      <pubDate>Wed, 23 Aug 2023 15:24:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2228997</guid>
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