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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>Relationship between Shear Strength and Snow Properties at the Base of Snowpack</title>
      <link>https://trid.trb.org/View/2635985</link>
      <description><![CDATA[This study investigates how snow properties affect the shear strength at the base of snowpack. Field measurements of the shear strengths showed values ranging from 0.3 to 3.8 kN/m², with an average value of 1.5 kN/m². Despite the variation, it was confirmed that shear strength was positively correlated with dry snow density and snow hardness, and negatively correlated with liquid water content. Additionally, to understand the shear strength of snowpack under rainfall or rapid snowmelt, we measured the shear strength of an area of snowpack sprayed with water.]]></description>
      <pubDate>Tue, 24 Feb 2026 15:39:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635985</guid>
    </item>
    <item>
      <title>Lightweight Regression Model With Prediction Interval Estimation for Computer Vision-Based Winter Road Surface Condition Monitoring</title>
      <link>https://trid.trb.org/View/2598812</link>
      <description><![CDATA[Winter conditions pose several challenges for automated driving applications. A key challenge during winter is accurate assessment of road surface condition, as its impact on friction is a critical parameter for safely and reliably controlling a vehicle. This paper proposes a deep learning regression model, SIWNet, capable of estimating road surface friction properties from camera images. SIWNet extends state of the art by including an uncertainty estimation mechanism in the architecture. This is achieved by including an additional head in the network, which estimates a prediction interval. The prediction interval head is trained with a maximum likelihood loss function. The model was trained and tested with the SeeingThroughFog dataset, which features corresponding road friction sensor readings and images from an instrumented vehicle. Acquired results highlight the functionality of the prediction interval estimation of SIWNet, while the network also achieved similar point estimate accuracy as the previous state of the art. Furthermore, the SIWNet architecture offers a more favourable balance of accuracy and computational load than previous state-of-the-art models.]]></description>
      <pubDate>Mon, 08 Dec 2025 17:05:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598812</guid>
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    <item>
      <title>In Situ Monitoring and Numerical Studies on the Performance of Gentle Slopes to Preserve the Transportation Infrastructure in Northern Canada</title>
      <link>https://trid.trb.org/View/2596411</link>
      <description><![CDATA[Permafrost degradation occurs underneath the side slope due to snow accumulation, which prevents heat from being extracted from the ground in winter. This paper presents the in situ snow dynamics and thermal behavior of an airstrip in an area of continuous permafrost, in Nunavik, Quebec, Canada. The dynamic change of snow thickness around the embankment toe was measured in the field during 2014–2015. Thermistor strings were installed under the side slope and around the toe of the embankment. An empirical relationship between snowpack thickness and the freezing 𝘯-factor was proposed. The ground temperature data collected were used to calibrate a thermal conduction model, which was then used to develop the design chart for gentle slopes. The design chart was further validated using data from another experimental site at Tasiujaq airstrip in Nunavik, Canada. This work enhances the design capacity of gentle slopes to stabilize thaw-sensitive permafrost beneath the embankment shoulder.]]></description>
      <pubDate>Fri, 21 Nov 2025 17:09:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596411</guid>
    </item>
    <item>
      <title>Effect of Snow Cover on Slope Stability of Embankment during Rainfall and Snowmelt</title>
      <link>https://trid.trb.org/View/2594259</link>
      <description><![CDATA[In this study, the effect of snow cover on slope stability was examined to more accurately evaluate the risk of snowmelt hazards. Firstly, laboratory tests were carried out to determine strength characteristics of snow. In addition to laboratory tests, sprinkling model experiments were conducted on a snow-covered embankment model to observe the moisture response and deformation of the embankment. In addition, slope stability analysis using a finite element method was carried out on the snow-covered embankment model. The result confirmed that snow cover could restrict the surface layer of an embankment and slightly improve the slope stability.]]></description>
      <pubDate>Tue, 28 Oct 2025 13:46:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594259</guid>
    </item>
    <item>
      <title>Advancing winter road maintenance: An AI-driven web platform for real-time road condition monitoring and spatial analysis</title>
      <link>https://trid.trb.org/View/2590210</link>
      <description><![CDATA[Winter weather conditions pose significant challenges for transportation agencies, impacting road safety, traffic flow, and winter road maintenance (WRM) operations. Traditional methods for monitoring road surface conditions (RSCs) often involve time-consuming processes that require significant personnel. To address these challenges and maximize the utility of existing infrastructure, this paper presents a web-based system for real-time RSC monitoring. The system combines convolutional neural networks (CNNs) for RSC classification, a novel Nested Indicator Kriging (NIK) method for spatial interpolation, and modern web technologies to provide an intuitive interface. The system seamlessly integrates CNN models for real-time classifications using automated vehicle location (AVL) and road weather information system (RWIS) imagery. The NIK method enhances spatial coverage by classifying multiple RSC categories through two layers: the first identifies basic road conditions as bare or non-bare, while the second discriminates between more complex states, such as partially or fully snow-covered. Validated through simulations using historical data, the integrated AVL CNN model achieved a training accuracy of 99.89% and a validation accuracy of 94.62% during training, while the RWIS model reached a maximum accuracy of 98.46% and an F1 Score of 97.19%. Furthermore, the NIK method showed cross-validation accuracies averaging 73.5% for the first layer, and 86.0% for the second layer. This unified system represents an advancement in WRM decision support by automating RSC classifications and closing gaps in spatial data coverage, thus improving the efficiency and sustainability of operations and enhancing the ability of safety professionals and operators to respond to roadway hazards in real-time.]]></description>
      <pubDate>Wed, 17 Sep 2025 10:55:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2590210</guid>
    </item>
    <item>
      <title>Simulation of Snow Redistribution on the Surface of a Long-Span Box Girder Bridge Based on CFD-DEM Coupling</title>
      <link>https://trid.trb.org/View/2550298</link>
      <description><![CDATA[Bridge structures are typically elevated above the ground, with lower temperatures on the bridge deck. Accumulated snow particles can drift under the influence of strong winds, posing a substantial threat to traffic safety. The study of wind-induced snow hazards on bridge structures is significant for ensuring the safe operation of transportation in high-latitude and cold regions. Due to the complex mechanisms of snow motion, many unresolved issues remain. This study focuses on a large-span highway box girder bridge and investigates wind-induced snow redistribution on the girder surface using a three-dimensional CFD approach. The nondimensional wind-induced redistribution coefficients of the snow particles were obtained. In addition, a detailed analysis of the mechanisms behind the wind-induced snow redistribution was conducted from a flow field perspective. The results indicate that auxiliary components of the bridge model, such as railings and sidewalk pavement layers, directly influence the redistribution of particles on their surfaces. Furthermore, scaled model tests were conducted in a wind tunnel to validate the accuracy of the numerical simulations. Polyethylene particles were used to simulate snow particles. Moreover, to reveal the mechanism of how wind attack angles affect, the redistribution of particles and flow field information were analyzed under different wind attack angles (0° and ± 3°). The results demonstrate that different wind attack angles have a significant impact, particularly on the windward side of the bridge. The erosion extent of snow particles under the negative wind attack angle is higher than that under the 0° attack angle, and the erosion extents under both the negative and 0° attack angles are higher than that under the positive attack angle. The average and maximum differences in the nondimensional distribution coefficients of particles among the three conditions reach 34.5% and 77.8%, respectively. These findings not only provide data support for practical engineering applications but also offer methods and insights for further research on wind–snow interactions on bridge structures.]]></description>
      <pubDate>Tue, 29 Jul 2025 09:27:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2550298</guid>
    </item>
    <item>
      <title>Two-Way Coupled Aerodynamics and Vehicle Dynamics Crosswind Simulation of a Heavy Ground Vehicle in Winter Road Conditions</title>
      <link>https://trid.trb.org/View/2407510</link>
      <description><![CDATA[The present study investigates the dynamic characteristics of a heavy ground vehicle subjected to crosswind with various frequencies using two-way coupled simulations between aerodynamics and vehicle dynamics, including the driver’s steering inputs. Four different reduced frequencies of crosswind, which are equal to 4.2, 2.1, 1.3 and 0.9, are used. The results show that the absolute maximum magnitudes of the vehicle’s lateral dynamic and aerodynamic characteristics increase inversely proportional with the reduced frequencies of crosswinds without the driver’s steering input. However, when the driver’s steering input is included in the vehicle’s response to the crosswind disturbances, the vehicle’s lateral dynamic characteristics increase by the largest amount for reduced frequencies of 1.3 and 2.1. Furthermore, for a reduced frequency of 0.9, the driver’s steering inputs attenuate the unfavorable amplifications in the vehicle’s lateral dynamic characteristics. In the present study, the reasons for the increase in the vehicle’s lateral dynamic characteristics in the cases of reduced frequencies equal or greater than 1.3 with the driver’s steering input are explained. Additionally, the results show that the vehicle is not controlled within the 0.5 m lateral margins of the road when the steering starts with a time delay of 1.0 s for reduced frequencies equal to or lower than 2.1.]]></description>
      <pubDate>Mon, 28 Jul 2025 08:55:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407510</guid>
    </item>
    <item>
      <title>Reducing EMS Response Time to Crash Sites under Adverse Pavement Surface Conditions</title>
      <link>https://trid.trb.org/View/2553126</link>
      <description><![CDATA[Emergency medical services (EMS), also known as ambulance or paramedics services, play a critical role in saving lives by providing patients with the earliest urgent medical care and transportation to medical facilities. It is crucial that the EMS response time (EMS-RT), defined as the time required for EMS to reach the site after notification of the crash, be kept at a minimum to the chances of fatalities. This study aims to identify the factors that affect the EMS-RT during adverse weather conditions using descriptive and statistical modeling. Logistic and multiple linear regression techniques were utilized to develop the models. More than 58,000 EMS-RTs from 2016 to 2020 in an urban area were investigated. Results revealed that EMS-RT is significantly higher (p-value<0.05) in snowy pavement surface conditions and identified the factors that may reduce the EMS-RT. Results also showed that the resources are sufficient in the study area to keep the EMS-RT the same on a day with higher crashes compared to typical days.]]></description>
      <pubDate>Tue, 17 Jun 2025 09:58:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553126</guid>
    </item>
    <item>
      <title>Field measurements at a road embankment during a winter season in northern Sweden</title>
      <link>https://trid.trb.org/View/2530078</link>
      <description><![CDATA[Understanding the thermal regime of road embankments in cold climates during winter is essential for efficient road design and accurate estimation of maintenance frequencies to reduce freeze-induced damage. In response to the challenging climate conditions in northern Sweden, an experimental field setup was designed to assess the thermal impact of culverts and accumulated snow in ditches on the thermal regime of road embankments during a winter season. This study provides detailed information on the experimental setup, highlights potential challenges from installation phase to data acquisition, and addresses measurement errors. Methods to ensure accuracy and obtain reliable data are also presented. Additionally, some of the obtained measurement results are included in this paper. The results show that snow impacts the thermal regime of the embankment from the onset of accumulation in the ditch, when the snow cover is still thin, until it reaches a depth of 65 cm. Beyond this depth, the soil beneath the snow remains almost unfrozen throughout the winter season. Additionally, the temperature distribution measurements within the embankment indicate that freezing progresses faster near the culvert compared to the rest of the embankment. However, once the culvert ends are insulated by snow cover, the frost depth in the soil near the culvert does not increase significantly, while the rest of the road continues to freeze gradually to greater depths throughout the winter season. The measurement results presented in this study provide researchers with a reliable dataset for validating numerical models in related research areas simulating cold-climate conditions. Additionally, these results enhance the understanding of the thermal regime of road embankments in typical cold climates and offer valuable insights for planning road maintenance and construction in such regions. Furthermore, this study provides essential information for researchers aiming to design and optimize experimental measurement setups in similar investigations.]]></description>
      <pubDate>Mon, 12 May 2025 09:46:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2530078</guid>
    </item>
    <item>
      <title>Snow Depth Retrieval with an Autonomous UAV-Mounted Software-Defined Radar</title>
      <link>https://trid.trb.org/View/2520988</link>
      <description><![CDATA[The authors present results from a field campaign to measure seasonal snow depth at Cameron Pass, Colorado, using a synthetic ultrawideband software-defined radar (SDRadar) implemented in commercially available Universal Software Radio Peripheral (USRP) software-defined radio hardware and flown on a small hexacopter unmanned aerial vehicle (UAV). The authors coherently synthesize an ultrawideband signal from stepped frequency 50-MHz subpulses across 600-2100-MHz frequency bands using a novel nonuniform nonlinear synthetic wideband waveform reconstruction technique that minimizes sweep time and completely eliminates problematic grating lobes and other processing artifacts traditionally seen in stepped waveform synthesis. The authors image seasonal snow across two transects: a 400-m open Meadow Transect and a 380-m forested transect. The authors present a surface detection algorithm that fuses data from LiDAR, global navigation satellite system (GNSS)/global positioning system (GPS), and features in the radargram itself to obtain high precision estimates of both snow and ground surface reflections, and thus total snow depth, represented as two-way travel time. The measurements are validated against independent ground-based ground-penetrating radar measurements with correlations coefficients as high as 𝜌 = 0.9 demonstrated. Finally, the authors compare backscattered radar data collected by the UAV-SDRadar while hovering proximal to a known snow pit with in situ measured snow dielectric profiles and demonstrate imaging of snow stratigraphy.]]></description>
      <pubDate>Fri, 21 Mar 2025 09:03:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2520988</guid>
    </item>
    <item>
      <title>Snow Plow Performance Measures in Non-RWIS Locations</title>
      <link>https://trid.trb.org/View/2494865</link>
      <description><![CDATA[This project aims to enhance traffic safety by developing an Artificial Intelligence (AI) model and the weather interpolation method to evaluate snow cover conditions on road surfaces using existing roadside Closed-Circuit Television system (CCTV) images in non-Road Weather Information System (RWIS) locations. Snow-cover significantly impacts traffic safety, contributing to 24 percent of annual weather-related vehicle crashes. In Utah, where snow seasons can last up to seven months with over 25 winter storms annually, understanding snow-cover condition in real time is crucial for effective snow plow management by the Utah Department of Transportation (UDOT) and for public safety. Traditional methods for assessing road conditions rely on RWIS, which has limited coverage. The project will utilize image data from CCTV cameras to train a deep learning-based AI model for automatic evaluation of snow-cover condition. The methodology includes processing images to create labeled datasets, developing AI models based on AlexNet as the deep learning algorithms, and implementing these models to analyze real-time snow-cover conditions. Additionally, a weather interpolation method is developed to estimate real-time weather status in non-RWIS areas. The validation results show that the developed AI model can achieve over 97 percent accuracy in identifying various snow-cover states, including clear, partial snow, and full snow conditions. Furthermore, the developed weather interpolation method can provide a detailed view of the spatial distribution of air temperature, relative humidity, wind speed, precipitation, and snow accumulation. By leveraging existing CCTV networks, this project offers a cost-effective solution for large-scale, real-time road condition monitoring, closing information gaps, and enhancing winter road safety management.]]></description>
      <pubDate>Thu, 30 Jan 2025 17:01:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2494865</guid>
    </item>
    <item>
      <title>Snow- or ice-covered road detection in winter road surface conditions using deep neural networks</title>
      <link>https://trid.trb.org/View/2431588</link>
      <description><![CDATA[In cold regions during winter, traffic accidents frequently occur on snow- or ice-covered roads. Snow- or ice-covered roads are monitored by fixed-point cameras to capture surface conditions, but in this paper the authors propose a method for detection that uses the deep convolutional autoencoding Gaussian mixture model (DCAGMM) with structural similarity (SSIM). The DCAGMM is an unsupervised method for detection of anomalies, unaffected by imbalances in its training data. A convolutional neural network implemented in the DCAGMM captures the unique features of pavement surface images. By reconstructing input data as a normal image, input and reconstructed images can be compared, enabling identification of snow- or ice-covered road areas. The pavement surface data include complex characteristics for reconstruction, and SSIM enables preservation of image quality. Experimental results obtained on road surface images demonstrate the proposed method’s effectiveness.]]></description>
      <pubDate>Sat, 30 Nov 2024 15:26:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2431588</guid>
    </item>
    <item>
      <title>Estimation of Line Capacity for Railway</title>
      <link>https://trid.trb.org/View/1975932</link>
      <description><![CDATA[To estimate the line capacity of a railway, the influencing factors on railway line capacity are investigated. First, various states of the China Train Control System (CTCS) under severe ice and snow conditions are discussed. Second, descriptive and analysis models of train operation under adverse situations are given based on a stochastic Petri network. Since the SPN and Markov chain model (MCM) are isomorphic, the transition probability of train operation conditions is analyzed in depth by MCM. Then dynamic line capacity transition probability and fuzzy comprehensive evaluation matrixes are constructed for analyzing the capacity variation of a railway line.]]></description>
      <pubDate>Thu, 26 Sep 2024 16:50:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/1975932</guid>
    </item>
    <item>
      <title>Vehicle-mounted Dynamic Detection and Prejudgment of Railway Snow Cover Conditions in Remote Cold Regions</title>
      <link>https://trid.trb.org/View/1975877</link>
      <description><![CDATA[In view of the safety threat of sudden snowfall in the remote cold regions to railway transportation, China Railway Corporation has formulated strict speed limit regulations for trains in snow and ice weather. Real-time detection of snow cover on railway during train operation has become a key technology. In this regard, through the analysis of the detection methods and methods of railway snow cover status under heavy snow conditions in the remote cold regions, a dynamic detection technology of vehicle-mounted railway snow cover status is proposed. Based on real-time detection of local railway snow cover status, the dynamic prediction of the snow cover status in front of the railway is made, so as to provide effective information for railway transportation dispatching and safety management departments.]]></description>
      <pubDate>Fri, 23 Aug 2024 16:53:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/1975877</guid>
    </item>
    <item>
      <title>Analyzing Wind-Induced Snow Redistribution on Box Girder Bridges Using Wind Tunnel Tests</title>
      <link>https://trid.trb.org/View/2402317</link>
      <description><![CDATA[With the increase in span, the flexibility and sensitivity to aerodynamic damage of bridges significantly increase. However, current research studies primarily focus on analyzing the single environment, leaving the safety and durability of bridges under extreme weather relatively understudied, such as snowdrift. This study selects four typical box girder bridges as test models to conduct wind tunnel tests. By analyzing similarity principles and particle properties, polyethylene particles are chosen as the test medium, and their simulation accuracy is validated using a step-type flat roof model. The study then explores wind-induced snow redistribution on the surfaces of the four box girder bridges under different horizontal wind speeds, initial particle heights, and wind attack angles. The results demonstrate that as the surface configurations of the test models become more complex, the particle redistribution becomes more chaotic. Among the three test conditions, the wind attack angle exerts the greatest influence, followed by horizontal wind speed and initial particle height. Notably, the dimensionless redistribution coefficients of particles on the surface of the large-span highway box girder bridge show the largest differences under these test conditions, with average differences and maximum differences reaching 50.3% and 63.5%, respectively, for different wind attack angles. These findings provide data support for the investigation of the safety and durability of real bridges under extreme weather conditions.]]></description>
      <pubDate>Tue, 30 Jul 2024 09:55:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2402317</guid>
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