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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <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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    <item>
      <title>Retail form and vegetation interactions in street canyon cooling: A case study on 3D urban form using JL1KF01A data</title>
      <link>https://trid.trb.org/View/2641140</link>
      <description><![CDATA[Street canyons in commercial districts can be characterized by complex 3D forms in which building structures, retail activities, and vegetation interact to shape thermal environments. The cooling effect in canyons is provided by roadside tree canopies; however, it is rarely evaluated precisely at a high resolution in the commercial districts of a metropolis. In this study, Beijing was chosen as the city to investigate the cooling effect of roadside tree canopies in relation to varied retail densities and commercial types. JL1KF01A satellite imagery was used with a 3-meter multispectral resolution, and the 3D-GloBFP dataset was used to integrate fine-scale 2D and 3D urban form data. This study identified street canyon structures and estimated land surface temperature (LST) across road and roadside buffers. The findings demonstrated that green and blue spaces significantly contributed to temperature reduction, with cooling effects reaching –4.34 °C. In contrast, impervious surfaces increased LSTs by as much as 3.70 °C. Building height and green space area followed a consistent inverse U-shaped trend across retail density gradients and peaked in zones with 10–30 % and 70–90 % retail densities, where cooling was most effective. Among retail types, entertainment and hotel zones were associated with stronger cooling owing to the higher proportion of green and blue spaces, whereas shopping and life service areas exhibited greater heat accumulation. Maximum likelihood estimation confirmed that green and blue spaces had the strongest negative contribution to LST, whereas impervious surfaces had a consistently positive influence. Building height offers a modest but stable cooling effect. This study employs high-resolution 3 m JL1KF01A multispectral imagery integrated with 3D-GloBFP building data to precisely evaluate roadside tree cooling in relation to retail densities and types, addressing gaps in the existing literature that often overlook commercial-specific interactions and rely on coarser resolutions. The results clearly indicate that urban thermal regulation in commercial street canyons depends on the interplay between the retail function, vegetation coverage, and vertical urban form.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:55:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2641140</guid>
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    <item>
      <title>Deep Learning-Based Automated Slope Condition Assessment Using Satellite Imagery</title>
      <link>https://trid.trb.org/View/2562225</link>
      <description><![CDATA[Manual inspection of highway slopes is labor-intensive and time-consuming, making monitoring and consistently assessing slope conditions difficult. Early indicators of slope failure, such as erosion, vegetation loss, cracks, and water runoff, can significantly affect slope stability and are challenging to detect manually. While traditional methods rely on direct observation, they do not effectively leverage the advancements in satellite imagery and deep learning for consistent and scalable monitoring of slope conditions. The objective of this research is to develop an automated, deep learning-based system using high-resolution satellite imagery to identify early slope failure indicators and improve monitoring accuracy. The methodology utilizes the DeepLabV3+ convolutional neural network (CNN) architecture, augmented with a Modified ResNet101 backbone, Enhanced Channel Attention (ECA) modules, and Atrous Spatial Pyramid Pooling (ASPP) specifically designed to improve feature extraction and classification accuracy. A custom data set of annotated high-resolution slope images, derived from the Landslide4Sense data set, was used for training and validation. The model achieved a validation accuracy of 97.76%, a mean Intersection over Union (IoU) of 87.9%, and a Dice Coefficient of 90.8%, highlighting its capability to detect critical slope failure indicators, such as erosion, cracks, and vegetation loss, with high precision. This approach was applied to analyze slope imagery, detecting erosion, cracks, and vegetation loss with high precision and robustness. Consequently, the results demonstrate the scalability and effectiveness of the method in improving slope condition monitoring across transportation infrastructure. Detecting early signs of slope failure like erosion, cracks, and vegetation changes helps transportation agencies ensure safety, optimize maintenance resources, and prevent infrastructure damage.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562225</guid>
    </item>
    <item>
      <title>Land Cover Classification Using U-Net for Calibration of Rainfall-Induced Slope Susceptibility Maps</title>
      <link>https://trid.trb.org/View/2562222</link>
      <description><![CDATA[Land cover classification is essential in roadway design and environmental conservation. The convolutional neural network (CNN) model named U-Net, with its ability to capture fine-grained spatial details, has been proven particularly effective in performing accurate and efficient classifications from satellite imagery. These advancements are particularly valuable in geophysical hazard assessment, where physics-driven predictive models evaluate rainfall-triggered slope susceptibility and identify slope failure hazards. Existing physics-driven models for predicting rainfall-induced slope susceptibilities often focus on factors like slope geometry, soil characteristics, and precipitation but tend to overlook the influence of land cover features such as vegetation, retaining structures, and wetlands on slope stability. The primary objective of this study is to address this gap by developing an accurate semantic segmentation method for aerial imagery using the U-Net convolutional neural network (CNN) model and integrating it with physics-driven models to account for the influence of land cover on slope stability. The methodology includes preprocessing aerial imagery by segmenting images into 256 × 256 pixel patches, normalizing pixel values for consistency across the data set, and using the U-Net architecture to achieve fine-grained segmentation details. This method integrates with the current physics-driven model by converting the classified image into geospatial formats and conducting overlay analysis on slope susceptibility maps to enhance the accuracy of slope susceptibility levels. The results demonstrate high accuracy and a strong Intersection over Union (IoU), highlighting the U-Net model’s effectiveness in identifying and classifying complex land cover types. By integrating detailed land cover data, this research enhances the predictive accuracy of traditional slope failure predictive models and broadens the applicability of U-Net in geophysical modeling, facilitating the identification of critical slopes and enhancing proactive maintenance strategies.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562222</guid>
    </item>
    <item>
      <title>Evaluation of Rolled Erosion Control Products and Seed Mixes for Vegetation Establishment on Slopes</title>
      <link>https://trid.trb.org/View/2652069</link>
      <description><![CDATA[Because transportation construction projects often result in steep slopes with disturbed, bare soils that are sensitive to runoff and erosion processes, vegetation establishment is a continual challenge for transportation departments. The Virginia Department of Transportation (VDOT) regularly uses rolled erosion control products (RECPs) to mitigate erosion and provide an environment for vegetation to establish. RECPs range from erosion control blankets made of degradable natural or polymer fibers to non-degradable mats for permanent erosion protection. RECPs that promote rapid and reliable vegetation establishment help expedite environmental compliance for VDOT, a key criterion for project closeout for VDOT projects. The purpose of this study was to (1) assess and compare the performances of RECPs in promoting vegetation establishment, (2) evaluate the performance of VDOT’s basic seed mix design when supplemented with specialty seed mixes such as pollinator and strip mixes, and (3) examine the influence of air temperature, precipitation, soil temperature, and soil moisture on vegetation establishment. RECPs were installed at four VDOT project sites, and vegetation was monitored across the spring, summer, and fall seasons. Evaluations of four commonly applied RECPs and three seed mixes were conducted on geotechnically stable 2:1 slopes with varying soil types. Selected RECPS included two degradable EC-2 mats (straw-based or coconut-based soil stabilization blankets) and two EC-3 mats (non-degradable plastic matting). An image analysis program was used to determine the percentage of vegetative cover. Results showed that degradable EC-2 mats reliably supported vegetation growth and met permanent stabilization thresholds on 2:1 slopes, with EC-2 Type 2 (jute netting and straw fiber) reaching the 75% final stabilization criterion earliest and sustaining the highest percent cover across study sites. Findings pointed to two primary reasons for the superior performance of EC-2 mats, particularly the EC-2 Type 2: (1) soil temperature results indicated that the consistently higher temperatures in EC-3 plots contributed to slower and less consistent vegetative cover relative to EC-2 plots and (2) RECP material characteristics appeared to influence vegetation establishment. EC-2 mats, and the EC-2 Type 2 mat in particular, were more flexible and conformed better to uneven or rocky slopes, whereas EC-3 mats were more rigid, prone to folding and bunching, inhibited plant emergence in some areas, and required more careful installation procedures. This report recommends that VDOT prioritize the use of EC-2 Type 2 RECPs on 2:1 slopes in place of EC-3 mats because this approach would promote faster and more reliable vegetation establishment. Because of the lower purchase cost of EC-2 Type 2 compared with EC-3 mats, VDOT would save an estimated $400,000 during a 10-year period. However, the more important benefit of using EC-2 Type 2 mats is the reduction in reseeding or permit delays. This report also recommends that VDOT continue using its updated framework for seed mix selection, which includes options for pollinator and strip specialty mixes, with selections based on specific project goals.]]></description>
      <pubDate>Sat, 10 Jan 2026 11:18:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652069</guid>
    </item>
    <item>
      <title>Design of Cut-off Wall for Groundwater and Vegetation Protection during Tunnel Excavation in High Groundwater Level Areas</title>
      <link>https://trid.trb.org/View/2640245</link>
      <description><![CDATA[Research into protective measures for groundwater and vegetation under tunnel excavation has mainly focused on grouting methods around the tunnel, with little attention given to viable external measures such as constructing cut-off walls. This study proposed a block calculation method for the analytical solution of groundwater level with a finite depth cut-off wall, based on the analytical solution of groundwater level with an infinite depth cut-off wall. A design method for the cut-off wall is proposed under the tunnel excavation and vegetation protection. The results show that only when the depth of the cut-off wall exceeds the groundwater level can it provide a cut-off effect, and the small permeability can increase the cut-off effect. The proposed design method for cut-off walls was applied under various conditions and demonstrated excellent applicability in the context of groundwater and vegetation protection (at least 41% of the vegetation was protected from water shortage).]]></description>
      <pubDate>Mon, 29 Dec 2025 09:32:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640245</guid>
    </item>
    <item>
      <title>Investigation of Tools and Technology for Roadside Vegetation Asset Management</title>
      <link>https://trid.trb.org/View/2637517</link>
      <description><![CDATA[This report presents an investigation of the tools and technology for Roadside Vegetation Asset Management (RVAM) in four chapters. Chapter one defines and describes RVAM and details the study’s objectives and scope. This chapter contains an overview of RVAM, and discusses research conducted on RVAM funding issues, funding sources, and associated budgetary constraints; the use of structural and vegetative assets; and barriers to implementing new technology. Finally, chapter one touches on the benefits of RVAM that will be explained in the report. Chapter two reviews the research approach for the literature review, nationwide surveys, case studies, guidebook work plan, Interim Report, Technical Memorandum, and final project deliverables. Chapter three provides an overview of the results and subsequent recommendations from the literature review, nationwide surveys, and case studies. The literature review identified both commonly available and new and evolving technology for state department of transportation (DOT) use. This chapter covers funding sources and opportunities available at the time of the writing of this report. Finally, chapter three lists results from the nationwide surveys and a short comparison of the five case studies showcasing similarities and differences between state DOTs that were identified during the project. Chapter four outlines suggestions for state DOTs to improve their RVAM practices based on the findings of the project including tools and technology implementation, terminology usage and consistency, asset condition rating systems, staffing and funding opportunities, and public outreach. This chapter also provides research recommendations to the Transportation Research Board (TRB) for possible future research projects, the formation of a subcommittee, and an RVAM transportation pooled fund research study.]]></description>
      <pubDate>Sat, 13 Dec 2025 16:59:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2637517</guid>
    </item>
    <item>
      <title>Tools and Technology for Roadside Vegetation Asset Management: A Guide</title>
      <link>https://trid.trb.org/View/2637507</link>
      <description><![CDATA[This report describes how state departments of transportation (DOTs) can promote and implement roadside vegetation asset management (RVAM). The guide presents the types of tools and technology available for roadside vegetation asset management, identifies potential implementation issues, and makes suggestions for state DOTs with different organizational structures and different levels of Information Technology (IT) support. In developing this guide, the authors incorporated information from “white” and “grey” literature reviews, surveys, and case studies. This guide will be of immediate interest to landscape asset management practitioners. State DOTs can use this guide in creating or updating RVAM plans.]]></description>
      <pubDate>Sat, 13 Dec 2025 16:59:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2637507</guid>
    </item>
    <item>
      <title>Quick Guide for Native Planting</title>
      <link>https://trid.trb.org/View/2636115</link>
      <description><![CDATA[Native vegetation is the preferred long-term solution for roadside stability, water quality, and habitat restoration. When implemented with proper establishment practices, native plantings reduce maintenance costs, improve ecological function, and support resilient transportation infrastructure. This guide is a practical reference for both seasoned staff and new team members. It is just one consideration of an integrated vegetation management approach. Please reference other quick guides in this series including Noxious Weed Control, Herbicide Application, and Mowing and Brush Control.]]></description>
      <pubDate>Thu, 11 Dec 2025 16:08:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636115</guid>
    </item>
    <item>
      <title>Exploring the effects of street canyon morphology on LST within different street types using causal inference and machine learning</title>
      <link>https://trid.trb.org/View/2602053</link>
      <description><![CDATA[There is currently a lack of classification methods for street canyon morphology at the street-scale level. This can impede the development of targeted cooling strategies tailored to the specific characteristics of different street morphologies. This study quantifies street canyon morphology using street-view hemisphere images and compares multiple clustering models to identify the optimal model and parameters. Subsequently, machine learning is coupled with causal inference models to explore the associative mechanisms between different street canyon morphology indices and multi-time land surface temperature (LST). The results reveal that spectral clustering divides streets into three categories of wide streets and two categories of narrower alleys. Different street types exhibit distinct correlation trends with LST, highlighting the importance of clustering algorithms. In conjunction with the results of causal inference, it is observed that alleys with high canopy coverage and broad streets equipped with road-center hedges demonstrate superior cooling capabilities, with cooling effects of 23.56 % and 18.81 %, respectively. Conversely, for broad streets with lower levels of greening, increasing the height of roadside buildings can be an effective strategy to maximize the utilization of building shadows and wind for cooling purposes. This study emphasizes vegetation as a key factor in altering street canyon morphology to achieve cooling effects, particularly in stock developments.]]></description>
      <pubDate>Thu, 20 Nov 2025 17:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2602053</guid>
    </item>
    <item>
      <title>In-Stream Vegetation as a Nature-Based Approach to Scour Control at Bridge Crossings
</title>
      <link>https://trid.trb.org/View/2627352</link>
      <description><![CDATA[Scour and erosion are well-established as leading causes of bridge failures in North America. While bridge crossings often include countermeasures for scour control and mitigation, the majority of existing scour countermeasures are considered expensive, impractical, and ineffective. Although routinely commented on in state-of-practice reports and highly beneficial in building and maintaining sustainable communities and ecosystems, nature-based solutions have been overlooked as an approach to scour control. A knowledge base which provides scientific evidence of the efficacy of green infrastructure such as in-stream vegetation for scour control at bridge crossings is not currently available. Evaluation based on detailed bathymetric and flow field measurements is necessary for future development of practical guidelines.
The proposed research will employ extensive physical modelling to explore the efficacy of in-stream vegetation for scour control at bridge crossings. Experiments will be conducted in the laboratory facilities of IIHR – Hydroscience & Engineering, which include a high-gradient sediment-capable tilting flume with a sediment recess. Robust flow measurement techniques, including particle image velocimetry (PIV) and acoustic Doppler velocimetry (ADV), will provide insight into distribution of velocity components, shear and normal stresses, and higher-order turbulence moments in the flow field of interest due to inclusion of vegetated sections in the channel. The results of the physical modelling efforts will enhance the severely limited understanding of the influence of green infrastructure elements on the scour mechanism. The primary anticipated product is the initiation of a knowledge base for the development of a framework of guidelines to be used in practice.
]]></description>
      <pubDate>Wed, 19 Nov 2025 14:20:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627352</guid>
    </item>
    <item>
      <title>Entwicklung der Grundlagen für ein Baumkataster an Bundesfernstraßen im BISStra</title>
      <link>https://trid.trb.org/View/2612888</link>
      <description><![CDATA[Mit der Veröffentlichung wird der Forderung Rechnung getragen, ein bundesweit einheitliches Baumkataster zu realisieren und regelmäßig zu aktualisieren. Die Entwicklung des Katasters ist ein Querschnittsprojekt, in welchem durch die Vernetzung von Informationen die Effizienz/Wirtschaftlichkeit verbessert und der Wissensstand erhöht wird. Es wurde ein Datenmodell entwickelt, das als neue Fachschale im Bundesinformationssystem Straße (BISStra) integriert wird. Die Datengrundlagen für das bundesweite Baum- und Alleenkataster an Bundesfernstraßen wurden unter Berücksichtigung umwelt- als auch verkehrstechnischer Aspekte definiert und mit Datenlieferanten und Interessengruppen abgestimmt. Bei der Identifikation der Grundlagen wurden die Bereiche Verkehrssicherheit, Landschaftselement/Kulturgut, Schutzstatus und Schutzwürdigkeit, Umwelt, Natur und Ökologie mit vergleichbarer Intensität analysiert. Um die einheitliche Verwendung der Begriffe sicherzustellen, wurden die Begriffe „Allee“ und „Baumreihe“ im Kontext des bundesweiten Baumkatasters definiert. Früher analog geführte Baumkataster liegen heute digital vor und sind eine unabdingbare Hilfe bei der Baumkontrolle und -pflege. Neben Sicherstellung und Erhalt eines gesunden Zustands der Bäume ist der Verkehrssicherungspflicht und der Gefahrenabwehr Rechnung zu tragen. Bis auf Baden-Württemberg führen alle Flächenbundesländer ein digitales Baumkataster oder bauen ein solches auf. Daten zu Straßenbäumen der Stadtstaaten Berlin, Hamburg, Bremen werden von der Autobahn GmbH geführt. Basierend auf der Literaturrecherche und in Abstimmung mit den Stakeholdern (Bundesländer, Autobahn GmbH, Fernstraßen-Bundesamt, BASt) wurde ein logisches Datenmodell erstellt und die Lieferbarkeit der Attribute (kurz-, mittel-, oder langfristig) ermittelt. Die vier Aufgabenbereiche Verkehrssicherheit, Schutzstatus, Umwelt/Natur und Landschaftselement/Kulturgut wurden auf fachlich zusammengehörende Komponenten verteilt. Mit der Umsetzung in ein datenbankspezifisches Datenmodell wurde die ASB-Konformität sichergestellt. Neben der Schreibweise zur Modellgestaltung und Definition von Tabellen wurden Mechanismen zum Umgang mit Datenlücken und Inhomogenitäten berücksichtigt. Aus dem Datenmodell wurden SQL-Skripte für die Integration ins BISStra erstellt. Mit Testdaten aus den Bundesländern Rheinland-Pfalz, Nordrhein-Westfalen und Mecklenburg-Vorpommern wurde die Datenübernahme länderspezifischer Baumkatasterdaten in das bundesweit einheitliche Datenmodell geprüft. Inhomogene Daten und Datenlücken wurden identifiziert und mit den vorgesehenen Mechanismen erfolgreich bearbeitet. Alle Testdaten konnten in das Modell überführt werden. Die gegenwärtig verfügbaren Daten in der BISStra-Fachschale Baumkataster ermöglichen bereits die Durchführung zahlreicher Analysen und Monitoringaufgaben. Beispiele aus den Bereichen Umwelt/Natur sind die geographische Verteilung der Baumarten, inklusive Alter und Zustand sowie die Auswirkung von Trockenheit oder Streusalz auf die Baumgesundheit. Die vorgeschlagene Alleedefinition ermöglicht erstmals ein bundesweites Monitoring zum Bestand und zur Entwicklung der Alleen, inklusive der Identifikation von Lücken und der Ermittlung des Ausdünnungsgrad der Alleen. Mit den Baumstandorten, den Abständen der Bäume zueinander und zum Fahrbahnrand, sowie dem Stammdurchmesser stehen wichtige Eingangsgrößen für die netzweite Sicherheitsbewertung im Sinne der EU-Direktive 2019/1936 zur Verfügung. Die netzweite Sicherheitsbewertung für Bundesfernstraßen erfordert die Kenntnis aller ortsfesten nicht verformbaren Hindernisse im Straßenseitenraum, die nicht durch Fahrzeugrückhaltesysteme geschützt sind. Neben Bäumen sind dies Ingenieurbauwerke (Brücken, Tunnelportale) und Objekte der Straßeninfrastruktur (Signalmaste, Notrufsäulen, etc.). Alle genannten Hindernisse sind digital in unterschiedlichen Fachsystemen verfügbar und könnten über Schnittstellen in ein Gefahrenkataster überführt werden. Inhalte, Schnittstellen und Anwendungen für ein Gefahrenkataster werden in einer Ideenskizze beschrieben. Folgerungen aus den Untersuchungsergebnissen, Vorschläge für die Nutzung der Forschungsresultate in der Praxis sowie Ideen für neue Forschungsthemen, mit welchen die Daten das Baumkatasters in Wert gesetzt werden, finden sich in der Schlussbetrachtung. Mit Methoden der Künstlichen Intelligenz (KI) könnten im umfangreichen Datenbestand des Baumkatasters Muster und Charakteristiken für umwelt- und naturrelevante Fragestellungen identifiziert werden. KI könnte auch dazu dienen, die Baumkontrolle effizienter und wirtschaftlicher zu gestalten. Solche Forschungsprojekte könnten im europäischen Kontext lanciert werden. ABSTRACT IN ENGLISH: The report addresses the demand to implement and maintain a uniform national avenue and tree database. The development of the database is a cross-sectional project, improving efficiency and increasing knowledge through synthesis of information from several sources. A data model has been developed and will be integrated as a new specialist layer in the Bundesinformationssystem Straße (BISStra). The data basis has been defined by considering both environmental and traffic-related aspects, and was coordinated with data suppliers and interest groups. The database was considered from an overarching perspective, where elements include traffic safety, landscape elements, cultural heritage, protection status and worthiness, environment, nature and ecology. To ensure the uniform use of terms, the terms "avenue" and "tree row" were defined in the context of the nationwide tree register. The currently available data in the BISStra tree register module already enable numerous analyses and monitoring tasks. Examples from the environment/nature sector include the geographical distribution of tree species, including age and condition of individual trees, as well as the impact of drought or road salt on tree health. The proposed definition of “avenue” allows for nationwide monitoring of the status and development of avenues, including identifying gaps (i.e. stretches of missing trees) and determining the time evolution (e.g. thinning rate) of avenues. The location of trees, their trunk diameter, distances between adjacent trees as well as distance between trees and the road are all important input parameters for a network-wide safety assessment in accordance with EU Directive 2019/1936. A network-wide safety assessment for federal highways requires knowledge about all fixed, non-deformable obstacles along the roadside that are not protected by vehicle safety systems. In addition to trees, these include engineering structures (e.g. bridges, tunnel portals) and road infrastructure objects (road signage elements, emergency call pillars, edge barriers, etc.). Information about such obstacles are already digitally registered and available from specialized databases. This information could be integrated into a centralized hazard register through application programming interfaces (APIs). The data content, interfaces, and potential applications for such a hazard register are outlined in this report. Conclusions from the research results, suggestions for the practical use of the research findings, and ideas for new research topics that could utilize the tree register data are also found in the summary section. In particular, methods utilizing artificial intelligence (AI) could potentially identify patterns and characteristics in the extensive tree register data relevant to environmental and nature-related questions. AI could also make tree inspections more efficient and economical. Such research projects could be launched in a European wide context.]]></description>
      <pubDate>Mon, 27 Oct 2025 14:00:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612888</guid>
    </item>
    <item>
      <title>Climate Resilience for Low-Volume Roads</title>
      <link>https://trid.trb.org/View/2593744</link>
      <description><![CDATA[Low-volume roads—often unpaved—are increasingly affected by extreme weather events and shifting precipitation patterns, raising concerns about their resilience. Enhancing the climate resilience of these roads is vital for maintaining transportation networks and ensuring the well-being of communities that rely on them. Climate resilience refers to a community’s ability to anticipate, prepare for, respond to, and recover from adverse climate impacts. For low-volume roads, this means that local road agencies implement strategies that not only protect the infrastructure but also ensure continued accessibility for local populations. Improving resilience involves assessing vulnerabilities, implementing adaptive designs, and investing in sustainable materials and construction practices. Specific strategies discussed in this article include: upgrading drainage systems, using permeable pavements, enhancing road durability with recycled aggregates and geosynthetic materials, elevating or realigning roads, preventive maintenance, and planting vegetation along road edges.]]></description>
      <pubDate>Tue, 07 Oct 2025 13:15:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593744</guid>
    </item>
    <item>
      <title>Dynamics, Drivers and Mitigation of Submerged Aquatic Vegetation (SAV) and Shoreline Change: Data Needs Associated with the Mid-Currituck Bridge</title>
      <link>https://trid.trb.org/View/2604610</link>
      <description><![CDATA[Recent research (S.A.V.E. Currituck, 2020) has shown that in Currituck Sound, the primary factor limiting submerged aquatic vegetation (SAV) distribution is water clarity. SAV in the sound will likely be impacted by construction and shading post construction of the Mid-Currituck Bridge. In addition, studies have shown that shorelines near the Mid-Currituck Bridge terminus on the east and west side are experiencing significant erosion rates. Bridge design and ultimate construction may change local dynamics (i.e., wave field, water clarity) that can influence SAV distribution and shoreline position. This project has three primary objectives relevant to understanding water quality, SAV and shoreline mitigation strategies: (1) Quantify temporal and spatial changes in water quality, specifically CDOM abundance in surface waters of northern Currituck Sound; (2) evaluate shoreline change rates on multiple timescales, including influence of storms, near bridge landing; and (3) synthesize remote sensing and field data to provide information to limit impacts to water quality, SAV and shorelines during bridge construction and maintenance and to prioritize mitigation for maximum benefit. It is critical to have more information on the current local water quality, shoreline change and SAV dynamics to better predict and limit damage associated with bridge construction and to prioritize required mitigation activities for maximum benefit. This research will focus on expanding data collection near the bridge corridor and broadening understanding of water clarity/quality dynamics and its potential role in changing SAV distribution.]]></description>
      <pubDate>Tue, 30 Sep 2025 14:53:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604610</guid>
    </item>
    <item>
      <title>Using Unmanned Aerial Systems to Identify and Manage Protected Species Habitat</title>
      <link>https://trid.trb.org/View/2604538</link>
      <description><![CDATA[Unmanned aerial systems (UAS) or drones provide a unique but mostly unexploited opportunity to improve the Texas Department of Transportation (TxDOT)'s right-of-way management and its current practices for planning, designing, constructing, and maintaining habitat. The research team will develop guidance on the use of UAS to identify and manage protected species habitat and determine the efficacy of multi- and hyper-spectral imagery for identifying and mapping endangered plant populations and potential pollinator conservation areas.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:28:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604538</guid>
    </item>
    <item>
      <title>Soil Erosion Control on New Construction Projects: Bibliography</title>
      <link>https://trid.trb.org/View/2576937</link>
      <description><![CDATA[This bibliography contains 57 citations on the subject of soil erosion control on new construction projects.]]></description>
      <pubDate>Mon, 29 Sep 2025 11:14:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2576937</guid>
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