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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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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>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Traffic Control Device Analysis, Testing, and Evaluation Program</title>
      <link>https://rip.trb.org/View/2731978</link>
      <description><![CDATA[Traffic control devices (TCDs) are the primary means of communicating highway information to road users and play a key role in highway automation. The design, application, and maintenance of TCDs is under constant transformation as new technologies, methodologies, and policies are introduced. In addition, vehicle technologies and the roadway infrastructure industry are rapidly evolving, spurred by technology advancements, customer demand, changes in the vehicle fleet, and changes in national and state policies. The research team will provide Texas Department of Transportation (TxDOT) a mechanism to quickly and effectively conduct high priority evaluations of issues related to TCDs. The TCD issues to be evaluated in this project could represent new devices or technologies, new applications of an existing device or technology, TCD material performance, changes in TxDOT’s practices regarding a TCD, or other TCD related needs. Examples of various evaluations include human factors, machine vision performance, safety and operational effects, visibility assessments, and cost effectiveness analyses. The activities conducted through this project will support the development of TCD related policy, specifications, guidelines, handbooks, and training.]]></description>
      <pubDate>Fri, 17 Jul 2026 16:11:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731978</guid>
    </item>
    <item>
      <title>Large Language Model-Driven Crash Risk Analysis System for Rural and Tribal Roadways</title>
      <link>https://rip.trb.org/View/2731923</link>
      <description><![CDATA[Rural and tribal roadways in the United States experience disproportionately high crash incidents due to a combination of infrastructure challenges, limited resources, and incomplete crash reporting. Traditional statistical, machine learning (ML), and deep learning (DL) models have struggled to address these issues because they rely heavily on structured data, perform poorly with incomplete or imbalanced datasets, and often fail to leverage the rich information contained in crash narratives. Large language models (LLMs) offer an alternative by reframing crash risk analysis as a text reasoning problem, enabling the extraction of contextual insights from narratives, the imputation of missing or inconsistent fields, and the integration of structured and unstructured data into unified predictive frameworks. This proposal aims to develop an LLM-based crash risk analysis system utilizing North Carolina’s statewide police-reported crash data as a foundation, with the goal of enhancing the accuracy, interpretability, and robustness of rural crash risk assessment. The project will proceed through four main tasks: crash data enhancement and model adaptation, predictive model development, model validation, and the design of an implementation plan for real-time crash risk warnings in connected vehicle (CV) environments. ]]></description>
      <pubDate>Fri, 17 Jul 2026 16:08:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731923</guid>
    </item>
    <item>
      <title> Evaluate PVC Water Main Materials in Roadway Projects</title>
      <link>https://rip.trb.org/View/2731924</link>
      <description><![CDATA[Water main breaks within Michigan Department of Transportation (MDOT) R.O.W. pose significant risks to the Department and stakeholders, including complete road
closures, detours, as well as boil water advisories. MDOT is obligated to replace municipal water mains that are impacted by
Road and Bridge projects, typically at Project costs. The Department currently only specifies ductile iron water main (DIWM)
materials within the influence of its roadways. Rising costs of and supply issues with DIWM in recent years have caused
significant project delays. Municipalities are increasingly requesting the use of PVC water main materials within MDOT R.O.W.
to maintain material continuity of their facilities. Allowing use of alternative materials could reduce costs and/or delays to the
Department. MDOT needs data to address Municipal Engineers and Industry questions on the suitability of allowing PVC water
main on MDOT projects. Several factors must be evaluated in comparison to DIWM; the durability and expected design life,
historical leakage and breakage rates, cause of failures, the long-term safety of PVC water main materials on public health,
and life cycle costs. The research must provide data guidance and recommendations on the advantages and disadvantages of
PVC versus DIWM to allow consideration of a change to current policy.]]></description>
      <pubDate>Fri, 17 Jul 2026 14:29:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731924</guid>
    </item>
    <item>
      <title>Connected and Automated Vehicle (CAV) Readiness Survey: Are MDOT Roads Machine Readable</title>
      <link>https://rip.trb.org/View/2731918</link>
      <description><![CDATA[Considering Michigan Department of Transportation (MDOT) Mission, Values, and Vision, with the evolving landscape of technologies within the connected and automated
vehicle (CAV) industry, there is a pressing need to investigate the requisite support from DOTs to enable seamless integration of
CAVs with infrastructure. As core sensors and systems defining these technologies become more established, understanding the
precise infrastructure requirements becomes paramount. Therefore, the research aims to address the question: "What specific
support and infrastructure enhancements are necessary from MDOT to facilitate effective detection and interaction of connected and
automated vehicles with the surrounding infrastructure?]]></description>
      <pubDate>Fri, 17 Jul 2026 11:47:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731918</guid>
    </item>
    <item>
      <title>Artificial Intelligence for Pavement Condition Assessment from 2D/3D Surface Images</title>
      <link>https://rip.trb.org/View/2727389</link>
      <description><![CDATA[In phase I, the research team selected/annotated a library of two-dimensional/three-dimensional (2D/3D) pavement surface images in AASHTO standard and developed Artificial Intelligence (AI) Machine Learning (ML) models, using the established dataset. Phase I achieved a Technology Readiness Level (TRL) of 6, significantly below TRL 8 required for implementation. This gap was compounded by the lack of sufficient data for several pavement distress types and a new functional requirement requested by TxDOT to include distress segmentation to the scope of work. In phase II, the research team will prepare more pavement image data provided by TxDOT to achieve the needed diversity on all pavement distress types in the 2D/3D image data library. The research team will revisit the tasks of literature review and AI/ML model selection, revise and optimize the trained models to make improvements, and develop new models to more accurately detect/segment and quantify pavement distresses for TxDOT.]]></description>
      <pubDate>Fri, 10 Jul 2026 17:50:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727389</guid>
    </item>
    <item>
      <title>Leveraging Telematics Data for Enhanced Traffic Safety: Unveiling Crash-Prone Hotspots and Mitigating Incidents - Phase 2</title>
      <link>https://rip.trb.org/View/2727388</link>
      <description><![CDATA[Cutting-edge connected-vehicle (CV) telematics now stream billions of instantaneous speed, heading, and hard- maneuver records across Texas roadways—an untapped resource for proactive safety management. Phase I of Project 0-7200 capitalized on this opportunity by (1) surveying and vetting statewide CV data sources, (2) building rigorous preprocessing pipelines and a strategic data-archiving scheme with the Receiving Agency, (3) defining data-driven “near-crash” events, and (4) creating proof-of-concept analytics that locate and rank high-risk corridors. Two single-user prototype web tools—TTI’s near-crash explorer and UTA’s multi-criteria hotspot-ranking dashboard—proved the approach valid, with results aligning closely with the Crash Records Information System (CRIS). Phase II will transform those prototypes into a secure, cloud-based, multi-user platform capable of statewide, high-volume ingestion and real-time analytics—advancing the solution to TRL 8 (actual system completed and “TxDOT-pilot ready”). The research teams will, optimize the data-processing engine for scalability, integrate interactive visualizations with enterprise authentication, automate continuous data refresh and long-term archiving, and embed crash- prediction models that fuse telematics with CRIS and roadway inventory. The research teams will develop a decision-support tool that lets TxDOT’s districts quickly pinpoint emerging crash-prone hotspots and deploy targeted countermeasures.]]></description>
      <pubDate>Fri, 10 Jul 2026 17:07:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727388</guid>
    </item>
    <item>
      <title>Linking Landslide Triggering and Runout Hazard with Surface Deformations for Optimized Infrastructure Systems Resiliency</title>
      <link>https://rip.trb.org/View/2726550</link>
      <description><![CDATA[Landslides are one of the most significant geohazards impacting North Carolina's transportation network, causing fatalities, property loss, and long-term economic disruption. These events are frequently triggered by extreme precipitation from hurricanes and tropical storms, which have historically produced hundreds to thousands of debris during a single event. For example, Hurricane Helene (2024) triggered more than 2,000 reported landslides across the Southern Appalachians, resulting in widespread road closures, bridge damage, and tens of billions of dollars in direct and indirect losses. As the frequency and intensity of extreme precipitation events increase, the risk of cascading infrastructure failures is expected to grow. Current North Carolina Department of Transportation (NCDOT) Geotechnical Asset Management (GAM) tools primarily operate reactively— tracking known unstable sites and coordinating post-disaster repairs. Therefore, there is a critical need for proactive capabilities to anticipate landslide hazards before they disrupt the network.

The objective of this project is to create a robust, scalable, and computationally efficient framework to predict landslide triggering and runout at a regional scale, supporting optimized maintenance, emergency response, and risk-informed investment decisions. This work will integrate the North Carolina Geological Survey (NCGS) Post-Helene Landslide Inventory, surface deformation mapping, and AI enhanced triggering predictions. The research will pursue four main objectives: (1) consolidate and curate a high-quality georeferenced dataset of landslide and debris flow events in North Carolina; (2) develop machine-learning models informed by physics to predict triggering susceptibility based on rainfall thresholds, slope geometry, and hydrologic conditions; (3) link surface deformation signals to slope stability through finite-element-based surrogate models; and (4) compute landslide runout using depth-averaged Material Point Method (DA-MPM) simulations that account for three-dimensional topographic effects and infrastructure exposure.

The approach follows a hierarchical and computationally efficient workflow. Regional-scale data-driven models will rapidly screen the entire state for slopes with high triggering potential. For these critical sites, limit equilibrium analysis (LEA) using existing NCGS models will identify likely failure surfaces and factors of safety. The outputs will serve as inputs to physics-based DA-MPM simulations that predict debris flow runout, impact zones, and potential consequences for NCDOT-managed assets. This strategy maximizes coverage while focusing on high-fidelity simulations where they are most needed, thereby balancing predictive power with computational cost.

The anticipated products include trained machine-learning models, enhanced infinite-slope analysis incorporating AI training, a verified and validated DA-MPM module, and geographic information system (GIS)-integrated hazard/risk maps. Integration into NCDOT's existing GAM system will enable decision-makers to: (i) develop watchlists of critical slopes, (ii) anticipate maintenance and debris removal needs, (iii) coordinate detour planning and emergency response, and (iv) communicate risk more transparently to stakeholders. Training workshops will be held with NCDOT and NCGS engineers and geologists to ensure usability and gather feedback for future system enhancements.

This project represents the first step toward a real-time, data- and physics-informed landslide early warning and infrastructure risk management system. By combining machine learning, geotechnical modeling, and large-deformation simulation, this work will strengthen North Carolina's landslide risk assessment and improve transportation resiliency, reduce lifecycle maintenance costs, and protect the safety and mobility of the traveling public.]]></description>
      <pubDate>Thu, 09 Jul 2026 09:02:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726550</guid>
    </item>
    <item>
      <title>Load Capacity of Temporary Railcar Bridges in Western North Carolina</title>
      <link>https://rip.trb.org/View/2726549</link>
      <description><![CDATA[As a result of Hurricane Helene in late 2024, the spanning members of nearly three dozen temporary bridges in Western North Carolina (WNC) are (or were recently) comprised fully or partially of repurposed flatbed railroad cars.  Many of these bridges will need to stay in operation for long durations before permanent replacements can be completed, necessitating a detailed study of these temporary structures, especially from the perspective of load rating.  The bulk of published literature on railroad flatcar (RRFC) bridges is generally focused on permanent structures with composite concrete decks, which the temporary structures in WNC do not have.  Additionally, prior work on RRFC bridges with non-composite steel or timber decks and/or non-composite asphalt wearing surfaces is generally focused on RRFCs of a different geometry and/or span than the RRFCs currently in use in WNC.  As such, proposed study of the WNC RRFCs is justified by a goal to develop load rating methods and tools for the current temporary applications (or similar applications in the future).  In addition, a secondary goal involves determining the rated load capacities of existing RRFCs in a variety of potential future use scenarios, as the Department plans to store RRFCs for future use once existing temporary structures are removed from service. A chart of load ratings for different types of flatcars in different conditions supported on different spans would be useful for future deployments.

The proposed work will accomplish the goals by first measuring the geometry and documenting the condition of a wide range of existing RRFC bridges in WNC.  An estimated 10-15 bridges will be measured using traditional methods.  These measurements are necessary because railroad flatcar designs are not standardized – many manufacturers have existed over the years that have produced many specific flatcar designs for different railroad specifications.  Thus, while the RRFCs in use in WNC appear to be of one general type (Type FM, F-class, 90’ length), at least two variations are currently in temporary bridge service in the state.

With the likely range of geometry and structural condition of the WNC railcar fleet documented, finite element models will be developed in Abaqus to reflect idealized versions of this geometry for all significant flatcar variants in service.  Rolling loads will be applied numerically to the models, enabling a detailed study of RRFC bridge performance under a variety of conditions. Parameters can then be varied in the models to study the effects of material properties, span lengths, damage levels, adjacent connected flatcars, and other relevant factors.

Critical to developing a finite element model (FEM) will be validating that model with experimental data prior to running the parametric analyses.  Data from limited field testing will be available from recently completed preliminary work, but full-scale experimental tests are proposed as part of this research.  Two full-scale railroad flatcars are proposed to be tested to failure in the structures laboratory at NC State University or at a North Carolina Department of Transportation storage yard.  Data will be collected during each test to include loads, deflections, and strains, enabling validation and benchmarking of the FEM.  Together, the experimental and analytical results will be used to create outputs useful for load rating temporary bridges, including proposed load rating methods and simplified design tools, such as capacity charts, specific to the types of cars in WNC and a variety of possible RRFC bridge configurations. 
]]></description>
      <pubDate>Thu, 09 Jul 2026 08:53:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726549</guid>
    </item>
    <item>
      <title>Empirical Modeling for Improved Ground Failure Analysis</title>
      <link>https://rip.trb.org/View/2726232</link>
      <description><![CDATA[Problem Statement: Numerous bridge approaches and substructures, highway and railway embankments, and particularly roads in low-lying areas adjacent to rivers and their corresponding traffic sign and signal poles are underlain by the silt soils of the Willamette and Columbia River Valleys and below Oregon's coastal communities. These soils are susceptible to liquefaction or cyclic softening during earthquakes and will produce varying degrees of severity in the consequences such as lateral spreading displacement, global instability, and settlement. Settlement of soils will produce drag loads to bridge and traffic sign and signal pole foundations. Such damage has the potential to severely impact our critical surface transportation lifelines and reduce the efficacy of emergency responders and reduce the rate of economic recovery. The risk of seismic ground failure is exacerbated by groundwater table rise, which occurs during short-term, acute events (flooding) and the long-term effects of potential rising sea levels. Application of ground failure models to silty soils that were developed based on the responses of sandy soils can result in over-conservative estimates of the effects seismic ground failure and lead to inefficient use of limited resources as Oregon strives to maintain and improve its current resilience.
This work aims to develop the types of empirical relationships that the geotechnical community are well-familiar with but geared towards transitional silty soils, which can exhibit differing behaviors from the soils which are presently represented in available models. The objectives of this research are to produce specific design guidance, models, and spreadsheet-based tools to: (1) account for the effects of sloping ground on the calculation of the factor of safety against liquefaction/cyclic softening during earthquakes, (2) compute lateral displacements of sloping ground, and (3) calculate vertical settlements of level and sloping ground and any foundations buried within, to (4) culminate in a decision matrix for Oregon Department of Transportation (ODOT) engineers and their consultants to guide the selection of a particular model when assessing the seismic vulnerabilities of existing surface transportation infrastructure. The decision matrix and specific guidelines for conducting cyclic failure analyses and simplified displacement estimates will guide cost-effective measures to assess and improve existing surface transportation infrastructure and improve community and infrastructure resilience to increasingly combined natural hazards.
]]></description>
      <pubDate>Wed, 08 Jul 2026 17:38:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726232</guid>
    </item>
    <item>
      <title>Enhancing Commercial Motor Vehicle Safety and Compliance: Evaluating The Aries Pilot and Illegal Bypass Behavior in Oregon</title>
      <link>https://rip.trb.org/View/2726122</link>
      <description><![CDATA[Illegal bypass of weigh stations and roadside inspection facilities poses a measurable safety and compliance risk within Oregon’s commercial motor vehicle (CMV) system. When vehicles evade inspection, potential violations such as overweight operations, equipment deficiencies, and hours-of-service noncompliance may go undetected, increasing crash exposure and infrastructure damage risk. Oregon Department of Transportation's (ODOT’s) Commerce and Compliance Division (CCD) currently lacks a standardized, integrated methodology to quantify illegal bypass behavior or link bypass events to inspection outcomes, crash involvement, and carrier safety history.
OBJECTIVES: This research will deliver to ODOT: (1) A standardized and replicable data integration framework linking ARIES pilot data with CCD inspection, violation, crash, and carrier safety records. (2) Measurable and trackable performance indicators to support ongoing internal monitoring of illegal bypass activity and automated enforcement effectiveness. (3) Quantitative analysis of the magnitude, characteristics, and safety implications of illegal bypass behavior in Oregon. (4) Evaluation of ARIES pilot impacts on compliance rates, inspection targeting efficiency, enforcement productivity, and CMV safety outcomes. (5) Implementation guidance and best-practice recommendations to inform strategic investment decisions, future site deployments, and FMCSA Innovative Technology Deployment (ITD) funding applications.
This research will strengthen ODOT’s ability to detect and deter illegal bypass behavior, directly advancing Oregon’s transportation safety goals. By integrating ARIES data with inspection and crash records, CCD will be able to target high-risk vehicles more effectively, reduce unnecessary inspections of compliant carriers, and improve enforcement productivity. The project supports ODOT priorities related to Safety, Innovative Technologies, Process Improvement, and Stewardship of Public Resources by providing measurable evidence to guide enforcement modernization.]]></description>
      <pubDate>Wed, 08 Jul 2026 17:24:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726122</guid>
    </item>
    <item>
      <title>Systemic Safety Analysis and Assessment of Bicycle and Pedestrian Crash Risk: Developing Risk Factors using the Multimodal Inventory Project Data</title>
      <link>https://rip.trb.org/View/2725665</link>
      <description><![CDATA[Inconsistent and incomplete data on multimodal infrastructure and operations limits 
Oregon Department of Transportation's (ODOT’s) ability to develop data-driven risk factors. Accurate and up-to-date bicycle and pedestrian risk factors are necessary inputs for ODOT programs aiming to proactively address active transportation safety, as they can help identify locations with geometric and operational characteristics that lead to increased crash risk for active transportation users. The Multimodal Inventory Project offers new data and a unique opportunity to develop more rigorous, data-driven bicycle and pedestrian risk factors. Leveraging these new data and methodologies, in addition to crash data and exposure data, will enable analysis that can identify roadway and operational characteristics most strongly associated with bicycle and pedestrian crash risk.
OBJECTIVES: This research will provide ODOT with up-to-date, high-quality bicycle and pedestrian risk factors to be used for proactive safety analysis. The anticipated outcome of this research is to develop these new bicycle and pedestrian risk factors by leveraging new multimodal data from the Multimodal Inventory Project and by applying more rigorous risk factor methodologies. The latter will be accomplished by developing a Risk Factor Tool that relies on data inputs and analysis results to provide site-specific bicycle and pedestrian risk assessments. Objectives of this research include: (1) A comprehensive review of studies that develop bicycle and pedestrian risk factors and/or apply them, methods used to derive bicycle and pedestrian risk factors, and current policies and practices implemented through Active Transportation Safety Plans and Vulnerable Road User Safety Assessments; (2) A data collection and fusion process that combines existing and new Multimodal Inventory Project data; and (3) Development of bicycle and pedestrian risk factors using an integrated approach that leverages descriptive statistics and safety modeling techniques, resulting in a Risk Factor Tool to conduct site-specific risk assessments.
This research will provide ODOT with up-to-date data and risk factors to improve bicycle and pedestrian safety, addressing Transportation Plan Safety Objectives, Social Equity Objectives, and Mobility Objectives.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:52:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725665</guid>
    </item>
    <item>
      <title>IMG2Speed: Generative AI and Multimodal Machine Learning for Predicting Operating Speed Distributions from Roadway Design and Context</title>
      <link>https://rip.trb.org/View/2725360</link>
      <description><![CDATA[Designers set target speeds to achieve safe operations, yet observed operating speeds often diverge because the influence of geometric and contextual elements (e.g., lane width, medians, trees, curb extensions, etc.) is not quantified in a way that is practical for design. A modern data-driven machine learning approach can be a potential solution to learn the quantitative mapping from observable design elements to operating speed distributions. This project proposes to (i) automate data curation from spot-speed reports using Generative Artificial Intelligence (AI) like Large/Vision Language Models (LLMs/VLMs); (ii) fuse the curated evidence base with street-view imagery and Geographic Information System (GIS)/context layers to extract geometric and streetscape attributes; and (iii) develop a machine learning (ML) model that estimate the percentiles of operating speeds used in practice (e.g, median, 85th) from cross-section and visual/context features.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:24:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725360</guid>
    </item>
    <item>
      <title>Phase III Wickiup Junction: Diatomaceous Soil Numerical Modeling to Support Design, Performance, and Feasibility</title>
      <link>https://rip.trb.org/View/2724820</link>
      <description><![CDATA[Diatomaceous soils exist at many Oregon Department of Transportation (ODOT) projects in Oregon, including the Wickiup Junction overpass site. Construction challenges have been encountered for ODOT projects on and in diatomaceous soils, including pile freeze, overlength piles, and excessive settlement. Ongoing Wickiup Junction embankment monitoring indicates that these embankments are undergoing continuous settlement at about 1.75 inches per year. Recently, a consultant’s feasibility study estimated that settlement mitigation for future overpass construction will cost $47M to $63M. This high mitigation cost is attributable to extensive deposits of soft and compressible diatomaceous soils that underlay the site. Considering that diatomaceous soils are non-standard geomaterials, limited literature, standards, or case histories exist to guide design and construction in these materials. However, this Wickiup Junction location may provide a prime translational research opportunity to improve engineering practice through development of a case history report with associated design charts for diatomaceous soils.

This highly applied research proposal will investigate the recently released design options at Wickiup Junction using advanced soil numerical modeling as a case study for design in diatomaceous material. This work will build on previous ODOT diatomaceous soil research to develop design tools that can be applied for construction in and on these deposits. Specific objectives include: (1) develop settlement model of the Wickiup Junction Overpass, and (2) develop design charts for diatomaceous soils.]]></description>
      <pubDate>Wed, 08 Jul 2026 13:53:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2724820</guid>
    </item>
    <item>
      <title>Updating Streamflow Statistics for Central and Eastern Oregon to Reduce Flooding Risk</title>
      <link>https://rip.trb.org/View/2724818</link>
      <description><![CDATA[Regional flood frequency equations are needed to plan, maintain, and protect critical infrastructure against flood risks across Oregon. When designing and maintaining hydraulic infrastructure in central and eastern Oregon, Oregon Department of Transportation
(ODOT) professionals face persistent challenges of sparse streamflow data, highly variable precipitation, diverse geologic and topographic features, and irregularities due to large water withdrawals for agriculture. While reliable streamflow statistics can be obtained for western Oregon locations using the ODOT funded U.S. Geological Survey (USGS) StreamStats tool, the current accuracy of the underlying regression equations for locations in central and eastern Oregon are much less reliable, and in some cases not available. Further, though the StreamStats tool may be helpful for some central and eastern Oregon locations, these regression equations—now more than 20 years old—may not accurately reflect present-day conditions, particularly where basins have experienced significant shifts in long-term precipitation and temperature patterns, land use, or water withdrawals. Accurate streamflow statistics are essential for sizing bridges, culverts, and roadside drainage, ensuring infrastructure longevity through variable flow conditions and extreme weather events.
The objective of this research is to update Oregon streamflow statistics and the heavily used StreamStats tool so that this tool can be relied upon for ODOT hydraulic design in central and eastern Oregon. This update process will employ new machine-learning and refined statistical approaches, together with more expansive data from states that share central and eastern Oregon’s hydraulic and hydrologic characteristics. Specifically, this research aims to: (1) enhance design accuracy, (2) support infrastructure longevity under future conditions, (3) optimize resource allocation, (4) improve planning and reduce maintenance, and (5) facilitate regulatory compliance and environmental stewardship with effective fish passage design and habitat protection.]]></description>
      <pubDate>Wed, 08 Jul 2026 12:19:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2724818</guid>
    </item>
    <item>
      <title>Evaluating Mowing Practices for Pollinator Habitat Enhancement: Highway Vegetation Management and Its Impact on Endangered Polinator</title>
      <link>https://rip.trb.org/View/2724815</link>
      <description><![CDATA[ORS 634.045 requires several state agencies, including Oregon Department of Transportation (ODOT), to maintain and revise a bee pollinator safety plan to educate the public and increase pollinator habitat. It is not clear to ODOT how to document habitat on their properties, if current mowing practices enhance or reduce pollinator habitat, and how to prioritize areas for compliance of conservation practices. The US DOT has guidelines for vegetation management for right-of-ways (ROWs), but there have been contrasting results for key practices, such as mowing and efforts at establishing pollinator-attractive plants have not resulted in long lasting habitat and may result in costly landscaping. Finally, there are concerns that the habitats near roadway locations with vehicles and environmental pollutants may result low mortality rates to pollinators. Consequently, it is unclear how ODOT routine mowing practices, which covers an estimated 20,000 acres annually, enhances the vegetation that is important to threatened or endangered pollinators. This research seeks to identify mowing practices that encourage these plants and to help meet protection targets without increased costs.
The research will study pollinator activity in three geographic regions and develop “high benefit” pollinator vegetation management practices with a neutral (or lessened) cost to ODOT. The research will be conducted over three seasons and have three parts: (1) In-field vegetation documentation using new app and method analysis; (2) Cost-Benefit Analysis; and (3) Feasibility analysis. 
The in-field tasks include selecting 27 site locations on ODOT secondary or tertiary roads, with appropriate ROWs, across three ODOT regions (i.e., 9 per region).  The study will have three levels of mowing intensity: high (at least once per year), medium (once every other year) and low (less than once every two years). Each location will be monitored for plant diversity and density and bee pollinator activity during the appropriate seasons – with particular measurement directly before and after mowing. The research will determine if mowing intensity and date of mowing influences the density of plants of highest value to pollinators by: (1) relating plants found to Melittflora records filtered for the region; and (2) calculating the richness of bee species found at each site. The research will also use historic estimates of average seasonal traffic volume at each site as a covariate to investigate the impact of traffic on the diversity of the bee community for a given plant community. Site characteristics will also be documented (e.g. distance from the roadway). The cost-benefit analysis will focus on detailing normal vs pollinator staff/resource costs, timing for mowing, and comparing to outcomes of high pollinator activity. The feasibility analysis will compare normal maintenance resource availability and use compared to recommended optimal mowing for pollinator benefits.

Finally, the project would result in a regional pocket guide of the most important plants for pollinators that vegetation management crews will encounter, as well as new continuing education trainings for vegetation management staff. This information could be used in future construction (or Maintenance) locations to identify plant species that are both of high value to pollinators and known to persist under ROW conditions that could help inform how to modify seed blends following road construction.]]></description>
      <pubDate>Wed, 08 Jul 2026 11:39:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2724815</guid>
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