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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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    <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>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>Ensemble Radar Nowcasts for Probabilistic Road Disruption Prediction</title>
      <link>https://rip.trb.org/View/2706035</link>
      <description><![CDATA[Heavy precipitation and flash flooding can rapidly degrade roadway operating conditions, causing speed reductions, lane closures, detours, and secondary crashes. Current traffic management systems largely confirm disruptions after they have already developed, limiting the ability of transportation operators to act proactively. Deterministic weather products also provide limited information about forecast uncertainty, which is critical for risk-based operational decision-making.
This project develops a probabilistic road disruption nowcasting system that integrates ensemble radar precipitation forecasts with traffic observations and roadway attributes to produce segment-level disruption probabilities at lead times of 30 to 180 minutes. Using a multi-member ensemble framework applied to real-time radar precipitation data, the system will generate exceedance probabilities and persistence metrics that quantify near-term hazard likelihood. These probabilistic precipitation indicators will be fused with traffic state variables and roadway characteristics to estimate the likelihood of operational disruption. The result is a calibrated, segment-level decision-support tool that provides actionable lead time and quantified uncertainty to support safer and more reliable corridor operations.

]]></description>
      <pubDate>Sat, 23 May 2026 18:00:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706035</guid>
    </item>
    <item>
      <title>Advanced InSAR–UAV-LiDAR Flood-Deformation Risk Monitoring for Efficient Mobility</title>
      <link>https://rip.trb.org/View/2669656</link>
      <description><![CDATA[El Paso’s critical transportation corridors face compounding risks from ground deformation and flash flooding that can severely disrupt efficient mobility, impede traffic flow, and challenge infrastructure reliability. Such infrastructure disruptions compromise public safety by delaying emergency response access and increase collision risk on compromised roadways. Despite advances in satellite monitoring and hydrologic modeling, no integrated system currently provides transportation agencies with rapid and actionable, near-real-time alerts for combined flood-deformation hazards. This project is designed to support uninterrupted mobility directly by developing and demonstrating a unified monitoring framework that fuses millimeter-precision Interferometric Synthetic Aperture Radar (InSAR) deformation maps with Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR) terrain models and Synthetic Aperture Radar (SAR)-derived soil-moisture indices to deliver actionable risk assessments. The research addresses a core challenge in maintaining efficient mobility: predicting when and where infrastructure vulnerabilities will coincide with flood conditions. Using validated Persistent Scatterer (PS) and Small Baseline Subset (SBAS) InSAR processing chains, high-resolution UAV-LiDAR surveys, and machine learning algorithms trained on historical events, the proposed system will provide transportation agencies with advanced warning, which enables proactive response and traffic management. The project will produce a composite flood-deformation risk index with demonstrated 90% accuracy in hazard detection. An edge-computing prototype will be deployed in partnership with the Texas Department of Transportation (TxDOT) to operationalize the fusion algorithms, enabling 24-hour processing turnaround and secure web-based risk visualization. Through formal partnerships with TxDOT and El Paso Water, the system will integrate real-time flow gauge data and infrastructure databases to enhance model calibration and validation. The project includes comprehensive technology transfer components, such as Docker-containerized software, training workshops for state Department of Transportation (DOT) engineers, and a commercialization brief outlining licensing pathways for rapid deployment across additional corridors.  ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:40:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669656</guid>
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    <item>
      <title>From perception to preparedness: Virtual reality simulations of flooded roadways in coastal communities (UPRM)</title>
      <link>https://rip.trb.org/View/2663232</link>
      <description><![CDATA[Project Description: Coastal flooding regularly disrupts transportation networks, damages infrastructure, and limits access to essential services through storm surge, tidal inundation, and extreme precipitation. These events result in vehicle failures, stranded motorists, pavement damage, and delays in emergency response and daily mobility. Communities with aging infrastructure, limited resources, or constrained evacuation options face heightened vulnerability. The total annual economic burden of flooding in the U.S. ranges from $179.8 to $496.0 billion (US Congress JEC, 2024). In addition, the National Weather Service and the Centers for Disease Control and Prevention report that over half of all flood-related drownings occur when a vehicle is driven into hazardous floodwater. Understanding how drivers decide whether to cross or avoid flooded roads is essential for designing warnings, signage, and roadway treatments that reduce risky behavior and improve outcomes. The use of virtual reality (VR) and immersive 360° scenarios can let residents experience rising water, blocked routes, and mitigation measures without real-world risk, increasing realism and emotional stimulus. Scenario-based VR visualizations can help translate technical flood data into intuitive, actionable information for nontechnical audiences. Local resilience depends not only on infrastructure but also on household-level preparedness and decision-making, including how individuals interpret alerts and respond to flood risks. Chacon-Hurtado (2013) advocates for embedding community preferences and preparedness considerations directly into transportation decision-making frameworks, arguing that investments should be evaluated not only on engineering metrics but also on how they advance local capacity to act under hazard conditions. 
This project will employ virtual reality (VR) simulations of flooded highways that are being developed by the University of Puerto Rico at Mayagüez (UPRM) team to study human behavior and perception in flood scenarios, with three main goals: (1) Enhance public understanding of flood risks by immersing participants in realistic coastal flooding scenarios, (2) Evaluate driver decision-making when encountering flooded roadways, analyzing how variables such as water depth, roadway conditions, and alert systems (e.g., signage, ADAS, in-vehicle alerts) influence choices, and 
(3) Assess community preferences for flood mitigation strategies, using immersive experiences to gather feedback on potential interventions. 
Two VR approaches will be implemented. The first involves a driver simulator with 24–36 participants navigating flooded roadway scenarios to assess behavioral responses under controlled conditions. The second approach will engage community members from coastal municipalities like Isabela, Puerto Rico, in immersive 360° simulations to explore perceptions of flood risk and mitigation strategies. Pre- and post-tests will measure changes in knowledge, perception, and behavioral intent. Insights from both simulations will inform the design of more effective alert systems and flood mitigation strategies that reflect community preferences and improve safety. The findings will support transportation and emergency planning professionals in developing human-centered solutions for flood-prone coastal areas.

]]></description>
      <pubDate>Sat, 31 Jan 2026 12:03:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663232</guid>
    </item>
    <item>
      <title>Vulnerability assessment and durability of coastal freight networks (UPRM)</title>
      <link>https://rip.trb.org/View/2663230</link>
      <description><![CDATA[Project Description: Freight networks, including ports, coastal highways, bridges, and distribution hubs, are critical lifelines that sustain regional economies, enable everyday commerce, and support emergency response after catastrophic events. The coastal location of this essential transportation infrastructure makes these assets uniquely vulnerable to extreme natural events such as flooding, storm surge, coastal erosion, and compound hazards. The Puerto Rico’s 2050 Long Range Transportation Plan explicitly calls for reducing transportation vulnerabilities to extreme weather effects and improving connectivity. Puerto Rico could serve as a critical logistics hub for U.S. freight operations in the Caribbean, offering strategic access to regional markets and maritime routes. But recent storms Hurricane María (2017) and Hurricane Fiona (2021) have highlighted the freight network’s fragility and the urgent need for targeted resilience measures. 
The assessment of Puerto Rico’s freight network, one that relies solely on the performance of the highway system, can be a case study to evaluate the system vulnerabilities derived from natural flood hazards, aging infrastructure, urbanization in coastal areas, and congestion in strategic corridors. A rigorous vulnerability assessment combines data from hydrologic and coastal flood modeling with traffic flows, asset condition inventories, and safety records to identify critical and single-point-of-failure links. This integrated analysis can provide a method to reveal which corridors and nodes are most likely to fail under different flood scenarios, how congestion and limited redundancy amplify delays, and which assets require immediate reinforcement or operational changes. It can also uncover system-level interdependencies among ports, road networks, and distribution hubs that are not visible from isolated asset inspections. This project can assist local transportation agencies, freight operators, and decision-makers in identifying risks to the freight network, improving the assessment of infrastructure assets by including the interdependence between ports, road networks, and distribution hubs, and prioritize improvements in strategic planning and project development. This project is envisioned as a two-year program. Year 1 will define Puerto Rico’s primary freight network anchored at the ports of San Juan and Ponce, map major distribution points, and develop an interactive dashboard showing asset condition, corridor flows, crash hotspots, and flood-vulnerable links and nodes. Four analytical dimensions will be assessed: infrastructure condition, traffic flows, safety, and durability, using official data, operational reports, and geospatial analysis to identify hotspots and critical vulnerabilities. Year 2 will focus on network optimization and investment prioritization, applying stochastic and optimization models to produce a prioritized, implementable resilience strategy. A Texas State University team will collaborate in the review of stochastic and optimization approaches, the evaluation of data requirements and computational complexity, and provide recommendations about the best model(s) for optimizing freight flows and prioritizing investments from ports to distributors.

]]></description>
      <pubDate>Sat, 31 Jan 2026 11:32:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663230</guid>
    </item>
    <item>
      <title>COLLABORATIVE: Quantifying erosion and load transfer mechanisms of geosynthetic reinforced coastal pavement subgrades and embankments during inundation events (TAMU/TXST)</title>
      <link>https://rip.trb.org/View/2663227</link>
      <description><![CDATA[Project Description: Transportation infrastructure in coastal regions is highly susceptible to soil erosion and subgrade degradation under frequent inundation events caused by storm surges. Fines within the subgrade are washed out due to flood-induced subsurface flow, while overflowing water along embankments results in overtopping and eventually leads to surficial erosion and complete collapse. These processes result in embankment and pavement failures; addressing these issues requires novel and innovative infrastructure durability solutions. One approach that combines hydraulic protection of subsoils with reduced soil erosion and provides drainage to recede floodwaters from infrastructure is geosynthetics. Geosynthetics, like geocomposites and turf-reinforced mats (TRMs), are often used to control erosion in slopes and levees from overtopping and rainfall. Also, the use of geosynthetics is increasingly growing for pavement reinforcement applications. These well-established benefits of geosynthetics can be combined and effectively applied for coastal transportation infrastructure that often sees failures following inundation events. Hence, this research study focuses on evaluating geosynthetics to solve both embankment erosion and maintain drainable and resilient subgrade foundations to support coastal transportation infrastructure. 
Geosynthetic Reinforcement of Coastal Embankment Slopes: TRMs and geocomposites will be studied for this application. Texas State University (TXST) will measure the erosion characteristics of the test materials using the erosion function apparatus (EFA). The EFA will quantify the erosion rates of the soil with and without the protection of these geosynthetic layers under varying hydraulic stresses, providing insights into soil erodibility and material performance. Texas A&M (TAMU) will conduct small-scale flume erosion studies on model embankment slopes using a coastal, sandy soil. Flume studies on embankment slopes built with and without geosynthetic reinforcements will be subjected to overtopping and inundation flow conditions for various time periods. Erosion patterns will be studied via laser and digital image scans. These data will also assess the role of geocomposites and TRMs on mitigating soil erosion and enhancing slope stability.  
Geosynthetic Reinforcement of Coastal Pavement Subgrade Foundations: TAMU flume study results will yield erosion patterns, more specifically void patterns, that will be used to create an  “eroded” pavement structure. These artificial voids will be created inside a large box setup, with 12 to 18 in. of subgrade supporting a flexbase aggregate base layer. These box samples will be instrumented with moisture probes, pressure cells, and MEMS deformation sensors. Each model pavement will be subjected to cyclic plate load tests to study and evaluate the load-bearing capacity and load transfer mechanism from repeated loads to the underlying subgrades. The same tests will be performed on the samples after they are inundated. The role of geocomposites both before and after exposure to moisture inundation, as well as load transfer mechanisms on subgrades with erosion-simulated voids, will be evaluated.
This is a collaborative project between Texas A&M University (TAMU) and Texas State University (TXST). Flume and large-scale box studies will be performed at TAMU Galveston campus and Center for Infrastructure Research (CIR) laboratories, respectively. TXST will perform the EFA with geosynthetic layers experiments. EFA studies focus on evaluating the critical shear stresses (i.e., hydraulic shear stresses at which soil erosion initiates) of the reinforced/unreinforced subsoils. Changes in critical shear stress at discontinuities such as gravel/sand interfaces will be of particular interest.  These combined results will generate a comprehensive understanding of the potential improvements of embankment and foundation reinforcement using advanced geosynthetic materials in providing resilient support to transportation infrastructure in coastal corridors. The results of this project will be used to design Phase II with coastal railroad track embankments.
]]></description>
      <pubDate>Sat, 31 Jan 2026 11:12:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663227</guid>
    </item>
    <item>
      <title>Assessing Transportation Infrastructure Exposure to Flooding Using Next-Generation Flood Maps in Eastern Oklahoma </title>
      <link>https://rip.trb.org/View/2646941</link>
      <description><![CDATA[Federal Emergency Management Agency (FEMA) flood maps are widely used as a primary reference for the planning, design, and risk assessment of transportation infrastructure, particularly for evaluating flood exposure to roads, bridges, and overall network performance during extreme weather events. While FEMA maps are routinely used by stakeholders, they have come under increasing scrutiny due to a key limitation: FEMA’s 100-year flood maps assume that the “100-year flood” is produced by a single design storm, commonly referred to as the “100-year storm.” As a result, these maps are deterministic, indicating only whether an area is flooded or not under the 100-year storm event. This approach fails to represent the full range of meteorological and hydrologic variability. To address this limitation, FEMA, in collaboration with U.S. Army Corps of Engineers (USACE), National Oceanic and Atmospheric Administration (NOAA), and U.S. Geological Survey (USGS), launched the Future of Flood Risk Data Initiative (FFRDI) project. This initiative represents a paradigm shift: moving from deterministic to probabilistic flood hazard maps. Yet, there remains a critical question: how will these next-generation probabilistic flood maps impact transportation infrastructure risk analyses compared to the traditional, deterministic FEMA products? Addressing this question is urgent. Understanding how the new probabilistic maps alter flood exposure assessments is essential for transportation agencies to update resilience strategies, design standards, and emergency management plans. Without proactive evaluation, agencies risk facing misalignments between outdated flood data assumptions and modern hazard realities. This project aims to lead the first assessment of transportation infrastructure flood hazard exposure using probabilistic flood maps by using the FEMA’s FFRDI framework. The study will focus on the Illinois River watershed in eastern Oklahoma, covering approximately 800 km² from the urban center of Tahlequah to the Arkansas state line. This area includes critical transportation corridors such as State Highways 10 and 82, U.S. Highway 59 and 412. The domain was strategically selected based on the availability of pre-calibrated and validated hydrologic and hydraulic models provided by the USACE Tulsa District, ensuring realistic implementation within the project timeline. 
Using the Illinois River Basin in eastern Oklahoma as a case study, this project has three primary objectives: (1) generate high-resolution probabilistic flood hazard maps following the FFRDI methodology; (2) assess transportation infrastructure flood exposure by intersecting these maps with road and bridge datasets; and (3) quantify differences between traditional and probabilistic flood maps, with a focus on transportation-related impacts. The project will be carried out through five main tasks: Task 1 involves generating synthetic storm events using stochastic storm transposition methods. Task 2 includes hydrologic and hydraulic simulations using HEC-HMS and HEC-RAS models. Task 3 focuses on developing probabilistic flood maps. Task 4 evaluates infrastructure exposure under both traditional and probabilistic mapping approaches. Task 5 tracks progress across all tasks and compiles key deliverables through mid-year and final reporting. Expected outcomes include a publicly available dataset of probabilistic flood hazard maps and a catalog of flood-exposed transportation assets within the study area. Ultimately, this work will demonstrate the added value of probabilistic flood products for improving hazard characterization and will offer practical guidance for DOTs and planners seeking to integrate next-generation flood data into transportation resilience planning. ]]></description>
      <pubDate>Mon, 05 Jan 2026 23:01:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646941</guid>
    </item>
    <item>
      <title>A Framework for Rapid Performance Assessment of Bridges Under Flood Hazard Using Machine Learning </title>
      <link>https://rip.trb.org/View/2646940</link>
      <description><![CDATA[Recent heavy rainfall and flood events highlight the growing threat to transportation infrastructure and their surrounding communities. Floods can severely damage bridges through pier scour, hydraulic loads, or debris impact. These effects may lead to sudden failures and impede critical emergency response and recovery operations. Traditional flood risk assessment methods often rely on expert opinion or visual inspection results, which may not accurately capture the true structural condition. Additionally, conventional stochastic approaches, while more robust, are computationally expensive and impractical for time-sensitive applications. To address these challenges, the proposed research develops a framework for rapid fragility quantification of bridges under flood conditions. The framework utilizes machine learning models that are trained to capture complex flood-structure interactions and generate predictive fragility curves for failure assessment and/or risk-based decision-making. The framework considers flood and bridge attributes to generate the fragility profile directly in real-time. Accordingly, this approach enables faster, more reliable, and computationally efficient assessment compared to traditional methods. Rather than focusing on a single bridge, the framework will be trained using high-fidelity finite element analysis results from an ensemble of models that covers a particular class of bridges. After training, the framework can generate the fragility profiles of any bridge within the class given the bridge attributes (e.g., span length, number of lanes, foundation type, etc.). At this stage, focus will be placed on single- or multi-span, simply supported girder bridges (steel or concrete). Other bridge types may be considered in subsequent studies. ]]></description>
      <pubDate>Mon, 05 Jan 2026 22:41:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646940</guid>
    </item>
    <item>
      <title>Hydrologic and Hydraulic Software Enhancements 2 (SMS, WMS, Hydraulic Toolbox, and HY-8)</title>
      <link>https://rip.trb.org/View/2640674</link>
      <description><![CDATA[This Transportation Pooled Fund (TPF) project will: 1) Enhance the capabilities of the four Federal Highway Administration (FHWA) sponsored software programs and ensure they remain consistent with the latest FHWA technical reference documents; 2) Update the software user manual documentation; 3) Make new software versions publicly available; 4) Develop and deploy technology transfer materials and workshops to test and demonstrate new software content and features; 5) Inform users of the availability of new software versions and features through website postings, email notifications, newsletter articles, conference presentations, and other avenues.]]></description>
      <pubDate>Wed, 17 Dec 2025 15:42:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640674</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Highway Practices. Topic 57-03. Practices for Monitoring POA-Required Bridges During and After Floods</title>
      <link>https://rip.trb.org/View/2630487</link>
      <description><![CDATA[Scour is the most common cause of bridge failures. Per 23 CFR 650.313, state departments of transportation (DOTs) must have programs to manage the risks of scour vulnerability in their bridge inventories. Specifically, this regulation requires state DOTs to maintain a documented plan of action (POA) for every scour-critical bridge and every bridge with unknown foundations. A POA typically includes a plan for monitoring the bridge during or after flooding to ensure it is safe for traffic or closed if found to be unsafe.

The 23 CFR 650.313 does not prescribe specific monitoring methods, so state DOTs use a variety of approaches and software in their POAs. These approaches can vary depending on factors such as data availability, funding, and resources for scour monitoring. Therefore, a synthesis study documenting state DOT practices for meeting POA monitoring requirements for both on- and off-system bridges will help state DOTs implement monitoring scour POAs within their unique organizational contexts.

The objective of this synthesis is to document state DOT practices and policies for POA implementation, including monitoring methods, software, instrumentation, and other tools used in these efforts.

Information to be gathered includes (but is not limited to): (1) Units within the DOT responsible for POA monitoring; (2) Requirements in POAs for on-site bridge monitoring during flooding; (3) Procedures for maintaining and updating POA monitoring protocols for individual bridges over time; (4) Practices for prioritizing bridges for POA monitoring when multiple POA bridges are affected by flooding; (5) Roles and responsibilities for initiating POA monitoring at bridges; (6) Conditions or triggers that initiate on-site monitoring at a bridge; (7) Use of tools (e.g., GIS-based systems or other software) that leverage online rainfall or streamflow data to trigger monitoring; (8) Qualifications and/or training for personnel performing POA monitoring; (9) Observations and measurements made during on-site POA monitoring; (10) Practices and procedures for emergency and formal bridge closures; (11) Responsibilities for POA monitoring of non–state-owned bridges; (12) Use of fixed instrumentation in POA monitoring; (13) Benefits and challenges of POA monitoring and inspection; and (14) Written policies and procedures supporting POA implementation.
Information will be gathered through a literature review, a survey of state DOTs, and follow-up interviews with selected DOTs for the development of case examples. Information gaps and suggestions for research to address those gaps will be identified.

Information sources (partial): 23 CFR 650.313 Inspection procedures. https://www.ecfr.gov/current/title-23/chapter-I/subchapter-G/part-650/subpart-C/section-650.313]]></description>
      <pubDate>Wed, 26 Nov 2025 16:33:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2630487</guid>
    </item>
    <item>
      <title>Equitable Resilience-Informed Strategies for Flood Risk Mitigation in Nebraska's Transportation Infrastructure
</title>
      <link>https://rip.trb.org/View/2627065</link>
      <description><![CDATA[This proposal focuses on enhancing the equitable resilience of Nebraska's transportation infrastructure against flood hazards. In light of the substantial damage caused by flooding, the project aims to introduce a robust framework for designing and retrofitting, especially bridges, to withstand flood impacts. A novel simulation based approach promoting equitable and resilience-informed decision-making will be implemented, focusing on individual bridges within Nebraska. The methodology developed will produce cost estimates for implementing flood design upgrades, ensuring new retrofit level requirements are met for existing bridges. The research will contribute to the Mid-America Transportation Center for Transportation Safety and Equity (MATC-TSE) theme of improving transportation resilience and align with the U.S. Department of Transportation's strategic infrastructure investment and innovation goals. The anticipated results include a robust simulation-based framework for designing and retrofitting bridges, an optimized policy framework for flood hazard mitigation, cost estimates for implementing flood design upgrades, and a comprehensive report detailing the research findings.
]]></description>
      <pubDate>Wed, 19 Nov 2025 15:04:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2627065</guid>
    </item>
    <item>
      <title>Automatic Boundary Detection and Change Analysis Using Static and Dynamic Imagery</title>
      <link>https://rip.trb.org/View/2616823</link>
      <description><![CDATA[This project aims to develop an automated segmentation and boundary detection system capable of identifying geometric features and changes in river boundaries using advanced image processing techniques on satellite imagery, digital photographs, and videos. Current aerial remote sensing and data collection techniques use LiDAR, photogrammetry, or other methods that require significant time and computational power to assess and identify key features of interest. Therefore, rapid or real-time monitoring of dynamic conditions such as flooding is difficult or impossible. By combining a fast segmentation algorithm with novel edge detection and artificial intelligence (AI)-based classification methods to analyze boundary changes, the proposed system will allow for temporal monitoring of river conditions and adjacent infrastructure, and aid in the detection of any deviations from established boundary norms. While this system has numerous potential use cases, the main focus of this research will be the creation and training of a system that can identify and quantify a number of key features used for asset management, flood monitoring, and disaster response associated with levees and adjacent transportation infrastructure.]]></description>
      <pubDate>Thu, 30 Oct 2025 14:39:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2616823</guid>
    </item>
    <item>
      <title>Evaluating isolated areas, alternative routing, and economic impact for resilient transportation in North Carolina</title>
      <link>https://rip.trb.org/View/2604572</link>
      <description><![CDATA[Natural disasters, such as flooding, landslides, storm surge, and wildfire can cause severe impacts to the social, environmental, economic, and transportation systems of North Carolina. At the same time, transportation infrastructure plays a critical role in natural disaster response and recovery efforts during these natural disaster events. Unfortunately, extreme hazard events such as these are occurring with greater frequency and intensity. These events can negatively impact road functionality and lead to the loss of essential services. According to the National Oceanic and Atmospheric Administration (NOAA), weather-related disasters have cost over $1.875 trillion since 1980. The built environment isn’t designed to handle many of the impacts that are happening due to extreme hazard events. For example, stormwater systems, culverts, and tidal pumps were all designed for past events— not current and future conditions. The failure of these systems will impact  communities to a level where they may not be able to return to normal for months or years.

Transportation planners and engineers from North Carolina Department of Transportation (NCDOT), as well as other federal, state, and local agencies across the state, and in close collaboration with emergency managers, are increasingly looking for better ways to address these issues and become more resilient, while simultaneously planning for a more reliable transportation network. Planning for extreme events is about finding ways for systems to bounce back to normal as quickly as possible after the negative impacts of an event. One particular issue that NCDOT faces is the rerouting of traffic during and immediately after natural disaster events. Typical considerations include traffic volumes, current conditions, roadway capacities, and overall safety. However, there are other considerations such as the overall economic impact, including issues like commerce, commute times for individuals traveling between work and home, access to essential services, and disruption to local businesses, that should also be taken into account. These impacts can be further compounded in areas where entire networks of roads, such as a neighborhood or community, become cut-off due and thus isolated. This isolation can be due to such factors as a damaged bridge or road washout. Worse yet, these impacts can often last for days or even months. By identifying these areas ahead of time, and better understanding the potential economic impacts, NCDOT and other agencies can be better equipped when planning for a more resilient and sustainable transportation infrastructure system.

The joint proposal team, consisting of researchers from the University of North Carolina at Asheville’s National Environmental Mapping and Applications Center (NEMAC) and the University of North Carolina at Charlotte, proposes a comprehensive and innovative approach to helping NCDOT better understand the forces behind transportation route and commute pattern disruptions, and their effects on local economies, in the face of an increase in extreme hazard events. Through comprehensive user research and discovery, data analysis, and the development of decision-making workflows, the project team seeks to provide NCDOT with actionable insights to better plan and respond to disruptions related to extreme hazard events, ultimately improving infrastructure reliability and community access.]]></description>
      <pubDate>Tue, 30 Sep 2025 11:13:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604572</guid>
    </item>
    <item>
      <title>Synthesis: Increase Awareness and Use of Transportation Nature-Based Solutions</title>
      <link>https://rip.trb.org/View/2604506</link>
      <description><![CDATA[Transportation systems and facilities face challenges and impacts from extreme weather, changes in precipitation, sea level rise, and storm surge. There have been many discussions including the Statewide Resiliency Plan that identify the stressors and provide a summary of solutions to help bolster system resiliency; however, nature-based solutions is a tool and methodology that has little awareness at the Texas Department of Transportation (TxDOT) and limited use. Currently, TxDOT does not have guidelines for implementing nature-based solutions. This project will investigate how nature-based solutions can be used to protect roadway facilities from flooding, storm surge, and sea level rise. The research will begin with a comprehensive literature review of case studies on the successful use of nature-based solutions in transportation systems. It will then summarize existing resources, tools, research, and national and international guidance, with a focus on applicability to various Texas regions. The project will identify use cases and opportunities specific to TxDOT roadway facilities, aiming to enhance awareness and provide a foundation for future implementation guidelines.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:06:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604506</guid>
    </item>
    <item>
      <title>Traffic Safety Improvements at Low Water Crossings</title>
      <link>https://rip.trb.org/View/2593185</link>
      <description><![CDATA[The Texas Department of Transportation (TxDOT) Project 0-6992 "Traffic Safety Improvements at Low Water Crossings (LWCs)" proved how easy, low-cost countermeasures improve safety at LWCs by focusing on LWC delineation, using flood-detection sensors, and warning systems to alert travelers of flooded crossings. In addition to recommending the use of raised retroreflective pavement markers (RRPMs) to improve longitudinal markings at low-water crossings, the research team recommended experimenting with internally illuminated raised pavement markers (IRPMs) at problematic locations where drivers regularly drive through high water conditions. The research team will improve safety and operations at LWCs by incorporating a tool which integrates the National Oceanic and Atmospheric Administration (NOAA's) Multi-Radar/Multi-Sensor (MRMS) system, which combines radar, stream gauges, and environmental data to estimate rainfall rates with 1-kilometer resolution across the United States. By leveraging virtual sensor data, TxDOT will enhance the effectiveness of roadway flood warning systems, making early flood detection more robust and improving overall driver safety.]]></description>
      <pubDate>Tue, 26 Aug 2025 12:29:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593185</guid>
    </item>
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