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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>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <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>
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    <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>Likelihood of Unplanned Bridge Posting and Closing

</title>
      <link>https://rip.trb.org/View/2558386</link>
      <description><![CDATA[Federally required state department of transportation (DOT) Transportation Asset Management Plans (TAMPs) must include a process for risk management analysis. State DOTs are tasked with prioritizing bridges for preservation, rehabilitation, and replacement within available budgets. Investment strategies result from evaluating various levels of funding to achieve targets for bridge condition and performance effectiveness at a minimum practicable cost while managing risks. Risks include those associated with performance due to extreme events and bridge conditions. While probabilistic data, methods, and tools exist to quantitatively assess the response of bridges to extreme events using system-wide data (e.g., fragility curves), equivalent approaches for condition-related risks remain underdeveloped. Addressing this gap requires a clearer understanding of the mechanisms and circumstances that lead to unplanned bridge postings and closings due to bridge conditions.

Unplanned postings and closings related to conditions may result from different causes, including (1) discovery of severe deficiency affecting strength or stability, (2) substantial change in condition since the previous inspection or due to accelerated deterioration, and (3) degradation from normal traffic or environmental loading on compromised members. These situations may be more prevalent in certain bridge types and materials (e.g., timber), component or element types (e.g., truss), element defect types (e.g., corrosion, fatigue), site locations (e.g., wet vs. dry), and so forth. Research is needed to develop procedures and tools for state DOTs to quantify the likelihood of unplanned bridge postings and closings as a function of bridge condition and defining attributes.

OBJECTIVE: The objective of this project is to develop procedures and tools for state DOTs to quantify the likelihood of unplanned bridge posting and closing as a function of bridge condition and defining attributes. The research will quantify this likelihood in terms of annual probability values that can be applied to individual bridges.

]]></description>
      <pubDate>Wed, 28 May 2025 14:04:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558386</guid>
    </item>
    <item>
      <title>Probabilistic Performance Modeling and Optimum Maintenance Planning of Plastic Pipeline with Piezoelectric Based NDE Updating</title>
      <link>https://rip.trb.org/View/2085758</link>
      <description><![CDATA[The project will develop non-destructive evaluation (NDE)-updated probabilistic modeling and decision-making framework for plastic pipeline, which integrates novel NDE method for crack detection, probabilistic deterioration modeling, and risk-based optimization of maintenance planning.]]></description>
      <pubDate>Fri, 16 Dec 2022 14:15:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2085758</guid>
    </item>
    <item>
      <title>SPR-4430:  Multiple Water Course Joint Probability Analysis Procedure Development for Indiana Specific Watersheds</title>
      <link>https://rip.trb.org/View/1653582</link>
      <description><![CDATA[Design of hydraulic structures near a stream confluence requires the use of joint probability of flow exceedances. This project will develop joint probabilities based on Indiana streams for multiple probabilities including 1%, 2%, 4% and 10%. This project will deliver a report detailing the scope of the study, methodology and results related to development of joint probabilities and a recommended procedure on how the newly developed joint probabilities can be implemented in Indiana Department of Transportation (INDOT) projects.
]]></description>
      <pubDate>Wed, 25 Sep 2019 11:05:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/1653582</guid>
    </item>
    <item>
      <title>Phased Array Probability of Detection Study - Phase IV (FOR NDE LAB)</title>
      <link>https://rip.trb.org/View/1512647</link>
      <description><![CDATA[Currently relying on radiographic testing (RT) for acceptance/rejection of groove and full penetration welds causing delays during fabrication. Phased Array Ultrasonic Testing (PAUT) should be able to accomplish the same with little delay. There is a need to develop acceptance criteria for PAUT for weld inspection]]></description>
      <pubDate>Tue, 15 May 2018 15:34:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1512647</guid>
    </item>
    <item>
      <title>Modeling Driver Behavior and Driver Aggressiveness Using Biobehavioral Methods - Phase I</title>
      <link>https://rip.trb.org/View/1501846</link>
      <description><![CDATA[It is well known that driver inattention and human error are the primary causes of traffic accidents. In addition, existing driver behavioral modeling algorithms (e.g., car-following, lane changing) assume that driver variability is expressed through various distributions and random number generators. What constitutes aggressive driving, and which are the actions of aggressive drivers that negatively affect safety and traffic instability, are some of the topics that have not been studied thoroughly. At the same time, significant work has been done in the field of cognitive science and psychology, with emphasis in understanding, modeling, and predicting drivers’ intended actions.
The goal of this research is to investigate the linkage between different driver profiles with both traffic stability and the probability of being involved in risk-taking behaviors, borrowing concepts from the fields of cognitive science and psychology. Participants with different driving habits and levels of aggressiveness will be invited to participate in driving simulator experiments, where they will be asked to drive under different geometric, control, and traffic scenarios, that may additionally vary on the level of moral decision making involved. Various metrics related to drivers’ reaction times, gap acceptance, car-following, and lane changing activity will be measured through the driving simulator experiments. Additional behavioral and psychophysical measures will be collected through electroencephalogram recordings (EEG) during the simulator experiments, and through questionnaires.
These data will result in the identification of measurable behavioral parameters and their inter-driver heterogeneity. It is expected that these parameters will be used in subsequent projects to refine or develop enhanced driver behavior models that account for both safety and traffic instabilities.
]]></description>
      <pubDate>Thu, 08 Feb 2018 19:16:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/1501846</guid>
    </item>
    <item>
      <title>Reliability-Based Assessment of Landslide Risk Along Roadways</title>
      <link>https://rip.trb.org/View/1494118</link>
      <description><![CDATA[This research will adapt a procedure that the principal investigators (PIs) have previously developed for assessment of underseepage and internal erosion risk for levees (Boulware and Rice 2017, Polanco and Rice 2014, 2012) to the problem of landslide disruption of roadways. Models will be developed for each geologic feature type that can assess the stability of the feature for ranges of geometric parameters (depth of deposit, slope inclination, groundwater level, etc.) and material properties (unit weight, strength, etc.). Depending on the complexity of the analysis, the landslide model may be represented by a closed-form equation or, in the case of a large number of input parameters, a response surface (a multi-dimensional function representing the relationship between input parameters and the failure potential). A Monte Carlo analyses will then be performed for each feature along the stretch of roadway using probability density functions (pdfs) representing the likelihood of a given parameter having a certain value over the range of possible values. The failure probability can be annualized by considering triggering events (such as rainfall events having calculated return frequencies) and assessing the effects of multiple levels of these events. The resulting fragility curve ties the probability of failure to the likelihood of the triggering event. This method will provide a tool for agencies to assess their annual risk due to landslide hazards and will give these agencies a means for optimizing their mitigation efforts.]]></description>
      <pubDate>Wed, 03 Jan 2018 15:07:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/1494118</guid>
    </item>
    <item>
      <title>Probabilistic Integrity and Risk Assessment of Turbine Engines</title>
      <link>https://rip.trb.org/View/1360973</link>
      <description><![CDATA[No summary provided.]]></description>
      <pubDate>Wed, 15 Jul 2015 01:01:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/1360973</guid>
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