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    <title>Research in Progress (RIP)</title>
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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>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>AI-assisted Condition Assessment of Roads</title>
      <link>https://rip.trb.org/View/2752288</link>
      <description><![CDATA[The objective of this project is to develop an AI-assisted road monitoring system that enables low-cost, autonomous, and frequent condition-based assessments using a network of mobile sensing units. The system will use computer vision and machine learning to detect and quantify pavement defects, replacing traditional schedule-based inspections with continuous, data-driven monitoring. The proposed system provides transportation agencies with an affordable, scalable, and intelligent tool for real-time pavement monitoring. By using low-cost sensors on existing vehicles and automated data interpretation, it delivers accurate condition insights, reduces inspection costs, and supports timely maintenance decisions.]]></description>
      <pubDate>Thu, 13 Aug 2026 15:31:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2752288</guid>
    </item>
    <item>
      <title>Modernization of Idaho StreamStats: Updates to Statewide Basin Characteristics and At-Site Peak Streamflow Statistics</title>
      <link>https://rip.trb.org/View/2742769</link>
      <description><![CDATA[The Idaho Transportation Department (ITD) is seeking to enhance the Idaho StreamStats application by incorporating updated basin characteristics, hydrography, and at-site peak streamflow statistics using current datasets, advanced geospatial technologies, and modern hydrologic analysis methods. StreamStats is an important tool used by transportation engineers, planners, and water resource professionals to estimate streamflow characteristics that support the design and maintenance of bridges, culverts, drainage infrastructure, and other transportation assets. This project will leverage high-resolution lidar-derived elevation data, updated land cover and climate datasets, improved hydrography, and current flood-frequency analysis methods to strengthen the accuracy, consistency, and reliability of hydrologic information available through StreamStats. Research activities will include updating basin characteristics, recalculating peak-flow statistics using recent streamflow records, integrating updated information into the StreamStats application, and publishing supporting datasets to promote transparency and future use. The resulting tools and workflows will provide ITD and its partners with enhanced hydrologic information to support infrastructure design, flood risk assessment, regulatory compliance, and long-term transportation planning.]]></description>
      <pubDate>Tue, 04 Aug 2026 16:20:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742769</guid>
    </item>
    <item>
      <title>StreetsAPI: A Connected-Vehicle Interface for Planning-Informed Street Management</title>
      <link>https://rip.trb.org/View/2739299</link>
      <description><![CDATA[One of the promises of connected vehicle (CV) technologies, such as vehicle-to-infrastructure (V2I) and vehicle-to-everything (V2X), is the ability to manage streets more nimbly using real-time and continuous data, changing street operating profiles based on data-informed conditions, a promise that, with few exceptions, has yet to be realized. Most streets remain static, using CV data only indirectly through traditional planning processes.

StreetsAPI creates an interface and an extensible markup language (XML) for managing streets with real-time and long-run CV data. Building on empirically grounded models for street and mobility management, such as variable pricing of congestion or parking, it implements a system that uses CV data to inform parametrically modifiable street operating profiles. The resulting interface, the project’s primary distributable product, will enable localities, state departments of transportation (DOTs), and other stakeholders to establish parameters for how streets respond to CV-informed flows across a range of use cases.]]></description>
      <pubDate>Thu, 30 Jul 2026 16:08:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2739299</guid>
    </item>
    <item>
      <title>Using Artificial Intelligence-Based Computer Vision for Traffic Monitoring </title>
      <link>https://rip.trb.org/View/2736590</link>
      <description><![CDATA[The proposed research project would employ Artificial Intelligence (AI)-based Computer Vision to collect traffic data including vehicle count (traffic volume), Federal Highway Administration (FHWA) vehicle classification, and turning movements. Using video footage taken by existing roadside cameras (such as the Ohio Department of Transportation (ODOT)'s Milestone cameras) or other temporary cameras to collect traffic data allows for a safer and less expensive option compared with other methods. Computer Vision is an important AI application and using it to automatically collect traffic data will save significant amount of time and cost. This project epitomizes innovation as it will use cutting edge technologies to perform practical tasks while improving ODOT workers' and driving public's safety, reducing cost, and enhancing operational efficiency. 

A previous project conducted by the University of Toledo for ODOT Office of Technical Services developed a prototype tool that used AI-Computer Vision to collect vehicle count and classification data from recorded roadway videos. This research would investigate ways to more efficiently access videos from Milestone cameras and improve the previous work by developing a web-based traffic monitoring application, so that it can be used for routine traffic flow data collection. The outcome of this research will provide ODOT a safe, efficient, accurate, and economical alternative for collecting traffic data.

The overall goal of the project is to develop a safer method to collect accurate traffic data, while reducing costs and increasing operational efficiency.
                  ]]></description>
      <pubDate>Mon, 27 Jul 2026 13:57:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2736590</guid>
    </item>
    <item>
      <title>SPR-5108: Automated Highway Asset Inventory Using High-Resolution Aerial Imagery and LiDAR Data</title>
      <link>https://rip.trb.org/View/2732647</link>
      <description><![CDATA[This project proposes to develop, validate, and scale an automated framework for extracting and mapping key highway assets from 3-inch aerial imagery and QL1 LiDAR data. Target assets include the number of travel lanes in each direction, pavement section areas, guardrails, and sound barrier walls. Hamilton County will serve as the pilot implementation area due to its diverse mix of Interstate, U.S., and State routes across urban and suburban environments.]]></description>
      <pubDate>Wed, 22 Jul 2026 15:22:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732647</guid>
    </item>
    <item>
      <title>Evaluation of Traffic Speed Deflectometer Data (TSD) for Potential Use in Michigan</title>
      <link>https://rip.trb.org/View/2731922</link>
      <description><![CDATA[The collection of continuous pavement deflection data is a technology that is starting to mature and is getting a lot of
attention from many state agencies around the country. The cost to obtain these data collection services can be significant.
The Michigan Department of Transportation (MI DOT) would like to explore whether there is a benefit in spending money on this type of data. Does it provide
sufficient structural information that can be useful in the scoping or design phases of upcoming projects? Does it provide
useful information in the evaluation of the performance of in-situ pavements? In what situations does it make sense to
gather this data? These are some of the questions that need to be answered so that a determination can be made on the
cost effectiveness of moving forward with this relatively new technology.]]></description>
      <pubDate>Fri, 17 Jul 2026 13:59:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731922</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>Early Warning for Oregon's Aging Post Tensioned Bridges: Proactive Detection, Longer Life, Lower Risk</title>
      <link>https://rip.trb.org/View/2725349</link>
      <description><![CDATA[This research tackles the urgent need to safely manage Oregon's aging post-tensioned (PT) concrete bridges, which rely on high-strength steel tendons but are prone to hidden corrosion from grout voids, water ingress, and outdated grouting methods. Rising risks of tendon failure, cracking, prestress loss, or collapse drive the development of a risk-based, scalable protocol. It includes a vulnerability screening score, a centralized PT bridge database with corrosion-relevant attributes, structural modeling linking observable changes (camber, strains, natural frequencies) to internal damage, proven nondestructive evaluation (NDE) methods (ultrasound, ground penetrating radar (GPR)), and practical inspection/monitoring guidelines demonstrated on a case study bridge. Integration into the Oregon Department of Transportation (ODOT) Bridge Inspection Program Manual supports proactive network-level screening, prioritized inspections, service life extension, and risk reduction—enhancing safety and reliability of Oregon transportation infrastructure.
OBJECTIVES 
The project equips ODOT with practical, risk-based tools to proactively manage PT bridge safety and serviceability. Main objectives are to: (1) create a vulnerability screening score that prioritizes bridges by corrosion risk factors (grout quality, duct material, exposure conditions); (2) build a centralized statewide PT bridge database for efficient network assessment; (3) develop a scalable protocol integrating visual inspections, NDE techniques (ultrasound, GPR), and damage-tolerance analysis to detect defects, predict remaining service life, and direct interventions; and (4) field-test the approach on a case study bridge and embed the resulting guidance in the ODOT Bridge Inspection Program Manual. These steps will extend bridge life, reduce hidden corrosion risks, optimize inspection efforts, lower unexpected failure potential, and enable cost-effective statewide maintenance.
This research equips ODOT with risk-based tools for safer, more efficient PT bridge management. Key benefits include early detection of tendon corrosion, extended service life through targeted inspections, improved efficiency via network screening and prioritization, major cost savings by avoiding emergencies and premature replacements, consistent statewide protocols in ODOT manuals, and reduced risks to workers and the public. Overall, it supports safer, more resilient, and cost-effective stewardship of Oregon’s transportation infrastructure.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:13:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725349</guid>
    </item>
    <item>
      <title>Phase 2: Leveraging Surface Monitoring to Guide Emergency Response and Long-Term Strategic Planning</title>
      <link>https://rip.trb.org/View/2725299</link>
      <description><![CDATA[Conventional monitoring, site investigation, and assessment of landslides for mitigation often requires drilling along with installation of piezometers and inclinometers which is not always feasible due to the significant expense of drilling, short lifespan of subsurface instruments, safety concerns about working on an active landslide, and difficult site access. A continued need exists to expand the capabilities of near-real-time surface monitoring of ground movements for (1) existing, monitored landslides to gather longer time-series of landslide response to variable wet seasons, and (2) newly-monitored landslides that reflect uncharacterized climatic and geologic conditions to extrapolate a spectrum of landslide impacts to Oregon Department of Transportation (ODOT) right-of-way, and (3) landslide events that occur and require quantitative information for decision-making. Such data is key for evaluating highway safety, repair and mitigation needs, and strategies for reopening after failure. The absence of this information places critical infrastructure and ultimately ODOT customers at risk.

The data from this expanded monitoring would inform important ODOT planning activities and research questions. For planning purposes, this data has already demonstrated benefits for emergency response to landslide events, planning around mitigation plans and Goal 18 discussions, maintenance considerations, and reduced ODOT time for inspection of landslides. Expanding these efforts would further support ODOT’s ability to monitor problematic slopes and make informed decisions regarding prioritization, planning, and sustaining mobility.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:07:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725299</guid>
    </item>
    <item>
      <title>Development of Methods to Produce High-Quality Household-Based VMT Dataset</title>
      <link>https://rip.trb.org/View/2724823</link>
      <description><![CDATA[Household-based Vehicle Miles Traveled (VMT) is essential for Oregon Department of Transportation's (ODOT’s) strategic initiatives. Traditionally, ODOT has reported on-road VMT to the Federal Highway Administration (FHWA) by monitoring traffic on public roads, but new planning and modeling requirements now emphasize household-based, light vehicle VMT—tracking passenger vehicle travel by Oregon residents, regardless of location. A variety of policy actions may have an impact on resident-generated VMT.  Accurate household-based VMT data is critical as ODOT implements new local and metropolitan reporting requirements, tracks VMT per capita as a Key Performance Target in the 2022 Oregon Transportation Plan, and reports vehicle type VMT to the legislature. Without this research, the agency lacks an empirical method for measuring household-based VMT.

The research will establish a framework for developing a high-quality household-based VMT measurements by integrating available empirical data and evaluating each dataset’s strengths and weaknesses for monitoring household-based.  This research will leverage Oregon’s 2023-2024 household travel survey, which collected 1- to 7-day travel diaries from 22,000 households—a rare opportunity, as such surveys occur only about every 13 years. Additional VMT data sources include DMV and DEQ odometer readings, which require evaluation for completeness, privacy, and accuracy; ODOT’s OreGO program, which provides high-quality VMT data but has limited participation and self-selection bias; and third-party data vendors, which have been used by other state DOTs but remain untested for accuracy in Oregon.  This research will assess the strengths and limitations of these data sources and develop a methodology for integrating reliable household-based VMT data. If critical flaws are identified, it will provide recommendations to improve VMT measurement through future data collection and administrative processes.]]></description>
      <pubDate>Wed, 08 Jul 2026 14:46:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2724823</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>A Data-Driven and Region-Specific Optimization Framework for CCS and WIM Planning
</title>
      <link>https://rip.trb.org/View/2719328</link>
      <description><![CDATA[The primary objective is to enhance and operationalize a data-driven process for systematically managing Office of Transportation Data’s Continuous Count Stations and Weigh-in-Motion sites programs. This project will provide improved network coverage with more accurate representation of  statewide traffic and freight flow patterns, tailored approaches that account for distinct characteristics of Atlanta metropolitan area and the rest of Georgia, and a streamlined, ready to implement decision making process for Continuous Count Stations and Weigh-in-Motion sites planning and deployment.
]]></description>
      <pubDate>Thu, 25 Jun 2026 11:46:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719328</guid>
    </item>
    <item>
      <title>Automated QA/QC and Guidance for Inspecting Robotically-Welded Steel Structures
</title>
      <link>https://rip.trb.org/View/2719306</link>
      <description><![CDATA[The objective of this research is to develop a quality assurance/quality control (QA/QC) process for inspecting welded steel structures using infrared thermography (IRT), automate the front-end (i.e., data collection) and back-end (i.e., data analysis and decision-making) of the QA/QC process, and create publicly accessible resources and guidance on implementing IRT-based assessment.
]]></description>
      <pubDate>Thu, 25 Jun 2026 09:25:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719306</guid>
    </item>
    <item>
      <title>SPR 783 Contractor Performance Evaluation 2.0</title>
      <link>https://rip.trb.org/View/2719304</link>
      <description><![CDATA[This project aims to develop a data-driven Contractor Performance Evaluation System 2.0 (CPES 2.0) for the South Carolina Department of Transportation (SCDOT) to support performance-based contractor prequalification. Building on existing methodologies and incorporating lessons from peer agencies, the research will focus on improving fairness, accuracy, and adaptability to evolving project delivery methods.
The primary objectives are: (1) Review and analyze current SCDOT contractor evaluation processes and data sources to identify strengths, gaps, and improvement opportunities.
(2) Benchmark contractor performance evaluation systems used by other state DOTs to identify effective practices, especially those accommodating alternative delivery methods and project complexities. (3) Develop a comprehensive evaluation framework with data-driven scoring algorithms that ensure fairness, consistency, regulatory compliance, and adaptability across project types and delivery methods. (4) Develop revised Resident Construction Engineer and contractor questionnaires to support enhanced data collection and evaluation accuracy.]]></description>
      <pubDate>Thu, 25 Jun 2026 08:59:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719304</guid>
    </item>
    <item>
      <title>Risk-Based and Cost-Effective Agency Verification of Contractor-Collected Pavement and Bridge Profiles</title>
      <link>https://rip.trb.org/View/2712193</link>
      <description><![CDATA[State departments of transportation (DOTs) recognize that pavement and bridge smoothness is a key indicator of performance and public satisfaction. As state DOT staffing levels have declined, contractors have become increasingly responsible for collecting profile data, calculating smoothness indices, and sometimes determining pay factors. While federal regulations require independent verification of contractor data used for acceptance decisions, agencies remain uncertain about the level of verification needed to ensure accuracy and judicious allocation of public funds.

Current practices for validation and verification vary widely across state DOTs. Some agencies collect independent profiles on a subset of projects, while others rely on partial sampling, comparisons with contractor data, or limited review processes. The statistical reliability and risk implications of these approaches are not well understood. Additionally, advances in data collection technologies, such as high-speed profilers, have increased the volume of data, challenging traditional verification approaches. There is a need for research that helps state DOTs accurately determine pavement life through the potential use of emerging technologies and improved verification of contractor-collected pavement and bridge profile data.

The objective of this research is to develop a guide and supporting tool to assist state DOTs in conducting cost-effective, risk-based verification of contractor-collected pavement and bridge profiles.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:10:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712193</guid>
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