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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>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>2504 Evaluating Pavement Friction using Dynamic Friction Tester (DFT)</title>
      <link>https://rip.trb.org/View/2786776</link>
      <description><![CDATA[ODOT has taken significant steps toward implementing a balanced mix design (BMD) approach, which evaluates asphalt mixtures based on performance characteristics including rutting and cracking. One critical performance measure is friction. Current ODOT specifications require aggregates to satisfy certain criteria, such as insoluble residue limits, to help ensure adequate polishing resistance. However, the insoluble residue test and other tests such as the L.A. abrasion and Micro Deval are intended to assess the aggregate properties and may not fully represent the overall friction resistance of the asphalt mixture. Overall pavement friction is also influenced by factors such as aggregate gradation, surface texture, aggregate blending, and mixture design. This project will investigate the use of the Dynamic Friction Test (DFT) along with the three-wheel polisher (TWP) device to assess friction resistance of asphalt mixes in Oklahoma. To accomplish this, the laboratory and field friction performance of Oklahoma asphalt mixtures will be evaluated by examining the effects of mixture type, aggregate source, RAP content, and binder type. Asphalt mixtures containing recycled asphalt pavement (RAP) will be tested to determine the frictional contribution of RAP aggregates, since ODOT specifications assume zero insoluble residue for RAP materials. The correlation between insoluble residue and DFT friction values will be investigated for different traffic levels to establish whether current insoluble residue limits are appropriate. Preliminary threshold friction values will be proposed for ODOT asphalt mixtures for incorporation into the BMD provisional specification.]]></description>
      <pubDate>Wed, 07 Oct 2026 11:19:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786776</guid>
    </item>
    <item>
      <title>Strategic Interactions in Mixed-Autonomy Networks: Multi-Company AV/HV Fleet Operations and Policy Implications</title>
      <link>https://rip.trb.org/View/2786713</link>
      <description><![CDATA[Autonomous vehicle (AV) ride-hailing fleets are no longer hypothetical: Waymo, Uber, and competing operators are deploying mixed AV and human-driven fleets across U.S. cities. State and local agencies must now decide which operating rules to impose, such as permit caps, congestion pricing zones, and limits on empty repositioning trips, without analytical tools that capture how multiple competing fleet operators actually respond to those rules and to one another. Existing models treat AV fleets as if they were human-driven fleets with different cost structures, missing the central control, repositioning capability, and regulatory susceptibility that distinguish AV operations. This project develops a two-level modeling framework for operational constraints and regulatory design in mixed AV and human-driven ride-hailing networks. The lower level represents the strategic interaction among competing fleet operators, travelers, and the road network, extending the team's prior network equilibrium model to include empty-vehicle repositioning that loads network links and human drivers who work for multiple platforms simultaneously. The upper level represents a regulator selecting permit caps, congestion-pricing tolls, and repositioning limits to optimize a system-level objective such as total travel time or system congestion. The team will develop solution methods, release an open-source solver, and apply the framework to representative California networks. The work will give state and local agencies a transparent, defensible tool to evaluate regulatory levers before deployment. ]]></description>
      <pubDate>Mon, 05 Oct 2026 13:38:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786713</guid>
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    <item>
      <title>Roadmap for Virtual Traffic Light Control at Intersections Using Connectivity, AI and Digital Twins</title>
      <link>https://rip.trb.org/View/2786712</link>
      <description><![CDATA[Vehicle automation, connectivity, artificial intelligence (AI), and advanced sensing enable new approaches to intersection management. This project investigates a virtual traffic light (VTL) framework that over time can replace conventional signalized intersections, which carry high capital, maintenance, and operating costs. In the proposed system, connected and automated vehicles (CAVs), along with connected pedestrians and bicyclists, communicate continuously with a virtual control agent that maintains real-time estimates of their positions, speeds, and intended trajectories. Using these data, the agent computes individualized right-of-way assignments—equivalent to virtual green, yellow, and red indications—and transmits them directly via vehicle-to-everything (V2X) communication to each user. By coordinating movements through communication rather than fixed infrastructure, the system can improve traffic efficiency, reduce delays, enhance safety, and lower costs. An additional advantage of VTL systems is their flexibility: control logic and policies can be updated through software, enabling rapid, low-cost adaptation compared with conventional traffic signals. Because full connectivity will be achieved gradually, the research first establishes upper-bound performance under ideal, fully connected conditions without failures, providing quantitative benchmarks. It then develops a roadmap for transitioning from today’s traffic control to increasingly connected environments, accommodating mixed traffic with varying levels of connectivity. The results will guide phased deployment strategies aligned with USDOT priorities in safety, mobility, sustainability, and cost-effective infrastructure modernization.

]]></description>
      <pubDate>Mon, 05 Oct 2026 13:32:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786712</guid>
    </item>
    <item>
      <title>Data Driven Analysis and Modeling of Merging Behavior and Traffic Breakdown at Freeway Merging Bottlenecks</title>
      <link>https://rip.trb.org/View/2786711</link>
      <description><![CDATA[Freeway merging bottlenecks (e.g., lane-drop, entry ramps) are major sources of congestion, where traffic breakdown is often triggered by complex interactions among merging vehicles. While congestion has been widely studied, existing models for bottleneck congestion frequently rely on simplified behavioral assumptions and do not adequately capture the mechanisms that lead to congestion onset. This limits the reliability of simulation tools used for operational analysis and policy evaluation. This project proposes a data-driven, simulation-based framework to model merging behavior and quantify its role in congestion formation at merging bottlenecks. Using high-resolution drone-based trajectory data collected during congestion onset, the study will characterize key aspects of human merging behavior, including gap acceptance, lane-changing dynamics, and speed adaptation. Additional drone data collection will be conducted as needed to enrich the dataset. A probabilistic model will be developed to estimate the likelihood of congestion onset as a function of traffic conditions and merging behavior. This model will be integrated into a microscopic traffic simulation platform to reproduce realistic merging dynamics and congestion formation as an emergent phenomenon. The resulting framework will provide transportation agencies with a practical tool for analyzing congestion mechanisms and evaluating operational strategies. The project also establishes a foundation for future research on control strategies in mixed traffic systems, including automated vehicle applications.

]]></description>
      <pubDate>Mon, 05 Oct 2026 13:30:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786711</guid>
    </item>
    <item>
      <title>Measuring Household Mobility and Travel Patterns with Partial Vehicle Automation</title>
      <link>https://rip.trb.org/View/2786708</link>
      <description><![CDATA[Vehicle automation has the potential to fundamentally reshape travel behavior by reducing the cognitive and physical burden of driving. A large body of research has explored the potential impacts of fully autonomous vehicles on travel demand. However, fully autonomous systems have not yet achieved widespread market adoption, limiting researchers’ ability to study these behavioral changes using real-world data and limiting the current potential of this technology to benefit society. Meanwhile, partial vehicle automation is being adopted and used by consumers. This project uses partial vehicle automation systems (SAE Level 2) as a real-world setting for studying how automation technologies may influence household mobility, travel demand, and transportation system performance. Unlike fully autonomous vehicles, partial automation systems are already integrated into millions of vehicles across a wide range of vehicle types and price points, allowing the study of automation-related behavioral changes under real market conditions. The project combines a nationwide cross-sectional survey merged with Vehicle Identification Number-decoded vehicle specifications and a longitudinal panel of California households. Using econometric analysis, the study will identify the types of travel associated with partial automation adoption and examine which households are most likely to benefit from these technologies. Findings will support transportation agencies and planners seeking to better understand how emerging vehicle technologies may shape regional travel patterns, infrastructure demand, and economic activity.]]></description>
      <pubDate>Mon, 05 Oct 2026 13:28:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786708</guid>
    </item>
    <item>
      <title>Multi-modal Surveillance and Behavioral Interventions for Micromobility Safety: A Data-Driven "Safety Ecosystem" to Mitigate Traumatic Brain Injury</title>
      <link>https://rip.trb.org/View/2786707</link>
      <description><![CDATA[The rapid proliferation of micromobility (electronic scooters and bikes) represents a transformative shift in transportation use, yet its adoption has outpaced the safety frameworks required to protect users of these transportation modes. Traumatic brain injury (TBI) remains a leading cause of disability in California, and at the University of California, Davis Level 1 Trauma Hospital, the patient registry of encounters reveals that a new epidemic has emerged: a 100% increase in electronic scooter-related traumatic brain injuries within a single year (2023-2024), where only 8% of injured riders were helmeted, and 100% of patients who died were unhelmeted riders. This project will establish a multidisciplinary "Safety Ecosystem" that moves beyond reactive trauma treatment toward a proactive, data-driven prevention model for innovative policy development. By integrating retrospective clinical outcomes from the registry and Fire Department prehospital data with innovative video-based behavioral surveillance, the research team will identify the intersection of high-risk infrastructure and rider behavior (e.g., helmet use, rider speed) that may influence patient outcomes, thereby reducing morbidity and mortality related to micromobility. A targeted microlearning intervention will be designed to bridge the educational gap between clinical risk and public perception. This multidisciplinary approach will provide a scalable framework for leveraging innovative data to inform infrastructure management and safety policy, as it relates to the rising burden of brain injury among vulnerable populations who rely on micromobility.]]></description>
      <pubDate>Mon, 05 Oct 2026 13:26:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786707</guid>
    </item>
    <item>
      <title>Advancing Autonomous Vehicle Deployment in U.S. Cities: A Decision-Support Tool for Implementation and System Performance</title>
      <link>https://rip.trb.org/View/2786705</link>
      <description><![CDATA[Cities across the U.S. are central to advancing the next phase of autonomous vehicle (AV) deployment as these technologies move from pilot programs to broader implementation. As industry investment and activity accelerate, public agencies are responsible for enabling safe, efficient, and scalable operations within transportation systems. While some cities have developed operational experience, many are still establishing the policies, processes, and institutional capacity needed to support deployment at scale. This creates a timely opportunity to provide practical, evidence-based guidance to help agencies manage operations, coordinate with industry, and integrate autonomous systems effectively. This project will develop an AV Deployment Decision-Support Tool for Cities to support the efficient expansion of AV services across U.S. markets. The research will engage cities grouped into four categories: mature, growing, new, and potential markets. The study follows a structured three-phase approach. First, a baseline review will document deployment practices, regulatory approaches, and operational processes across cities. Second, targeted interviews with public agencies and national organizations will be conducted to identify key issues and refine discussion topics, followed by four online focus groups to examine common challenges, operational needs, and scalable strategies across different stages of deployment. Third, a one-day in-person workshop will validate findings, compare approaches, and identify shared priorities. The project will deliver a practical, structured decision-support tool that helps cities navigate deployment processes and reduce uncertainty as AV technologies scale. By supporting streamlined and efficient deployment practices, this work contributes to improved system performance and strengthens U.S. leadership in transportation innovation.]]></description>
      <pubDate>Mon, 05 Oct 2026 05:39:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786705</guid>
    </item>
    <item>
      <title>Clear Roads Winter Highway Operations Phase IV Pooled Fund</title>
      <link>https://rip.trb.org/View/2784243</link>
      <description><![CDATA[Phase IV will maintain Clear Road’s long-term goal mission to advance winter highway operations nationally by undertaking practical, practice-ready research related to materials, equipment, and methods. This supports state DOTs’ pursuit of new technologies, practices, tools, and programs that improve winter highway operations and safety while maintaining fiscal responsibility.

Emerging priority areas of interest in FFYs 2027-2031 are likely to include advanced imagery, machine learning and artificial intelligence technologies, improved decision-making tools, operations coordination among states, updates to content and format of Clear Roads training materials, as well as others that cannot yet be anticipated over the next half decade.

In phase IV, Clear Roads will: (1) Address both operational and management research needs, investigating the most effective tools and practices for clearing snow and ice and for managing program resources, budgets, and performance measures. (2) Continue to promote an even stronger emphasis on the use of research results in the field. (3)         Provide expanded support for implementation and technology transfer through the development of user manuals, training modules, peer exchanges, and quick turnaround syntheses of the most effective state practices from around the country. (4) Give additional attention to new member states from the southeastern and southwestern portions of the country and their unique needs and challenges. (5) Address emerging winter challenges in traditional winter-weather states, such as operational effectiveness in extreme cold conditions and changes in salt supply, availability and cost. (6) Continue to collaborate with other organizations focused on winter maintenance (such as the Aurora Pooled Fund, AASHTO, FHWA, TRB, APWA, and NPRA) to further leverage the research funding investment.

In achieving these objectives, state and local winter maintenance organizations will be able to make informed operational and purchasing decisions based on objective evaluations of promising materials and equipment.

By pooling resources, agencies can conduct more extensive studies across a greater range of conditions than could be done by a single agency with only its own funds. Moreover, by collaborating, sharing information, and conducting impromptu surveys, agencies benefit from each other's experiences and avoid the duplication of research efforts. The outcomes of these projects will help agencies get the maximum financial benefit out of their investments in materials, equipment, and technologies. ]]></description>
      <pubDate>Mon, 05 Oct 2026 04:59:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784243</guid>
    </item>
    <item>
      <title>Performance-Based Evaluation of Expanded Polystyrene (EPS) Thickness in Integral Abutment Bridges</title>
      <link>https://rip.trb.org/View/2784732</link>
      <description><![CDATA[The objective of this study is to establish performance-based framework for calculating the required thickness of Expanded Polystyrene (EPS) elastic inclusion at integral bridge abutments. The results will be implemented in Chapter 17 of the Virginia Department of Transportation's (VDOT's) Manual of the Structure and Bridge Division. Reductions in integral bridge construction and maintenance costs are expected to be derived from this study. ]]></description>
      <pubDate>Thu, 01 Oct 2026 08:42:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784732</guid>
    </item>
    <item>
      <title>Evaluation of Traffic Speed Deflection Devices (TSDD) for Collecting, Reporting, and Utilizing Pavement Network and Project Level Structural Data in Utah</title>
      <link>https://rip.trb.org/View/2784729</link>
      <description><![CDATA[Evaluate the potential of Traffic Speed Deflection Devices (TSDD) for collecting, reporting, and utilizing pavement network and project level structural data in Utah.]]></description>
      <pubDate>Wed, 30 Sep 2026 19:13:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784729</guid>
    </item>
    <item>
      <title>Solar Noise Barrier Industry Discovery and Prototyping</title>
      <link>https://rip.trb.org/View/2784616</link>
      <description><![CDATA[The North Carolina Clean Energy Technology Center (NCCETC) is proposing to support the North Carolina Department of Transportation (NCDOT) by conducting background research, technical modeling and a prototype implementation plan for implementation of proposed solar noise barriers along to be determined highway corridors in North Carolina. The scope of work provides a framework that outlines a comprehensive, phased approach to evaluate the technical, economic, and regulatory feasibility of integrating solar photovoltaic technology into highway noise barriers. The work will conclude with a project report that documents the findings regarding the energy production modeling, stakeholder and utility engagement, capital and O&M cost analysis, and long-term monitoring considerations to successfully transition from a pilot-scale test to full-scale regional deployment.]]></description>
      <pubDate>Wed, 30 Sep 2026 11:25:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784616</guid>
    </item>
    <item>
      <title>AI Applications for Developing Contractors Prequalification Criteria and Safety Matrix for NCDOT</title>
      <link>https://rip.trb.org/View/2784615</link>
      <description><![CDATA[Different state DOTs have attempted to use safety leading indicators for heavy construction projects. The North Carolina Department of Transportation (NCDOT) has contractors’ prequalification guidelines and a formal process for constructability review meetings. However, the safety component is not much considered, which may result in disruption of construction activities due to potential site safety violations. The current prequalification process for contractors requires multiple changes to improve the contractors’ selection process and secure a safe job site. Prequalification process may be revised to incorporate a safety scoring system that considers the contractors history, Occupational Safety and Health Administration (OSHA) citations, availability of in-house safety plans and safety audits, availability of competent persons and OSHA trainers, and formal safety training attended by the contractor’s company personnel. Similarly, NCDOT formal CR meeting needs to incorporate a safety matrix to be revised by project stakeholders during the meeting. The safety matrix should tie different site activities – listed in NCDOT standard bid template – with the respective OSHA big four construction hazard. Performance-based contractor prequalification allows NCDOT personnel to evaluate contractors past performance on similar work to ensure high-quality work in future projects. Occupational safety and health criteria are somehow included in such prequalification to limit construction site accidents and violations. However, current matrix/safety prequalification criteria have significant shortfalls. In this proposed research (implementation plan), the project research team will focus on utilizing artificial intelligence (AI) and machine learning (ML) applications to decide on the attributes relevant to job site safety and the most important parameters to be used in contractors’ prequalification procedures. The research tasks will use the developed criteria through the current project phase, collect information from NCDOT, other state DOTs, and OSHA to train a machine learning model to be developed specifically to evaluate the correlation between different attributes and possible “future” performance of the contractor. Attributes include – but not limited to – contractor’s size, budget, safety practices, previous experience, employee turnover, OSHA records, and experience modification rating. Predictive analytics will provide NCDOT personnel with proper algorithm to assess/predict possible contractor’s performance. The outcomes of this research will result in seamless flow of construction site activities, reduced accidents, and avoiding arbitration and litigation. ]]></description>
      <pubDate>Wed, 30 Sep 2026 11:09:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784615</guid>
    </item>
    <item>
      <title>Advancing the Extended T-FAST Test for Assessing ASR Risk in Hardened Concrete</title>
      <link>https://rip.trb.org/View/2784610</link>
      <description><![CDATA[Alkali-Silica Reaction (ASR) remains a significant durability concern for concrete infrastructure and continues to present challenges for transportation agencies. Existing ASR test methods often involve the tradeoff between speed and reliability. Accelerated methods can produce false positives and false negatives, while more reliable expansion-based methods may require several months or years to complete. Research project RP#2024-11 evaluated an extended version of the T-FAST test (Turner-Fairbank Alkali Silica Reaction (ASR) Aggregate Susceptibility Test) by AASHTO TP144, for quantifying ASR risk in hardened concrete. Significant progress has been made through the evaluation of aggregates with known reactivity, development of and experimental database, and assessment of variables affecting the Reactivity Index (RI), including curing conditions, immersion solutions, and use of fly ash and silica fume for ASR mitigation. As the research progressed, several new variables were identified that were not included in the original scope but are important for continued validation of the method. This Technical Transfer project will further advance the Extended T-FAST method by: (i) incorporating additional aggregates with documented reactivity; (ii) evaluating interactions between fine and coarse aggregates; (iii) refining the RI classification framework using classification algorithms; and (iv) supporting the statistical analysis needed to develop a standardized test procedure.​]]></description>
      <pubDate>Wed, 30 Sep 2026 10:32:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784610</guid>
    </item>
    <item>
      <title>Reassessing the Subsurface Conditions and the Durability of Buried Foundation Elements at a Texas National Geotechnical Experimentation Site</title>
      <link>https://rip.trb.org/View/2780471</link>
      <description><![CDATA[A research site at Texas A&M University (TAMU) was one of six National Geotechnical Experimentation Sites (NGESs) established across the United States for geotechnical experiments. The NGESs were initially funded in 1992, through a partnership between the National Science Foundation and the Federal Highway Administration, to establish sites with extensive characterization data so research could focus on advancing state-of-the-art in situ testing, field instrumentation, foundation testing, and behavior predictions of soil and geotechnical elements. Many of the NGESs have since been decommissioned with TAMU being one of the last remaining. Before this site becomes decommissioned, an opportunity exists to reassess and complete geotechnical experiments.

OBJECTIVES: The objectives of this project are to quantify temporal changes in soil properties, assess advancements in geotechnical site characterization analysis, evaluate soil aggressiveness and the durability of buried concrete and steel, assess available prediction methods for corrosion and remaining strength given the soil in which the foundation elements are buried, and to produce a relational database of test results and measurements.]]></description>
      <pubDate>Wed, 23 Sep 2026 18:20:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780471</guid>
    </item>
    <item>
      <title>Applied Generative AI Pilot: Establishing the Rapid Applied Research Environment (RARE) for Construction &amp; Materials
</title>
      <link>https://rip.trb.org/View/2780490</link>
      <description><![CDATA[This project will establish the Rapid Applied Research Environment (RARE), an internal generative AI capability designed to help the Idaho Transportation Department (ITD) address complex transportation challenges through applied artificial intelligence (AI) research. The project will use the Expert AI Co Research framework to develop and evaluate AI solutions for Construction and Materials challenges, including specification modernization, data extraction, technical research, and historical data recovery. The results will be used to measure efficiency improvements, evaluate the feasibility of AI tools, and develop a repeatable framework for using AI in future ITD research and operations.]]></description>
      <pubDate>Mon, 21 Sep 2026 16:09:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780490</guid>
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