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    <title>Research in Progress (RIP)</title>
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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>
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      <title>Research in Progress (RIP)</title>
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    <item>
      <title>Improving Active Transportation Asset Data Management for UDOT Using AI and Computer Vision</title>
      <link>https://rip.trb.org/View/2786773</link>
      <description><![CDATA[Develop computer vision models to collect essential data on high-priority assets in the active transportation (AT) system to create an intelligent system designed to address the challenges of current methods for inventorying AT assets.]]></description>
      <pubDate>Tue, 06 Oct 2026 17:32:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786773</guid>
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    <item>
      <title>Establishing Performance Measures for Transportation and Quality of Life</title>
      <link>https://rip.trb.org/View/2786772</link>
      <description><![CDATA[Evaluate the performance measures used in Utah Department of Transportation's (UDOT's) transportation planning process by contextualizing the Utah Well-Being survey and ensuring that travel demand models produce relevant performance measures that are sensitive to transportation policies and alternatives.]]></description>
      <pubDate>Tue, 06 Oct 2026 17:22:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786772</guid>
    </item>
    <item>
      <title>Drone Network Design for Comprehensive Emergency Response in Rural Utah</title>
      <link>https://rip.trb.org/View/2786771</link>
      <description><![CDATA[Develop an optimized drone network design for comprehensive emergency response in rural Utah to deliver much needed supplies during times of emergency.]]></description>
      <pubDate>Tue, 06 Oct 2026 17:13:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786771</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>
    </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>
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    <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>Improving the Performance of Transportation Networks for Disaster Evacuation by Accounting for Friction Factors</title>
      <link>https://rip.trb.org/View/2786706</link>
      <description><![CDATA[The performance of the transportation system under the stress of natural and man-made disasters is one of the most important aspects of transportation system design. In light of recent failures, there has been a push at the state and federal levels to improve U.S. infrastructure resilience. As things stand, disaster preparedness is a secondary consideration, and the models and best practices on which it is based are insufficient. Planning for disasters is a complex process that must account for transportation network design, disaster dynamics, and human behavior under duress. Tools that assist in planning must account for the process holistically, and planners must be willing to consider fundamental reconfiguration of transportation systems that give residents a greater chance of survival in the worst-case scenarios. This seed grant will focus on fundamental methodological development and kick off research into the holistic optimization of evacuation plans and the systems on which they depend, accounting for the various friction factors that often get in the way. The methodology developed in this project and subsequent work will substantially advance the state of the art towards a safer, better-performing transportation network. Expected results are new mathematical modeling/optimization techniques, open-source code, collected and processed datasets, publications, and targeted stakeholder engagement.]]></description>
      <pubDate>Mon, 05 Oct 2026 13:24:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786706</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>Quantifying the Impacts of UTA's Frequent Transit Network on Congestion, Safety, and Mode Shift</title>
      <link>https://rip.trb.org/View/2784728</link>
      <description><![CDATA[Optimize the Frequent Transit Network (FTN) through a comprehensive, data-driven evaluation of its impact on regional transportation dynamics and inform future transportation planning decisions.]]></description>
      <pubDate>Wed, 30 Sep 2026 18:16:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784728</guid>
    </item>
    <item>
      <title>Evaluation of Safety Elements for Improved Pedestrian Safety at At-Grade Rail Crossings</title>
      <link>https://rip.trb.org/View/2784727</link>
      <description><![CDATA[Provide insight into how other transit/transportation agencies improve safety at at-grade crossings, particularly with helping pedestrians be more aware of train movements before crossing tracks.]]></description>
      <pubDate>Wed, 30 Sep 2026 18:03:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784727</guid>
    </item>
    <item>
      <title>How is Utah Doing with Transit Performance?</title>
      <link>https://rip.trb.org/View/2784725</link>
      <description><![CDATA[Evaluate Utah transit performance to understand the current state of the transit system, assess how effectively it serves the population statewide, and support decisions on future improvements and investments.]]></description>
      <pubDate>Wed, 30 Sep 2026 17:28:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784725</guid>
    </item>
    <item>
      <title>The Impact of Light Rail in Utah - A Before-and-After Study</title>
      <link>https://rip.trb.org/View/2784724</link>
      <description><![CDATA[Measure the impact of light rail (TRAX) by estimating changes in land use that took place within TRAX corridor between two years before the line opened and 10 years after and identify all new multifamily and commercial projects built in that period. In addition, use Salt Lake County Tax Assessors valuations to assess the value of this new development.]]></description>
      <pubDate>Wed, 30 Sep 2026 17:12:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784724</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>
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