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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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>3D-Printed Architected Concrete Materials  </title>
      <link>https://rip.trb.org/View/2752287</link>
      <description><![CDATA[The objective of this project is to develop and evaluate concrete-based acoustic metamaterials specifically designed to mitigate low- to mid-frequency traffic noise. By leveraging extrusion-based concrete 3D printing (C3DP) and Helmholtz resonator geometry, the project designs, fabricates, and acoustically characterizes scalable noise barrier specimens with near-perfect absorption performance. This project combines innovative acoustic design, advanced concrete 3D printing, and tailored material formulations to deliver next-generation noise barriers capable of addressing complex traffic noise patterns. The outcomes include: Concrete-based metamaterials with proven low-frequency noise reduction capabilities; Scalable barrier designs; Durable, sustainable materials enhanced with additives and supplementary cementitious materials (SCMs); and Strong interdisciplinary collaboration and educational impact through specialized workshops and coursework.
]]></description>
      <pubDate>Thu, 13 Aug 2026 15:31:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2752287</guid>
    </item>
    <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>Feasibility Studies of the Conceptual Novel Elevated High-Speed Rail System with a Case Study in Texas</title>
      <link>https://rip.trb.org/View/2736754</link>
      <description><![CDATA[The high-speed rail system is a powerful rapid transportation tool with significant mobility, financial, and accessibility benefits. The United States is one of the first countries to develop high-speed trains, dating back to 1969. Recently, there has been another wave of inventions and studies of high-speed rail systems, including the California and Texas high-speed rail plans. Among them, the conceptual novel elevated high-speed rail (CNE-HSR) is unique due to its (1) fast construction speed (3 miles per day); (2) elevated design that affords minimal ground field land needs and minimal disruption to existing infrastructure and property, and no displacement of existing communities; (3) independent pods for exclusively nonstop travel and flexible allocation between passenger and cargo needs; and (4) deep last-mile reach. This seed grant project will conduct feasibility studies of CNE-HSR with a case study in Texas along the Houston-San Antonio corridor. Transportation, land use, socioeconomic, and other information will be collected with the assistance of local transportation agencies. The retrieved information will be fully employed to test the identified assessment tools for studies of impacts on the regional economy, housing and real estate development, job market growth, as well as socioeconomic progress. The research team will conduct engagement activities to incorporate advisory comments from policymakers, planners, engineers, community leaders, and others. Eventually, this project will develop recommendations and implementation strategies for the evaluated CNE-HSR system in Texas.]]></description>
      <pubDate>Wed, 29 Jul 2026 14:42:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2736754</guid>
    </item>
    <item>
      <title>Implementation of Seal Coat Project Selection Guidelines</title>
      <link>https://rip.trb.org/View/2727313</link>
      <description><![CDATA[The research team will assist TxDOT with implementing the guidelines for seal coat project selection and the draft ball penetration test method developed in research project 0-7106 “Quantify Maximum Accumulated Seal Coat Layers for Stability“. The research team will then use the findings and data generated from this implementation project to develop and teach implementation workshops for TxDOT.]]></description>
      <pubDate>Fri, 10 Jul 2026 12:40:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727313</guid>
    </item>
    <item>
      <title>Low-Cost AI-Based System for Temporary Traffic Control Review</title>
      <link>https://rip.trb.org/View/2717331</link>
      <description><![CDATA[Work-zone fatalities in the United States increased by about 50% between 2013 and 2022, with a surge of 33% in 2021 alone. To protect workers and guide drivers safely through modified traffic patterns, temporary traffic controls (TTCs) are used that must be regularly inspected for proper functioning. However, the current inspection process is manual and resource intensive, typically involving a three-person team: one person to drive, another to photograph, and a third to document observations. This staffing requirement limits the frequency and the geographic coverage of safety reviews. 

For NCHRP 20-30/IDEA 264, the research team will develop an open-source artificial intelligence (AI)-powered TTC inspection software that leverages Vision Language Models and requires just one inspector with a dashcam and an internet-connected computer. The inspector will drive through the work zone, upload dashcam footage for processing, and review AI-detected issues on an interactive video. The system will transform TTC reviews in several significant ways: (1) Convert a three-person operation into a streamlined, single-operator system, enabling more frequent inspections across wider geographic areas without additional labor cost. (2) Apply assessment criteria uniformly and objectively across all work zones. This offers the potential to deliver more consistent and accurate evaluations regardless of inspector fatigue, regional staffing differences, and complex work zones. (3)       Harness the growing availability of crowdsourced dashcam footage from commercial fleets and autonomous vehicles. This integration transforms work-zone monitoring by ensuring remote, widespread, continuous geographical coverage, including during challenging conditions such as nighttime and adverse weather.

The team will focus on high-priority deficiencies the Texas Department of Transportation identified that carry the highest penalties and represent significant safety hazards in work zones. A structured database of historical review reports will be created for use for training, validation, and prototype testing. Working with Texas Department of Transportation, an evaluation metric will be established and refined. This will be followed by a system engineering task in which core components or modules of the proposed system will be developed and tested individually and in end-to-end testing using a dataset. The system’s report generation component will synthesize component outputs into standardized inspection documentation, incorporating observations, images, regulatory citations, and location data for each identified deficiency. Finally, the AI-based system will be tested and validated across at least three active work zones in Texas that have ongoing, traditional TTC inspections.]]></description>
      <pubDate>Tue, 23 Jun 2026 13:44:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2717331</guid>
    </item>
    <item>
      <title>Advanced Transportation Optimization and Modeling (ATOM)</title>
      <link>https://rip.trb.org/View/2676009</link>
      <description><![CDATA[The U.S. transportation system is experiencing increasing complexity driven by evolving infrastructure, land-use patterns, travel demand, demographic shifts, and rapid advances in vehicle and mobility technologies. Emerging behaviors such as telecommuting, ridesharing, and micromobility, along with changing attitudes toward public transit and vehicle ownership, are reshaping how people and goods move across regions. To ensure that transportation investments remain efficient, resilient, and cost-effective, transportation agencies require advanced, data-driven tools to anticipate and evaluate the system-level impacts of these changes.  

This project develops an advanced transportation modeling and optimization pipeline in Austin, Texas, to evaluate the impacts of alternative strategies and technologies through scenario-based analysis. The system will be built around the Behavior, Energy, Autonomy, and Mobility (BEAM) model. BEAM is an open-source, agent-based regional transportation model that enables realistic simulation of travel behavior, mode choice, fuel consumption, and system performance, and associated community-level impacts under different “what-if” scenarios.  

By leveraging BEAM’s scalable, modular architecture, the project will address key limitations of conventional four-step and activity-based transportation models, providing a robust framework for testing strategies such as emerging technologies, infrastructure enhancements, and new mobility services before deployment. The pipeline will be developed and extended to assess additional impacts (via coupling to additional models) and therefore to serve as a decision-support tool for engineers, planners, and service providers, allowing them to evaluate performance outcomes and trade-offs across multiple metrics relevant to both economic productivity and community outcomes. Model calibration and validation of the Austin BEAM Core pipelines will utilize highly resolved local datasets on traffic flows, speeds, and network performance. These data will enable precise representation of real-world operating conditions in the Austin region and ensure the model’s reliability for planning and investment analysis.  

Scenario development will be coordinated with implementation partners regional stakeholders identified through a stakeholder mapping exercise. These scenarios will reflect practical policy and technology options under active consideration in Texas, ensuring alignment with state and regional priorities. The resulting pipeline will be structured for extensibility, allowing future integration with additional datasets and modeling components for use in other applications. Project outcomes will be shared broadly through technical reports, workshops, and data portals to facilitate adoption by other agencies, research institutions, and industry partners.  

Ultimately, this project supports goals of enhancing efficiency, safety, and reliability, while strengthening economic competitiveness and enabling informed, data-driven investment decisions. By combining open-source modeling innovation with public–private collaboration, the project will provide a replicable framework for modern, performance-based transportation system management.  

Moreover, the pipeline embraces and deploys advanced and transformative research: using an open-source, agent-based framework (BEAM) exceeds conventional planning methods. The stakeholder-co-development model (with public and industry partners) ensures that this research is not only theoretically innovative but also rooted in real-world deployment potential. This initiative empowers decision-makers to implement policies that enhance safety, the economy, and with various co-benefits to communities.  ]]></description>
      <pubDate>Tue, 03 Mar 2026 16:42:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676009</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>
    </item>
    <item>
      <title>Enabling Mobility of Emergency Medical Service through Connected and Automated Vehicle Preemption</title>
      <link>https://rip.trb.org/View/2669655</link>
      <description><![CDATA[Emergency Medical Service (EMS) vehicles, typically ambulances, have time-critical transportation roles when responding to traffic incidents by bringing first medical responders and equipment from their bases to the incident scenes, and transferring injured persons from the scenes to medical facilities. Addressing the mobility of EMS vehicles supports but public health and safety goals, as well as those related to efficient mobility.     

The traditional way for EMS vehicles to reach their destinations faster is to use audible sirens to alert drivers of their presence. Upon hearing an EMS vehicle’s siren, drivers must yield the right of way to facilitate its passage. Previous research on traffic signal preemption for EMS vehicles has demonstrated its effectiveness in reducing delays at signalized intersections. With the advent of Connected and Automated Vehicle (CAV) technology, vehicles can now communicate directly with each other. EMS vehicles equipped as CAVs could leverage vehicle-to-vehicle (V2V) communication technology to transmit warning messages to the CAVs downstream along their routes, beyond the range of audible sirens. The CAVs that have received these messages can proactively move aside to create a clear lane for the EMS vehicle to pass. This “CAV preemption” concept has the potential to significantly improve EMS mobility, resulting in faster response times, earlier on-scene medical aid, and quicker patient transfer to hospitals. Furthermore, the proposed CAV preemption will accelerate incident clearance and the restoration of highway capacity.  

This research is based on an envisioned CAV preemption system in which an EMS vehicle broadcasts its impending arrival to downstream CAVs, while simultaneously sounding its siren and emitting high-intensity strobe light to request signal preemptions. All CAVs receiving this V2V message will automatically move to the right lane, while only a portion of the non-CAV drivers will comply and respond to the siren. The efficiency of this system depends the following factors: (1) The broadcast range of the warning messages to CAVs, (2) The market penetration rate of CAVs, (3) The move-aside compliance rate of non-CAV drivers, (4) The level of traffic congestion.  

This research will simulate and quantify the efficiency of the proposed CAV preemption system under varying operating conditions. An agent-based simulation model of the El Paso highway network will be used to assess the EMS vehicle’s travel time. Mobility efficiency is defined as the percentage reduction in the average travel time. The travel times of EMS vehicles from their bases (selected fire stations that house ambulances) to multiple incident sites (selected highway locations) will be simulated, extracted, and analyzed. The analyses will assess the impacts of broadcast range, CAV market penetration, non-CAV compliance rate, and traffic volume.   ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:34:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669655</guid>
    </item>
    <item>
      <title>Center for Efficient Mobility (CEM) Innovation Accelerator</title>
      <link>https://rip.trb.org/View/2636170</link>
      <description><![CDATA[This project will establish an “Innovation Accelerator” for the Center for Efficient Mobility (CEM)  to act as an incubator for commercializing technologies related to healthy and efficient mobility. The CEM consortium led by the Texas A&M Transportation Institute (TTI) has already laid the groundwork for this effort through the identification of stakeholders and partners and the establishment of an innovation ecosystem to accelerate the development, adoption, and commercialization of new transportation technologies, in partnership with the Texas Department of Transportation.  This project will formalize the innovation ecosystem within CEM, supported by commercialization support and stakeholder engagement. It will include commercialization support from technology commercialization and licensing offices at TTI and the A&M System, with support from facilities at our partner institutions, and input and advice from stakeholders. Through support, seed funding, commercialization grants, and the necessary legal and business support, CEM will champion students, faculty, and researchers in their efforts to commercialize any intellectual property developed as part of the grant. CEM will leverage the support of Texas A&M’s Innovation Office (https://innovation.tamus.edu/). CEM will also work with their counterparts at other consortium members such as Georgia Tech's CREATE-X and Quadrant-i initiatives (https://commercialization.gatech.edu/ ) and work with researchers, entrepreneurs, and investors to spin off new companies based on CEM research.   The key aspects of this project include:  Stakeholder Engagement – CEM will formalize a stakeholder engagement and advisory function to identify needs and problems that can be solved through research and technology developed by CEM; Innovation Ecosystem – Students, researchers, and faculty will be supported as they advance research outcomes. The innovation ecosystem will connect them to experts, entrepreneurs, and business communities. Testing facilities and seed funding will also be made available as needed to support technology development.  Commercialization Support -  Commercialization experts from within the consortium  will provide education,  technical support, legal and business support   to researchers who develop technologies with potential for commercialization.  ]]></description>
      <pubDate>Thu, 12 Feb 2026 15:56:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2636170</guid>
    </item>
    <item>
      <title>Data-driven assessment of rigid pavement vulnerability in Texas coastal regions</title>
      <link>https://rip.trb.org/View/2663108</link>
      <description><![CDATA[This research aims to evaluate the vulnerability of rigid pavements in two major coastal districts of Texas (i.e., Beaumont and Houston) spanning about 900 miles using data-driven approaches. Particularly, the study will (1) identify the key factors contributing to rigid pavement distress under dynamic coastal weather conditions, and (2) develop data-driven strategies to enhance the durability and performance of these pavement networks. Multi-source datasets, such as weather, geotechnical, traffic, coastal proximity, and pavement conditions, will be collected and integrated to support this analysis. Weather data, including temperature and precipitation, will be obtained from national and global databases such as NOAA’s National Centers for Environmental Information (NCEI) and NASA Earthdata/GES DISC. Soil classification and geotechnical attributes will be sourced from the NRCS SSURGO (Soil Survey Geographic Database), while coastal proximity data will be derived from Google Earth. Traffic volumes and loading data will be gathered from TxDOT’s Statewide Traffic Analysis and Reporting System (STARS II). Pavement condition metrics, including distress quantity, distress score, condition score, and ride quality, will be extracted from the Texas Department of Transportation (TxDOT)’s Pavement Management Information System (PMIS) and supplemented with satellite imagery. By integrating these datasets, the project will perform statistical and spatial analyses to establish correlations between weather variables, geotechnical conditions, traffic patterns, and pavement performance indicators.]]></description>
      <pubDate>Thu, 29 Jan 2026 19:58:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663108</guid>
    </item>
    <item>
      <title>Assessment of hydroplaning potential in coastal regions using roadway characteristics and related datasets</title>
      <link>https://rip.trb.org/View/2663101</link>
      <description><![CDATA[Hydroplaning is a critical pavement safety concern that occurs when a layer of water builds up between the vehicle's tires and the pavement surface, leading to a loss of traction and vehicle control. It is a significant contributor to wet-weather crashes and thereby poses a serious challenge to highway safety, especially for coastal regions where rainfall is more abundant and more frequent. Hydroplaning risk assessment fundamentally depends on the integration of multiple diverse datasets that reflect the interaction among crash occurrences, pavement conditions, and vehicle dynamics. These data items are typically recorded in different datasets maintained by various owners or agencies, each with their unique collection methods and standards. This research will develop data-driven likelihood models based on a verification check of the reliability of the important data variables, and a fusion of the available history data from diverse data sources to assess hydroplaning risks for coastal highways. The proposed research will also develop recommendations to be considered for roadway design and construction in association with wet-weather accident reduction procedures for transportation agencies.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:13:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663101</guid>
    </item>
    <item>
      <title>Role of emerging transportation technologies and safety initiatives in mitigating crashes in coastal communities</title>
      <link>https://rip.trb.org/View/2661744</link>
      <description><![CDATA[Coastal communities face heightened crash risks due to hazards such as hurricanes, flooding, and roadway degradation. Traditional safety countermeasures often fail to address these compounded risks, especially where evacuation routes are limited. This project will investigate how emerging transportation technologies (e.g., connected vehicle systems, advanced driver assistance systems, smart corridors) and safety initiatives (e.g., hazard-responsive traffic management, roadway design measures) can mitigate crash risks in coastal regions. Using literature review, geospatial screening of coastal corridors, and expert validation, the team will develop a prototype decision-support tool linking crash scenarios common in coastal environments with candidate technologies and initiatives. The outcome will provide agencies with a concise, practical framework to assess and prioritize safety solutions that improve infrastructure durability and resilience under coastal hazards.]]></description>
      <pubDate>Thu, 29 Jan 2026 16:13:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2661744</guid>
    </item>
    <item>
      <title>Coastal pavement maintenance and rehabilitation decision making based on both surface and subsurface conditions</title>
      <link>https://rip.trb.org/View/2662938</link>
      <description><![CDATA[Texas has approximately 3,359 miles of coastline spanning five geographically distinct districts. Pavements in these regions are exposed to highly variable subgrade soils, diverse traffic loading levels, and unique climatic challenges, including hurricanes, storm surges, and recurrent flooding. Effective decision-making for pavement Maintenance and Rehabilitation (M&R) is therefore critical to ensuring resilient infrastructure, optimizing project selection, and allocating limited resources efficiently. Current M&R selection practices primarily rely on surface-level indicators—such as distress manifestations (cracking, rutting, etc.) and ride quality. While these measures are useful, they fail to provide a comprehensive understanding of the pavement’s structural health. To address this limitation, this study will propose an integrated framework that combines both surface and subsurface information for M&R decision-making. In particular, subsurface conditions derived from non-destructive testing will be emphasized as a means to bridge the existing knowledge gap, enabling a more holistic and data-driven approach to pavement management.]]></description>
      <pubDate>Thu, 29 Jan 2026 15:57:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2662938</guid>
    </item>
    <item>
      <title>Health and Activity Impacts of Student Commute Modes</title>
      <link>https://rip.trb.org/View/2652176</link>
      <description><![CDATA[Active school transportation can profoundly influence children’s health, safety, and wellbeing. This project will investigate how different school commute modes – walking, bicycling, school bus, or private car – affect student physical activity and health, exposure to traffic-related air pollutants, safety, and travel disparity. Focusing on Texas school districts that currently or historically participate in Safe Routes to School (SRTS) programs, we will combine new data collection with existing evidence to evaluate the benefits and challenges of various commute modes. The study will also examine how shifting school trips to active modes may reduce vehicle emissions near schools and improve air quality. We will conduct surveys to quantify students’ physical activity during commutes, assess their exposure to emissions, and gauge perceptions of safety. Recent literature (2015–2025) will be synthesized to identify how school transportation choices affect student health (e.g. obesity, respiratory health, mental wellbeing) and safety outcomes, including disparities by socioeconomic status and geography. By evaluating SRTS interventions’ effectiveness in Texas communities, the project will fill critical gaps in understanding the multi-faceted impacts of commute mode on student wellbeing. Expected outcomes include practical recommendations for school districts and transportation agencies to design safer, healthier school travel environments. ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:25:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652176</guid>
    </item>
    <item>
      <title>Transportation and Mental Health in Central Texas Using 211 Call Center Data – An Exploratory Analysis</title>
      <link>https://rip.trb.org/View/2652177</link>
      <description><![CDATA[Mental health is an important part of an individual’s well-being and has been included as a key topic by the U.S. Centers for Disease Control and Prevention. Lack of access to affordable and efficient transportation can isolate individuals, limiting their ability to maintain employment, attend healthcare appointments, or engage in social and recreational activities—all of which are vital for mental well-being.  Long commutes, traffic congestion, and unreliable transit can contribute to chronic stress, anxiety, and fatigue, especially in urban environments. Active transportation options like walking and cycling not only reduce stress but also promote physical activity, which can reduce symptoms of depression.  
The research project aims to understand the multifaceted relationships between transportation and mental health by conducting a literature review using Latent Dirichlet Allocation (LDA) in topic modeling to identify prevailing themes and research trends in transportation and mental health. Also, through collaboration with United Way for Greater Austin, this project will incorporate insights from 211 Call Center staff and volunteers to better understand transportation-related mental health concerns, from a frontline service perspective. The project will then analyze 211 Call Center data provided by the United Way for Greater Austin. This analysis will explore spatial and temporal variations in mental health-related issues and examine how transportation correlates with mental health concerns. Caller comments, when available, will complement the quantitative data by providing personal context and deepening the understanding of lived experiences. Ultimately, the findings will inform policy recommendations aimed at addressing transportation barriers as a means to improve mental health outcomes in communities.    
]]></description>
      <pubDate>Tue, 13 Jan 2026 15:19:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652177</guid>
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