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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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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>Cannabis Use and Fitness-for-Duty Standards for Aviation Personnel</title>
      <link>https://rip.trb.org/View/2724767</link>
      <description><![CDATA[The Federal Aviation Administration (FAA) Office of Aerospace Medicine faces an urgent need to establish clear, scientifically defensible, and operationally actionable fitness-for-duty standards for pilots and air traffic control specialists (ATCS) in the context of evolving federal cannabis policy. Recent federal actions, including Executive Order 14370 and subsequent partial rescheduling of cannabis, have created a dual-status regulatory environment (Schedule I recreational vs. Schedule III medical use), while DOT drug testing and FAA medical certification standards remain unchanged. This misalignment introduces significant operational risk by: (1) increasing the likelihood and normalization of cannabis use; (2) complicating disclosure and compliance; and (3) leaving the FAA without validated criteria to determine when individuals are no longer impaired. The central policy question is: Following cannabis use, what elapsed time ensures both (1) absence of operational impairment and (2) compliance with DOT drug testing requirements? Due to the urgency of near-term policy decisions and the inability to conduct new primary research in the required timeframe, the FAA requires structured, policy-relevant interpretation of existing scientific evidence. This project will deliver that capability through a National Academies–facilitated expert meeting series over ~12 months, enabling FAA policy development and implementation within 12–18 months.
]]></description>
      <pubDate>Tue, 07 Jul 2026 09:41:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2724767</guid>
    </item>
    <item>
      <title>Operational Impacts of Women’s Health Factors on Pilot Availability and Certification</title>
      <link>https://rip.trb.org/View/2719307</link>
      <description><![CDATA[The number of certificated female pilots in the U.S. aviation system continues to increase and is projected to reach approximately 15–20 percent of the pilot workforce within the next decade. Current Federal Aviation Administration (FAA) medical certification policy, Aviation Medical Examiner (AME) guidance, and aeromedical training materials contain limited and inconsistent information addressing health factors specific to women pilots.
Women’s health factors—including pregnancy, postpartum recovery, hormonal therapies, menstrual disorders, and menopause—may temporarily affect medical eligibility, certification timelines, and operational availability. In the absence of a systematic, evidence-informed understanding of these factors, the FAA lacks the ability to anticipate certification impacts, promote consistency in AME decision-making, and support pilot workforce planning. This research will identify and characterize women’s health factors that influence pilot medical certification and availability, providing actionable insight to improve certification efficiency, reduce variability in aeromedical decision-making, and enhance forecasting of pilot availability. Results will inform targeted updates to FAA guidance, training, and policy materials without establishing new certification standards.
There are no statutory deadlines associated with this research; however, completion within 12 months is required to support near-term improvements to aeromedical guidance as the female pilot population continues to grow. The research requires collaboration with an external academic partner and Institutional Review Board (IRB) approval due to the use of human-subjects data.]]></description>
      <pubDate>Thu, 25 Jun 2026 09:31:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719307</guid>
    </item>
    <item>
      <title>Deep Learning–Based Digital Image Correlation for Fatigue Crack  Characterization in Steel Structures
</title>
      <link>https://rip.trb.org/View/2703927</link>
      <description><![CDATA[This proposal presents a strategic approach to improving transportation safety through the advancement of deep learning–based Digital Image Correlation (DIC) for fatigue crack characterization in steel structural components. With aging transportation infrastructure and increasing cumulative traffic loading, fatigue-related deterioration in steel bridges and related systems presents ongoing safety risks. Accurate measurement of crack-induced displacement fields is critical for reliable structural assessment and informed maintenance decisions. The primary objectives of this proposal are to advance artificial intelligence (AI)-driven DIC methods beyond the limitations of conventional correlation-based approaches by enabling sub-pixel displacement learning through synthetic data generation, incorporating physics-informed modeling of crack-induced displacement discontinuities, and supporting high-resolution analysis of large image regions without loss of spatial detail. The methodology involves grayscale synthetic speckle data generation for sub-pixel displacement learning, mechanics-based displacement field modeling using finite element simulations, and development of an attention-enhanced deep learning architecture for full-field displacement prediction. Experimental validation against commercial DIC systems will establish a transferable methodology supporting safer fatigue crack evaluation practices.
]]></description>
      <pubDate>Tue, 19 May 2026 13:48:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703927</guid>
    </item>
    <item>
      <title>Phase II Pilot Program for UAS-Enabled Component Level Bridge Inspection in New Mexico</title>
      <link>https://rip.trb.org/View/2703713</link>
      <description><![CDATA[Building on the success of Phase I, Phase II of the project seeks to expand unmanned aircraft system (UAS) inspection capabilities to focus on bridge superstructures. This is a more complex and critical component of overall structural performance, because superstructures, comprising elements such as girders, beams, and trusses, are responsible for transferring deck loads to substructures and ultimately to the ground. Their integrity is essential for bridge safety and serviceability.]]></description>
      <pubDate>Fri, 15 May 2026 13:14:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703713</guid>
    </item>
    <item>
      <title>Artificial Intelligence (AI) Technologies for Data-Driven Bridge Management</title>
      <link>https://rip.trb.org/View/2703687</link>
      <description><![CDATA[Artificial intelligence (AI) is showing promise for providing quick analysis, summary and documentation of field conditions for bridges. This project will provide the Illinois Department of Transportation (IDOT) with an overview of AI products available for bridge inspection and management. Researchers will review other state agencies’ practices and policies for use of AI in this field as well as develop recommendations for IDOT. Aid in formulation of AI policy for bridge inspection within IDOT may be considered if the department deems the technology essential. Effective use of AI in bridge inspection and management systems will provide cost and time savings to the state, allowing for quicker bridge inspections, diagnosis of issues and documentation.]]></description>
      <pubDate>Fri, 15 May 2026 09:24:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703687</guid>
    </item>
    <item>
      <title>Data-Driven Tools for Transportation Efficiency and Community Health</title>
      <link>https://rip.trb.org/View/2677559</link>
      <description><![CDATA[Factors related to transportation and mobility of people and goods have an impact on communities. Atlanta’s twenty-five Neighborhood Planning Units (NPUs) play an important role in providing input to variety of issues and help shape transportation planning and prioritize development efforts in the city.  However, NPU’s often lack access to comprehensive, data-driven granular insights into transportation infrastructure, mobility and safety, and community impacts. This project will identify, inventory, and analyze available data to describe transportation efficiency, safety, and community issues and challenges in the city of Atlanta and present analytical results aimed to inform NPU leaders and promote effective understanding of transportation priorities at the local community level. The developed tools, rooted in geospatially driven analysis will empower NPU leaders to effectively advocate for local interest with city administrators and policymakers.  

In conjunction with viewing Atlanta holistically, this research aims to look closer and break down interconnected issues related to efficient mobility, safety, community health and derive detailed, localized strategies and metrics. By integrating transportation and other relevant datasets into a geospatial framework, this project will assist NPU leadership and stakeholders to prioritize and recommend targeted strategies to enhance mobility options and improve overall community well-being.   ]]></description>
      <pubDate>Thu, 05 Mar 2026 12:24:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2677559</guid>
    </item>
    <item>
      <title>Communicable disease preparedness: Aircraft cabin disease dispersion study for model validation</title>
      <link>https://rip.trb.org/View/2675923</link>
      <description><![CDATA[This research supports the Federal Aviation Administration's (FAA’s) Aviation Safety Research Strategy Public Health Preparedness thrust and depends on access to National Research Council Canada’s Centre for Air Travel Research facility. To strengthen public health preparedness, the Office of Aerospace Medicine must quantitatively model disease transmission risk in commercial aviation and evaluate mitigation strategies. Building on preliminary work under prior work, risk analysis models have been developed for interagency Safety Risk Management (SRM) use, with broader dissemination planned to public health planners, industry, and academia.

This project directly responds to the final recommendation of GAO-22-104579, which highlighted critical gaps in prior models. The project will publish key human behavior and ventilation datasets, enabling peer review, independent replication, and expanded application.]]></description>
      <pubDate>Mon, 02 Mar 2026 10:19:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2675923</guid>
    </item>
    <item>
      <title>Customizing Transportation Services and Technologies Based on Rural Patient Healthcare Needs</title>
      <link>https://rip.trb.org/View/2667211</link>
      <description><![CDATA[The purpose of this project is to build on previous research to better understand the various linkages between specific comorbidities, lifestyle habits and targeted public transportation-related services and technologies. In addition, the project will demonstrate how these services and technologies can be adapted specifically for rural populations to secure better health outcomes. The primary research methods for this project will be as follows: 1) obtain literature about risk factors for specific comorbidities and lifestyle habits and how they interact with healthcare system access ; 2) use data from the University of Kentucky’s Healthcare’s Center for Clinical and Translational Studies and other Southeastern health systems to create a panel analysis of health outcomes based on University of Kentucky’s patient surveys, lifestyle habits, known comorbidities and diagnoses, and patient histories; 3) review materials as needed to determine best practices and synthesize findings for transportation-based support for specific medical conditions for rural residents; and 4) work with technology transfer programs and other stakeholders to develop a tool and/or outreach materials based on project findings for technology transfer professionals to improve transportation efficiency, technology and system innovation in trainings for transit and healthcare providers. The goal is to help both health and transportation providers to implement customizable healthcare mobility strategies based on their logistical capacity and patient needs.]]></description>
      <pubDate>Mon, 23 Feb 2026 14:19:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2667211</guid>
    </item>
    <item>
      <title>Driver to Non-Driver Transitions: Related Health, Mobility and Safety Outcomes</title>
      <link>https://rip.trb.org/View/2671991</link>
      <description><![CDATA[This project involves analyzing the impacts of becoming a non-driver (suddenly or gradually) in Wisconsin and nationally and effects on health, mobility, and safety outcomes. The project will analyze health, quality of life and mobility outcomes for drivers who are no longer able to drive. The researchers will analyze the safety, mobility, and quality of life outcomes for those who have suddenly or gradually become non-drivers. Analysis should focus on adult non-drivers of all ages and demographics, with particular emphasis on adults aging in place and urban versus rural areas. Once the analyses are conducted and complete, the researchers will report findings and provide recommendations for policies that lead to improved outcomes—namely increases in mobility and safety benefits for the entire state. Recommendations will help Wisconsin Department of Transportation (WisDOT) understand how to best offset impacts to mobility for individuals suddenly or gradually transitioning from being drivers to non-drivers.]]></description>
      <pubDate>Wed, 18 Feb 2026 11:39:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2671991</guid>
    </item>
    <item>
      <title>Cybersecurity Assurance via AI-Driven Digital Twins for Transportation Safety  </title>
      <link>https://rip.trb.org/View/2663600</link>
      <description><![CDATA[Transportation infrastructure increasingly depends on networked sensor systems for structural health monitoring, yet many operational deployments lack robust data-integrity protections, rendering them vulnerable to cyber-physical attacks. Manipulated sensor readings can misrepresent bridge health, rail conditions, or load limits, thereby creating risks of undetected structural failure, service closures, or catastrophic crashes. Because cyber manipulation directly produces false-safe readings, delays critical maintenance actions, and conceals structural distress, cybersecurity protection constitutes a core safety requirement, not an ancillary concern, for modern monitoring infrastructure.
This project develops a secure, artificial intelligence (AI)-driven digital twin framework that continuously compares real-time sensor data against expected behavioral responses to detect spoofing, tampering, replay, and delay manipulation, and other cyber-physical disruptions. The digital twin is intentionally implemented as a lightweight behavioral model; its purpose is not full structural simulation but rather the generation of expected-response profiles that serve as the ground-truth reference for anomaly detection. Combined with secure sensing hardware, AI-based detection algorithms, and survivability logic, the integrated system maintains reliable monitoring capability even under partial cyber compromise. The framework supports the U.S. Department of Transportation (USDOT) Safe System Approach by preventing cyber-induced safety failures and provides a clear pathway to pilot deployment through a Python-based prototype, agency demonstrations, and structured partner engagement.

Key milestones include the twin baseline model, secure sensing validation, AI detection module completion , and a survivability demonstration with partner input. The resulting system provides transportation agencies with a low-cost cybersecurity layer that protects safety-critical sensing systems from data manipulation and disruption. Deliverables include a Python detection module, interactive dashboard, and validated datasets compatible with existing DOT workflows. By ensuring the trustworthiness of monitoring data, the proposed approach reduces hazard risk, strengthens maintenance decision-making, and scales across bridges, tunnels, and rail systems, offering a realistic and immediate path to pilot adoption within USDOT transportation-cybersecurity priorities.
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:23:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663600</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>Healthy Micromobility: Moving From Crisis to Opportunity</title>
      <link>https://rip.trb.org/View/2652680</link>
      <description><![CDATA[Micromobility, including e-scooters and e-bikes, is an emerging transportation mode with the potential to alleviate congestion and improve urban mobility. However, prior research has primarily focused on safety risks and injury rates, with less attention given to its potential benefits, such as improved accessibility, reduced vehicle miles traveled (VMT), and enhanced health through active transportation. This project aims to provide a more comprehensive assessment of both the risks and benefits of electric micromobility within the U.S. transportation system using a combination of literature review, survey research, and systems dynamic modeling. The study examines how electric micromobility reduces VMT while also evaluating the health trade-offs related to safety risks and active transportation benefits. The project consists of three main aims: (1) a targeted literature review to synthesize existing evidence on electrified micromobility’s health impacts, (2) a nationally representative survey to capture user behavior, trip substitution patterns, and safety concerns, and (3) the development of a system dynamics simulation model to quantify the net health effects across diverse urban settings.     ]]></description>
      <pubDate>Tue, 13 Jan 2026 16:27:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652680</guid>
    </item>
    <item>
      <title>Bridging Data Gaps with Modeled Data from Generative AI: Advancing Health in Transportation Research</title>
      <link>https://rip.trb.org/View/2652171</link>
      <description><![CDATA[Transportation-related factors, such as air quality changes and exposure disparities, have significant impact on health outcome. Communities near high-traffic corridors experience elevated exposure levels, yet efforts to assess these impacts are hindered by the lack of high-resolution health and socio-demographic datasets. Traditional air quality models, such as dispersion and interpolation techniques, estimate pollutant distributions but struggle to capture localized exposure variations and real-world uncertainties due to their reliance on static assumptions. These limitations reduce the precision of transportation health impact assessments. 

This project addresses data gaps in air quality and health outcomes by integrating AI-generated data with  traditional modeling techniques. Bridging the data gap is essential to improving exposure assessments and provide a more comprehensive understanding of transportation-related health effects. The research develops and trains generative AI models for data augmentation, using harmonized datasets to create high-fidelity modeled data that reflects real-world patterns. Furthermore, we integrate the trained AI models with air quality simulation models to estimated transportation-related air quality scenarios and assess potential health impacts.
 
The project produces a validated generative AI model for data augmentation, generating high-resolution datasets that enhance geographic and demographic granularity in transportation health research. The application of scenario-based health impact simulations provides new insights into the relationships between air quality and health outcomes, improving the ability to evaluate transportation-related interventions. By combining AI-driven data synthesis with traditional modeling approaches, this research advances methodologies for transportation and environmental health assessments, providing more reliable data for exposure studies and policy evaluations. 
]]></description>
      <pubDate>Tue, 13 Jan 2026 16:10:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652171</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>Integrated Transportation and Health Impact Modeling Tool for U.S. Cities </title>
      <link>https://rip.trb.org/View/2652180</link>
      <description><![CDATA[The health of the American people is a national priority, and ensuring that transportation policies support strong communities, economic prosperity, and public well-being is a critical challenge that requires holistic solutions. This project will deliver groundbreaking research that directly informs transportation policies to improve traffic safety, air quality, and physical activity among transportation users in major American cities. These policies will help reduce preventable health burdens, cut healthcare costs, and enhance both community well-being and the cost-efficiency of our transportation systems. In the first stage of this project, we will review and update the underlying literature to refine and potentially extend the framework. We will develop updated visualizations to help transportation and public health agencies identify and communicate the various pathways linking transportation and health. By incorporating new evidence and addressing critical gaps, we will ensure the framework remains relevant for shaping future transportation policies at local, state, and national levels. During this stage, we will engage key stakeholders—such as transportation and public health agencies—by presenting our updated model, gathering their feedback, and enhancing our understanding of how transportation choices impact health outcomes. 
In the second stage, we will systematically collect, clean, quality-assess, harmonize, and integrate data from diverse sources to underpin subsequent quantitative modeling. This modeling exercise will examine pathways related to vehicle crashes/traffic safety, transportation-related air pollution, transportation-related physical activity, and any additional pathways deemed feasible for quantitative modeling based on data availability and strength of evidence. The data sources will include census population counts, geographic information system layers, transportation network layers and average vehicle speed data, household travel surveys, physical activity surveys, police crash records for fatal and non-fatal incidents, baseline health outcome rates, and associations between transportation factors and health outcomes as derived from systematic reviews and meta-analyses (i.e., dose- and exposure-response functions). This will allow us to construct a detailed and representative model of American mobility patterns, their health impacts through safety, air quality, and physical activity, and how targeted policies can mitigate risks and enhance benefits holistically across these pathways. We will focus on practical solutions that include policy instruments such as shifting a portion of trips to electric vehicles, electric buses, and electric bikes—while ensuring alignment with existing travel survey data for realism. 
]]></description>
      <pubDate>Tue, 13 Jan 2026 15:05:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652180</guid>
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