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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>Task Order Support for ACRP Insight Events. Forum on Airport Roles in Reducing Transmission of Communicable Diseases</title>
      <link>https://rip.trb.org/View/2784517</link>
      <description><![CDATA[Airports are the global gateways for passengers and cargo via aircraft, usually the quickest way to get overseas from one continent to the other. In addition to carrying passengers’ luggage and commercial goods, aircraft can be the vehicle by which diseases can quickly spread around the world. The Transportation Research Board has conducted several studies on this topic. In 2007, Interagency-Aviation Industry Collaboration on planning for Pandemic Outbreak workshop was organized to explore the state-of-the-practice for pandemic planning by airports and airlines. In 2009, Research on Transmission of Disease in Airports and on Aircraft symposium was conducted that examined the current research related to the transmission of disease on aircraft and in airports. ACRP also conducted two research projects that has resulted in ACRP Report 91: Infectious Disease Mitigation in Airports and on Aircraft, which provides guidance to airports and air carriers to mitigating the risk of spreading disease, and ACRP CRP-CD-137: The Vector-Borne Disease Airport Importation Risk Tool, which is an interactive tool to assist airports and airlines in understanding their roles in reducing the spread and transmission of diseases.
 
The objectives of this research are to convene an industry forum and build upon the established knowledge to discuss the challenges airports face in reducing the transmission of communicable diseases, quantify data to determine the risk of specific infectious diseases for airlines and airports, identify specific measures to mitigate the transmissions of diseases, and implementation priorities and other potential solutions and/or areas for further research. The presenters will include public health officials, epidemiologists, and airline environmental and occupational health experts.]]></description>
      <pubDate>Mon, 05 Oct 2026 14:25:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784517</guid>
    </item>
    <item>
      <title>Enterprise AI Governance Strategy and Roadmap: Enhancing Operational Efficiency and Data Safety</title>
      <link>https://rip.trb.org/View/2780484</link>
      <description><![CDATA[This project will develop an enterprise artificial intelligence (AI) governance strategy and implementation roadmap for the Idaho Transportation Department (ITD). The study will assess current AI applications within ITD, including related risks, opportunities, governance needs, and data privacy considerations. The project will review AI governance practices used by transportation agencies, state governments, and industry organizations. The results will be used to develop an enterprise AI governance framework with policies, accountability structures, risk management procedures, and guidance for responsible AI use, along with recommendations for training, compliance monitoring, and future AI adoption.]]></description>
      <pubDate>Mon, 21 Sep 2026 15:18:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780484</guid>
    </item>
    <item>
      <title>Integrating Remote Sensing and Ecological Monitoring for Wildlife-Resilient Roadside Maintenance</title>
      <link>https://rip.trb.org/View/2780408</link>
      <description><![CDATA[The primary objective of this project is to develop an automated, multi-scale decision-support framework that transforms satellite and vehicle-acquired remote sensing data into actionable roadside vegetation management workflows. Specifically, the project will achieve the following technical milestones: (1) Develop a Multi-Scale Sensing Architecture: Combine wide-area daily satellite imagery for macro-scale screening with meso-scale mobile sensor rigs mounted to daily Arizona Department of Transportation (ADOT) fleet vehicles (trucks, sweepers, mowers) for high-resolution edge diagnostics. (2) Engineer Multi-Modal Phenological Classification Models: Utilize Visible & Near-Infrared (VNIR) spectral bands (400–900 nm) to track rapid, post-monsoon green-up windows unique to invasive grasses and invasive forbs across all phenological growth stages, alongside Short-Wave Infrared (SWIR) imaging (900–1700 nm) and Long-Wave Infrared (LWIR) thermal sensing to calculate accurate Live Fuel Moisture Content (LFMC). (3) Construct a Predictive Prioritization Matrix: Build an automated roadside inventory system that fuses structural vegetation canopy data (via oblique terrestrial LiDAR/RGB), fuel moisture maps, wildfire risk indices, and traffic data to automate maintenance work orders and preserve clear zones, seamlessly integrated with an Interactive Fire Risk Dashboard.]]></description>
      <pubDate>Fri, 18 Sep 2026 17:11:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780408</guid>
    </item>
    <item>
      <title>Enhanced Crash Risk Estimation in Urban Environments: Integrating Multi-Source Data and Advanced Modeling Approaches for the City of Pittsburgh</title>
      <link>https://rip.trb.org/View/2778230</link>
      <description><![CDATA[Crash risk estimation is a critical aspect of transportation safety study, aimed at identifying high-risk locations and mitigating potential hazards/risks for road users. Limited resources can be allocated to those high-risk areas for effectively improving safety for the entire network. As travel demand grow, new mobility options emerges and traffic volumes increase, the complexity of factors influencing crash risk has also grown. Historically, crash risk estimation has been approached using statistical models, such as Safety Performance Functions (SPFs), which predict the expected crash frequency or risk on road segments based on exposure and other factors. However, the dynamic nature of urban environments and the growing availability of diverse data streams, such as video analytics, near-miss reports, real-time traffic data, and social media, present opportunities to enhance traditional methods. In particular, data directly measuring near-misses in addition to observed and rare accidents enable a more holistic and accurate measure of crash risks. These sensing and methodological advancements can improve model accuracy, reduce biases, and provide more granular insights into high-risk locations for both motorized and non-motorized traffic.

By leveraging long-term multi-source data related to crash risks, diverse sources such as police reports, traffic stops, and near-miss incidents from the city of Pittsburgh, and applying both statistical and machine learning approaches, this project aims to enhance the precision of crash risk estimations. It addresses key challenges related to data aggregation, modeling performance, and bias mitigation to provide more reliable safety insights that can inform future interventions.

Methodology and steps

The approach is to estimate SPFs for both motorized and non-motorized crash risks in the city of Pittsburgh with multiple data sources, including historical crash data, UPMC urgent care data, police traffic stop reports, 311 near miss report, 911 Dispatch data, Waze reports (social media), Velo.ai video analytics data, and so on. Specifically, the research team processes the video, medical, police and Waze data among all locations and in the past few years to either improve or validate the SPFs (namely current risk maps). The team applies both statistical and machine learning models and select the best model based on model performance. The team develops a new crash map and compare it with the the current risk map network. The team explores the potential biases and how to improve the accuracy of crash risk estimation in general. This work will be conduct particularly for the City of Pittsburgh, but can be replicated in any other communities in general. The detail for each step is listed below.
Step 1: Conduct a comprehensive literature review on risk factors and video analytics algorithm for near-miss detection
The team plans to carry out a comprehensive literature review on types of risk factors that make significant contributions to the crash risk (particularly near-misses related to vulnerable road users). This literature review helps collecting necessary datasets for building robust SPFs to estimate crash risk for streets in the following task. In addition, the team will review the approaches of detecting incidents from the video data, which could help improve the algorithm in detecting near-miss cases from the camera videos.

Step 2: Data collection and video processing for near-miss incidents
In this step, the team will collect the related datasets which could benefit the safety performance function estimation from multiple data sources. Table 1 lists potential datasets that could be applied in this research project. Furthermore, the team plans to carry out video processing with advanced deep learning techniques to detect the near-miss incidents from the camera videos from selected intersections. The result of the number of near-miss incidents and driving factors could be either used to improve the SPFs or validate the estimated SPFs.

Table 1. Source and year of the datasets
Dataset	Data source	Year
High injury network	City of Pittsburgh	-
Crash data	PennDOT	2017-2023
UPMC urgent care	UPMC	In request
Police traffic stop report	City of Pittsburgh	2018-2022
311 near miss report	City of Pittsburgh	2019-2023
911 Dispatch data	WPRDC	2015-2022
Network of bike facilities	City of Pittsburgh	-
Built environment data	EPA	-
Socio-demographic data	NHGIS	-
Waze data	Waze for Cities	2018-2024
Cyclists’ waypoints and distances to vehicles	Velo.ai	2022-2024
Camera video from selected intersections	City of Pittsburgh	In request
Strava pedestrian and bicyclist count data	Strava Metro	2023-2024

Step 3: Estimate SPFs with both statistical and machine learning modeling approaches.
The team will estimate two sets of SPFs for motorized crash risk and non-motorized crash risk, respectively, with the data collected and processed in Step 2. The team plans to apply both statistical (e.g., linear regression, Poisson regression, and Negative Binomial regression) models and machine learning models (e.g., random forest, gradient boosting decision tree, and neural network). The team will compare the model performance and select the best one as the final model.

Step 4: Develop a crash map for all street segments in Pittsburgh, compare it with the original high injury network, and prioritize high-risk locations. 
The team plans to use the estimated SPFs to predict the expected crash risk (both motorized and non-motorized) for all streets in the city of Pittsburgh and develop a crash map dashboard. The team will compare the crash risk with those estimated in the high injury network and find the differences. The team will explore the sources of bias and study how to improve crash risk estimation. Finally, the team will rank the locations and prioritize high-risk ones for further examination.]]></description>
      <pubDate>Thu, 17 Sep 2026 16:28:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778230</guid>
    </item>
    <item>
      <title>UTC Safety21: Improve Transportation Infrastructure (Geotechnical Asset) Safety by Reducing the Risks of Landslides - Phase 3</title>
      <link>https://rip.trb.org/View/2778223</link>
      <description><![CDATA[Geologic hazards including slope failures, landslides, mudflows, debris flows, etc. and hydrological hazards related to floods and stormwater surge can be destructive to transportation infrastructure (geotechnical asset) and threaten property and human life along the highway, railroad and road ways. Landslides alone cause thousands of deaths and many billions of dollars in damage every year. 

Morgan State University team proposes a multi-phase (multi-year) project focusing on safety of transportation infrastructure systems by preventing geohazard, specifically slope failure and landslides and minimizing impacts of geohazard. This project will employ an integrated approach of geotechnical and Artificial Intelligence (AI)/Machine Learning methods for assessing conditions of geotechnical assets, such as cut slopes and embankment of the DOT SHA and delineating landslides and high-risk areas. 

The objectives (tasks) of the proposal include: (1) with AI/Machine Learning approaches assess the risks of landslides based on soil/rock types, weather conditions, mechanical properties of slope materials, stream gage station flow data, pavement material and design, and the status of existing retaining structures along the selected highway sections, using Maryland as case studies and railroad, (2) identify and map the high-risk areas based on controlling factors such as geometry and mechanical properties of soil or rock, and triggering factors, including gravitational and hydraulic forces, using available survey data, remote sensing and LiDAR and InSAR data and other factors like transportation modes, (3) design and test protocols for real time monitoring at selected sites in consultation with agencies and organizations (such as DOT SHA, Federal Railroad Administration), and (4) recommend strategies for reducing the risks of landslides with real-time monitoring for the high-risk areas, and improving the safety of the transportation infrastructure. All the methods and strategies can be transferred to other states or regions with similar geological conditions and engineering configurations. Phase 1 of this project covered task 1 and part of task 2. Phase 2 covers part of task 1 and task 2. Phase 3 will cover task 3 and will also include literature review and summary of landslides along the railroad. This project will primarily complement the ongoing project sponsored by the Maryland DOT (MDOT) SHA (see more information in TRID) led by Zhuping Sheng in collaboration with Carnegie Mellon University (CMU) (Dr. Sean Qian). In this phase the research team will also expand their collaboration with CMU team (Dr. Christoph Mertz) by including technology transfer in photographical images processing to build conceptual models and identify slope failures.   

As PI Dr. Sheng will coordinate the efforts in collaboration with MDOT/SHA and advise other faculty and postdoctoral research associates and graduate students to carry out the project under Tasks 1 and 2. Dr. Zhuping Sheng has experience in geohazards assessment and mitigation, geotechnical and water resources engineering.  The team includes Co-PIs, Dr. Oludare Owolabi with experience in transportation engineering and resilient infrastructure and will mainly cover Task 1 and Dr. Yi Liu with experience in geohazards, land subsidence and landslides and geotechnical engineering and will cover Task 2. They have conducted and are currently conducting research supported by MDOT SHA, which provides a strong foundation for future collaboration with the partner MDOT SHA and others for technical transfer. Dr. Kofi Nyarko, Professor of the Electrical and Computer Engineering and Director of Morgan AI/ML program, has extensive experience in AI/ML research and applications, including automating complex systems through computer vision and machine learning as well as scientific/engineering simulation & visualization, predictive visual analytics and will mainly cover Task 3.

The Morgan AI/ML program will provide in-kind support (Dr. Nyarko’s time) and support for one student who will help with sensor tests, calibration and assembling as part of technical transfer at selected sites in consultation with Maryland DOT/SHA. The team will also explore opportunity for summer intern program with both UTC and the AI/ML program's support, which is designed for development of future workforce in transportation safety. Students have participated in and will continue to participate in exchange programs and deployment partner symposium and other activities. Through this project the MSU team will continue to expand collaboration with CMU and other partner institutes via faculty meetings, seminars, national summit, and other venues, which provides great opportunity for professional development.]]></description>
      <pubDate>Thu, 17 Sep 2026 14:52:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778223</guid>
    </item>
    <item>
      <title>Air Traffic Controller Human Performance Assurance Framework</title>
      <link>https://rip.trb.org/View/2778050</link>
      <description><![CDATA[The Federal Aviation Administration (FAA) requires a systems-level understanding of how medical qualification, fatigue management, staffing, training, procedures, reporting, and organizational controls collectively prevent, detect, mitigate, and support recovery from degraded air traffic controller performance. These controls have not been systematically characterized as an integrated barrier system, limiting the FAA’s ability to assess their effectiveness, dependencies, vulnerabilities, and contribution to overall safety.

This research will identify the essential human-performance barriers supporting air traffic control operations and establish methods for evaluating and monitoring their individual and collective effectiveness. It will also determine how controller performance events and underlying medical, physiological, cognitive, psychosocial, and operational factors can inform continuous improvement and system resilience.

Results are required within 12 months to support the FAA Reauthorization Act Section 411 Aviation Workforce Mental Health Task Group. The resulting FAA-endorsed Human Performance Assurance Framework will support evaluation of controller mental health protocols, medical qualification requirements, assistance programs, and related Aviation Rulemaking Committee recommendations. The framework will enable transparent, evidence-based decisions about controller health and human-performance risks within the FAA Safety Management System.]]></description>
      <pubDate>Tue, 15 Sep 2026 11:22:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778050</guid>
    </item>
    <item>
      <title>AI-Enabled Aeromedical Safety Assessment Decision Support</title>
      <link>https://rip.trb.org/View/2778047</link>
      <description><![CDATA[This project will evaluate how artificial intelligence can support the Federal Aviation Administration's (FAA’s) Aeromedical Qualitative Hazard Assessment Framework while preserving human oversight, scientific rigor, transparency, traceability, and regulatory defensibility. The framework provides a structured method for assessing how medical conditions, treatments, and operational mitigations may affect pilot performance and aviation safety, but its application requires substantial expert effort.

The research will identify assessment activities suitable for artificial intelligence (AI) assistance, including evidence identification, impairment-pathway mapping, operational scenario development, bowtie modeling, barrier evaluation, confidence assessment, and report preparation. Candidate technologies—including large language models, knowledge-retrieval systems, and automated reasoning tools—will be compared with expert-led assessments using representative aeromedical policy cases. Evaluation will address accuracy, completeness, consistency, reproducibility, traceability, workload reduction, and timeliness.

The project will also examine potential biases, failure modes, evidence-quality concerns, and governance requirements. It will define the human-review responsibilities, technical architecture, data resources, validation methods, and controls needed for responsible operational use. Deliverables will include an assessment of AI applicability across the framework, a prototype AI-assisted workflow and decision-support architecture, case-study performance results, and an implementation roadmap. The findings will guide future FAA investments while ensuring that AI augments, rather than replaces, accountable expert judgment in aeromedical safety decisions.]]></description>
      <pubDate>Mon, 14 Sep 2026 20:55:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778047</guid>
    </item>
    <item>
      <title>Human Performance Risk Assurance through FAA–Industry Collaboration</title>
      <link>https://rip.trb.org/View/2778046</link>
      <description><![CDATA[This project will develop an evidence-based method for evaluating how the Federal Aviation Administration (FAA), air carrier, labor, and peer-support controls collectively manage aeromedical and human-performance risks. Current FAA aeromedical assessments do not consistently account for operator Safety Management System controls or establish when those controls can be credited as effective and reliable safeguards.

The research will define how medical, operational, organizational, and peer-support controls should be represented in aeromedical safety assessments. It will identify the evidence needed to demonstrate control effectiveness, determine how operator SMS processes may contribute to pilot performance assurance, and establish appropriate safety-assurance methods, metrics, and leading indicators for human-performance hazards. The research will also evaluate the potential safety contributions and assurance requirements of voluntary reporting and peer-support programs.

Preliminary findings will be delivered in fiscal year 2027, with final recommendations completed by the end of fiscal year 2028. The FAA will use the results to update its Aeromedical Qualitative Hazard Assessment Framework, develop guidance for evaluating operator SMS controls, support pilot performance assurance initiatives, and inform implementation of voluntary reporting and peer-support programs. The resulting framework will provide a more comprehensive and transparent basis for integrating aeromedical, operational, and organizational safeguards while identifying residual risk that remains subject to FAA oversight.]]></description>
      <pubDate>Mon, 14 Sep 2026 20:49:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778046</guid>
    </item>
    <item>
      <title>Pilot Performance Assurance System Development and Evaluation</title>
      <link>https://rip.trb.org/View/2778045</link>
      <description><![CDATA[This project will develop a repeatable Pilot Performance Assurance System method to support modernization of Federal Aviation Administration (FAA) aeromedical certification under Section 411 of the FAA Reauthorization Act of 2024. Current aeromedical evaluations generally consider individual medical conditions, treatments, or certification requirements without systematically accounting for the operational, organizational, and regulatory safeguards that also support airline pilot performance.

The research will define how these safeguards should be represented and establish criteria for evaluating their effectiveness, independence, reliability, and supporting evidence. The method will also provide a structured approach for communicating uncertainty, evidence limitations, and differences in expert judgment. Application to selected policy questions will help determine whether identified risks should be managed through medical certification, operational safeguards, or a layered combination of controls.

Beginning in October 2026, the project will produce an initial review and policy-case materials for November 2026 working-group sessions. By September 2027, it will deliver the completed model, assessment tools, case analyses, decision records, and draft findings and recommendations. Approved results will support the Section 411 report and future FAA decisions concerning aeromedical policy, guidance, procedures, analytical tools, and training. The project is intended to make aeromedical safety decisions more transparent, consistent, evidence-based, and responsive to the full system of safeguards protecting Part 121 operations.]]></description>
      <pubDate>Mon, 14 Sep 2026 20:43:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778045</guid>
    </item>
    <item>
      <title>Aeromedical Oversight Performance Framework and Measurement</title>
      <link>https://rip.trb.org/View/2778044</link>
      <description><![CDATA[This project will develop a performance-based framework for determining whether the 
Federal Aviation Administration's (FAA's) aeromedical oversight system is effectively managing safety risk across the National Airspace System. Existing approaches emphasize compliance activities and rare adverse outcomes, which provide limited insight into whether oversight actions are preventing complex, latent medical and human-performance risks.

The research will adapt the FAA’s broader oversight strategy to the aeromedical environment. It will define the populations and activities subject to oversight, clarify the objectives of the FAA’s regulatory, guidance, approval, surveillance, and corrective functions, and trace how these activities are expected to influence safety. Researchers will identify operational signals, data sources, outcome pathways, and indicators that collectively demonstrate whether oversight is working as intended.

The project will produce an aeromedical oversight performance framework, an implementation-ready metrics dictionary, and a phased integration roadmap. These products will support continuous monitoring, earlier identification of emerging risks, and more informed decisions regarding medical certification policy, surveillance priorities, and resource allocation. By making preventive performance more visible, the framework will help the FAA transition from measuring oversight activity to evaluating its safety effects and managing aeromedical risk as an adaptive system.]]></description>
      <pubDate>Mon, 14 Sep 2026 20:32:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778044</guid>
    </item>
    <item>
      <title>Tipping-Point Analysis of Air Traffic Controller Impairment in Terminal Airspace Operations</title>
      <link>https://rip.trb.org/View/2778042</link>
      <description><![CDATA[This project will develop and validate a computational model for assessing how cognitive impairment among air traffic controllers may affect the safety and efficiency of terminal airspace operations. Current Federal Aviation Administration (FAA) oversight relies largely on procedural safeguards and retrospective monitoring and cannot quantitatively predict how fatigue, illness, medication effects, or other impairments influence controller performance or aircraft separation.

The research will first construct a representative model of air traffic controller tasks using operational procedures, realistic traffic scenarios, simulation, and subject-matter expert input. After confirming that the model accurately represents unimpaired controller performance, researchers will introduce impairment-related changes in cognitive performance and evaluate their effects on loss-of-separation risk, conflict-resolution time, delay, and throughput. The analysis will examine whether safety margins decline gradually or exhibit “tipping points” at which relatively small additional impairments produce disproportionate operational effects.

The resulting model will provide the FAA with a reusable predictive capability to support safety risk assessments, fatigue risk management, staffing evaluations, and medical oversight policy. It may also be adapted to evaluate other human-performance factors, including training, aging, procedural changes, and new technologies. Findings will help determine whether current safeguards remain adequate or whether performance-based policy changes should be considered.]]></description>
      <pubDate>Mon, 14 Sep 2026 20:19:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778042</guid>
    </item>
    <item>
      <title>Implementation of Riley’s Qualitative Safety Evaluation Method for Pilot Impairment Risk</title>
      <link>https://rip.trb.org/View/2778043</link>
      <description><![CDATA[This project will develop and demonstrate a standardized qualitative method for evaluating how medical conditions may affect pilot performance and aviation safety. The research addresses partial impairment, in which physiological or cognitive effects may degrade a pilot’s ability to perform specific operational tasks without causing total incapacitation.

Using the landing phase of airline operations as a bounded case study, researchers will work with airline transport pilots, aeromedical specialists, neuropsychologists, human-factors experts, and safety-management professionals. The team will identify critical cognitive and performance requirements, map potential impairment mechanisms to vulnerable tasks, and apply Riley’s qualitative safety evaluation method to construct explicit safety claims supported by structured arguments, evidence, assumptions, uncertainties, and confidence considerations.

The project will produce a fully documented example evaluation, task and impairment mapping, reusable templates, evidence checklists, and implementation guidance. These products are intended to help the Federal Aviation Administration (FAA) Office of Aerospace Medicine evaluate medical risks more consistently, transparently, and defensibly while supporting modernization of aeromedical policy and oversight. Following completion in fiscal year 2027, the FAA plans to incorporate the method into guidance, workforce training, and additional pilot medical-risk evaluations to assess its scalability and practical value.]]></description>
      <pubDate>Mon, 14 Sep 2026 16:53:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778043</guid>
    </item>
    <item>
      <title>STPA-Based Risk Assessment for Mental Health–Related Aviation Performance Hazards</title>
      <link>https://rip.trb.org/View/2679033</link>
      <description><![CDATA[This project develops and demonstrates a systems-theoretic approach for assessing aviation safety risks associated with mental health–related performance degradation among pilots and air traffic controllers. Current aviation safety frameworks recognize that many human performance hazards—particularly those associated with mental health—cannot be reliably quantified using traditional probabilistic risk methods, creating a gap in Safety Management System (SMS) risk assessment and oversight. The research applies System-Theoretic Process Analysis (STPA) to model aeromedical mental health oversight as a sociotechnical control structure involving the Federal Aviation Administration (FAA)  Office of Aerospace Medicine, airline operators, labor organizations, peer-support programs, and healthcare interfaces. Through structured analysis and stakeholder engagement, the project will identify hazards, unsafe control actions, and systemic contributors that may degrade the effectiveness of existing oversight mechanisms. The research will then develop a modernized control structure aligned with SMS principles, including improved regulator–industry interfaces, qualitative risk evaluation methods for non-probabilistic hazards, and leading indicators to monitor oversight system health. The resulting framework is intended to support more consistent, transparent, and defensible evaluation of mental health–related aviation performance hazards while enabling proactive safety assurance within the broader aviation oversight architecture.]]></description>
      <pubDate>Mon, 14 Sep 2026 16:38:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2679033</guid>
    </item>
    <item>
      <title>Human-AI Collaborative Bridge Inspection for Advanced Damage Detection
and Defect Quantification</title>
      <link>https://rip.trb.org/View/2689757</link>
      <description><![CDATA[The aging concrete bridge infrastructure across the U.S. poses increasing risks to safety and reliability, amplifying the urgency to modernize inspection processes. Traditional manual inspection methods are labor-intensive, subjective, and inconsistent, creating barriers to effective infrastructure management. This research proposes an innovative human-AI collaborative framework designed to enhance the durability, resiliency, and technological capabilities of bridge inspections. The project integrates advanced artificial intelligence (AI) and computer vision technologies to automate damage detection and defect quantification. To ensure reliability and trustworthiness, the AI system incorporates rigorous uncertainty quantification methods, increasing transparency and inspector confidence. An active learning component strategically facilitates human-AI collaboration, enabling inspectors to focus on the most critical inspection cases identified by the AI, significantly reducing manual workloads. Additionally, engineering domain expertise, particularly in structural reliability and risk assessment, will inform and prioritize inspection processes within the AI framework. The outcomes of this project will substantially improve the accuracy, efficiency, and reliability of bridge inspections, promoting safer, longer-lasting, and more resilient transportation infrastructure. Anticipated benefits include reduced inspection costs, enhanced public safety, and a proactive approach to infrastructure management that significantly extends service life and supports sustainability. ]]></description>
      <pubDate>Thu, 10 Sep 2026 09:04:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689757</guid>
    </item>
    <item>
      <title>Cost Effectively Building Resilience into Freight Transportation Operations and
Capital/Infrastructure Investments</title>
      <link>https://rip.trb.org/View/2692316</link>
      <description><![CDATA[Resilience and disruption response have become part of our daily lexicon.  However, cost-effectively and continuously including resilience considerations in freight operations and infrastructure investments is still developing.  This research effort focuses on identifying the key steps that can be taken by regions to work resiliency from a supply chain perspective in planning and infrastructure investment efforts.  While approaches such as risk registers and “bow tie” analyses exist, many regions and organizations do not have a consistent understanding of how to incorporate resilience cost-effectively into existing programs in terms of freight operations and investments. The research will involve surveys, interviews, and case studies of effective practices involving supply chains and various forms of disruptive events. The large volume of existing resources will be noted and arranged in a format that makes them easily used by public agencies.  The result will be a document to assist public sector entities with considering and incorporating resilience into freight planning and infrastructure investments. ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:39:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692316</guid>
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