<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/common/TRIS Suite/feeds/RSS.aspx?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzcxIiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8%2BPHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8%2BPHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8%2BPC9wYXJhbXM%2BPGZpbHRlcnMgLz48cmFuZ2VzIC8%2BPHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM%2BPHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8%2BPC9wZXJzaXN0cz48L3NlYXJjaD4%3D" rel="self" type="application/rss+xml" />
    <description></description>
    <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>
    </image>
    <item>
      <title>Drone Network Design for Comprehensive Emergency Response in Rural Utah</title>
      <link>https://rip.trb.org/View/2786771</link>
      <description><![CDATA[Develop an optimized drone network design for comprehensive emergency response in rural Utah to deliver much needed supplies during times of emergency.]]></description>
      <pubDate>Tue, 06 Oct 2026 17:13:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786771</guid>
    </item>
    <item>
      <title>Strategic Mapping of UAV Test Sites and Corridors to Support Emerging Development and Workforce Training</title>
      <link>https://rip.trb.org/View/2784730</link>
      <description><![CDATA[Develop a comprehensive network of test sites and corridors for electric aviation to assist policymakers, developers, and workforce training institutions in making informed, long-term investments.]]></description>
      <pubDate>Wed, 30 Sep 2026 18:29:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2784730</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>FAA AME Color Test Reliability Study</title>
      <link>https://rip.trb.org/View/2778048</link>
      <description><![CDATA[This project will evaluate whether Federal Aviation Administration (FAA)-approved color vision screening methods produce consistent, reliable, and scientifically defensible aeromedical certification outcomes. Aviation Medical Examiners may use multiple approved tests, but applicants can potentially receive different results depending on the test, device, or testing location. The effects of equipment calibration, maintenance, and other sources of test variability are not sufficiently understood.

The research will compare approved screening methods across devices and locations to determine their reproducibility and equivalence. It will assess how equipment calibration and device-specific variation influence test results and whether these differences materially affect medical certification decisions. The project will also identify actions needed to improve the reliability, consistency, and quality assurance of color vision screening.

Preliminary findings are expected in fiscal year 2028, with final results required by June 2028. The FAA will use the evidence to determine whether changes are warranted to screening policy, approved testing methods, Aviation Medical Examiner guidance, training materials, equipment requirements, or quality assurance practices. The expected outcome is a stronger scientific foundation for color vision screening that supports consistent and equitable certification decisions while ensuring that pilots can safely perform aviation tasks requiring accurate color discrimination.]]></description>
      <pubDate>Mon, 14 Sep 2026 21:03:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778048</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>Advanced Air Mobility (AAM) Strategic Plan for New Hampshire</title>
      <link>https://rip.trb.org/View/2775489</link>
      <description><![CDATA[This research will position New Hamphshire to quickly integrate Advanced Air Mobility (AAM) safely and expeditiously into the state’s transportation system.  AAM will provide alternative transportation means for the movement of goods and passengers thereby reducing congestion in other transportation modes. This project will identify strategies for incorporating Advanced Air Mobility (AAM) operations that provide service through scheduled, on-demand, or as part of intermodal transportation links throughout NH’s communities and across state boundaries including how to accommodate a wide range of AAM operations, infrastructure, and legislative needs. A Strategic Plan detailing the current and expected trajectory of AAM in New Hampshire and throughout the industry will provide a roadmap for stakeholders to use in developing flexible solutions to integrating AAM technologies into NH’s existing transportation system.]]></description>
      <pubDate>Wed, 09 Sep 2026 10:41:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775489</guid>
    </item>
    <item>
      <title>Drone-in-a-Box: Enhancing Public Safety and Environmental Monitoring on Cape Cod</title>
      <link>https://rip.trb.org/View/2775052</link>
      <description><![CDATA[Cape Cod, a popular tourist destination, faces unique challenges due to its geographic isolation, seasonal population fluctuations, and increasing environmental concerns. This research proposes leveraging drone-in-a-box (DiB) technology, an autonomous drone system with automated launch, landing, and charging capabilities, to address these challenges. DiB systems offer persistent aerial surveillance and rapid response capabilities, making them ideal for various applications in remote and dynamic environments.

The research team for this project includes representatives from 
Massachusetts Department of Transportation (MassDOT) Aeronautics, MassAutonomy, and Endicott College.  Endicott College will identify appropriate team members based on the expertise needed for each aspect of the project.

This project is significantly strengthened by the collaboration and support of the Wellfleet Police and Fire Departments, who will provide valuable expertise, operational insights, and access to critical resources.

The objectives of this project are to: (1) Evaluate the technical feasibility of deploying DiB systems in the Cape Cod environment, considering factors such as weather conditions, Federal Aviation Administration (FAA) regulations, and communication infrastructure.
(2) Assess the operational effectiveness of DiB systems in each use case, measuring improvements in traffic flow, emergency response times, and shark detection accuracy.
(3) Analyze the cost-benefit of DiB implementation compared to traditional methods. 
(4) Investigate public perception and acceptance of drone technology for public safety and environmental monitoring.
The project will yield data and associated reports detailing the effectiveness of the use of drone-in-a-box (DiB) technology, an autonomous drone system with automated launch, landing, and charging capabilities, to address these challenges.
]]></description>
      <pubDate>Fri, 04 Sep 2026 14:40:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775052</guid>
    </item>
    <item>
      <title>Governance and Risk Management Framework for AI Adoption in Airport Operations</title>
      <link>https://rip.trb.org/View/2772535</link>
      <description><![CDATA[Airports are increasingly adopting artificial intelligence (AI) across a wide range of operational and administrative functions, including baggage screening, biometric passenger processing, security threat detection, predictive maintenance, autonomous mobility devices, passenger-facing chatbots, and operations optimization. Although AI adoption continues to accelerate, airports lack a common governance structure to guide responsible implementation, define organizational roles and accountability, assess AI-related risks, and align AI deployment with existing regulatory obligations. These challenges extend beyond information technology and affect multiple airport functions, including operations, security, legal, workforce planning, airline relations, customer experience, and public trust. Small and medium commercial service airports face additional challenges because they often lack the staff and resources needed to develop governance structures independently.

This research will develop a practical governance and risk management framework for AI adoption in airport operations. The project will be conducted as a phased research effort, providing an expedited initial deliverable followed by completion of the full research project. The research will produce an enterprise governance model, an airport-specific AI risk assessment methodology, regulatory compliance guidance aligned with existing federal and international frameworks, a stakeholder accountability model, implementation toolkits scalable for airports of different sizes, and workforce guidance addressing AI implementation, retraining, human oversight, algorithm transparency, and public communication.]]></description>
      <pubDate>Thu, 03 Sep 2026 08:41:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772535</guid>
    </item>
    <item>
      <title>Update of ACRP Report 4: Ground Access to Airports by Public Transportation</title>
      <link>https://rip.trb.org/View/2772536</link>
      <description><![CDATA[Airport operators continue to strengthen public transportation access for passengers and employees to enhance customer experience, support regional mobility goals, expand access to airport employment opportunities, reduce roadway congestion, and improve the environmental performance of the transportation system. The Airport Cooperative Research Program (ACRP) has produced several reports documenting strategies for improving public transportation access to airports, including ACRP Report 4 and related Cooperative Research Program publications. Since these reports were published, the transportation landscape has evolved with the emergence of transportation network companies and shared micromobility services, advancements in autonomous vehicle technologies, expanded rail connections at many U.S. airports, and new approaches to data collection and analysis. In addition, current airport transit ridership and passenger and employee mode share data are needed to support benchmarking, planning, and decision-making. Research is needed to update the existing body of knowledge, document current practices and emerging approaches, and compile current airport transit ridership and passenger and employee mode share data. The objective of this research is to update the strategies and information contained in ACRP Report 4 and related Cooperative Research Program reports, with a focus on airport passenger and employee mode shares and bus and rail transit ridership. The research should document current transit strategies, practices, and lessons learned; examine relevant changes and emerging approaches in airport ground access; and compile and analyze current airport transit ridership and passenger and employee mode share data using current survey methods and other available data sources. The resulting guidance should address airports of varying sizes and operating environments and be designed for use by airport planners and operators, transit agencies, regional planners, and other stakeholders involved in improving public transportation access to airports.  ]]></description>
      <pubDate>Thu, 03 Sep 2026 08:39:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772536</guid>
    </item>
  </channel>
</rss>