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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>
    <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>Coordinating the Airport Workforce Across Multiple Employers</title>
      <link>https://rip.trb.org/View/2772537</link>
      <description><![CDATA[Airport operators are increasingly held accountable for operational and customer service outcomes that depend on a workforce they do not directly employ. Passengers interact with employees from numerous organizations operating at an airport—including airport staff, airlines, concessionaires, contractors, ground handlers, TSA, and other federal agencies—while generally viewing the airport as a single entity. Although airports have limited direct authority over many of these employees, they are expected to provide a consistent customer experience and coordinate expectations, standards, training, and performance across this multi-employer environment.

The objective of this research is to develop practical, airport-controlled coordination mechanisms that help airport sponsors improve workforce outcomes without relying on policy changes or new technology. The research will develop governance and influence models, best practices and template language for workforce coordination agreements, and tools for aligning training, service standards, and performance expectations across multiple employers to support a consistent organizational culture across the airport community. The project will also produce an implementation toolkit with templates, playbooks, metrics, and case studies to help airports of varying sizes coordinate a fragmented workforce, including airports that have limited resources to support these efforts.]]></description>
      <pubDate>Thu, 03 Sep 2026 08:36:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772537</guid>
    </item>
    <item>
      <title>Best Practices for Combined ARFF and Airport Operations Departments</title>
      <link>https://rip.trb.org/View/2772558</link>
      <description><![CDATA[Many medium, small, and non-hub airports use staffing models that combine Aircraft Rescue and Fire Fighting (ARFF) and airport operations functions to improve staffing efficiency while maintaining required emergency response capabilities. Personnel in these roles are often responsible for multiple operational functions, creating challenges related to balancing emergency response readiness with routine airport operations, maintaining regulatory compliance, supporting a consistent safety culture, and managing competing operational priorities. Airports operating with combined staffing models require practical guidance to address these operational challenges while complementing existing FAA regulatory requirements.

The objective of this research is to evaluate the operational, safety, and regulatory implications of combined ARFF/Operations staffing models and develop practical, data-driven guidance for airports. The research should examine implementation approaches for combined ARFF and operations functions, training practices, task saturation, operational staffing models, and the use of emerging technologies that support airport operations and emergency response. The project will produce best practice recommendations, staffing frameworks, risk assessment tools, and decision-making guidance to assist airports that currently use or are considering combined ARFF/Operations departments while complementing, rather than interpreting or replacing, existing FAA regulatory requirements.]]></description>
      <pubDate>Tue, 01 Sep 2026 11:51:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772558</guid>
    </item>
    <item>
      <title>Preparing the Airport Workforce for Emerging Technologies and Data-Informed Decision Making</title>
      <link>https://rip.trb.org/View/2772531</link>
      <description><![CDATA[Airports are operating in an increasingly complex environment driven by rapid technological change, expanding data availability, evolving safety expectations, and growing operational demands. At the same time, airports continue to face workforce challenges, including retirements, competition for skilled labor, recruitment and retention issues, and the loss of institutional knowledge. Although airports collect increasing amounts of workforce and operational data, these data often remain fragmented across separate systems, limiting their ability to align workforce capabilities and staffing decisions with operational needs and the adoption of emerging technologies.

The objective of this research is to develop practical guidance, tools, and implementation resources that help airports modernize workforce development while improving data-informed workforce decision-making. The research will produce an airport workforce framework, role-based training pathways, guidance for integrating workforce and operational data, and implementation resources that airports of varying size and complexity can use to strengthen workforce capabilities, support staffing and operational decisions, and complement existing workforce research while minimizing duplication.]]></description>
      <pubDate>Tue, 01 Sep 2026 10:02:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772531</guid>
    </item>
    <item>
      <title>Strengthening Cross-Disciplinary Collaboration Throughout Project Development and Delivery</title>
      <link>https://rip.trb.org/View/2768407</link>
      <description><![CDATA[Project development/design and construction activities at the Kentucky Transportation Cabinet (KYTC) tend to be siloed. During project design and development, and construction personnel have few if any opportunities to provide input. And once construction begins, project managers and designers have little involvement. The lack of sustained engagement has multiple consequences. Contract plans prepared without construction input can be difficult to build in the field. Similarly, if designers and project managers proceed without early feedback from construction subject matter experts and do not participate during the construction phase, they will not gain knowledge of how to minimize or mitigate constructibility issues that contractors sometimes confront on project sites. They will also have little role in finalizing as-built plans, which are integral to guiding future maintenance and construction efforts. The absence of cross-disciplinary collaboration throughout project development and delivery also undercuts quality management and can leave key risks undetected (e.g., complex traffic control scenarios, construction spanning multiple seasons), and thus unaddressed and un-mitigated. Resolving these challenges requires putting into place integrated workflows that foster cross-disciplinary dialogue and reciprocity throughout the project life cycle.]]></description>
      <pubDate>Wed, 26 Aug 2026 17:04:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2768407</guid>
    </item>
    <item>
      <title>A Succession Planning and Knowledge Transfer Strategy for KYTC</title>
      <link>https://rip.trb.org/View/2768405</link>
      <description><![CDATA[Retirements and staff turnover imperil the institutional knowledge base at the Kentucky Transportation Cabinet, and by extension project development and delivery. To cope with these dynamics, KYTC must put in place a succession planning framework that helps preserve experience, catalogue institutional knowledge, and strengthen personnel performance. Researchers previously worked with KYTC to identify and define core competencies, which encompass the knowledge and skills an employee must have to successfully fulfill a position’s responsibilities. Additional work is needed to identify critical tasks, knowledge gaps, and training needs; distill knowledge and experiences of veteran staff into formats that new employees can engage with; and create digital, updatable reference guides staff and project managers can use to improve their efficiency and reduce the likelihood for risk and rework.]]></description>
      <pubDate>Wed, 26 Aug 2026 17:04:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2768405</guid>
    </item>
    <item>
      <title>Workers as Co-designers of Workforce Development: Lessons for Transit Workforce Planning</title>
      <link>https://rip.trb.org/View/2761610</link>
      <description><![CDATA[This project documents and shares insights from a comparative study of workforce co-design initiatives in other regions. The main objective is to enhance hiring, retention, and leadership promotion within job areas and positions at regional transit agencies that are currently the hardest to staff and keep full. The research is designed to assist regional transit agencies in determining where there is the greatest potential for worker-informed co-design processes to make an impact on hiring and retention, and to support implementation of high-impact processes within regional transit organizations and partner organizations contracted for workforce hiring.

Co-design efforts recognize that current and prospective workers hold the key to unlocking access to new generations of workers because of their lived experience, cultural and social connections, and career awareness. Their involvement is helping to elevate public organizations and their hiring partners as "employers of choice," ensuring more diverse populations recognize and benefit from the quality of the jobs and career opportunities being offered. Equally, by giving workers a voice in workforce development design, these organizations amplify the dignity of work, ensuring employees gain a greater sense of purpose and meaning from their daily work and thus feel empowered to improve the work lives of others. ]]></description>
      <pubDate>Mon, 17 Aug 2026 10:49:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2761610</guid>
    </item>
    <item>
      <title>Development and Evaluation of a Large Language Model and Virtual Reality Framework for Improving Flagger Training
</title>
      <link>https://rip.trb.org/View/2739298</link>
      <description><![CDATA[Flaggers are essential for maintaining traffic safety in work zones, serving as human traffic controllers who coordinate alternating traffic through the work zone, yet they work under extremely hazardous conditions in close proximity to high-speed traffic and heavy equipment. Traditional classroom-based training is often insufficient for developing situational awareness, hazard recognition, and communication skills, while real-world training exposes trainees to significant risk. Virtual reality (VR) offers immersive, hands-on practice without danger, but current VR systems rely on preprogrammed scenarios and require instructors to manually identify trainee errors.

This project develops a large language model (LLM)-based virtual flagger that provides dynamic, realistic interactions within a VR training environment rather than rigid, pre-scripted scenarios. The virtual flagger engages in natural radio communication, responds contextually to trainees’ actions, asks clarifying questions when communication is unclear, and adapts its behavior to create diverse, progressively challenging training experiences.

]]></description>
      <pubDate>Thu, 30 Jul 2026 16:26:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2739298</guid>
    </item>
    <item>
      <title>Flood-resilient Transport System Through Integrated Modeling, ML &amp; Immersive AR/VR </title>
      <link>https://rip.trb.org/View/2732359</link>
      <description><![CDATA[Extreme rainfall events increasingly disrupt urban transportation systems by overwhelming drainage infrastructure and causing localized road flooding that impedes last-mile freight delivery, delays emergency response, and disrupts the broader multimodal supply chain. These disruptions limit access to essential services, delay emergency response, and threaten public safety. Building on the research team's previously developed framework (F25-26), this project advances a data-driven approach for high-resolution prediction of urban road flooding in Jackson, Mississippi, integrating geospatial databases, process-based H-H modeling (aligning with rigorous U.S. Army ERDC methodologies), and machine learning (ML) and artificial intelligence (AI) techniques to identify flood-prone road and railway segments. The ML-based surrogate models will maintain computational efficiency, enable timely identification of vulnerable transportation networks, and support emergency response by feeding into JSU Water Lab’s broader web-based-visualization interfaces. This project introduces an interactive K–12 STEM module, age-appropriate hands-on activities along with STEM curriculum module designed for upper-level Civil Engineering undergraduate and graduate students at JSU. This initiative transforms research outcomes into the classroom to modernize workforce training using integrated Augmented Reality (AR) and Virtual Reality (VR) and will be tested with summer student exchange programs. Students can explore and 3D print several transportation infrastructure components, such as culverts, bridges, and urban drainage systems, and evaluate their performance under simulated flood conditions, and experience AR/VR based immersive simulators. Using AR/VR tools, including the Meta Quest platform, available in the PI lab, future transportation engineers will visualize flood scenarios in immersive 3D environments built from existing topographical assets in Unity or Unreal Engine. By combining advanced predictive modeling with experiential learning, the project promotes advanced STEM engagement, and high-tech workforce development, aligning with broader goals of improving multimodal transportation system resilience. While the primary focus is on urban road and rail flooding, these transportation corridors serve as critical connectors to Mississippi's inland waterway freight network, including facilities linked to the Pearl River system and regional multimodal freight movements. Roadway disruptions during extreme rainfall events can delay freight access to ports, intermodal terminals, water-dependent industrial facilities, and affect supply-chain resilience. By identifying flood-vulnerable roadway and railway segments, the proposed framework will support more reliable connectivity between surface transportation infrastructure and maritime freight operations]]></description>
      <pubDate>Tue, 21 Jul 2026 16:34:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732359</guid>
    </item>
    <item>
      <title>Community Response Enhanced Education on Disasters (CREED): Virtual Reality Training to Enhance Transportation</title>
      <link>https://rip.trb.org/View/2732358</link>
      <description><![CDATA[Artificial Intelligence/Virtual Reality (AI/VR) simulation activities enhance critical emergency management functions—planning, forecasting, threat detection, security, and information sharing—ultimately improving the preservation of life and property. These tools create a safe, immersive environment where participants build problem-solving skills, decision-making ability, and confidence in disaster response. This project will deliver education, professional development, and continuous improvement in emergency preparedness for both aspiring and current professionals. The research team will invite high school and community college students to participate alongside university students and practitioners. Participants will engage in hands-on training and live demonstrations using advanced technologies such as virtual reality (VR) and artificial intelligence (AI)-driven simulations. The program fosters multidisciplinary collaboration among disciplines: Emergency Management Technology, Meteorology, Computer Science/Engineering, Health Science, and Journalism, and Media Studies. Through classroom instruction, workshops, and interactive training demos, students will work directly with emergency management professionals, strengthening real-world skills, supporting school-to-work transitions, and enhancing career readiness.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:29:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732358</guid>
    </item>
    <item>
      <title>Update Standardized Measurement Systems and Laboratory Test Procedures to Improve Motorist Safety</title>
      <link>https://rip.trb.org/View/2727316</link>
      <description><![CDATA[The growth in construction and maintenance activities throughout the state has prompted an increase in the throughput for standardized measurements, laboratory tests, and similar processes performed by TxDOT on a routine basis. TxDOT, as well as its affiliated stakeholders, must meet this increased demand with a limited availability of trained workforce while ensuring high quality and consistent work products. While many of these processes have been developed and used for several decades, recent advances in technologies such as sensors, computing, logic controllers, machine vision, and actuators have now made it feasible to fully or partially automate several of the existing processes or, where feasible, replace some archaic processes with modern advanced and automated methods. The research teams will conduct one or more scouting tours of divisions and districts to create an inventory of processes and identify their potential to be automated. The research teams will use a scoring system with input from TxDOT to prioritize processes that can be partially or fully automated and benefit TxDOT. The research teams will develop, validate, and facilitate implementation of selected solutions with appropriate workflow integration and training of TxDOT’s personnel.]]></description>
      <pubDate>Fri, 10 Jul 2026 14:52:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727316</guid>
    </item>
    <item>
      <title>Building a Sustainable Workforce: Enhancing Employee Retention at GDOT
</title>
      <link>https://rip.trb.org/View/2719324</link>
      <description><![CDATA[
This research project aims to improve employee retention, particularly Transportation Engineers and Transportation Specialists. By identifying and analyzing current practices alongside employee experiences and expectations, it will offer evidence-based insights to inform decision-makers, including the Office of HR.
]]></description>
      <pubDate>Thu, 25 Jun 2026 11:39:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719324</guid>
    </item>
    <item>
      <title>SPR 782 Improving Worker Safety in Work Zones with Effective Proximity Sensing Technology</title>
      <link>https://rip.trb.org/View/2719302</link>
      <description><![CDATA[The project will synthesize SWZ technologies and analyze their benefits and challenges —specifically focusing on intrusion alert and motorist alert systems—based on their use by other highway agencies and the construction industry. These findings will then be tailored to fit the needs of the South Carolina Department of Transportation's (SCDOT’s) internal and contract workforce. To meet the research goals, the team will focus on three key objectives: (1) Investigate various intrusion alert technologies to identify those best suited for highway worker safety, motorist alert systems effective in work zones, and solutions that can reduce unsafe driving behaviors in South Carolina. (2) Assess at least three intrusion alert and three motorist alert technologies for suitability to the SCDOT, evaluating their capabilities, feasibility, and providing cost estimates for initial implementation and life cycle comparisons against current practices.
(3) Evaluate compliance by workers in using these technologies and assess motorist response and behavioral changes prompted by driver-facing alert devices.]]></description>
      <pubDate>Thu, 25 Jun 2026 08:57:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719302</guid>
    </item>
    <item>
      <title>Evaluating Construction Workforce Conditions and Their Effects on Productivity and Project Scheduling</title>
      <link>https://rip.trb.org/View/2712203</link>
      <description><![CDATA[Transportation construction projects often involve accelerated schedules, extended work hours, and work performed in challenging environmental and safety-sensitive conditions. These conditions can affect the physical and mental well-being of state department of transportation (DOT) and contractor staff and may negatively influence workforce productivity, safety, and project delivery outcomes.

Long work hours, demanding schedules, and changing environmental conditions have contributed to growing concerns regarding workforce stress, burnout, fatigue, and mental health challenges within the construction industry. Research and industry surveys have highlighted the need to better understand how workforce conditions influence productivity and project performance. However, there is limited guidance on incorporating workforce well-being considerations into project scheduling, phasing, and construction management practices.

The objective of this research is to develop a guide to assist state DOTs in evaluating environmental, physical, and mental conditions affecting the transportation construction workforce and their impact on productivity and scheduling expectations. The research is intended to support healthier, safer, and more sustainable project delivery practices.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:14:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712203</guid>
    </item>
    <item>
      <title>Developing Data Literacy Competencies and Practices for State Transportation Workforce</title>
      <link>https://rip.trb.org/View/2712190</link>
      <description><![CDATA[State departments of transportation (DOTs) are undergoing a major transformation in how they collect, manage, and use data. Historically reliant on manual observations and field reports, DOTs now collect large and diverse datasets from traffic monitoring, asset condition assessments, maintenance records, freight compliance, Global Positioning System (GPS) probe data, light detection and ranging (LiDAR), drones, and video analytics. These technologies support more data-driven decisions related to infrastructure management, operations, and planning.

The growing volume and diversity of transportation data have created significant challenges for integration, governance, and analysis. To address these issues, many DOTs are adopting standardized data formats and centralized governance structures that improve interoperability, reduce duplication, and support collaboration with external stakeholders. At the same time, data access has expanded across agencies, allowing planners, engineers, managers, and policy staff to work directly with increasingly complex datasets.

Artificial intelligence (AI) and machine learning applications are accelerating this shift, particularly in areas such as traffic incident detection, pavement performance prediction, asset management, and safety analysis. However, many transportation professionals lack foundational competencies in data governance, statistical reasoning, ethical data use, visualization, and interpretation of analytical outputs. The shortage of qualified data-science personnel within public agencies further increases reliance on undertrained staff and external consultants. Communication gaps between technical teams and transportation practitioners also hinder effective implementation of data-driven tools and practices.

The objective of this research is to improve data literacy within transportation agencies by identifying current skill gaps and workforce needs, evaluating data usage practices, and developing strategies to improve the ability of staff to collect, interpret, manage, and apply data effectively.

This research will identify baseline competencies required for transportation data literacy; examine barriers related to training, governance, and organizational silos; evaluate the impacts of limited data-science staffing; and explore best practices for training, communication, and knowledge management. The study will develop actionable recommendations for tailored training, improved data governance, reduced reliance on external consultants, and stronger data-driven decision-making across transportation agencies.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:01:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712190</guid>
    </item>
    <item>
      <title>Workforce Development in Digital Transportation and Infrastructure Technologies for State DOTs</title>
      <link>https://rip.trb.org/View/2712186</link>
      <description><![CDATA[State departments of transportation (DOTs) are undergoing rapid transformation as digital technologies—such as data analytics, sensor networks, connected and automated systems, artificial intelligence, and digital asset management—become integral to transportation systems. While these tools are reshaping how agencies plan, design, and operate infrastructure, they require new technical and interdisciplinary skill sets beyond traditional engineering roles.

Many state DOTs face challenges in keeping pace due to workforce constraints, including skill gaps, an aging workforce, and difficulties recruiting and retaining talent with digital expertise. Legacy workforce structures and limited training capacity further hinder agencies’ ability to adapt, creating a gap between technological advancement and workforce capability.  

The objective of this research is to develop a framework and practical tools to help state DOTs plan, implement, and sustain workforce development strategies aligned with digital transformation. The research will assess workforce capacity and skill gaps and develop guidance to support recruitment, reskilling, retention, and long-term workforce readiness.]]></description>
      <pubDate>Tue, 09 Jun 2026 16:10:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712186</guid>
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