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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>
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    <item>
      <title>Roadmap for Transportation Systems Digital Infrastructure
</title>
      <link>https://rip.trb.org/View/2719308</link>
      <description><![CDATA[This research is a roadmap for innovative application of Georgia Department of Transportation's (GDOT's) digital information assets in support of developing the digital transportation infrastructure.
]]></description>
      <pubDate>Thu, 25 Jun 2026 09:34:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719308</guid>
    </item>
    <item>
      <title>Identifying, Assessing, and Managing Events for Critical Infrastructure Resilience in Surface Transportation</title>
      <link>https://rip.trb.org/View/2712209</link>
      <description><![CDATA[State departments of transportation (DOTs) play an important role in protecting critical transportation infrastructure from natural hazards, human-caused events, and emerging threats. However, there is currently no consistent methodology for identifying and assessing critical infrastructure within surface transportation systems. Definitions of “criticality” vary across agencies, and existing approaches often lack integration with broader resilience, security, and emergency management frameworks.

Previous research has focused on specific threats, such as terrorism or cybersecurity, or on resilience to natural hazards, but gaps remain in developing proactive, risk-based approaches that address the full range of threats and system interdependencies. Transportation systems are closely linked with other infrastructure sectors, such as power and water systems, and disruptions can have cascading impacts across regions.

Research is needed to help state DOTs better define their role in coordinating with law enforcement, emergency responders, and other planning partners and system owners to enhance preparedness, response capabilities, proactive resilience planning, stakeholder coordination, and implementation of national infrastructure protection frameworks.

The objectives of this research are to (1) identify and assess surface transportation system interdependencies and develop a risk-based approach to managing a wide range of threats; and (2) develop an infrastructure resilience guide with case studies, a list of stakeholder roles and responsibilities, and decision-making tools to help state DOTs identify, assess, and manage risks to critical infrastructure within surface transportation systems.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:41:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712209</guid>
    </item>
    <item>
      <title>Evaluating Large-Scale UAS Technologies and Current Implementation Practices for State DOT Infrastructure Operations</title>
      <link>https://rip.trb.org/View/2709243</link>
      <description><![CDATA[The use of unmanned aircraft systems (UASs) in transportation operations has expanded rapidly over the past decade. State departments of transportation (DOTs) now routinely deploy small UASs for asset inspection, mapping, and incident management. However, most operations remain limited to small-scale, pilot-controlled flights under visual line-of-sight (VLOS) conditions. The next major advancement involves the integration of large-scale (group 3+) UASs capable of extended range, higher payloads, and broader data collection, transforming how agencies monitor and maintain transportation infrastructure.

 Larger UASs equipped with advanced sensors, automated navigation, and real-time data analytics offer significant potential benefits for DOTs. These systems could enable persistent infrastructure monitoring, automated right-of-way surveys, and rapid disaster response across large geographic areas. When deployed strategically, fleets of UASs could conduct scheduled bridge inspections, detect pavement distress, or assess storm damage in near-real time.

 While the private sector and federal agencies such as the Federal Aviation Administration (FAA), National Aeronautics and Space Administration (NASA), and Department of Homeland Security (DHS) are advancing research in large-scale UAS technologies, few studies have assessed how these capabilities align with state DOT operational needs and regulatory environments. Key challenges remain in areas such as beyond visual line of sight (BVLOS) authorization, cybersecurity, airspace integration, data management, and interoperability with existing DOT information technology systems.

 The objective of this research is to evaluate large-scale (group 3+) UAS technologies and assess how these systems can be effectively integrated into state DOT support operations. The research will identify current and emerging UAS platforms suitable for infrastructure inspection, traffic monitoring, emergency response, and environmental assessment.

 This research will review existing implementations by U.S. state DOTs, universities, and other public agencies, identifying best practices, operational benefits, budgeting, funding acquisition, challenges, and gaps in policy, technology, and workforce capability. Key outcomes will include operational frameworks for safe deployment and actionable recommendations for regulatory alignment, interagency coordination, and technology adoption strategies.]]></description>
      <pubDate>Wed, 03 Jun 2026 11:09:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709243</guid>
    </item>
    <item>
      <title>Capital Investment, Financing, Flood Risk, and Transportation Safety in the MidAmerica Region
</title>
      <link>https://rip.trb.org/View/2706033</link>
      <description><![CDATA[Transportation agencies in the MidAmerica region face increasing pressure to manage aging infrastructure under fiscal constraints while improving transportation safety. Rural highways, freight-intensive corridors, and aging bridges experience elevated crash severity, yet capital investment timing and financing decisions are rarely evaluated through a safety-risk lens.
This project develops an integrated empirical and probabilistic framework to quantify how capital investment timing, financing mechanisms, and flood-related hazards influence lifecycle transportation safety outcomes. The study constructs project- and asset-level datasets linking capital programming records, delivery timelines, financing mechanisms, infrastructure characteristics, crash outcomes, and flood risk indicators. Econometric models estimate statistical relationships between investment timing and safety performance. Monte Carlo simulation propagates uncertainty in delivery delays, cost escalation, traffic growth, and flood exposure to produce distributions of lifecycle safety risk and cost. Results will support safety-oriented capital planning and risk-informed decision-making for transportation agencies in the MidAmerica region.

]]></description>
      <pubDate>Sat, 23 May 2026 17:36:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706033</guid>
    </item>
    <item>
      <title>Investigating the Critical Success Factors for Technology Adoption in Transportation Safety Systems</title>
      <link>https://rip.trb.org/View/2703924</link>
      <description><![CDATA[The United States continues to face challenges in maintaining safe and efficient transportation networks, as roadway crashes remain a leading cause of death and economic loss. Although considerable advancements have been made in vehicle and roadway safety, emerging technologies remain underutilized in practice. This limited adoption highlights a critical need to better understand the factors that influence the successful implementation of transportation safety technologies. This research addresses this gap by identifying the critical success factors and barriers affecting the adoption of digital transportation safety technologies in the U.S. The study adopts a multi-stage approach consisting of a literature review, an expert survey, and network analysis to identify key adoption factors and their interrelationships. The expected outcomes include the development of a structured framework of adoption factors and practical implementation guidelines to support transportation agencies in deploying digital safety technologies more effectively. By facilitating the transition from technological development to real-world implementation, the research supports safer and more resilient transportation networks.
]]></description>
      <pubDate>Wed, 20 May 2026 09:18:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703924</guid>
    </item>
    <item>
      <title>A Performance- and Cost-Based Framework to Evaluate the Value of Multimodal Logistics Infrastructure</title>
      <link>https://rip.trb.org/View/2703796</link>
      <description><![CDATA[This project develops a practical, data-driven framework to evaluate the value of logistics infrastructure in a multimodal freight region. Focusing on the St. Louis metropolitan area, the framework integrates freight performance measurement with generalized logistics cost modeling to translate travel time, reliability, and terminal access improvements into economic outcomes. Methods include assembling a regional freight network representation, computing corridor-level travel time and variability metrics, and applying scenario-based valuation to estimate marginal benefits of targeted investments. The project also includes a private-sector truck–rail–barge use case to quantify multimodal tradeoffs and assess the competitiveness of inland waterway transportation under alternative infrastructure scenarios. The resulting workflow provides agencies and regional partners with transparent, repeatable methods to support freight investment prioritization and decision-making.]]></description>
      <pubDate>Sat, 16 May 2026 11:52:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703796</guid>
    </item>
    <item>
      <title>The Effects of Street Repurposing on Pedestrian, Vehicle and Visitor Patterns</title>
      <link>https://rip.trb.org/View/2702858</link>
      <description><![CDATA[COVID is a crisis that is unanticipated both in its occurrence and also its length of impact. In the early days, many office employers implemented work-from-home policies while retail businesses shuttered, leading to deserted downtowns across the country. Yet crisis is also an opportunity, and municipalities and businesses innovated in response to the fears of infection. In particular, many cities changed transportation infrastructure, including permitting sidewalk cafes that accommodated outdoor dining, reallocating street space from travel or parking to outdoor dining, and redesigning streets to accommodate a wide variety of users etc. What are the effects of these urban infrastructure innovations? How well do they draw visitors and support businesses nearby? What are their effects on the region’s traffic patterns? Are there spillover effects spatially? As cities emerge from COVID and re-imagine the future of our urban cores, answers to these questions are critical. Though the existing literature has a wealth of knowledge on the built environment effect on travel behavior, they are nearly exclusively at much larger scale (e.g., census tracts) and static (comparing different behavioral patterns between places with different built environment characteristics. There is little to no insight on how block-level urban infrastructure innovations lead to changes in visit patterns as well as nearby businesses. And yet, changes at this scale (block-level) are where local policy changes take place. This proposal is to answer these questions.]]></description>
      <pubDate>Thu, 14 May 2026 15:19:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702858</guid>
    </item>
    <item>
      <title>Algorithms to Convert Basic Safety Messages into Traffic Measures</title>
      <link>https://rip.trb.org/View/2701259</link>
      <description><![CDATA[Connected vehicles rely on short-range messaging using Basic Safety Message (BSM) data that includes information about vehicle size, speed, position, and heading (direction). In the future, all vehicles will be expected to send and receive this information to enhance safety and mobility. Exchange of BSM data among vehicles and traffic management systems will have the potential to generate traffic information that could be used to support current or develop new traffic measures such as travel time, end of queue information for work zones, road weather delay impact, and enhanced traffic signal control. This will be particularly valuable for arterial roadways and work zones in areas without instrumentation or where transportation systems management instrumentation is disrupted by construction. The aim of this research was to develop and validate algorithms that will use BSM data to estimate selected traffic measures that could be used for performance monitoring, traffic control, and traveler information.

]]></description>
      <pubDate>Tue, 12 May 2026 15:54:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701259</guid>
    </item>
    <item>
      <title>Hydrologic and Hydraulic Software Enhancements (SMS, WMS, Hydraulic Toolbox, and HY-8)</title>
      <link>https://rip.trb.org/View/2698362</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) sponsors ongoing development of four computer programs that perform both routine and complex hydrologic and hydraulic analyses of watersheds, river and stream systems, and transportation infrastructure. 
This Transportation Pooled Fund (TPF) project will: 1. Enhance the capabilities of the four FHWA sponsored software programs and ensure they remain consistent with the latest FHWA technical reference documents. 2. Update the software user manual documentation. 3. Make new software versions publicly available. 4. Develop and deploy technology transfer materials and workshops to test and demonstrate new software content and features. 5. Inform users of the availability of new software versions and features through website postings, email notifications, newsletter articles, conference presentations, and other avenues.]]></description>
      <pubDate>Fri, 01 May 2026 19:48:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2698362</guid>
    </item>
    <item>
      <title>Modeling Bicyclist Behavioral Patterns and Multi-Faceted Decision-Making Strategies in Urban Settings with Limited Infrastructure: Guidance for Future Development</title>
      <link>https://rip.trb.org/View/2691667</link>
      <description><![CDATA[While bicycling is an essential mode of urban transportation, most parts of U.S. cities lack adequate infrastructure to keep bicyclists safe and allow them to travel efficiently. This research aims to model how psychological, street, and infrastructure characteristics influence bicyclist behavior in urban settings with inadequate bicycling infrastructure, such as in the Greater Houston area (Houston-The Woodlands-Sugar Land), Texas. This research will integrate quantitative and qualitative methods to develop a model that supports adaptive decision-making for bicycling in urban areas with limited infrastructure. The project will recruit 40 adult bicyclists to participate in surveys and bicycle simulator testing. A realistic urban network will be simulated in the bicycle simulator to replicate bicycling conditions under varying (infrastructure quality, traffic volume, visibility, etc.), psychological (risk perception, motivation, and attitudes, etc.), and operational (route choice, adaptation, and interaction with other modes of transportation, etc.) scenarios. Various techniques, including both qualitative and quantitative methods, can be used to identify key drivers of route choice and to develop optimal strategies for efficiency and safety. The findings will inform action-oriented urban planning and policy recommendations for enhancing bicycle infrastructure and safety. The outcomes have the potential to offer a replicable methodology for implementation in similarly challenged cities, providing active urban transportation and improved public health.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:34:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691667</guid>
    </item>
    <item>
      <title>Tribal &amp; Rural Autonomous Vehicles for Efficiency, Livability and Safety (TRAVELS) </title>
      <link>https://rip.trb.org/View/2687129</link>
      <description><![CDATA[Tribal & Rural Autonomous Vehicles for Efficiency, Livability and Safety (TRAVELS) is the passenger transportation component of the U.S. Department of Transportation’s Rural Autonomous Vehicle (RAV) Research Program, led by the University of Wisconsin–Madison. The project addresses persistent mobility challenges in rural and Tribal communities, where residents often face limited transportation options, long-distance travel needs, restricted access to healthcare and essential services, and fewer after-hours or on-demand services. These challenges are further complicated by roadway, infrastructure, weather, and economic constraints that can limit the feasibility of conventional and emerging mobility solutions.


This research examines how automated vehicle technologies, supportive infrastructure, communications systems, and service design strategies can be integrated to improve safety, accessibility, and operational efficiency in rural settings. The project includes demonstration and deployment activities in Wisconsin, Georgia, and Oklahoma, focusing on use cases such as healthcare access, economic transportation, and tourist & event shuttle services. Through testing, evaluation, and before-and-after analyses, the project seeks to identify practical pathways to accelerate automated mobility deployment in rural and tribal areas.
]]></description>
      <pubDate>Fri, 03 Apr 2026 16:46:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2687129</guid>
    </item>
    <item>
      <title>Data Integration to Support Digital Infrastructure and Efficient Mobility Insights</title>
      <link>https://rip.trb.org/View/2669548</link>
      <description><![CDATA[Data is at the core of understanding mobility trends, modeling and optimizing transportation systems and informing policy and decision-making. Understanding data is also key to advancing digital infrastructure for transportation, providing technological systems and frameworks to complement physical infrastructure. This project aims to support two other initiatives (the CEM Innovation Accelerator and the Advanced Transportation Optimization and Modeling project) by serving as the “data engine” to support both modeling as well as potential innovation projects. However, this project also stands alone as a data integration effort that will support the expansion and improvement of the previously-developed CEM Data Hub and serve to provide mobility data insights and digital infrastructure frameworks geared towards understanding transportation system efficiency.   

This project will develop a robust data integration framework, collect, assemble, and harmonize diverse transportation datasets to support the development of actionable mobility insights. By integrating data from traffic sensors, transit systems, GPS traces, and mobile applications, the project will create a comprehensive data ecosystem that reflects real-world travel behavior, congestion patterns, and modal interactions. This foundation will enable the development of analytical tools and decision-support systems that help agencies and planners optimize transportation networks for efficiency. The integrated data platform will support advanced analytics, visualization tools, and decision-support systems that can be used by planners, engineers, and policymakers to evaluate mobility strategies.   

Key components of the project include:  

Data Ecosystem Development – Establishing a scalable and secure data architecture that supports integration of diverse transportation datasets   

Collaboration – Engaging with ongoing projects across the CEM consortium to ensure collaborative use of available data   

Analytical Tools and Insights – Developing dashboards, predictive models, and scenario planning tools to support efficient and healthy mobility decisions.  

Ultimately, this effort will position data as a central asset in advancing efficient mobility across urban and rural contexts.  ]]></description>
      <pubDate>Thu, 12 Feb 2026 15:35:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669548</guid>
    </item>
    <item>
      <title>Novel surge barriers for coastal protection (TAMU)</title>
      <link>https://rip.trb.org/View/2663229</link>
      <description><![CDATA[Surge barriers are large hydraulic structures designed to protect infrastructure from coastal storm surges and high tides. Preventing surges from moving into bays and estuaries minimizes the need for other expensive elements of a flood control system, such as levees and floodwalls. Surge barriers can provide cost-effective protection critical transportation infrastructure, such as ports, roads, and bridges. Conventional surge barriers comprise a fixed structure with movable vertically or horizontally opening gates that can be closed during extreme storms and tidal events. Disadvantages of fixed barriers include high cost, sensitivity to waste and silt, potential debris blockage, and constraints to marine traffic. Temporary surge barriers can avoid these disadvantages. This research evaluates three novel temporary barrier concepts: flexible membrane barriers, sinkable floating barriers, and shade curtain barriers. Flexible membrane barriers are self-deploying and permanently located on shore. Buried when not deployed, they rise with rising water due to their buoyancy. Sinkable floating barriers rest on the seabed when not deployed and, when needed, are raised to the surface by pumping air into a tube. Shade curtains are fabric barriers attached to an existing bridge. When not deployed, it is secured to the underside of the bridge deck. In advance of a surge, the fabric curtain is lowered using a sinker-cable system to provide a vertical barrier extending from the bridge deck to the seabed. Hydraulic loads are transmitted from the barrier to the bridge and its foundations, which must be capable of resisting the added loads. This project addresses three key issues related to temporary surge barrier deployment: site conditions for which temporary surge barriers are appropriate, hydraulic loading on the barriers, and structural/geotechnical design considerations for the barriers.]]></description>
      <pubDate>Sat, 31 Jan 2026 11:29:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663229</guid>
    </item>
    <item>
      <title>An AI-Based Reasoning Framework for Proactive Infrastructure Monitoring and Preservation Using Connected Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2655750</link>
      <description><![CDATA[This research proposes the development of a Connected Autonomous Vehicles (CAV)-based Proactive Infrastructure Preserving (CAV-PIP) system to enhance the safety, resilience, and operational efficiency of transportation infrastructure. The system leverages the sensing and communication capabilities of CAVs to enable continuous, real-time detection and reporting of roadway anomalies, such as pavement distress and damaged traffic signage. By fusing multi-modal sensor data and incorporating a retrieval-augmented generation (RAG) framework with large language models (LLMs), the system constructs a dynamic prior knowledge base to reason about infrastructure conditions and recommend context-aware maintenance actions. The project aims to transform current reactive maintenance practices into a data-driven, proactive framework that improves decision-making for transportation agencies. The system will be validated through simulation in the CARLA (Car Learning to Act) environment and supported by curated real-world datasets. Expected outcomes include an integrated detection and reasoning framework, structured maintenance reporting tools, and publicly shareable datasets and software packages. The project's broader impact lies in advancing intelligent infrastructure monitoring technologies, reducing long-term maintenance costs, and contributing to safer and more sustainable transportation systems.]]></description>
      <pubDate>Mon, 19 Jan 2026 17:01:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655750</guid>
    </item>
    <item>
      <title>Data-Driven Resilience Planning for Transportation Infrastructure: Pilot Study in Texas</title>
      <link>https://rip.trb.org/View/2646954</link>
      <description><![CDATA[This one-year pilot proposes marrying three rich but rarely combined data streams—high-resolution weather data (freeze/thaw, temperature, rainfall, snow/ice, etc.) supplied by the Southern Regional Climate Center (SRCC), Connected-Vehicle Data (movements, windshield wiper events, delay, etc.) that capture real-time operating conditions, and Texas Department of Transportation's (TxDOT’s) own asset and condition inventories (e.g.  pavement condition data) into a cohesive, decision-ready framework. The research team will begin by geolinking these datasets and mining them for hazard frequency, traffic exposure, and structural vulnerability signals. Machine-learning and stochastic life-cycle cost models will then translate those signals into corridor-level risk profiles and economic damage curves under three strategies: do-nothing, reactive repair, and proactive hardening.  

Over the course of twelve months, the research team will iterate through four tightly coupled phases: (1) data assembly and quality control; (2) vulnerability assessment that fuses hazard intensity with deterioration and delay models; (3) scenario-based economic analysis to identify the most cost-effective resilience options; and finally, (4) delivery of an interactive web geographic information system (GIS)-based platform that maps risks, ranks projects, and lets engineers explore “what-if” funding scenarios. The researchers will ensure that methods align with agency workflows and that results are immediately actionable.  

Tangible pilot products—open-source modeling code, corridor-level risk maps, and a web-based GIS platform with an implementation guide and training workshop—will give Texas a clear blueprint for maximizing every resilience dollar. These outputs will enable TxDOT to pursue proactive adaptation and pave the way for multi-state deployment in the future. Expected benefits include lower lifecycle costs, fewer weather-related disruptions, and safer travel for Texans. Equally important, the modular design allows the Southern Plains Transportation Center to extend the framework to other Region 6 states in a potential follow-on effort, furthering USDOT goals for safety and infrastructure durability. ]]></description>
      <pubDate>Mon, 05 Jan 2026 23:27:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646954</guid>
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