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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>2508 Bridge Deck Evaluation</title>
      <link>https://rip.trb.org/View/2786854</link>
      <description><![CDATA[The overall goal of this project is to develop an integrated, multimodal bridge deck evaluation framework using existing statewide Performance Monitoring System (PMS) datasets collected annually by Oklahoma Department of Transportation, including high-resolution 2D pavement imagery, 3D surface range data, roadway longitudinal profile data, and right-of-way front-view and rear-view imagery. The proposed framework is intended to serve as a screening, prioritization, and decision-support tool that improves bridge inspection efficiency, enhances statewide bridge condition awareness, and supports more proactive bridge preservation and asset management practices. Unlike conventional pavement condition assessment, bridge-specific deterioration features including deck cracking, spalling, scaling, potholes, joint deterioration, and approach slab surface irregularities require bridge-oriented analytical methodologies and engineering interpretation approaches.]]></description>
      <pubDate>Thu, 08 Oct 2026 14:21:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786854</guid>
    </item>
    <item>
      <title>Improving Active Transportation Asset Data Management for UDOT Using AI and Computer Vision</title>
      <link>https://rip.trb.org/View/2786773</link>
      <description><![CDATA[Develop computer vision models to collect essential data on high-priority assets in the active transportation (AT) system to create an intelligent system designed to address the challenges of current methods for inventorying AT assets.]]></description>
      <pubDate>Tue, 06 Oct 2026 17:32:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786773</guid>
    </item>
    <item>
      <title>Broadband &amp; TSMO Integration: Next-Generation Fiber Network for ITS and Asset
Management</title>
      <link>https://rip.trb.org/View/2780487</link>
      <description><![CDATA[This project will develop a unified fiber optic network strategy and governance framework to support intelligent transportation systems (ITS), transportation systems management and operations (TSMO), and enterprise communications across the Idaho Transportation Deparement (ITD). The study will evaluate ITD’s existing fiber optic network, communications infrastructure, and asset management practices and review approaches used by other transportation agencies. The results will be used to develop network standards, governance processes, geographic information system (GIS) based asset management practices, and decision making tools to support future communication technology investments and improve network resilience and long term operations.]]></description>
      <pubDate>Mon, 21 Sep 2026 15:53:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780487</guid>
    </item>
    <item>
      <title>SHM-based Bridge Element Deterioration Modeling and Prediction</title>
      <link>https://rip.trb.org/View/2692311</link>
      <description><![CDATA[The transportation legislation MAP-21 and the FAST Act have advanced the integration of performance-based asset management into transportation decision-making across federal, state, and local levels. Through Transportation Asset Management (TAM), agencies are establishing systematic processes to operate, maintain, and preserve infrastructure assets throughout their lifecycle. The objective of the project described herein is to support these goals by developing a methodology for predicting bridge element deterioration using long-term Structural Health Monitoring (SHM) data. Current approaches rely on periodic inspections and NDT/NDE methods, which miss real-time changes and pose safety risks. By combining SHM data with traditional models (Markov chains, Weibull distributions) and advanced algorithms, this research aims to produce accurate, time-based deterioration curves for bridge elements. Using high-resolution data from the BEAST lab at Rutgers University, the models will be calibrated and validated to reflect real-world deterioration mechanisms. This work supports NCIT’s mission to improve durability and extend the life of transportation infrastructure. It directly addresses the topical pillars of Infrastructure Durability, Resiliency, and Technology, offering tools for predictive maintenance, life-cycle cost management, and improved safety. The outcome will aid bridge owners in making data-driven decisions that enhance long-term performance and resilience. ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:40:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692311</guid>
    </item>
    <item>
      <title>Infrastructure Asset Management Academy (IAMA) for Engineers and Planners</title>
      <link>https://rip.trb.org/View/2692314</link>
      <description><![CDATA[Spanning millions of miles, the United States is home to a vast public roadway network that is relied on every day to connect a growing population with jobs, communities, essential services, and goods. Over time this critical transportation network has aged, and now a significant portion of U.S. roads are in poor or mediocre condition. Previous federal legislation has recognized the need for investment in American roads and bridges, including mandating that state agencies develop and implement Transportation Asset Management Plans. During the last decade, significant progress has been made. However, the complexity and scale of managing modern transportation networks require specialized knowledge that expands into disciplines beyond traditional engineering. Today’s asset owners must also manage and analyze large amounts of data, consider lifecycle and economic costs, develop risk management programs, and strategically deploy maintenance activities and capital investments to ensure long-term performance. The goal of this project is to train today’s asset managers in these key areas and provide them with the knowledge and tools needed to make informed decisions that can extend the service life, improve durability, and minimize the risk to their assets. CAIT’s Infrastructure Asset Management Academy is a 4-course program that brings expert speakers together to enable state DOTs and their staff to utilize emerging asset management technologies; better collect, manage, and integrate large amounts of data; break down organizational silos; and ultimately implement a risk-based, data-driven asset management program that can extend the service life of their infrastructure.   ]]></description>
      <pubDate>Thu, 10 Sep 2026 08:40:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692314</guid>
    </item>
    <item>
      <title>Advanced Sensing and AI for Next-Generation Transportation Asset Management</title>
      <link>https://rip.trb.org/View/2745222</link>
      <description><![CDATA[The proposed study aims to create an artificial intelligence (AI)-based framework
that harness data from advanced sensing technologies to advance the next-generation of transportation asset management systems. The development of
AI-driven diagnostic tools for infrastructure is fundamental to optimizing
maintenance plans, thereby enhancing public safety and minimizing the
inefficient use of economic resources.
To address the limitations associated with the availability of training data for
deep learning algorithms, the proposed framework integrates heterogeneous
data collected from multiple sources. Beyond the deployment of conventional
fixed sensors, cutting-edge mobile sensing technologies will be incorporated to
achieve unprecedented temporal and spatial resolution, thereby ensuring the
scalability and adaptability of the AI-based strategy. The dynamic characteristics
extracted from acceleration data acquired through smartphones will be utilized
to: (i) calibrate the digital twin of the structure, (ii) identify and characterize
potential structural damage, and (iii) provide essential physics-based knowledge
to support the development of a physics-informed neural network for life-cycle
assessment.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:35:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2745222</guid>
    </item>
    <item>
      <title>AI-Powered Infrastructure Monitoring and Decision Support for Transportation Safety: Field Deployment and TDI Integration</title>
      <link>https://rip.trb.org/View/2742139</link>
      <description><![CDATA[In its fourth year, this project continues to advance transportation safety through the deployment and scaling of cutting-edge technologies for near real-time monitoring of infrastructure. Building on the foundational work of Year 1 (inverse problem framework development and vision-based structural health monitoring for bridges) and Year 3 (AI-powered video analytics scaled to urban infrastructure), the Year 4 initiative at Howard University integrates three funded years of research into a deployable, scalable monitoring solution.

The core focus of Year 4 is field deployment of the integrated monitoring platform on an operational bridge in Washington, DC, identified in coordination with the District Department of Transportation (DDOT). In parallel, the project will conduct exploratory trials of drone-based video and imagery for structural inspection, in collaboration with DDOT’s Drones team, to assess whether this complementary sensing approach warrants full integration in a potential Year 5. The work integrates civil engineering, computer vision, machine learning, and applied mathematics for AI-powered structural diagnosis.]]></description>
      <pubDate>Sat, 01 Aug 2026 09:43:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742139</guid>
    </item>
    <item>
      <title>SPR-5108: Automated Highway Asset Inventory Using High-Resolution Aerial Imagery and LiDAR Data</title>
      <link>https://rip.trb.org/View/2732647</link>
      <description><![CDATA[This project proposes to develop, validate, and scale an automated framework for extracting and mapping key highway assets from 3-inch aerial imagery and QL1 LiDAR data. Target assets include the number of travel lanes in each direction, pavement section areas, guardrails, and sound barrier walls. Hamilton County will serve as the pilot implementation area due to its diverse mix of Interstate, U.S., and State routes across urban and suburban environments.]]></description>
      <pubDate>Wed, 22 Jul 2026 15:22:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732647</guid>
    </item>
    <item>
      <title>Traffic Signal Pre-emption Effectiveness in Michigan</title>
      <link>https://rip.trb.org/View/2731919</link>
      <description><![CDATA[Current Michigan Department of Transportation (MDOT) policy allows signal pre-emption for emergency vehicles, but the emergency agency is responsible for paying
for the equipment required for implementation. However, MDOT still has costs associated with the operation and maintenance
of the system. It is unknown at this time how many of the emergency pre-emption implementations are working as designed.
Question- for existing systems that are operational, are the expected benefits being realized? As the complexity of traffic
signal technology continues to grow, the resources available to maintain the signal infrastructure are becoming more strained.
As such, it is important to ensure that the resources dedicated to implementing pre-emption are worthwhile and should be
prioritized over other competing needs. This project will investigate current state of pre-emption technologies and the various
practices underway in MI, as well as develop recommendations for the most effective and reliable systems to maximize the
return on investment with minimum disruptions to daily traffic operations. In addition to existing implementations, the
technology has developed to a point where more and more use cases are being considered for using signal pre-emption. In
cases where it is possible for multiple different use cases to exist along the same corridor, another question/unknown is- how
should the various pre-emption calls be prioritized?]]></description>
      <pubDate>Fri, 17 Jul 2026 11:58:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731919</guid>
    </item>
    <item>
      <title>Implementation of Seal Coat Project Selection Guidelines</title>
      <link>https://rip.trb.org/View/2727313</link>
      <description><![CDATA[The research team will assist TxDOT with implementing the guidelines for seal coat project selection and the draft ball penetration test method developed in research project 0-7106 “Quantify Maximum Accumulated Seal Coat Layers for Stability“. The research team will then use the findings and data generated from this implementation project to develop and teach implementation workshops for TxDOT.]]></description>
      <pubDate>Fri, 10 Jul 2026 12:40:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727313</guid>
    </item>
    <item>
      <title>Guidelines for Maintenance of Uncrewed Aircraft Systems Used in Transportation Infrastructure</title>
      <link>https://rip.trb.org/View/2714359</link>
      <description><![CDATA[The use of uncrewed aircraft systems (UAS), commonly known as drones, has increased exponentially in various industries due to their versatility and cost-effectiveness. However, as the use of UAS in commercial operations under Federal Aviation Administration (FAA) regulations continues to expand, ensuring the safety, reliability, and airworthiness of the UAS fleet becomes increasingly important. Maintaining a diverse fleet of UAS from multiple platforms, used across various industries, necessitates a comprehensive maintenance optimization strategy to address potential risks, ensure regulatory compliance, and maximize operational efficiency. Research is needed to help state departments of transportation (DOTs) manage UAS maintenance activities to enhance the design, maintenance, inspection, and construction of transportation infrastructure.

OBJECTIVE: The objective of this project is to develop guidelines for state DOTs for comprehensive maintenance of UAS to promote safe, reliable, and cost-effective operation. The guidelines shall include minimum maintenance standards and schedules adaptable to multiple UAS platforms and operational demands that can support streamlined maintenance management approaches.  ]]></description>
      <pubDate>Mon, 15 Jun 2026 16:45:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2714359</guid>
    </item>
    <item>
      <title>Transportation Asset Management Guidance and Framework for Ancillary Highway Assets</title>
      <link>https://rip.trb.org/View/2712210</link>
      <description><![CDATA[Ancillary assets represent a significant portion of state department of transportation (DOT) asset inventory. Current research on the state of practice summarizes ancillary asset management processes; however, state DOTs still face challenges in developing procedures and guidelines to establish comprehensive practices or improve existing programs. The Federal Highway Administration (FHWA) 2019 Handbook for Including Ancillary Assets in Transportation Asset Management Programs shows progress in this area, though knowledge gaps in data management and systems, life-cycle planning (LCP), and preservation and maintenance programs still exist for managing ancillary assets. Research is needed to identify and assess state DOT practices for performing ancillary asset assessments and managing these asset classes. 

The objective of this project is to develop a guide and framework for state DOTs to manage multiple ancillary asset classes, including hydraulics, signals, barriers, culverts, intelligent transportation systems, and other relevant assets.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:45:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712210</guid>
    </item>
    <item>
      <title>Development of Additional Risk Models for the Transportation Asset Risk and Resilience Manual</title>
      <link>https://rip.trb.org/View/2712191</link>
      <description><![CDATA[State departments of transportation (DOTs) are increasingly incorporating risk and resilience considerations into transportation asset management and system performance decisions. NCHRP Project 23-32, “Transportation Asset Risk and Resilience (TARR),” is developing quantitative models for evaluating risks associated with natural hazards and transportation assets; however, several priority threat-asset pairs were not included within the initial project scope because of funding and schedule constraints.

Additional modeling capabilities are needed to help agencies evaluate risks associated with human-caused threats, technological failures, and additional natural hazards affecting transportation systems. Together, these include threats such as cybersecurity incidents, wildfires, post-fire debris flows, coastal erosion, landslides, and critical infrastructure failures that can significantly affect infrastructure performance and resilience planning.

The objective of this research is to expand the Transportation Asset Risk and Resilience (TARR) Manual by developing additional quantitative threat-asset risk models to support transportation agencies in evaluating infrastructure risks and system resilience. The research will build upon the analytical framework developed through NCHRP Projects 23-09 and 23-32.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:04:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712191</guid>
    </item>
    <item>
      <title>Developing Workflows for Digital Project Delivery to Support Transportation Asset Management</title>
      <link>https://rip.trb.org/View/2712189</link>
      <description><![CDATA[Digital project delivery (DPD) is emerging to address challenges in the traditional delivery of transportation infrastructure projects, such as low productivity, workforce shortages, and the complexity of managing multiple stakeholders, vendors, and site-specific conditions. Utilizing DPD can enhance project outcomes related to schedule, cost, quality, and safety. A major component of DPD is the creation of digital design models during pre-construction, along with the collection of digital project data during construction to inspect and verify work against those models. While recent research has explored methods for creating digital as-builts (DABs) through field data collection, there is still a need for standardized workflows to transfer this information from construction into long-term operations and maintenance.

Data collected through DPD has significant value beyond project delivery and can be reused to support Transportation Asset Management (TAM) and life-cycle decision-making for transportation assets. State departments of transportation (DOTs) are already adopting DPD to improve project performance while also working to maintain and improve asset conditions with limited resources. As digital technologies continue to evolve, the need for practical strategies that connect project delivery data with long-term asset management is becoming increasingly important. Research is needed to (1) identify current practices and assess emerging strategies for integrating DPD data with TAM business needs, and (2) develop implementable strategies to streamline comprehensive workflows to improve user/owner outcomes.

The objectives of this research are to: (1) Identify approaches developed by state DOTs to implement DPD, (2) Identify challenges experienced by state DOTs in transitioning to DPD, (3) Identify approaches to generating DABs as part of DPD efforts to support TAM and the maintenance of data throughout the asset life cycle, and (4) Develop guidelines for bridging the gap between project delivery and asset management phases to better integrate available data and close the data loop.


]]></description>
      <pubDate>Tue, 09 Jun 2026 16:57:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712189</guid>
    </item>
    <item>
      <title>Elevate the Prioritization of Fleets and Equipment Within an Overall Asset Management Strategy</title>
      <link>https://rip.trb.org/View/2712185</link>
      <description><![CDATA[State department of transportation (DOT) fleets support highway maintenance, emergency response, and agency operations. However, fleet funding and prioritization can vary across agencies and may differ from approaches used for infrastructure assets such as roads and bridges. Funding constraints may contribute to increased lifecycle costs, additional maintenance requirements, and challenges related to fleet expansion and replacement planning. In some cases, fleet-related needs compete with other agency priorities during funding and resource allocation decisions.

Transportation Asset Management Plans (TAMPs), established under the Moving Ahead for Progress in the 21st Century Act (MAP-21), provide a framework for managing infrastructure assets using lifecycle-based approaches. Although these plans have traditionally focused on roads and bridges, some agencies are exploring ways to incorporate fleet assets into asset management practices. Current approaches vary across agencies, and practices for integrating fleet management into TAMPs are still evolving. In addition, fleet performance and utilization data may not always be fully integrated into planning and budgeting processes. Research is needed to identify critical equipment, performance measures, funding strategies, and data management methods to assist state DOTs with assessing fleet investment needs and managing fleet resources effectively.

The objectives of this research are to (1) develop a guide with reporting tools and successful practices for prioritizing fleet and equipment within state DOT asset management planning processes to improve lifecycle management, and (2) develop a framework for a Fleet Asset Management Plan (FAMP) to aid state DOT funding decisions.]]></description>
      <pubDate>Tue, 09 Jun 2026 16:07:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712185</guid>
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