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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzgyIiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Establishing a Unified Data Governance Framework for Reliable Project Prioritization in Transportation Agencies</title>
      <link>https://rip.trb.org/View/2712205</link>
      <description><![CDATA[Project prioritization is a cornerstone of effective transportation planning and investment. However, many state departments of transportation (DOTs) struggle with fragmented data systems, inconsistent data definitions, and unclear data ownership. These challenges result in decision-making that is often delayed, misinformed, or misaligned with strategic goals. The absence of  centralized, authoritative data source leads to duplication of effort, conflicting reports, and a lack of transparency. Moreover, undefined policies around data governance—such as who owns the data, who can access it, and how it should be maintained and secured—further exacerbate inefficiencies.

To address the fragmented data ecosystem that hinders effective decision-making, this research will examine successful strategies from leading transportation agencies, drawing on case studies that highlight the importance of interagency coordination, standardization of data formats, and robust digital delivery workflows. The research will identify scalable solutions to common workflow and workforce challenges, including the need for clear roles and responsibilities in data stewardship, ongoing workforce training, and adoption of compatible software infrastructure. The resulting data governance model and blueprint will be informed by national and international best practices, positioning state DOTs to deliver efficient, transparent, and high-impact transportation investments.

The objective of this research is to develop a unified data governance model and delivery framework that enables transportation agencies to provide timely, accurate, and reliable data for project prioritization.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:26:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712205</guid>
    </item>
    <item>
      <title>Advancing AI Applications for Knowledge Discovery, Capture, And Delivery at State DOTs</title>
      <link>https://rip.trb.org/View/2712201</link>
      <description><![CDATA[State departments of transportation (DOTs) are facing a critical workforce transition as large numbers of experienced engineers, planners, maintenance managers, and technical experts approach retirement. This demographic shift threatens the loss of institutional and tacit knowledge that supports effective decision-making, project delivery, operations, and innovation. Existing knowledge-management approaches are often fragmented and insufficient for systematically capturing and transferring experiential knowledge across agencies.

At the same time, transportation agencies are becoming increasingly digital and data-driven, relying on technologies such as intelligent transportation systems, analytics, digital twins, and artificial intelligence (AI)-enabled tools. Advances in AI, particularly in Large Language Models (LLMs), semantic models, and Retrieval Augmented Generation (RAG), offer opportunities to improve knowledge discovery, synthesis, retrieval, and delivery within transportation agencies. AI applications such as chatbots, intelligent assistants, semantic search, and interactive knowledge exploration tools can help employees quickly locate technical standards, business processes, lessons learned, datasets, and expert guidance.

Several DOTs are independently piloting AI-based knowledge discovery and delivery (KDD) applications, but there is limited research on scalable, transferable frameworks that support knowledge capture, workforce onboarding, training, and enterprise-wide information access. There is also a need to address governance, data quality, privacy, interoperability, model transparency, and long-term maintenance of AI-enabled knowledge systems.

The objective of this research is to advance AI applications for knowledge discovery, capture, and delivery within state DOTs by developing a scalable transportation-specific LLM framework that captures, organizes, synthesizes, and disseminates institutional knowledge. The research will assess current AI-based KDD practices; identify promising applications and use cases; develop standardized protocols for data ingestion, annotation, and evaluation; and establish governance frameworks for responsible AI deployment.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:08:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712201</guid>
    </item>
    <item>
      <title>Risk-Based and Cost-Effective Agency Verification of Contractor-Collected Pavement and Bridge Profiles</title>
      <link>https://rip.trb.org/View/2712193</link>
      <description><![CDATA[State departments of transportation (DOTs) recognize that pavement and bridge smoothness is a key indicator of performance and public satisfaction. As state DOT staffing levels have declined, contractors have become increasingly responsible for collecting profile data, calculating smoothness indices, and sometimes determining pay factors. While federal regulations require independent verification of contractor data used for acceptance decisions, agencies remain uncertain about the level of verification needed to ensure accuracy and judicious allocation of public funds.

Current practices for validation and verification vary widely across state DOTs. Some agencies collect independent profiles on a subset of projects, while others rely on partial sampling, comparisons with contractor data, or limited review processes. The statistical reliability and risk implications of these approaches are not well understood. Additionally, advances in data collection technologies, such as high-speed profilers, have increased the volume of data, challenging traditional verification approaches. There is a need for research that helps state DOTs accurately determine pavement life through the potential use of emerging technologies and improved verification of contractor-collected pavement and bridge profile data.

The objective of this research is to develop a guide and supporting tool to assist state DOTs in conducting cost-effective, risk-based verification of contractor-collected pavement and bridge profiles.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:10:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712193</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>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>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>
    </item>
    <item>
      <title>Error Assessment of Procedures for National Spatial Reference System Alignment of Highway Construction Surveying Projects</title>
      <link>https://rip.trb.org/View/2712173</link>
      <description><![CDATA[Highway construction surveying is facing an important transition as the industry prepares for modernization of the National Spatial Reference System (NSRS). The modernized NSRS, announced by the National Geodetic Survey, is scheduled for implementation in 2026. The new system will introduce geocentric and time-dependent terrestrial reference frames, replacing reliance on static datums such as NAD 83. For highway construction, this change is significant because many projects will need to reconcile existing designs based on NAD 83 (2011) and NAVD 88 with the modernized NSRS. Traditional site calibration tools may introduce additional discrepancies during this transition, particularly where control points are insufficient, poorly distributed, or affected by positional uncertainty.

Errors in construction surveying can propagate through multiple phases of a project and may lead to rework, delays, disputes, and safety concerns. To mitigate these risks and ensure the integrity of highway construction projects, research is needed to identify and minimize potential errors so the highway construction industry can better prepare for the transition to the modernized NSRS and ensure the continued accuracy and efficiency of infrastructure development. This research will ultimately safeguard the significant public investment in national transportation networks and support the establishment of accurate project control in an evolving geodetic landscape.

The objective of this research is to quantify and compare the errors induced by common best-fit coordinate transformation procedures, such as site calibration or localization, against least-squares transforms and other more rigorous workflows. The research will establish guidelines for selecting the optimal coordinate transformation procedures for highway construction surveying within the modernized NSRS framework.]]></description>
      <pubDate>Tue, 09 Jun 2026 12:53:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712173</guid>
    </item>
    <item>
      <title>Notification Systems to Enhance Construction Vehicle Safety at Work Zone Access Points</title>
      <link>https://rip.trb.org/View/2712172</link>
      <description><![CDATA[Commercial motor vehicles (CMVs) are consistently overrepresented in fatal work zone crashes. Improving CMV safety in work zones is a priority at federal and state levels. One aspect of particular interest is the interaction between slow-moving construction vehicles, delivering and removing materials from work zones, and traffic in the travel lanes. A sizable portion of rear-end collisions and sideswipe crashes involves construction vehicles exiting or entering travel lanes from the workspace, with speed differentials between through traffic and construction vehicles being a significant factor.

Advanced work zone technologies have the potential to reduce vehicle conflicts between slow-moving construction vehicles entering or exiting work zones and traffic in travel lanes. NCHRP Research Report 1142: Innovative Approaches to Enhancing Safety and Efficiency in Work Zones: A Guide,” documented use of entering/exiting vehicle notification as a work-zone safety application in Minnesota and Pennsylvania, with signs warning drivers of slow-moving construction or emergency vehicles entering or exiting the roadway to reduce crash risk.

Several states have reported plans to use smart work zone truck ingress-egress warning systems but noted inadequate information available regarding effectiveness, as well as issues with equipment availability and frequent malfunctions. In addition, pilot studies have been plagued with system performance issues.

The objective of this research is to conduct a scoping study to: (1) Document the state of knowledge regarding the design, operation, and effectiveness of smart work zone truck ingress-egress warning systems. (2) Identify research needs to fill knowledge gaps. (3) 
Propose a study design for use in potential future National Cooperative Highway Research Program (NCHRP) research to address knowledge gaps, with emphasis on safety performance metrics for the traveling public and construction vehicles.]]></description>
      <pubDate>Tue, 09 Jun 2026 12:49:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712172</guid>
    </item>
    <item>
      <title>Connected Corridor Advancement Initiative</title>
      <link>https://rip.trb.org/View/2712040</link>
      <description><![CDATA[The Connected Corridor Advancement Initiative (CCAI) aims to modernize corridor operations, enhance safety, and optimize economic efficiency by aligning efforts across state, federal, and private sectors. Objectives include developing and implementing open data standards for Work Zone Data Exchange (WZDx), Truck Parking Information Monitoring Systems (TPIMS), and national interoperability of communication data feeds to enable seamless communication across jurisdictions. Additionally, the initiative seeks to prepare the corridor for connected and automated vehicle (CAV) technologies by supporting data interoperability between states, agencies, emergency services, industry partners and the traveling public.]]></description>
      <pubDate>Mon, 08 Jun 2026 11:10:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712040</guid>
    </item>
    <item>
      <title>Advancing Pollinator Habitat Monitoring through Remote Sensing on Nebraska Roadsides</title>
      <link>https://rip.trb.org/View/2689394</link>
      <description><![CDATA[To meet monitoring and reporting requirements under the Monarch Candidate Conservation Agreement with Assurances (CCAA), Nebraska Department of Transportation (NDOT) must collect consistent data on milkweed stem density and nectar-plant cover across extensive roadside networks. Current field-based approaches, though effective, are resource-intensive, limited in spatial coverage, and require a specialized level of biological expertise. NDOT needs a scalable and cost-effective remote sensing strategy that can meet CCAA requirements. Furthermore, NDOT must understand the costs and benefits to applying this technology in-house or via external contract, and how the products could be applied to offer NDOT versatile imagery and data outputs that can support broader environmental review needs, planning, and maintenance decisions.]]></description>
      <pubDate>Fri, 05 Jun 2026 12:41:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689394</guid>
    </item>
    <item>
      <title>Advanced Technologies and Data Analytics for Safe, Smart, and Efficient Transportation (ASSET)</title>
      <link>https://rip.trb.org/View/2709572</link>
      <description><![CDATA[This project assists the Massachusetts Department of Transportation (MassDOT) with (A) calibrating safety models for urban and suburban arterial intersections and developing artificial intelligence models for (B) detecting sidewalks and (C) counting multimodal trips.  

There are three main goals:

(A) Calibrate the Safety Performance Functions (SPFs) in Chapter 16.6.4 of the Highway Safety Manual, 2nd Edition (HSM2), along with the associated parameters, for the twelve types of urban and suburban intersections in Massachusetts using the most recent data.

(B) Develop an Artificial Intelligence (AI) model to automate the detection and mapping of sidewalks from publicly available aerial imagery. Also, the model will be used to identify changes in sidewalks using aerial imagery from multiple years.

(C) Leverage AI to automate the counting of pedestrians, active transportation modes (such as bicycles and e-scooters), and site-generated trips from new developments. The results of this task will form the basis for developing AI and/or statistical models to estimate multimodal trip counts required for transportation planning purposes.]]></description>
      <pubDate>Wed, 03 Jun 2026 15:27:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709572</guid>
    </item>
    <item>
      <title>SPR-5042: Performance and Safety Evaluation of Truck Mounted Debris Clearing Systems</title>
      <link>https://rip.trb.org/View/2709430</link>
      <description><![CDATA[The principal investigators will help the Indiana Department of Transportation (INDOT) evaluate truck-mounted debris clearing systems by achieving the following three main objectives: 1) Development of an event-triggered, multi-sensor data collection framework integrating multi-camera video and Global Positioning System (GPS) to enable automated, machine vision-based performance assessment. 2) Quantitative evaluation of system performance through field testing to measure debris removal effectiveness, roadway interaction, and operational efficiency across real-world conditions. 3) Assessment of safety and traffic impacts by analyzing worker exposure, operational risks, and vehicle interactions to quantify how these systems influence roadway safety and deployment practices.]]></description>
      <pubDate>Wed, 03 Jun 2026 13:31:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709430</guid>
    </item>
    <item>
      <title>SPR-5135: CARSx Traveler Information Message (J-2735) Module Development</title>
      <link>https://rip.trb.org/View/2709429</link>
      <description><![CDATA[This project will facilitate joint engagement between Minnesota Department of Transportation (MnDOT), Indiana Department of Transportation (INDOT), and Castle Rock Associates. The objective of this proposal is to establish a collaborative, cost-sharing approach for work currently defined under an existing scope of work developed by Castle Rock for MnDOT, while ensuring that each state receives its own independent implementation.]]></description>
      <pubDate>Wed, 03 Jun 2026 13:27:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709429</guid>
    </item>
    <item>
      <title>SPR-5041: SPR-4517 Implementation: Wireless Data Collection and Model Development</title>
      <link>https://rip.trb.org/View/2709427</link>
      <description><![CDATA[SPR-4517 deployed an edge-enabled, solar-powered wireless monitoring system at the I-69 instrumentation section to evaluate pavement drainage and related performance indicators. This implementation study will sustain field hardware, formalize an automated data pipeline with quality assurance/quality control (QA/QC) protocols, develop and validate performance indicators and predictive models, and package the complete workflow for the Indiana Department of Transportation (INDOT). Outcomes include continued wireless monitoring, versioned data products with monthly health reports, validated drainage performance models, and a transferable implementation package enabling INDOT to maintain long-term field data collection and adopt similar capabilities at additional sites.]]></description>
      <pubDate>Wed, 03 Jun 2026 13:23:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709427</guid>
    </item>
    <item>
      <title>Developing a Standardized Framework for Real-Time Freight-Specific Traveler Information and Route Restrictions for Commercial Motor Vehicle Operators; Truck Parking Data Exchange Standards</title>
      <link>https://rip.trb.org/View/2709247</link>
      <description><![CDATA[Commercial motor vehicle (CMV) operations increasingly rely on maps and navigation systems that were not designed to address the unique needs of freight operations. This mismatch contributes to increased safety risks, including unplanned diversions, bridge strikes, congestion in freight corridors, lane geometry constraints, and other routing errors.

Today, the lack of a standard, consistent data structure or framework for sharing real-time freight-specific information remains a foundational challenge for public agencies and for the economy that depends heavily on the national roadway network. Public agencies currently lack a widely accepted standard or shared framework for communicating restrictions, alerts, and disruptions to CMV operators. Existing standards such as the Traffic Management Data Dictionary (TMDD) and SAE J2354 (Advanced Traveler Information Systems) support general traveler messaging but do not include freight-specific data elements.

In addition, the growing need for timely and reliable truck parking information, coupled with the rapid expansion of truck parking information systems, demonstrates the need for standardized methods to collect and disseminate truck parking data. As technologies used in these systems become increasingly ubiquitous, and as industry expectations and preferences continue to evolve, standardization of both information and dissemination tools becomes a critical next step.

OBJECTIVES: The objectives of this research are: (1) to develop a unified data framework for delivering time-sensitive, relevant, and actionable freight-specific traveler information messaging to CMV operators; and (2) to develop proposed data standards for real-time, public and private truck parking availability and attributes (including the number of spaces, size, hours of availability, and available amenities).

]]></description>
      <pubDate>Tue, 02 Jun 2026 14:33:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709247</guid>
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