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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>The New Mexico DOT Research and Climate Bureau Library Update: Welcome to the Story of Our Reopening! [video]</title>
      <link>https://trid.trb.org/View/2724652</link>
      <description><![CDATA[The New Mexico Department of Transportation re-opened its library in 2025 after being closed for several years. Speaker and solo librarian Amy Boggess shares an overview of the Research and Climate Bureau building that houses the library, the history and timeline of the library’s closing and re-opening, and progress made in establishing the new era of the library while operating in a multi-use space.]]></description>
      <pubDate>Tue, 14 Jul 2026 13:34:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724652</guid>
    </item>
    <item>
      <title>Practices for Transitioning to Digital Delivery Systems and Workflows</title>
      <link>https://trid.trb.org/View/2724648</link>
      <description><![CDATA[The transportation sector is undergoing a rapid transformation driven by digital technologies that are influencing how infrastructure is planned, designed, delivered, and managed. Across the United States, state departments of transportation (DOTs) have reported increasing interest in the potential of digital delivery, which is the exchange and use of digital information in standardized, accessible formats, to improve efficiency, reduce costs, and enhance lifecycle asset management. This shift is motivated by the need to overcome longstanding challenges associated with fragmented workflows, costly change orders, and inefficiencies tied to traditional processes and practices. This report, NCHRP Synthesis 664, documents state DOT practices for adopting and implementing digital delivery. Practices identified by the synthesis cover issues such as the status and maturity of digital delivery transitions, policy changes supporting implementation, documentation practices, organizational structures and champions, IT infrastructure, training, transition communications, data governance, and assessment. The report is organized as follows: chapter 2 is a review of existing literature on transitioning to digital delivery; chapter 3 summarizes survey results; chapter 4 provides five case examples of digital delivery transitions; chapter 5 summarizes findings from previous chapters and identifies the gaps in knowledge that could be addressed through future research.]]></description>
      <pubDate>Mon, 06 Jul 2026 16:28:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724648</guid>
    </item>
    <item>
      <title>Establishing a Unified Data Governance Framework for Reliable Project Prioritization in Transportation Agencies</title>
      <link>https://trid.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.

OBJECTIVE: 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://trid.trb.org/View/2712205</guid>
    </item>
    <item>
      <title>Advancing AI Applications for Knowledge Discovery, Capture, And Delivery at State DOTs</title>
      <link>https://trid.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://trid.trb.org/View/2712201</guid>
    </item>
    <item>
      <title>Streamlining Compliance Criteria and Preemptively Identifying Slowdowns for Hastened Project Delivery – Lessons from Efficient Agencies</title>
      <link>https://trid.trb.org/View/2712199</link>
      <description><![CDATA[Transportation agencies and decision-makers increasingly prioritize timely project delivery, including shorter durations between funding allocation and construction. This emphasis is reflected in a growing focus on schedule performance and transparent communication of project timelines. Stakeholder expectations&mdash;including those of elected officials and the public&mdash;underscore the importance of clearly understanding and managing factors that influence project schedules.
Quantitative, project-level data are essential for identifying patterns of delay, informing process improvements, and supporting the development of realistic and reliable schedules. With robust data, departments of transportation (DOTs) can more effectively assess project readiness, using performance-informed metrics to guide decision-making. In addition, information on the effectiveness of mitigation strategies&mdash;particularly measured reductions in delay duration&mdash;can help agencies prioritize resources and apply approaches that offer the greatest benefit.
State DOTs have developed a strong understanding of common sources of delay in areas such as environmental review and permitting. However, the availability of quantitative, project-level data for other types of delays remains limited. In particular, agencies may not consistently have data on the typical schedule impacts associated with specific issues or the relative effectiveness of different mitigation strategies. While existing research often identifies causes of delay, it less frequently quantifies their schedule impacts&mdash;especially for complex projects&mdash;or estimates potential time savings associated with mitigation measures. This limits agencies' ability to take a comprehensive, data-driven approach to comparing delay drivers, identifying process efficiencies, and evaluating tradeoffs between mitigation benefits and costs.
This scan will identify and examine organizations that have developed effective procedures to:
(1) Identify measurable sources of delay,
(2) Collect project-level quantitative data on the schedule impacts of those delays, and
(3) Apply mitigation strategies that support recovery of schedule time.
The resulting observations will provide practical, transferable lessons to support agencies in managing project development schedules and improving overall program efficiency.
Key factors to be investigated include:

Identification of measurable delay issues, 

Average and range of delay duration, by issue and project type, 

Mitigation strategies developed to address specific delay types, 

Average and range of time savings associated with mitigation, by issue and project type, and 

Approaches for applying these metrics to develop more reliable schedules for future projects.


]]></description>
      <pubDate>Wed, 10 Jun 2026 11:02:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712199</guid>
    </item>
    <item>
      <title>Making Knowledge Management Work for DOTs: A Guide to Fostering Collaboration, Learning and Adaptation</title>
      <link>https://trid.trb.org/View/2712194</link>
      <description><![CDATA[Knowledge management (KM) is growing among state departments of transportation (DOTs), and culture is an essential ingredient of KM. Agencies must value and support learning; otherwise, employees are not likely to share what they know or invest the time needed for effective collaboration. Previous National Cooperative Highway Research Program (NCHRP) studies on innovation and learning cultures have identified several factors that are conducive to a learning culture. These studies have acknowledged the importance of culture but have not explored it in detail. There is a need to build on prior research dealing with KM and innovation in transportation agencies, along with foundational studies of learning cultures, and exploring the intersection of KM, learning cultures, and organizational change at DOTs.

This objective of this research is to develop a guide for state DOTs to strengthen organizational cultures that foster collaboration, learning, and adaptability. ]]></description>
      <pubDate>Tue, 09 Jun 2026 17:13:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712194</guid>
    </item>
    <item>
      <title>Developing Workflows for Digital Project Delivery to Support Transportation Asset Management</title>
      <link>https://trid.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://trid.trb.org/View/2712189</guid>
    </item>
    <item>
      <title>Workforce Development in Digital Transportation and Infrastructure Technologies for State DOTs</title>
      <link>https://trid.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://trid.trb.org/View/2712186</guid>
    </item>
    <item>
      <title>Case Studies Documenting Lessons Learned and Identifying Opportunities for Maintenance of Traffic (MOT) Improvements</title>
      <link>https://trid.trb.org/View/2696160</link>
      <description><![CDATA[Over the past 18 months, the team has performed several dozen after-action reviews of interstate work zones and the associated maintenance of traffic (MOT). These after-action reports generally contain the following information: Date, location, visual images, connected vehicle summary graphics, qualitative discussion of activity, and in some cases press releases or media posts. In general, these themes emerged: (1) MOT plans in some cases do not accurately capture the geometry constraints placed by bridges, guardrail and or barrier wall. It is important that design reviews place careful focus on ensuring lane widths, shoulders, and shoulder treatments fit across the entire cross section of each phase or requests for design exceptions be initiated. In some cases, performing some type of LiDAR survey prior to design may assist MOT designers in effectively capturing more accurate location and dimensions of existing geometrics, particularly edge of pavement, edge of bridge, guardrails, and embankments. (2) When lane shifts are initiated, the designers should examine the path of the vehicle at both the start and end of the transition. In some cases, the end of the transition occurs quite close to either a guardrail or narrow section of pavement. In some cases, either gentler transitions or additional horizontal clearance should be considered. (3) There is relatively little dialog between the temporary traffic control contractors and designers on what are the best practices and requirements for implementing a change in MOT. (4) To sustain this engagement between designers, and MOT contractors, it is recommended that selected projects include MOT review with design engineers, and contractor as part of the close out process to document lessons learned. (5) Rolling slows downs have a role in MOT, but there appears to be inconsistent use of rolling slowdowns for short term closures. It is recommended that requirements for rolling slowdowns be identified in the MOT plans. If contractors are required to use rolling slowdowns not documented in MOT plans, they should be required to obtain approval from the Indiana Department of Transportation and notify the Traffic Management Center. (6) Barrier walls are an important tool for MOT. However, their set up can have significant impact on traffic capacity and create queues. Project plans should define days/time periods these are permitted to be installed and/or removed.]]></description>
      <pubDate>Mon, 18 May 2026 10:59:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696160</guid>
    </item>
    <item>
      <title>Supervisor's Handbook</title>
      <link>https://trid.trb.org/View/2701239</link>
      <description><![CDATA[Supervisors at the State Highway Administration (SHA) play an essential role in ensuring the continuity and effectiveness of daily operations. They enforce policies, mentor staff, evaluate performance, and uphold compliance with safety and procedural standards. Despite their critical responsibilities, many supervisors operate without a centralized, reliable, and up-to-date knowledge resource. Instead, they depend on fragmented intranet pages, institutional memory, email threads, and informal peer networks. This
patchwork approach often leads to inefficiencies, inconsistent interpretations, and frustration among staff. Because there is no robust process for curating, linking, and maintaining policy knowledge, supervisors spend valuable time searching through repositories or verifying outdated manuals. Decisions become inconsistent, fairness and compliance are compromised, and confidence in institutional processes weakens.
Over time, these inefficiencies not only hinder operational performance but also erode supervisors’ ability to coach and support their teams effectively. Despite the critical role supervisors play, SHA lacks a unified, authoritative, and maintainable knowledge
system to support them. Existing resources are fragmented, outdated, and inconsistently connected, leading to misinterpretation, wasted effort, and diminished trust. Supervisors often must interpret policy on their own, resulting in variations in implementation and uncertainty about compliance. The absence of a structured, well-maintained knowledge system represents a systemic challenge for SHA—one that undermines efficiency, workforce engagement, and institutional learning. Lack of compliance and proper
employee management also places the organization at risk for litigation. i.e., mismanaged employee performance issues expose SHA to litigation risk. Supervisors lack central and authoritative knowledge resources. Current resources are fragmented, outdated, and inconsistently applied leading to policy misinterpretation, inefficiency, and diminished trust. A structured, maintainable system is critical to ensure consistent implementation, reduce legal exposure, and strengthen workforce engagement and organizational success. National workforce research underscores the urgency of addressing these challenges. According to the 2025 Retention Report, 75% of employee departures are considered preventable, with management and communication deficiencies among the leading causes (Work Institute, 2025). Similarly, the 2025 SHRM State of the Workplace Report finds that nearly half of turnover intent is linked to weak engagement and workplace culture, with organizations that invest in management capacity achieving markedly higher retention (SHRM, 2025). For SHA operating under tight budgets, stringent regulations, and increasing workloads, the cost of fragmented knowledge is not merely administrative—it affects workforce stability, operational consistency, and public trust. To address this gap, this research proposes a structured knowledge management architecture grounded in Garfield’s (2022) knowledge management principles. The Curate → Connect → Cultivate (C3) framework translates these principles into an actionable model tailored for supervisory environments within SHA. It envisions a digital ecosystem that curates authoritative resources, connects users through accessible pathways, and cultivates continuous learning and collaboration. The following section introduces the framework and its three integrated phases, illustrating how each supports a more coherent and resilient
supervisory knowledge system. 
● Curate: the systematic gathering, validation, structuring, and versioning of policy and procedural
knowledge
● Connect: enabling supervisors to find, navigate, and relate to relevant content via search,
recommendations, linking, and social features
● Cultivate: establishing governance processes, review cycles, feedback loops, and incentives to
keep the system current, trusted, and sustainable

By mapping each stage to the known pain points in SHA supervisory practice, this framework guides both the design and evaluation of a “Supervisor’s Knowledge Hub.” The research will prototype, deploy, and assess features corresponding to each stage, measuring outcomes such as time to find authoritative guidance, user satisfaction, accuracy of interpretations, and frequency of contributions. For the purposes of
this study, supervisory competencies emphasize people leadership, team management, communication, performance feedback, and consistent policy application across SHA rather than technical engineering or design skills.]]></description>
      <pubDate>Wed, 13 May 2026 09:22:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701239</guid>
    </item>
    <item>
      <title>Coordination of Highway Safety Improvement Program and Highway Safety Office Activities</title>
      <link>https://trid.trb.org/View/2694541</link>
      <description><![CDATA[This report presents the state of practice of state departments of transportation (DOTs) on how they organize, manage, and align their Highway Safety Improvement Programs (HSIPs) and Highway Safety Offices (HSOs). The synthesis includes information on how state DOTs coordinate these functions through shared planning, data exchange, performance measures, and reporting processes. The synthesis also documents coordination practices related to funding, safety program implementation, public participation and engagement efforts, and the use of data tools and dashboards. Under NCHRP Project 20-05/Topic 56-19, “Practices on Coordination of HSIP and Highway Safety Office Activities,” the University of Missouri was asked to synthesize information to document current practices, challenges, and opportunities for improving coordination between HSIP and HSO management, practices, and associated funding. Information used in this study was attained through a literature review, a survey of state DOTs, and interviews to develop in-depth case examples. Chapter 4 provides six case examples that highlight how the interviewed state DOTs coordinate HSIP and HSO activities through shared performance measures, safety planning, crash data management, funding practices, and organizational structures, as well as challenges related to staffing, communication, and administrative processes.]]></description>
      <pubDate>Sun, 26 Apr 2026 17:37:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694541</guid>
    </item>
    <item>
      <title>Data Ontologies for Data-Driven Decision-Making: Development and Use</title>
      <link>https://trid.trb.org/View/2689752</link>
      <description><![CDATA[This report presents an advanced data representation and knowledge management guide, providing state departments of transportation (DOTs) with tools and techniques to treat data as a business asset. The guide also provides state DOTs with essential resources to develop and implement data ontologies that support data-driven decision-making. It was developed through a literature review, survey, case studies, and use cases. The information contained in the guide will be of immediate interest to transportation data practitioners and managers. The findings will serve as a valuable resource for state DOTs and other public transportation agencies.]]></description>
      <pubDate>Sat, 11 Apr 2026 11:01:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689752</guid>
    </item>
    <item>
      <title>Data Ontologies for Data-Driven Decision-Making: Research Approach and Findings</title>
      <link>https://trid.trb.org/View/2689753</link>
      <description><![CDATA[Transportation agencies are considering tools and techniques to treat data as a business asset. This shift to performance-based management necessitates the use of cross-cutting analytics and data-driven decision-making. In addition, the advancement of technology has left agencies with many legacy systems, architectures, and an accumulation of separate systems. Agencies are taking meaningful steps to make legacy systems more amenable to cross-functional decisions by developing “data lake” or “data warehouse” approaches. This research aimed to create a conceptual framework and a guide for State Department of Transportation (DOT) executive leadership and senior managers, mid-level managers, field staff, and others. The purpose was to help them design strategies for creating and using data ontologies. Data ontology is a framework for characterizing and defining classes, attributes, and their relationships in a domain to provide a shared meaning across multiple users and support agile, efficient, data-driven decision-making.]]></description>
      <pubDate>Sat, 11 Apr 2026 11:01:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689753</guid>
    </item>
    <item>
      <title>Electric Vehicle Adoption Leadership (EVAL) Program Final Case Study</title>
      <link>https://trid.trb.org/View/2685486</link>
      <description><![CDATA[The Electric Vehicle Adoption Leadership (EVAL) Program, led by Forth under the U.S. Department of Energy’s Leadership of Employers for Electrification Program (LEEP), established the first national certification and education framework recognizing employers for workplace-charging leadership. EVAL helps organizations assess, improve, and promote clean-transportation practices through a structured, four-tier certification system (Bronze, Silver, Gold, and Platinum). Key Metrics (as of October 2025): employers contacted: 20,000+, employers registered: 438, certified worksites: 521. This report includes: EVAL certification framework, program design and implementation, key achievements of the EVAL program, challenges and lessons learned, and next steps.]]></description>
      <pubDate>Tue, 07 Apr 2026 17:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685486</guid>
    </item>
    <item>
      <title>Strategies to Foster the Implementation of Knowledge Management</title>
      <link>https://trid.trb.org/View/2689398</link>
      <description><![CDATA[State departments of transportation (DOTs) began to explore knowledge management (KM) in the early 2000s. Since then, several state DOTs and U.S. DOT administrations have implemented KM activities and programs. The transportation community has conducted several research projects that examined how other industries have adopted and implemented KM. Also, NCHRP and others have published reports on the value of KM, including NCHRP Report 813, A Guide to Agency-Wide Knowledge Management for State Departments of Transportation (https://www.trb.org/Publications/Blurbs/173082.aspx).  

Despite substantial research on the use of KM in transportation, loss of institutional knowledge due to retirements and turnover, and other workforce changes, state DOTs have not widely adopted formal KM practices. Some state DOTs are trying to develop KM practices to capture this institutional knowledge quickly but need more resources and strategies for KM implementation. 

Research is needed to document the evolution of KM stewardship at state DOTs and insights into their successes and challenges in adopting and implementing KM. Strategies are needed to help state DOTs foster KM investment, development, and sustainability.

 OBJECTIVE: The objective of this research is to provide strategies and proven approaches to foster KM investment, development, and sustainability. The research shall, at minimum, (1) include a summary of the evolution of KM stewardship at state DOTs, and (2) identify and analyze successes and challenges state DOTs have encountered in adopting and implementing sustained KM programs.]]></description>
      <pubDate>Mon, 06 Apr 2026 18:33:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689398</guid>
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