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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>Automatic Signal Retiming for Large Scale Networks with Vehicle Trajectory Data</title>
      <link>https://trid.trb.org/View/2742152</link>
      <description><![CDATA[This report presents a real-world implementation of a recently developed traffic signal optimization system that uses a small percentage of vehicle trajectory data-also known as probe or telemetry data-as the only input. The system utilizes a new model for designing signal timing plans that leverages the temporal-spatial information of vehicle trajectory data as opposed to traditional software such as Synchro which relies on turning movement counts. This model allows us to accurately reconstruct average recurrent traffic states by aggregating sufficient historical data, even at low penetration rates. In the real-world deployment conducted in coordination with the Michigan Department of Transportation (MDOT) and the Road Commission for Oakland County (RCOC), researchers used vehicle trajectory data from an estimated 7% of vehicles in the traffic network to update cycle lengths, splits, offsets, and timing schedules at seven intersections along a 2.5-mile stretch of a coordinated-actuated arterial in Pontiac, Michigan. This corridor recently went through a traditional signal optimization based on vehicle count data, providing a unique opportunity to directly compare the performance of the new vehicle trajectory-based system against conventional methods. The vehicle trajectory-based system outperformed the traditional approach, reducing the overall control delay by 17.4% and the number of stops by 20.4%, compared to reductions of 13.7% and 14.9%, respectively, achieved by traditional optimization, and a cost analysis demonstrated savings of up to 35%. By utilizing increasingly available vehicle trajectory data as the only input and not requiring any additional infrastructure, we believe our system will provide a more scalable and economical solution to traffic signal optimization that could be applied worldwide.]]></description>
      <pubDate>Tue, 04 Aug 2026 11:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742152</guid>
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    <item>
      <title>Improve MDOT's Understanding of the Acceptance and Performance of Riprap</title>
      <link>https://trid.trb.org/View/2731977</link>
      <description><![CDATA[The long term performance of riprap has been an issue because some local sources of riprap have known durability issues
and will degrade/dissolve over time. In addition, the acceptance of riprap size and gradation is currently done by performing a
Wolman count. Performing the Wolman count involves walking over large rocks, which can be a safety hazard and takes a
significant amount of time to do. There are challenges in assessing the performance and durability of riprap in riverine, lightly
acidic and other environments that need to be addressed. The potential exists that there may be technological and electronic
solutions that need to be utilized to enhance or replace existing processes.]]></description>
      <pubDate>Fri, 17 Jul 2026 15:21:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731977</guid>
    </item>
    <item>
      <title>Guidelines and Best Practices for Determining the Life Cycle Cost of Various Superstructure Types</title>
      <link>https://trid.trb.org/View/2731917</link>
      <description><![CDATA[The selection of superstructure type during the study phase of a design project is currently made based on the estimated
construction cost and a subjective and inexact assessment of the life cycle cost of the structure. This method of selecting the
preferred alternative has led to the introduction of bias into the decision-making process and tends to lead to the selection of
concrete superstructures more often than steel superstructures. Rarely is this decision tied to objective data based on historic
maintenance records of similar superstructures and has never accounted for 
Michigan Department of Transportation's (MDOT’S) ability to extend the life of steel
superstructures by incorporating bolted and welded repairs, which are not possible on concrete superstructures. Disregarding
this information in the selection of a superstructure type increases the risk of not using the available bridge funding as
efficiently and effectively as possible.]]></description>
      <pubDate>Fri, 17 Jul 2026 10:05:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731917</guid>
    </item>
    <item>
      <title>Enhance Bridge Image Attribution Through Automated Post Image Processing</title>
      <link>https://trid.trb.org/View/2713582</link>
      <description><![CDATA[Highway agencies and consultants capture thousands of images during biennial, scoping, and request for action (RFA) inspections. Most of these files are stored with limited descriptions or inconsistent naming formats. As a result, a rich data source is underutilized. Understanding the value of this rich data source for asset management, MDOT initiated this project to explore how computer vision and artificial intelligence (AI) can automatically organize, label, and analyze bridge inspection images, turning unstructured images into structured data that can directly support the asset management program. The comprehensive review of state-of-the-art literature and practice showed that the commercially available tools are limited to roadside asset management. None of those tools is capable of performing semantic segmentation to detect bridge components. The foundation models, such as OpenAI’s GPT-5.1 and Google’s Gemini 3 Pro, are capable of delivering descriptive answers to given prompts. However, these models are not error-proof; hallucinations, non-determinism, and the reproducibility of results remain major issues when using them for highly specific and complex tasks such as bridge image analysis. The capabilities of the Integrated Bridge Analysis System, a comprehensive model for performing semantic segmentation and damage detection of bridge components, were demonstrated. Recommendations from this project include integrating the demonstrated model into the structural inspection program as a standalone application and using it to batch-process a large volume of images in the inspection database, converting them into a rich data source.]]></description>
      <pubDate>Mon, 22 Jun 2026 07:23:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713582</guid>
    </item>
    <item>
      <title>Michigan (One Point) Cone Test for Evaluating the Density of OpenGraded Drainage Course (OGDC) Materials</title>
      <link>https://trid.trb.org/View/2714102</link>
      <description><![CDATA[Pavement infrastructure is a significant public investment (over $60 billion annually in the U.S.), yet 39% of major roads are in poor condition, resulting in costly repairs and user expenses. Maximizing pavement longevity is critical to address a projected $684 billion funding gap in road maintenance, and improving compaction quality in pavement base layers has been identified as a high-return strategy to extend service life. This study investigates compaction of open-graded drainage course (OGDC) aggregates, which form the permeable base layers crucial to pavement stability and drainage, using the One-Point M-Cone Test (M-Cone). This collaborative study with the Michigan Department of Transportation (MDOT) compared M-Cone results with those from standard laboratory compaction methods across multiple OGDC aggregate sources. Statistical analyses were applied to evaluate the influence of different operators and material sources on the M-Cone outcomes within the controlled laboratory conditions of this study. The results showed that the M-Cone test can achieve maximum dry density (MDD) values comparable to those obtained with the Modified Proctor test, except for aggregates with a high water-absorption tendency. Statistical tests indicated no significant operator variability in M-Cone testing within the controlled laboratory conditions evaluated. Sieve analyses of particle size distributions before and after compaction confirmed that all methods produced some fine particles from aggregate breakage. Overall, the one-point M-Cone test, conducted by trained operators in a laboratory setting, is a reliable indicator of compaction.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:11:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714102</guid>
    </item>
    <item>
      <title>Local Roads Research Forum under TPF-5(495) 2023 Technology Exchange on Low Volume Road Design, Construction and Maintenance</title>
      <link>https://trid.trb.org/View/2709437</link>
      <description><![CDATA[The 2023 Technology Exchange on Low Volume Road Design, Construction and Maintenance pooled fund (TPF-5(495)) hosted a Local Roads Research Forum on March 3 and 4, 2026, with the Iowa Department of Transportation coordinating as lead state in partnership with the Iowa County Engineers Association Service Bureau. The purpose of this forum was to help participants understand each another's needs and challenges related to local roads research. The forum focused on four broad areas: Current state DOT local roads research, round table discussions on hot topics, national perspectives, and future planning.]]></description>
      <pubDate>Thu, 11 Jun 2026 09:16:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709437</guid>
    </item>
    <item>
      <title>A new Gaussian process regression-based approach to leverage non-destructive evaluation data in bridge deterioration prediction models</title>
      <link>https://trid.trb.org/View/2651559</link>
      <description><![CDATA[Current bridge deterioration models rely on subjective visual inspection ratings from the National Bridge Inventory, limiting their usefulness for maintenance planning. Non-destructive evaluation (NDE) tests, such as Impact-Echo (IE), offer objective and quantitative data for estimating deck delamination but remain underutilized due to limited data availability and accessibility. This study introduces a novel machine learning approach that leverages limited IE data from the Delaware Department of Transportation (DOT) and the Long-Term Bridge Performance (LTBP) database to predict bridge deck delamination as estimated by field IE measurements. Among several tested regression models spanning statistical and machine-learning approaches, Gaussian process regression (GP regression) achieved the highest accuracy on test bridges, with bridge age and deck protection code identified as key explanatory variables. When evaluated against the Michigan DOT Bridge Deck Preservation Matrix, the model correctly classified 80 % of test bridges into the recommended maintenance categories. The results highlight how limited NDE data can be used systematically to support data-driven inspection planning. Additionally, the model quantifies prediction uncertainty, enabling prioritization of bridges for future NDE testing and more strategic resource allocation. The study also highlights current challenges in NDE data collection and emphasizes the need for improved data curation and sharing to enhance modeling accuracy and support long-term bridge performance monitoring.]]></description>
      <pubDate>Tue, 21 Apr 2026 14:30:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651559</guid>
    </item>
    <item>
      <title>Multi-Objective Decision Analysis and Optimization Model for Transportation Investment Decision-Making at MDOT</title>
      <link>https://trid.trb.org/View/2688765</link>
      <description><![CDATA[Multi-objective decision analysis (MODA) provides transportation agencies with a structured process for identifying a set of investments that delivers the greatest impact toward their objectives. In the presented research, the research team developed a prototype MODA-based approach for prioritizing pavement rehabilitation and reconstruction projects at the Michigan Department of Transportation (MDOT). Research activities included a review of best practices and MODA applications, development of goals and measures for quantifying projects’ anticipated impacts, and implementation and evaluation of the proposed MODA approach using 39 test projects. The research demonstrated that a MODA-based approach can be used to assess and prioritize the target set of projects based on their anticipated asset condition, safety, accessibility and mobility, and environmental impacts. Further research could include implementation and evaluation of the proposed approach with additional data or decision-support tools, as well as adaptation of the MODA approach to other program areas at MDOT.]]></description>
      <pubDate>Fri, 10 Apr 2026 10:52:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688765</guid>
    </item>
    <item>
      <title>Capacity Prediction of Repaired and Unrepaired Bridge Beams with Deteriorated Ends</title>
      <link>https://trid.trb.org/View/2663105</link>
      <description><![CDATA[The Michigan Department of Transportation (MDOT) identified significant deterioration at the ends of steel and prestressed concrete (PSC) beams, requiring systematic evaluation and improved decision-making protocols for Requests for Action (RFAs). This comprehensive study examined 431 steel beam ends and 267 PSC bridges to develop capacity-based assessment methods and repair guidelines. For steel beam ends, the analysis revealed a strong preference for bolted repairs over welded repairs due to field welding challenges and concerns about fatigue. Section loss limits of 20% for webs and 10% for flanges were established as RFA thresholds. Finite element analysis of W30×108 beams with corrosion-induced holes resulted in capacity reduction factors ranging from 0.39 to 0.74, depending on hole geometry and the presence of stiffeners. Fatigue analysis revealed a significant reduction in fatigue life due to bolt holes with high surface roughness and preexisting cracks. For PSC beam ends, a Strut-and-Tie Method was implemented to model complex load transfer mechanisms, establishing 15% capacity reduction as the critical RFA threshold. Specific deterioration limits showed that spalls with ≥15% strand exposure or ≥40% section loss without strand exposure warrant immediate action. Accelerated corrosion testing of repair methods identified that combining latex-modified concrete patches with zinc-rich epoxy primer and elastomeric surface coatings provided optimal performance. Field evaluations demonstrated superior long-term durability of reinforced overcasts with FRP U-wraps and protective coatings when compared to unreinforced patches. Updated inspection guidelines, calculation tools, and comprehensive repair selection criteria were developed to enhance bridge safety while optimizing maintenance resource allocation through rational correlations between visual inspection data and structural performance.]]></description>
      <pubDate>Thu, 05 Feb 2026 09:18:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663105</guid>
    </item>
    <item>
      <title>Transit Agency Guidebook</title>
      <link>https://trid.trb.org/View/2658070</link>
      <description><![CDATA[This guidebook is a companion to the full report, Marketing and Education Budget for Implementation of New Transit Technology. The findings and Guidebook offer Michigan Department of Transportation (MDOT) and local transit providers an actionable framework and ready-to-use tools for planning, funding, and implementing technology solutions that improve efficiency, service reliability, and the rider experience statewide. The Guidebook sections are: Phase 1, assess your agency needs; Phase 2, plan, fund, and procure; Phase 3, employee and staff engagement, awareness, training, and internal communications; Phase 4, external technology marketing and training; and Phase 5, manage, maintain, and evaluate technology, resources, and funding.]]></description>
      <pubDate>Mon, 02 Feb 2026 14:13:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658070</guid>
    </item>
    <item>
      <title>Enhancing MDOT’s Pavement Management Tool (PMT) Inputs Through Data-Driven Analysis</title>
      <link>https://trid.trb.org/View/2658083</link>
      <description><![CDATA[This project was undertaken to support the Michigan Department of Transportation’s (MDOT) implementation of a new Pavement Management Tool (PMT), a decision-support system designed to optimize pavement project selection. The goal was to develop data-driven inputs for the PMT using MDOT’s extensive historical pavement condition and maintenance data, with a focus on five key General Condition Ratings (GCRs): International Roughness Index (IRI), cracking (CRK), rutting (RUT), faulting (FLT), and the Pavement Distress Score (PDS). The project began with the compilation and preprocessing of condition data from MDOT’s GroupRecords files, which represent pavement lifecycle histories categorized by treatment type. Condition data were cleaned, harmonized, and filtered to remove post-treatment values and outliers, enabling the development of deterioration models using both discrete (threshold-based) and continuous (mathematical) approaches. Models were calibrated and fitted at the individual-section level, and times to Good/Fair and Fair/Poor thresholds were estimated. Two methods—percentile-based aggregation and group-level curve fitting—were compared to generate representative deterioration trends. In addition, condition improvements were quantified for various fix types by analyzing pre- and post-treatment data, supporting the development of action-benefit profiles. Utility scoring practices from other agencies were reviewed to guide recommendations for scaling and weighting GCRs within the PMT. The analysis also enabled the identification of typical treatment-trigger thresholds to inform network policy decisions. All modeling outputs, thresholds, and supplementary analyses are provided in a digital appendix, including tools to support further customization and application of the results in MDOT’s pavement management practice.]]></description>
      <pubDate>Mon, 02 Feb 2026 14:13:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658083</guid>
    </item>
    <item>
      <title>Evaluation of MDOT’s Methodologies for both Quantifying Pavement Distress and Modeling Pavement Performance for Life-Cycle Cost and Remaining Service Life Estimation Purposes</title>
      <link>https://trid.trb.org/View/2658082</link>
      <description><![CDATA[Michigan Department of Transportation (MDOT) has been using the Distress Index (DI) since the inception of its pavement management system (PMS) in the early 1990s. DI was developed to help MDOT engineers decide, allocate budgets, and prioritize future maintenance or reconstruction activities. However, the raw data requirements for the DI are complicated (and somewhat unique compared to the rest of the nation). Over the last three decades, the pavement industry has seen many advances in data collection, distress identification, performance modeling, and other processes fundamental to PMSs. Consequently, there was a need to revisit the DI used by MDOT and revise it according to modern pavement data collection standards and calculation methodology. This study aimed to develop an enhanced pavement condition score and associated PMS data collection methodology for use by MDOT. To meet this objective, 2081 flexible and 741 rigid pavement sections were selected from MDOT’s performance database. Then, five different condition indices used by other state agencies were computed using the MDOT's PMS data and compared against MDOT’s Distress Index (DI). Maintenance records were used to compare the magnitudes of different indices right before maintenance activities were performed. The new pavement condition parameter was selected to follow the current state of the practice in its rating scale and consider major distresses. Furthermore, various performance models were used to predict the new condition index and International Roughness Index (IRI) data, and pavement fix lives were estimated for both asphalt and rigid pavements. Building on these advancements, network-level modeling methods were developed to project the future condition of MDOT’s pavement network in terms of IRI, cracking, rutting, and faulting. Using Markovian Transition Probability Matrices (TPMs) and multinomial logistic regression, the study established a robust analytical framework to forecast pavement performance under various maintenance and rehabilitation scenarios. These models enable MDOT to evaluate the long-term effects of different funding strategies, set realistic performance targets in alignment with federal requirements, and support data-driven decision-making for statewide pavement management.]]></description>
      <pubDate>Mon, 02 Feb 2026 14:13:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658082</guid>
    </item>
    <item>
      <title>Monitoring During the Unbraced Network Tied-Arch Bridge Construction Using ABC Techniques</title>
      <link>https://trid.trb.org/View/2630563</link>
      <description><![CDATA[A unique network tied-arch bridge with free-standing arches was designed to carry the 2nd Avenue traffic over the Interstate (I) 94 in Detroit, Michigan. This 245-ft-long, 96.5-ft-wide, and 18° skew bridge is the first skewed unbraced network arch bridge in the United States. The skeleton of the superstructure was erected off-site at a bridge staging area and moved and placed over the I-94 freeway using self-propelled modular transporters to complete construction. Based on the observations from the analysis models and the communications with the Michigan Department of Transportation, the Engineer of Records, and the peer review engineer, an instrumentation system was designed and installed in the bridge to: (i) monitor and record strains in major components during construction to determine the state of stress after construction; (ii) monitor and record the change in strains during service life to support bridge maintenance and load rating decisions; and (iii) collect adequate data to verify design assumptions. This paper discusses instrumentation system planning, design, and implementation, including sensor locations, data acquisition systems, cable and power management, data collection, and observations. The instrumentation system functions as expected. The monitoring data confirm that the structural component strains are within the design limits and the structure functions as expected.]]></description>
      <pubDate>Wed, 26 Nov 2025 10:13:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630563</guid>
    </item>
    <item>
      <title>Connected and Automated Vehicles (CAV) Readiness Survey</title>
      <link>https://trid.trb.org/View/2612891</link>
      <description><![CDATA[Considering Michigan Department of Transportation (MDOT) Mission, Values, and Vision, with the evolving landscape of technologies within the connected and automated vehicle (CAV) industry, there is a pressing need to investigate the requisite support from state DOTs to enable seamless integration of CAVs with infrastructure. As core sensors and systems defining these technologies become more established, understanding the precise infrastructure requirements becomes paramount. Therefore, the research aims to address the question: "What specific support and infrastructure enhancements are necessary from MDOT to facilitate effective detection and interaction of connected and automated vehicles with the surrounding infrastructure?"]]></description>
      <pubDate>Thu, 23 Oct 2025 07:09:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612891</guid>
    </item>
    <item>
      <title>Digital Collaboration using Industry Foundation Classes (IFC) and Building Information Modeling (BIM) Technology</title>
      <link>https://trid.trb.org/View/2611030</link>
      <description><![CDATA[The Michigan Department of Transportation (MDOT) manages a wide range of transportation assets throughout their lifecycle—spanning design, construction, operation, and maintenance. Effective asset management requires extensive data coordination across internal teams and external stakeholders. This creates challenges due to varied data storage practices, the use of various file formats, and manual data exchanges. These can lead to inefficiencies in asset-related information management. This report investigates these challenges, maps the digital data workflow and exchange requirements for multiple assets, and evaluates potential digital solutions—specifically Building Information Modeling (BIM), Industry Foundation Classes (IFC), and Common Data Environments (CDE)—as potential tools to improve workflows and improve data accuracy and consistency across all project phases. This project uses literature review to evaluate the state of the art, surveys to determine the current state of adoption of such technologies across state DOTs, interviews with MDOT personnel and contractors, and process mapping to identify key challenges. Such challenges include disconnected databases, manual data entry, and inconsistent updating of asset databases across the lifecycle of the studied assets. It also highlights the limitations of relying on 2D plan sets, where asset information is provided as text annotations, making retrieval and reuse difficult. From contractor interviews, feedback from contractors suggests the need for accurate, consistent data, whether in 2D plan sets or 3D models, and anticipates challenges related to technology adoption and workforce training, particularly for contractors, if such technologies are fully adopted. The report recommends potential pathways for adopting BIM, IFC, CDEs, including advantages and disadvantages of each. Finally, a detailed case study is presented on pavement asset management using IFC, supporting the feasibility and benefits of these digital approaches, but also demonstrating their limitations. Overall, the findings suggest that a transition toward integrated digital asset management systems would enhance efficiency, reduce manual errors, and support more effective long-term infrastructure management.]]></description>
      <pubDate>Tue, 21 Oct 2025 13:46:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611030</guid>
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