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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=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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>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Artificial Intelligence for Pavement Condition Assessment from 2D/3D Surface Images</title>
      <link>https://rip.trb.org/View/2727389</link>
      <description><![CDATA[In phase I, the Performing Agency selected/annotated a library of two-dimensional/three-dimensional (2D/3D) pavement surface images in AASHTO standard and developed Artificial Intelligence (AI) Machine Learning (ML) models, using the established dataset. Phase I achieved a Technology Readiness Level (TRL) of 6, significantly below TRL 8 required for implementation. This gap was compounded by the lack of sufficient data for several pavement distress types and a new functional requirement requested by the Receiving Agency to include distress segmentation to the scope of work. In phase II, the Performing Agency shall prepare more pavement image data provided by the Receiving Agency to achieve the needed diversity on all pavement distress types in the 2D/3D image data library. The Performing Agency shall revisit the tasks of literature review and AI/ML model selection, revise and optimize the trained models to make improvements, and develop new models to more accurately detect/segment and quantify pavement distresses for the Receiving Agency.]]></description>
      <pubDate>Fri, 10 Jul 2026 17:50:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727389</guid>
    </item>
    <item>
      <title>Evaluation of Road Ripples on North Carolina Roadways</title>
      <link>https://rip.trb.org/View/2726548</link>
      <description><![CDATA[Road ripples are an emerging and poorly understood form of asphalt pavement distress that has begun to appear with notable frequency on North Carolina roadways. Characterized by repeating surface undulations or shallow cracks that can form in the wheel paths or extend across entire lanes, they differ from conventional distress patterns and are not addressed in existing pavement distress manuals. They have been described as producing a “washboard-like” surface that degrades ride quality and draws the attention of both drivers and maintenance personnel. The phenomenon is particularly challenging because it does not have a single, well-established cause; instead, evidence suggests a complicated interplay of residual moisture in asphalt mixtures, moisture-sensitive subgrades, poor drainage or base support, and possibly traffic or environmental factors such as freeze-thaw cycling. This lack of clarity has meant that road ripples are frequently mischaracterized in pavement evaluations and, more importantly, that no standardized diagnostic or treatment methods exist to guide agency practice.

In light of this need, the proposed research plan will seek to achieve four objectives: (1) evaluate the extent and severity of road ripples and the causative factors in North Carolina, (2) evaluate the potential for residual moisture to exist in asphalt mixtures produced in North Carolina, (3) evaluate the potential long- and short-term implications of road ripples on pavement performance, and (4) develop a targeted research plan for a follow-up study to systematically evaluate and quantify the impacts of different causative factors on causing road ripples.

These objectives will be met with six tasks: (1) Perform a literature review to understand the state-of-the-art and state-of-the-knowledge with respect to road ripples. (2) Conduct an evaluation of pavements in North Carolina that are experiencing road ripples to understand the extent and severity of the distress in the state and also identify potential causative factors. (3) Conduct experiments to evaluate residual moisture contents in North Carolina mixtures. (4) Conduct an assessment to understand the potential long-term performance implications of road ripples. (5) Develop a detailed work plan for conducting a Phase II study to systematically evaluate the causative factors. (6) Prepare a final report summarizing the methodology, results, and recommendations. 

The primary outcome of this research will be the establishment of a foundational understanding of road ripple distress in North Carolina, including its likely mechanisms, diagnostic indicators, and performance implications. While road ripples have been observed across the state, there is currently no standardized method to investigate, quantify, or manage them. This study will combine field investigations, laboratory studies, and analytical assessments, to provide clarity on the potential causes of this distress. Ultimately, the outcome of this work will be a clear, evidence-based foundation for the North Carolina Department of Transportation (NCDOT) to use to make informed decisions about repair, prevention, and long-term management of pavements affected by road ripples.
]]></description>
      <pubDate>Thu, 09 Jul 2026 08:48:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726548</guid>
    </item>
    <item>
      <title>Feasibility Study of Zinc Diethyldithiocarbamate (ZDC) Modified Asphalt Mixture for Enhanced Safety through Improved Aging Resistance in Asphalt Pavement</title>
      <link>https://rip.trb.org/View/2703922</link>
      <description><![CDATA[Pavement surface distresses directly affect ride comfort and indirectly cause distraction to the driver resulting in loss of control of the vehicle, which may lead to injuries or deaths. Thermo-oxidative aging of asphalt binder is a key driver of asphalt pavement performance deterioration and distress development, which directly affect ride quality. As a result, mitigating asphalt aging is essential for maintaining pavement performance and ensuring roadway safety. Zinc diethyldithiocarbamate (ZDC), an emerging antioxidant, has shown stronger anti-aging effectiveness than many conventional antioxidants. However, existing studies have primarily focused on binder-level and mixture-level, while its impact on pavement structural performance remains unclear. This leads to a gap in that the effectiveness of ZDC has not yet been validated in terms of its ultimate objective — improving pavement structural performance and safety. The objective of this proposed study is to address this gap by linking laboratory aging characterization of ZDC-modified materials with pavement performance prediction. Comprehensive laboratory testing will be conducted to characterize the aging resistance and mechanical properties of ZDC-modified materials and to provide the required inputs for pavement performance prediction. The pavement structural analysis tool, FlexPAVE, which integrates the recently developed pavement aging model (PAM) and distress prediction models, will be used to predict pavement performance while explicitly incorporating aging mechanisms. ZDC-modified pavement structures will be simulated under representative U.S. climate zones to evaluate the effectiveness of ZDC under diverse environmental conditions, considering that aging rates and dominant distress modes may change with climate patterns. The results will offer insight into the practical use of ZDC for improving pavement performance and safety.]]></description>
      <pubDate>Tue, 19 May 2026 13:37:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703922</guid>
    </item>
    <item>
      <title>Improve pavement surface distress and transverse profile data collection and analysis, Phase III</title>
      <link>https://rip.trb.org/View/2666773</link>
      <description><![CDATA[The technical capabilities of systems to collect and analyze pavement surface distress and transverse profile (PSDATP) have increased dramatically in the last 5-10 years. Many state highway agencies (SHAs) are in the process of assessing the procurement of equipment/systems or procuring vendor services for network and project level pavement condition assessments. The collection of quality PSDATP is critical for pavement management and design. The current national and State efforts to develop and refine pavement performance measures highlight the high value provided by quality PSDATP. The implementation of new project delivery methods with medium- to long-term maintenance agreements (Design Build Maintain, Design Build Operate, etc.) justifies the need for high-quality PSDATP data. Accurate and repeatable measures are essential for proper planning and the allocation of funding. The implementation of the Mechanistic Empirical Pavement Design Guide (MEPDG) highlights the need for quality PSDATP to maximize the potential of the MEPDG and all other pavement design models. The emphasis on preventive pavement maintenance activities provides the opportunity for additional value from greater resolution of pavement surface distress quantification. TPF-5(299) and TPF-5(399) comes to end in 2026, and this pooled fund study will continue the work of that pooled fund study. The 24 State Highway Agencies of TPF-5(399) support starting this new pooled fund study. The activities of the pooled-fund study will be communicated with other appropriate committees and groups in the pavement community, such as, the Road Profiler User Group, the Federal Highway Administration (FHWA), the American Association of State Highway and Transportation Officials (AASHTO) Committee on Materials and Pavements (COMP), National Cooperative Research Program (NCHRP) and the Transportation Research Board (TRB). The AASHTO COMP currently manages several standards related to pavement surface characteristics measurement. Many of these standards continue to need refinement and updating. This pooled-fund study is being established to provide direction and funding to unify the strategies, support implementation efforts, and promote best practices that improve the accuracy and repeatability of the data collection and analysis systems, as well as advance the understanding of PSDATP measurements. It is expected that this study will be completed within 5 years.

OBJECTIVES: Improve the Quality of Pavement Surface Distress and Transverse Profile Data Collection and Analysis by assembling SHAs, the FHWA, and industry representatives to: Identify data collection integrity and quality issues; Identify data analysis needs; Suggest approaches to addressing identified issues and needs. Based on this information, the SHAs and the FHWA will: Initiate and monitor projects intended to address identified issues and needs; Disseminate results; Assist in solution deployment.
]]></description>
      <pubDate>Mon, 09 Feb 2026 19:52:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2666773</guid>
    </item>
    <item>
      <title>Data-driven assessment of rigid pavement vulnerability in Texas coastal regions</title>
      <link>https://rip.trb.org/View/2663108</link>
      <description><![CDATA[This research aims to evaluate the vulnerability of rigid pavements in two major coastal districts of Texas (i.e., Beaumont and Houston) spanning about 900 miles using data-driven approaches. Particularly, the study will (1) identify the key factors contributing to rigid pavement distress under dynamic coastal weather conditions, and (2) develop data-driven strategies to enhance the durability and performance of these pavement networks. Multi-source datasets, such as weather, geotechnical, traffic, coastal proximity, and pavement conditions, will be collected and integrated to support this analysis. Weather data, including temperature and precipitation, will be obtained from national and global databases such as NOAA’s National Centers for Environmental Information (NCEI) and NASA Earthdata/GES DISC. Soil classification and geotechnical attributes will be sourced from the NRCS SSURGO (Soil Survey Geographic Database), while coastal proximity data will be derived from Google Earth. Traffic volumes and loading data will be gathered from TxDOT’s Statewide Traffic Analysis and Reporting System (STARS II). Pavement condition metrics, including distress quantity, distress score, condition score, and ride quality, will be extracted from the Texas Department of Transportation (TxDOT)’s Pavement Management Information System (PMIS) and supplemented with satellite imagery. By integrating these datasets, the project will perform statistical and spatial analyses to establish correlations between weather variables, geotechnical conditions, traffic patterns, and pavement performance indicators.]]></description>
      <pubDate>Thu, 29 Jan 2026 19:58:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663108</guid>
    </item>
    <item>
      <title>Pavement Condition Rating Method and Use for Local Agencies 
</title>
      <link>https://rip.trb.org/View/2618201</link>
      <description><![CDATA[The Ohio Department of Transportation (ODOT) collects pavement condition ratings (PCR) on the state network annually and a subset of the local network that is federal aid eligible on a biennial basis. This data is made available to local public agencies (LPAs) through the TIMS system. Many LPAs also collect their own set of pavement condition ratings on all pavements within their jurisdiction to identify roads for resurfacing, repair, and other planning purposes. The data sets collected by LPAs may differ significantly from ODOT's PCR and in most cases the detailed level of distress information collected in ODOT PCR may not be necessary for their purposes. In addition, the collection methods, schedules, and data types differ from locality to locality statewide.

Metropolitan Planning Organizations (MPO's) use ODOT's PCR ratings to help compare the condition of various areas and for grant applications. While ODOT PCR may be helpful to MPOs, the feedback ODOT has received from LPAs who are responsible for maintaining the local roads is that ODOT's PCR data may not be helpful in many cases. In addition, LPAs would prefer to have data on the whole local network as opposed to a subset. Since ODOT collects and reports pavement data on federal aid eligible roads, identifying a pavement rating methodology that would be useful for all parties (LPAs and MPOs) is desired.
 
The goal of this research is to recommend pavement rating methods that would be useful to cities, counties, townships, and MPOs. Findings from this research will help ODOT to focus current efforts to collect local pavement condition ratings to be useful to the agencies responsible for the routes the data represents. Identifying and implementing a pavement rating methodology that would be useful for all parties (LPAs and MPOs) would help reduce duplication of effort and enhance data integrity and utilization. A more unified approach to pavement data collection can ultimately improve pavement management for local agencies.
                 ]]></description>
      <pubDate>Tue, 04 Nov 2025 15:32:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2618201</guid>
    </item>
    <item>
      <title>Are Current Rigid Pavement Roundabout Designs Working in Minnesota?</title>
      <link>https://rip.trb.org/View/2487311</link>
      <description><![CDATA[Rigid pavement roundabouts were initially designed with the expectation that they would experience distress similar to rigid pavements designed and built on motorways. However, distresses will occur differently due to the way traffic loads interact with them, the shape of panels, and drainage. The objective of this research is to provide guidance documentation for both the Minnesota Department of Transportation (MnDOT) and local agencies to improve roundabout designs for better long-term performance and reduced future costs in the maintenance and rehabilitation of these assets.]]></description>
      <pubDate>Wed, 08 Oct 2025 11:57:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487311</guid>
    </item>
    <item>
      <title>Evaluate Thick Lift High Performance Grade (HPG) Mixes for Intersections, Border Checkpoints, and Other Locations with Slow Moving Heavy Traffic</title>
      <link>https://rip.trb.org/View/2604525</link>
      <description><![CDATA[Several Districts of the Texas Department of Transportation (TxDOT) recently reported rutting problems greater than 2 inches at intersections, border checkpoints, and other locations where heavy truck traffic either moves very slowly or is stationary while waiting in queues, even though the best mixes (e.g., stone matrix asphalt) were used. There is an urgent need to address such premature rutting problem in those places in a timely manner. The research team will identify and evaluate the suitable mixes and single lift construction technique to avoid premature rutting failures at those places, resulting in major cost savings and reducing wet weather accidents. The research team will review literature to identify the critical factors for improving mix rutting resistance. Based on the findings from the literature, the research team will develop and execute an experimental design to identify the suitable mixes for the slow-moving heavy traffic and then construct field trials with those mixes in a single thick lift and follow up their field performance. In the end, the research team will recommend specification changes and develop guidelines for constructing single thick lift with suitable mixes at intersections, border checking points, or other places.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:16:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604525</guid>
    </item>
    <item>
      <title>An End-to-End Deep Learning System for Pavement Distress Detection, Severity Estimation, and Condition Reporting</title>
      <link>https://rip.trb.org/View/2578878</link>
      <description><![CDATA[Pavement condition assessment is essential for roadway asset management, yet current methods are fragmented, manual, and resource-intensive. Traditional workflows require separate tools for distress detection, severity and depth estimation, and overall condition classification, leading to inefficiencies. While deep learning models have emerged for tasks like crack segmentation or pavement condition index (PCI) prediction, most remain task-specific and lack integration. This project proposes a unified, end-to-end multitask framework using multimodal data for automated pavement assessment. By fusing high-resolution RGB images with stereo-derived depth maps, the system jointly performs distress detection, severity estimation, depth prediction, and condition classification. It also includes a natural language processing (NLP) module to generate human-readable reports tailored to agency workflows. The architecture features a shared encoder with four task-specific decoders, leveraging cross-task correlations to enhance generalization and reduce the need for separate models. Training will use a regional dataset annotated for all four outputs, with performance evaluated using intersection-over-union (IoU), mean absolute error (MAE), and F1-score. Report quality will be assessed using text similarity metrics and expert feedback. The central hypothesis is that multitask learning with multimodal inputs will improve accuracy and efficiency while reducing manual labor. This builds on the PI’s prior work in multitask PCI estimation, multimodal segmentation, and explainable reporting for infrastructure.]]></description>
      <pubDate>Thu, 24 Jul 2025 09:30:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2578878</guid>
    </item>
    <item>
      <title>Pavement Distress Evaluation and Cracking Indices Generation using Deep Learning</title>
      <link>https://rip.trb.org/View/2563770</link>
      <description><![CDATA[The North Carolina Department of Transportation (NCDOT) manages the nation's second-largest roadway network. To ensure safety and efficiency of this network, it is crucial to implement timely and effective maintenance strategies. This research project aims to address these needs. 

In this research, to help optimize maintenance strategies, non-crack distresses will be classified, segmented, and quantified using cutting-edge deep learning techniques. Since an on-going research project has already completed similar tasks for varying types of cracks, upon completion of this proposed study, all types (crack and non-crack) of distresses across the 14 Divisions monitored by NCDOT can be classified and quantified using deep learning models. With this approach, it is estimated that a comprehensive state-wide pavement performance assessment can be completed in one week. Consequently, the outcomes of this proposed study, combined with those from the on-going research, will enable timely updates of distress indices and Pavement Condition Rating (PCR) values. This enhanced responsiveness of NCDOT’s PMS will significantly benefit North Carolina’s roadway network in terms of durability and sustainability. In addition, specific crack metric and index, namely the Pavement Surface Cracking Metric (PSCM) and the Pavement Surface Cracking Index (PSCI), will be calculated using the ASTM E3303-21 standard. The calculated results will be highly accurate, as the length of every crack is quantified at a pixel level. Moreover, this task will standardize and enhance the reliability of crack assessments, contributing to a more effective PMS managed by NCDOT.

One potential challenge that the UNC Charlotte researchers face is identifying certain types of uncommon non-crack distresses from the raw images provided by NCDOT. The lack of training data for these distresses can directly impact the performance of the corresponding deep learning models. To address this issue, the researchers plan to work closely with NCDOT engineers to pinpoint the locations of these distresses and gather sufficient distress data for model training purposes. Another potential challenge is the time-consuming nature of the image annotation process, a common obstacle in studies utilizing deep learning techniques for image processing. Building on the experience gained from the on-going study, the researchers plan to evaluate both AI-based and self-supervised learning approaches to expedite the annotation process effectively. 

It should be noted that transferred learning from deep learning models developed in the on-going NCDOT research project will be used to develop new models in this study. This approach allows resources spent on one task to be transferred, reused, and adapted for other related tasks, significantly reducing the computational resources and time, and more importantly, leading to improved performance of newly developed models.

In summary, this research project is proposed to improve maintenance efficiency, reduce repair costs, and support NCDOT’s sustainability goals. Various approaches will be utilized to ensure the success of this project. The methods and tools developed in this project can be applied to address other challenges in the future.
]]></description>
      <pubDate>Fri, 13 Jun 2025 12:48:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2563770</guid>
    </item>
    <item>
      <title>SPR-5026: Evaluating the Effectiveness of Geosynthetics in Controlling the Detrimental Effects of Slow-Moving and Turning Traffic</title>
      <link>https://rip.trb.org/View/2553993</link>
      <description><![CDATA[This study presents a comprehensive evaluation of geosynthetics in slow-moving and turning traffic regions.  Phase 1 includes laboratory testing to assess geogrid effectiveness by quantifying lateral restraint, and stiffness enhancements under turning traffic stress conditions. Phase 2 focuses on field implementation with site selection, instrumentation, and in-situ performance monitoring. The study will develop optimized design strategies and construction practices, with findings disseminated through a final report and implementation guidance for effective integration into INDOT projects.]]></description>
      <pubDate>Thu, 15 May 2025 16:00:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553993</guid>
    </item>
    <item>
      <title>Cryosuction and Its Role in Infrastructure Distress from Freeze-Thaw Cycles</title>
      <link>https://rip.trb.org/View/2534019</link>
      <description><![CDATA[Pavement infrastructure in cold regions experiences significant distress due to freeze-thaw cycles, which govern moisture migration, frost heave, and post-thaw weakening. Cryosuction, the process by which water is drawn toward freezing fronts due to soil suction, plays a critical role in this phenomenon by intensifying frost heave and accelerating pavement deterioration. However, the influence of cryosuction on moisture migration and subsequent pavement damage remains insufficiently understood, particularly concerning varying soil properties, salinity levels, and environmental conditions. The proposed study aims to quantify the role of cryosuction in moisture distribution during freeze-thaw cycles and examine its effects on soil freezing characteristic curves (SFCC) and soil water characteristic curves (SWCC) across different salinity levels. An experimental approach will be employed, involving soil suction measurements, moisture content analysis, and frost heave observations using advanced geotechnical instrumentation in a setup that will be fabricated as a part of this study. The results will provide clarification regarding the relationship between cryosuction, soil properties, and pavement distress, enabling the development of advanced models and potential mitigation strategies. This study through its findings will contribute to the design of more resilient pavement systems, reducing maintenance costs and extending infrastructure lifespan in cold climates.]]></description>
      <pubDate>Thu, 03 Apr 2025 12:34:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2534019</guid>
    </item>
    <item>
      <title>Resiliency Assessment of Flood Damage in Pavements</title>
      <link>https://rip.trb.org/View/2534020</link>
      <description><![CDATA[There is an ongoing need to understand the impacts of flood inundation on pavement performance throughout the United States. This is especially true in coastal states like Virginia that have robust riverine systems that are subject to flooding after heavy rainfall events. This study seeks to identify flood susceptible areas through ongoing research efforts at Virginia Department of Transportation (VDOT) and document the pavement condition before flooding and after flooding. A quantification of pavement damage in terms of layer structural capacity and visual distress due to flooding will be performed, along with an identification of specific factors that influence the pavement damage. Inexpensive moisture sensors next to the roadway will be employed to understand the drainage characteristics of the pavement system as to connect the structural performance and eventual surface distresses. The outcome of this research is an understanding of the impact of flooding on VDOT’s roads and the potential impacts on maintenance and rehabilitation schedules. This will potentially benefit VDOT as it evaluates different pavement adaptation options for flooding and future budgets to accommodate shifting maintenance and rehabilitation schedules.]]></description>
      <pubDate>Thu, 03 Apr 2025 08:54:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2534020</guid>
    </item>
    <item>
      <title>RES2020-16: Evaluating Performance and Benefits-Costs of Road Diets in Tennessee</title>
      <link>https://rip.trb.org/View/2499160</link>
      <description><![CDATA[The Pavement Mechanistic Empirical Design (PMED) method was developed to address shortcomings experienced on the AASHTO Guide for Design of Pavement Structures (1993) including environmental/climate considerations. However, the implementation of PMED requires a large number of design inputs that characterize materials, traffic, and climatic conditions. This project was conducted to address the PMED climate input data for the state of Tennessee. Two climatic data sources were considered, North American Regional Reanalysis (NARR), and Modern-Era Retrospective Analysis for Research and
Application (MERRA). First, the sensitivity analysis using 2k factorial design method considering lower and higher extremes of each climatic input and water table was performed to determine climatic inputs sensitive to pavement distresses. Then, Virtual Weather stations (VWSs) were created, and their predicted performance was analyzed in comparison to the existing stations. Lastly, the performance analysis of NARR and MERRA
climatic data sources considered pavement distress predictions, and surface layer optimization. On sensitivity analysis of the EICM model, temperature was the most sensitive climatic input in PMED distress predictions, while humidity had no effect to pavement distress predictions. Performance evaluation of PMED VWSs indicated a significant difference in some of the predicted distresses when comparing PMED VWSs and MERRA stations at identical locations. The performance analysis of NARR and MERRA climatic data sources using surface layer optimization, indicated that MERRA optimized surface layer thicknesses were not significantly different from the original surfaces, while NARR and input Levels 2 and 3 thicknesses were significantly different from the original layer thicknesses.]]></description>
      <pubDate>Wed, 29 Jan 2025 15:57:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2499160</guid>
    </item>
    <item>
      <title>Update of Traffic Factor Equations for IDOT Mechanistic-Empirical Pavement Design</title>
      <link>https://rip.trb.org/View/2486928</link>
      <description><![CDATA[Transportation agencies must adapt pavement design procedures to meet changes in traffic and advances in new technologies such as electric vehicles and trucks, which are expected to accelerate pavement damage due to increased weight from batteries. Researchers will update the equations used by IDOT pavement designers to convert mixed-traffic axle loadings into traffic factors for asphalt and concrete pavements while accounting for current traffic conditions and axle configurations. Traffic factor represents the total number of 18-kip equivalent single-axle loads, expressed in millions, that a given pavement may be expected to carry. They will also incorporate the impact of e-trucks and platoons — a group or convoy of closely spaced vehicles — on pavement design. Updating the traffic factor equations to meet current and future demands will allow the agency to properly design pavements to carry the anticipated loadings.]]></description>
      <pubDate>Mon, 06 Jan 2025 12:32:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2486928</guid>
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