<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <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" />
    <description></description>
    <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>
    </image>
    <item>
      <title>Statewide Pavement Marking Condition Assessment Program</title>
      <link>https://rip.trb.org/View/2485381</link>
      <description><![CDATA[New driver-assist vehicle technologies—such as lane departure warnings, lane-centering systems, and lane-keep-assist systems—rely on pavement striping to function safely and effectively. Therefore, if these systems cannot detect pavement markings due to rain, snow, dust, or roadway damage, they may not operate correctly. Using retroreflective raised pavement markers (RRPM) is one solution to help maintain the visibility of pavement striping under these conditions, but there are tradeoffs. For instance, vehicle technology companies report that RRPMs are effective in helping their systems work, yet deployment is inconsistent throughout many states due to the high expense of installation and maintenance. 
This study would help Arizona Department of Transportation's (ADOT’s) Traffic Engineering Group determine appropriate design and installation guidelines for roadway striping to facilitate the use of new driver-assist vehicle technologies and develop a long-term striping maintenance plan that supports both legacy users and new technology, fine-tune striping assessment programs to optimize where and when new roadway striping should be implemented, and determine how best to inventory the striping’s current condition in order to prioritize striping maintenance activities. This research would then help provide the basis for a statewide system to make sure all Arizona roadways have pavement markings that support and perform at the levels needed for both human drivers and driver-assist vehicle technologies.]]></description>
      <pubDate>Fri, 03 Jan 2025 16:27:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2485381</guid>
    </item>
    <item>
      <title>Leveraging Vehicle Sensors for Pavement Condition Evaluation and Tracking</title>
      <link>https://rip.trb.org/View/2472693</link>
      <description><![CDATA[Pavement distresses like potholes and rutting pose significant safety risks and maintenance challenges for road networks. Traditional pavement monitoring methods rely on costly, intermittent surveys that fail to capture dynamic changes in conditions. This research explores the use of advanced sensors in modern and autonomous vehicles, including LiDAR, cameras, and inertial sensors, to gather real-time, high-resolution data for detecting and mapping pavement conditions. The project integrates data into Pavement Management Systems (PMS) through novel algorithms employing computer vision, machine learning, and statistical methods. Outputs include open-source algorithms and toolkits, enabling rapid identification and remediation of pavement issues, thereby improving road safety and advancing transportation technology.
]]></description>
      <pubDate>Mon, 09 Dec 2024 09:53:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2472693</guid>
    </item>
    <item>
      <title>Leverage AI for Asset Inventories &amp; Management</title>
      <link>https://rip.trb.org/View/2447052</link>
      <description><![CDATA[Texas Department of Transportation (TxDOT) owns and maintains a large number of safety and traffic-related devices including signs, delineators, guardrails, roadway lights, and traffic signals. While TxDOT maintains inventories for large infrastructure assets such as bridges, there is a need to develop more robust, dynamic inventory databases. This tool would enable TxDOT to make strides toward a cutting-edge proactive and holistic asset management practice, prioritize assets and corridors for maintenance, and maintain an updated understanding of asset location and condition. The main objective of this research is to develop a framework and prototype that uses data collected from CAVs to augment TxDOT's asset inventory and management. Integrating cutting-edge data sources and training AI to (1) identify assets and (2) detect changes to asset condition can improve TxDOT's awareness of the location and condition of assets. These data sources would augment, not replace, the traditional remote sensing and mobile mapping applications used by TxDOT teams. Components of the research work would included: [1] Compile asset inventory and condition summaries using one or multiple third party datasets. [2] Develop a condition rating system for each infrastructure to understand and monitor the status. [3] Collect data during nighttime operations in order to evaluate reflectivity. [4] Develop and end-to-end framework to integrate third-party data into TxDOT's asset inventory toolkit.]]></description>
      <pubDate>Wed, 30 Oct 2024 15:11:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447052</guid>
    </item>
    <item>
      <title>Developing Context-Aware Computer Vision Models for Robust Data-Informed Condition Assessment of Bridges</title>
      <link>https://rip.trb.org/View/2437397</link>
      <description><![CDATA[Visual inspection at regular intervals has traditionally been the primary method for assessing the condition of transportation assets to ensure they meet performance objectives. However, this method is labor-intensive, costly, poses safety risks to inspectors, and may suffer from quality inconsistencies. These challenges have driven the adoption of new inspection technologies such as drone imagery and LiDAR. However, the abundance of data generated from these technologies motivates the development of automatable and reliable methodologies for data processing to understand asset conditions and performance. Computer vision (CV) techniques offer an efficient means to process visual data and extract a high-level understanding of images and videos. However, the current CV-based techniques ignore the "context" of collected data, limiting their applicability and generalizability. This study aims to develop robust, context-aware CV models with low inference times that provide actionable insights on asset conditions. The proposed models will be applied to steel bridges, and the impact of various spatial and temporal contexts on CV model performance will be examined. The project outcome will advance the state of the art of using CV models for bridge inspection and provide opportunities for integrating these technologies into integrated asset management systems.]]></description>
      <pubDate>Wed, 02 Oct 2024 16:03:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2437397</guid>
    </item>
    <item>
      <title>A Review of Florida's FC-5 Raveling Condition Assessment and Measurement Methods</title>
      <link>https://rip.trb.org/View/2425104</link>
      <description><![CDATA[The objective of this project is to determine an appropriate method to account for raveling in Florida's pavement condition survey and subsequent pavement performance forecasting. The research should consider survey approaches as well as the rating system.]]></description>
      <pubDate>Tue, 03 Sep 2024 13:18:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425104</guid>
    </item>
    <item>
      <title>Transforming Infrastructure Inspection by Integrating a UAS with a Continuum Robotic Arm for Potential Contact-Based Damage Assessment</title>
      <link>https://rip.trb.org/View/2412947</link>
      <description><![CDATA[Uncrewed Aerial Systems (UAS) hold promise for revolutionizing the inspection of transportation infrastructure by enabling rapid and safe assessments. However, the application of UAS is predominantly limited to detecting surface-level defects, such as visible cracks, due to the reliance on vision sensors. This approach inherently misses subsurface damage, which, to date, requires direct contact-based methods (e.g., ultrasonic, magnetic, and radiographic techniques) that are currently carried out by manual inspection. This project aims to preliminarily investigate a transformative approach to infrastructure inspection by developing an integrated UAS platform equipped with a continuum robotic arm for contact-based inspection. The project will also conduct a preliminary evaluation of sensors suitable for contact-based infrastructure inspection, providing a basis for future sensor integration efforts. This proposed system aims to establish a foundational approach for future developments in multimodal and autonomous infrastructure inspection, advancing the field by overcoming current limitations in damage assessment capabilities.]]></description>
      <pubDate>Mon, 05 Aug 2024 19:03:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2412947</guid>
    </item>
    <item>
      <title>Leveraging Vehicle Camera Data for Road Condition Monitoring: A Crowdsourcing and Machine Learning Approach</title>
      <link>https://rip.trb.org/View/2408289</link>
      <description><![CDATA[One key part of pavement management is to assess the road condition and identify pavement distresses such as cracks and potholes. These road distresses, if not identified and repaired timely, could compromise road safety, cause expensive damage claims, and also lead to more expensive later repairs. To assess pavement condition, pavement condition data need to be collected first. However, traditional pavement data collection still relies on manual or specialized vehicles equipped with expensive sensors and requires personnel driving along each road in the road networks. Therefore, traditional road inspection methods are often costly, labor-intensive, and sporadic with limited coverage, leading to delayed maintenance and compromised safety. Recent advancements in machine learning (ML) and the proliferation of  vehicles equipped with various cameras (built-in or dashacams) and sensors offer a promising avenue for revolutionizing road condition assessment practices. This project will establish a framework for collecting and processing crowdsourcing vehicle camera data, and develop machine learning algorithms that uses such data to automatically assess road conditions and identify road damages such as cracks and potholes. The project has the potential to offer a more efficient, cost-effective, and real-time approach to road condition monitoring over large road networks and provide critical information for timely maintenance.]]></description>
      <pubDate>Fri, 26 Jul 2024 21:32:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2408289</guid>
    </item>
    <item>
      <title>Quant CR for Transformative Bridge Asset Management </title>
      <link>https://rip.trb.org/View/2404269</link>
      <description><![CDATA[The research team proposes developing an artificial intelligence (AI)-powered quantitative condition rating (QUANT CR) model which operates on a low-cost geographic information system (GIS) platform, aiding local and state bridge owners in maintenance, repair, and replacement (MRR) decisions while preserving the established inspection and condition rating practices. 
The next generation asset management system leverages the knowledge gained from 50+ years of bridge inspection practices but is predictive, forward-looking, and transformative. QUANT CR embodies insights gained from the understanding of human behavior to better assist bridge owners in decision-making. Thus, the team envisions QUANT CR will be operated in parallel with the existing bridge condition ratings and provide simple decision aids for bridge owners. 
The team believes bridge condition ratings can be better predicted by modern machine learning methods, leveraging the historic data, evolving element condition ratings, and detailed defect items. Additionally, deep learning widely used for text recognition enables an analysis of inspectors’ narratives describing bridge conditions. Lastly, computer vision and deep generative learning help bridge owners visualize the outcomes of their decisions - MRR actions/inactions, empowering bridge owners. ]]></description>
      <pubDate>Sun, 21 Jul 2024 15:03:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2404269</guid>
    </item>
    <item>
      <title>SPR-4907:  Systemwide Asset Condition Assessment using Connected Vehicle Data</title>
      <link>https://rip.trb.org/View/2389623</link>
      <description><![CDATA[This research project will investigate the feasibility of connected vehicle data to monitor the pavement conditions and ride quality on Indiana roads, in near real-time. The routine monitoring of pavement condition from this crowd-sourced data will be beneficial for agile prioritization of maintenance activities and longer term capital projects. It is envisioned the findings of this project could also be useful to local agencies. ]]></description>
      <pubDate>Wed, 12 Jun 2024 16:19:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2389623</guid>
    </item>
    <item>
      <title>Develop a Methodology for Pavement Drainage System Rating</title>
      <link>https://rip.trb.org/View/2379652</link>
      <description><![CDATA[The objective of this research is to explore the use of existing pavement and LiDAR data to develop a pavement drainage system rating index as part of pavement condition assessment in Louisiana, potentially by creating a drainage rating index as part of pavement condition assessment.]]></description>
      <pubDate>Tue, 14 May 2024 10:42:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2379652</guid>
    </item>
    <item>
      <title>New and Updated Statewide Historic Bridge Survey</title>
      <link>https://rip.trb.org/View/2342175</link>
      <description><![CDATA[Completed in 1995, the collaborative effort completed by the Iowa Department of Transportation (Iowa DOT), Federal Highway Administration (FHWA), and all Cities and Counties was the first statewide historic bridge study that inventoried all bridges in the state (with very few exceptions) that were built prior to 1942 (FraserDesign 1995).  Prior to this effort LPAs and the Iowa DOT had to evaluate all bridges over 50 years old individually as part of the project development process under the National Historic Preservation Act.  Under the premise that historical significance changes through time, historic preservation stakeholders such as the Iowa State Historic Preservation Office (Iowa SHPO) usually consider evaluations five years old or less valid.  A substantial amount of grace was extended to the 1995 statewide bridge study.  However, over the last several years it has become apparent that an update to the statewide bridge study is becoming increasingly necessary.  Either funded with support from FHWA or permitted through the USACE, more and more LPAs are being asked to evaluate their historic bridges one by one (recent examples can be provided).  We are likely entering a time were it will be much more effective (for both planning purposes and for costs) to complete an update to the statewide bridge survey whereby allowing LPAs to know if they are dealing with a historic bridge before they begin the project development process.  Advancing the bridge inventory cut-off date to 1975 would maximize the long-term efficiencies that an update could provide.  It’s recommended that LPAs and the Iowa DOT work to find a partnership that benefits all infrastructure owning agencies. Its important to note that the Iowa DOT did update its inventory of historic bridges in 2011, however, that effort only applied to primary system bridges.  This effort will allow for submission to any federal agency requiring a historical review including Corps, FHWA, FEMA.  All LPA's and Iowa DOT will benefit from this new updated study.  ]]></description>
      <pubDate>Tue, 20 Feb 2024 19:03:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2342175</guid>
    </item>
    <item>
      <title>Standardized Framework for Winter Weather Road Condition Indices</title>
      <link>https://rip.trb.org/View/2342044</link>
      <description><![CDATA[The lack of a national standard for winter weather road condition indices has led to inconsistencies in assessing road conditions and providing accurate information to drivers across the United States. This research aims to develop a national standard for winter weather road condition indices that is consistent, accurate, and reliable, enhancing driver safety and winter weather response effectiveness. The project involves conducting a comprehensive literature review, data analysis, and case study analysis, as well as engaging key stakeholders from public and private organizations. The anticipated outcomes include a national standard framework, implementation guide, training materials, monitoring and evaluation toolkit, best practices repository, communication templates, and an interactive map/dashboard for road conditions. The implementation of a national standard for winter weather road condition indices is expected to improve driver safety, reduce traffic crashes and congestion, and optimize winter weather response strategies by transportation agencies.]]></description>
      <pubDate>Mon, 19 Feb 2024 18:46:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2342044</guid>
    </item>
    <item>
      <title>Seamless Vehicle and Bridge Monitoring for Transportation and Infrastructure Safety
through a Wireless Internet-of-Things System – Phase I</title>
      <link>https://rip.trb.org/View/2341570</link>
      <description><![CDATA[The goal of this multi-phase project is to unite two traditionally separate vehicle and
bridge monitoring communities for a comprehensive evaluation of transportation and infrastructure safety. To achieve this goal, this project aims to (1) develop and validate a standalone, wireless Internet-of-Things (IoT) vehicle and bridge monitoring system for both collision and overstress detection, (2) deploy and calibrate the IoT system at a
highway bridge site with one type of representative trucks, (3) collect and store real-time traffic, meteorological, structural, and vehicle data, (4) cleanse and analyze heterogeneous data (numeric, image, audio, and video) through influence line analysis and machine learning for the extraction of features related to vehicle safety and infrastructure condition, and (5) develop and validate a visual mechanism to alert truck drivers as they drive underneath or across the highway bridge. The outcomes of this project are to mitigate collision-induced bridge damage, vehicle-related highway fatalities and injury rates through such an integrated vehicle and bridge monitoring in real time.

To address the first and second objectives, the scope of Phase I project includes, but is not limited to, (a) literature survey on bridge-weigh-in-motion (BWIM) and load tests, (b) development of a laboratory testbed of vehicle monitoring and BWIM system, and (c) scale-up of the laboratory testbed for field installation and validation.
]]></description>
      <pubDate>Mon, 19 Feb 2024 16:28:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2341570</guid>
    </item>
    <item>
      <title>Rapid Assessment of Network-Level Pavement Conditions Using Novel Tools</title>
      <link>https://rip.trb.org/View/2291289</link>
      <description><![CDATA[In this collaborative project, two leading Oklahoma universities – the University of Oklahoma (OU) and Oklahoma State University (OSU) – will work with the Texas A&M Transportation Institute (TTI) to assess network-level pavement conditions rapidly and cost-effectively, using novel tools. Roadway pavements constitute a critical element of surface transportation infrastructure. With a large portion of pavements in poor condition and reaching the end of their service lives, pavement maintenance and rehabilitation are becoming increasingly challenging tasks for many state DOTs, including DOTs in Region 6. 
Recent developments have spotlighted the Traffic Speed Deflection (TSD) Device as a valuable technology for measuring surface deflections at short intervals and capturing data on roughness, texture, and rutting at traffic speed. The evaluation of pavement conditions and their rating typically depend on such parameters as deflections, slope deflection indices, structural considerations, and remaining service life. In this context, the potential advantages of deriving network-level pavement condition ratings from TSD data could be enhanced through the implementation of other novel technologies developed by the consortium members collaborating on this project. Lack of access to a TSD device and high cost associated with data collection necessitate the pursuit of innovative in-house technologies, which will not only increase efficiency but reduce costs significantly.
As part of a pooled fund study (TPF-5 (385)) participated by ODOT, pavement conditions data from I-35 and I-40 in Oklahoma were collected recently using a TSD. The proposed study focuses on developing tools for analyzing these TSD data for network-level assessment or rating of the associated pavements. A complementary objective is to collect data from the same pavements using in-house technologies, namely Pave3D 8K available at OSU and an air-coupled Ground Penetrating Radar (GPR) and Fast Falling Weight Deflectometer (FFWD) available at TTI. 
For this purpose, with assistance of the Strategic Asset and Performance Management (SAPM) personnel at ODOT, the research team seeks to gain access to the TSD data from I-35 and I-40 and review these data closely. Leveraging different pavement condition indicators, the I-35 and I-40 pavement sections will be divided into five different categories, namely very poor, poor, fair, good, and excellent. This categorization will facilitate the subsequent selection of experimental sites for an in-depth evaluation, each spanning 3 to 5 miles. The OSU team will employ Pave3D 8K for the acquisition of 2D/3D surface images and detailed pavement roughness and texture data from the evaluation sites. The OSU team will then analyze the Pave3D 8K data and compare them with the TSD data. The results of these comparisons will assist in the establishment of definitive rating thresholds.
FFWD tests will be conducted by TTI at the selected I-35 and I-40 sections. Measured deﬂections will be used to determine structural conditions and remaining life and to compare with the corresponding TSD results. A subsurface GPR survey will be conducted on the above mentioned I-35 and I-40 sections with the help of TTI. The GPR data will be used to determine layer thicknesses and used to identify areas with subsurface defects. 
Based on the pavement conditions, cores will be extracted selectively from distressed locations as well as from some good locations. A visual observation of the extracted cores and limited laboratory test results will be used to validate the pavement rating from the TSD data and Pave3D 8K and FFWD data. The research teams from all three institutions will work together to establish pavement condition thresholds. These thresholds can be used readily by ODOT and other DOTs in Region 6. These thresholds can be adjusted in the future as more network-level data becomes available.

]]></description>
      <pubDate>Wed, 15 Nov 2023 21:46:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2291289</guid>
    </item>
    <item>
      <title>2295 ODOT Automated Bridge Survey</title>
      <link>https://rip.trb.org/View/2286453</link>
      <description><![CDATA[The purpose and scope of this research study will be to:  (1) develop an efficient, non-destructive, and cost-effective procedure to comprehensively evaluate the condition of approach slabs and bridge decks; (2) provide approach slab and bridge deck evaluations encompassing cracking and IRI data, ensuring a thorough understanding of their performance; (3) conduct deck surveys to document essential parameters such as crack size and location, spall locations, percentage of patches, and condition of expansion joints.  Identify areas requiring maintenance action based on a comprehensive assessment of ride quality, using 2D/3D images, roughness data, and right-of-way images to categorize conditions as Good, Fair, or Poor; and (4) develop a non-destructive and cost-effective approach to determine the actual dynamic impact factor (IM) on both the approach slab and bridge decks based on their condition.   ]]></description>
      <pubDate>Fri, 03 Nov 2023 11:56:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2286453</guid>
    </item>
  </channel>
</rss>