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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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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>National Accessibility Evaluation Phase II</title>
      <link>https://rip.trb.org/View/2703751</link>
      <description><![CDATA[This project implements activities for the National Accessibility Evaluation (NAE) pooled-fund study, performing accessibility evaluations describing conditions in 2020, 2021, 2022, 2023, and 2024. The National Accessibility Evaluation creates national census block-level accessibility datasets that can be used by partners in local transportation system evaluation, performance management, planning, and research efforts. The project produced a series of annual reports describing accessibility to jobs by driving, biking, walking, and by transit in metropolitan areas across America.

Accessibility calculations rely on detailed travel-time calculations for both driving and transit, using commercially available, global positioning system (GPS)-based speed measurements and published transit schedules. Each NAE partner received digital access to the accessibility datasets covering their jurisdictions. These datasets quantify access to jobs, health care, schools, grocery stores, and other essential destinations. The annual Access Across America reports provide summaries of the detailed job accessibility datasets for the 50 most populous metropolitan areas across America. These reports were released to national and local media outlets and supported by publicity and communications efforts.]]></description>
      <pubDate>Fri, 15 May 2026 15:29:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703751</guid>
    </item>
    <item>
      <title>How Much Do Attitudinal Variables Improve Travel Demand Models? Evaluation Using an Overlap Sample from an Attitude-rich Survey and the 2017 National Household Travel Survey</title>
      <link>https://rip.trb.org/View/2702856</link>
      <description><![CDATA[A line of research has recently been launched on attitude imputation using machine learning (ML) functions trained on variables common to two survey datasets (Mokhtarian, 2024). It was discovered that using a handful of attitudinal marker variables (i.e., the one or two attitudinal items most strongly associated with each attitude) as common variables for imputation (Shaw, 2021; Soria and Mokhtarian, 2024) far outperforms other approaches such as using socio-demographic and land-use variables (Malokin et al., 2019) and targeted marketing variables (Shaw, 2021). The basic idea is to use one survey dataset (the “donor sample”) to train an ML function that predicts attitudinal factor scores using marker variables, and then apply that function to another dataset (the “recipient sample”) that contains the same marker variables, to impute attitude scores into it. This allows attitudinal information to be attached to the respondents in the recipient sample without measuring the whole set of attitudinal variables used to reveal the attitudinal factor structure in the donor sample.]]></description>
      <pubDate>Thu, 14 May 2026 15:45:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702856</guid>
    </item>
    <item>
      <title>SPR-5031: Developing INDOT Road Crack Image Datasets for Advanced Analytics Research</title>
      <link>https://rip.trb.org/View/2691526</link>
      <description><![CDATA[The Indiana Department of Transportation (INDOT) requires a comprehensive, annotated crack image dataset from falling weight deflectometer (FWD) testing to enable advanced analytics for pavement management. This project delivers systematically labeled crack images to correlate with structural deflection data, and standardized annotation protocol. Dataset enables automated crack detection, enhanced structural assessment capabilities, and data-driven pavement management decisions while leveraging existing image archives cost-effectively through annotators.]]></description>
      <pubDate>Wed, 06 May 2026 14:55:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691526</guid>
    </item>
    <item>
      <title>Improving the Quality and Useability of Planned and Active Work Zone Data</title>
      <link>https://rip.trb.org/View/2683244</link>
      <description><![CDATA[Work zone data may be used to support efforts ranging from internal operational and safety analysis to public communications and connected vehicle navigation. Ensuring the quality and consistency of this data is vital to its usability. The Virginia Department of Transportation (VDOT)’s current systems,  VaTraffic and the Lane Closure Advisory Management System (LCAMS), require double entry of data, and the other data sets they feed into all display the data differently. This project will review data quality standards and create guidance that can be applied in LaneAware to ensure quality moving forward. In November 2024, the Federal Highway Administration (FHWA) updated its Work Zone Safety and Mobility Final Rule (23CFR630 Subpart J), which in part requires state departments of transportation (DOTs) to identify mobility and work-zone-exposure performance metrics that will be used to track performance and the statewide level and for specific major projects.  Best practices used by other DOTs will be gathered and recommended for adoption. Tools and scripts for data cleaning and analysis will improve the application of these data to operational and safety analysis, which is currently hampered by issues such as identifying data from planned work zones from active ones. By consulting with a wide range of stakeholders, these recommendations will consider the wide-ranging needs of both data producers and consumers in this system.     ]]></description>
      <pubDate>Tue, 24 Mar 2026 10:53:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683244</guid>
    </item>
    <item>
      <title>Toward Smarter Mobility: AI-Powered Safety Insights for AVs and Vulnerable Road Users</title>
      <link>https://rip.trb.org/View/2669549</link>
      <description><![CDATA[This project investigates the safety dynamics between autonomous vehicles (AVs) and vulnerable road users (VRUs)—including pedestrians, cyclists, and e-scooter riders—by applying advanced artificial intelligence (AI) and data fusion (DF) methods to high-resolution, real-world datasets. By understanding how AVs interact with diverse VRUs in complex urban environments, this project will generate critical insights into where and how conflicts occur, what environmental factors contribute to unsafe conditions, and how different VRUs respond to perceived threats. These findings will inform safety improvements that reduce crashes and injuries, leading to significant public health benefits such as fewer hospitalizations, reduced long-term disabilities, and lower healthcare costs.  Moreover, safer streets will encourage more pedestrian and active transportation activity, promoting healthier lifestyles and improving community well-being. As AVs become more integrated into urban mobility systems, their potential to provide efficient, reliable, and low-stress transportation will further enhance public health by reducing traffic congestion, energy use, and travel-related stress.  

The first dataset used for this study  Argoverse 2 3D Tracking, captures interactions between AVs and pedestrians/cyclists in Austin, Texas. The second dataset includes sensor data collected from e-scooters in San Antonio, Texas, by ScooterLab at the University of Texas at San Antonio.  

The project will begin by training an AI model on the Argoverse data to identify close encounters (e.g., within 2 meters), spatially aggregate them to locate dense near-miss zones, and analyze built environment features and vehicle movement characteristics. Next, the e-scooter data will be used to detect abrupt rider responses—such as hard braking, sudden acceleration, or sharp turning—using anomaly detection algorithms, which signal perceived or actual hazards. These data streams will be fused to perform a comparative analysis across different VRU types, enabling researchers to identify common risk patterns and mode-specific vulnerabilities.   

This project will advance the scientific understanding of AV-VRU safety interactions, and discuss how mobility and efficiency can be co-optimized with safety. It will lay the groundwork for future research and transportation interventions that support healthier, safer, and more efficient communities.  ]]></description>
      <pubDate>Thu, 12 Feb 2026 15:28:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669549</guid>
    </item>
    <item>
      <title>Assessment of hydroplaning potential in coastal regions using roadway characteristics and related datasets</title>
      <link>https://rip.trb.org/View/2663101</link>
      <description><![CDATA[Hydroplaning is a critical pavement safety concern that occurs when a layer of water builds up between the vehicle's tires and the pavement surface, leading to a loss of traction and vehicle control. It is a significant contributor to wet-weather crashes and thereby poses a serious challenge to highway safety, especially for coastal regions where rainfall is more abundant and more frequent. Hydroplaning risk assessment fundamentally depends on the integration of multiple diverse datasets that reflect the interaction among crash occurrences, pavement conditions, and vehicle dynamics. These data items are typically recorded in different datasets maintained by various owners or agencies, each with their unique collection methods and standards. This research will develop data-driven likelihood models based on a verification check of the reliability of the important data variables, and a fusion of the available history data from diverse data sources to assess hydroplaning risks for coastal highways. The proposed research will also develop recommendations to be considered for roadway design and construction in association with wet-weather accident reduction procedures for transportation agencies.]]></description>
      <pubDate>Thu, 29 Jan 2026 17:13:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663101</guid>
    </item>
    <item>
      <title>Bridging Data Gaps with Modeled Data from Generative AI: Advancing Health in Transportation Research</title>
      <link>https://rip.trb.org/View/2652171</link>
      <description><![CDATA[Transportation-related factors, such as air quality changes and exposure disparities, have significant impact on health outcome. Communities near high-traffic corridors experience elevated exposure levels, yet efforts to assess these impacts are hindered by the lack of high-resolution health and socio-demographic datasets. Traditional air quality models, such as dispersion and interpolation techniques, estimate pollutant distributions but struggle to capture localized exposure variations and real-world uncertainties due to their reliance on static assumptions. These limitations reduce the precision of transportation health impact assessments. 

This project addresses data gaps in air quality and health outcomes by integrating AI-generated data with  traditional modeling techniques. Bridging the data gap is essential to improving exposure assessments and provide a more comprehensive understanding of transportation-related health effects. The research develops and trains generative AI models for data augmentation, using harmonized datasets to create high-fidelity modeled data that reflects real-world patterns. Furthermore, we integrate the trained AI models with air quality simulation models to estimated transportation-related air quality scenarios and assess potential health impacts.
 
The project produces a validated generative AI model for data augmentation, generating high-resolution datasets that enhance geographic and demographic granularity in transportation health research. The application of scenario-based health impact simulations provides new insights into the relationships between air quality and health outcomes, improving the ability to evaluate transportation-related interventions. By combining AI-driven data synthesis with traditional modeling approaches, this research advances methodologies for transportation and environmental health assessments, providing more reliable data for exposure studies and policy evaluations. 
]]></description>
      <pubDate>Tue, 13 Jan 2026 16:10:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652171</guid>
    </item>
    <item>
      <title>Assessing the Value of LiDAR in Detecting Conflicts at Intersections to Enhance Safety</title>
      <link>https://rip.trb.org/View/2596528</link>
      <description><![CDATA[The goal of this research is to evaluate and compare the effectiveness of camera-based and LiDAR-based systems for detecting traffic conflicts. AI-based methods will be developed and applied separately  to datasets collected from each sensing platform. The study will assess the performance of each system in terms of conflict detection accuracy at three sites representing varied environmental and traffic conditions. In addition to technical performance, a cost-benefit assessment will be conducted to evaluate the practicality and scalability of each sensing approach. ]]></description>
      <pubDate>Tue, 09 Sep 2025 08:19:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2596528</guid>
    </item>
    <item>
      <title>Customization of a Mobile Field Data Collection Application and Linear Referencing System to Support Geospatial Data Collaboration </title>
      <link>https://rip.trb.org/View/2566964</link>
      <description><![CDATA[Under a previous research project, Project # SPR-2253, “Development of the Digital Design Environment (DDE) ProjectWise™ – Phase 2,” the Connecticut Department of Transportation (CTDOT) initiated and accomplished significant work towards customization of a web-based editing application to facilitate field data collection of geospatial data assets, specifically road network characteristics.  This application was known as the Mobile Asset Verification and Roadway Inventory Collection tool, otherwise known as “MAVRIC”.  Field data collection of roadway geometry and asset information, and more specifically, simultaneous multi-asset editing/collection (otherwise known as “parallel data collection”), is a critical component of timely management of the geospatially accurate road network and associated attribution (e.g., lanes, shoulders, curbs, intersections, intersection approaches, etc.).  This foundational data of the road network is the backbone upon which the CTDOT’s GIS is built, and a requirement under 23 CFR 924.17 for the Model Inventory Roadway Elements (MIRE) and All Roads Network of Linear Data (ARNOLD).  The initial project targeted field data collection utilization and was successful in meeting the project goals.  This next phase of the project looks to build upon the lessons learned during the first phase, incorporate additional customization to meet the needs to expanding CTDOT stakeholders, take advantage of additional technological advances CTDOT has made since the completion of the first project, and utilize the customized solution to help CTDOT personnel to enhance the overall quality and completeness of its critical roadway datasets, while providing metrics on time and resource savings that can be expected through implementation of a similar system.  For the different types of data collection, a fully configurable customized application is needed. ]]></description>
      <pubDate>Wed, 18 Jun 2025 16:28:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2566964</guid>
    </item>
    <item>
      <title>CTDOT Pavement Marking Inventory and Retro Reflectivity Compliance</title>
      <link>https://rip.trb.org/View/2566901</link>
      <description><![CDATA[The primary objective of this project is to develop a comprehensive baseline dataset and methodology to create a Pavement Marking Index. This index will provide a systematic approach to inventory, assess, and manage the pavement markings on all state roads in Connecticut. It will enable the Connecticut Department of Transportation (CTDOT) to ensure compliance with the Federal Register (87 FR 47921) and to efficiently plan and prioritize the maintenance and replacement of pavement markings. This will enhance road safety and optimize resource allocation. Additionally, this research may assist other states in developing similar programs.]]></description>
      <pubDate>Wed, 18 Jun 2025 13:24:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2566901</guid>
    </item>
    <item>
      <title>Applications of data science and big data analytics in underground transportation infrastructure (UTI-UTC 02)
</title>
      <link>https://rip.trb.org/View/2543307</link>
      <description><![CDATA[This project focuses on harnessing the power of data science, machine learning (ML), and big data analytics to enhance the construction, operation, and maintenance of underground transportation infrastructure (UTI). By collecting and processing large-scale datasets from tunneling projects—such as TBM performance data, geotechnical records, and operational logs—the research develops predictive models to assess ground conditions, detect anomalies, and forecast potential structural failures. Key objectives include refining data-driven methods for real-time TBM state prediction, designing algorithms to detect defects like cracks or rock incursions, and creating interactive visualization tools to support decision-making. The project emphasizes scalable ML architectures (e.g., deep learning, recurrent neural networks) to improve the resilience, safety, and cost-efficiency of UTI systems. Its outcome serves as a foundation for intelligent tunneling and infrastructure health monitoring frameworks in modern urban environments.
]]></description>
      <pubDate>Wed, 07 May 2025 19:00:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543307</guid>
    </item>
    <item>
      <title>Implementation of AASHTOWare BrR Program for Rating Iowa Bridges</title>
      <link>https://rip.trb.org/View/2484651</link>
      <description><![CDATA[Since the deployment of the National Bridge Inspection Oversight program, many States have performed a large majority of their load ratings in specific software for the benefits of ensuring quality and efficiency in re-utilizing the rating data and bridge models to manage the bridges over their life cycle. There is more emphasis from Federal Highway Administration (FHWA) this year in the assessment of Load rating quality control/quality assurance (QC/QA) program and procedures. A QC/QA program could be implemented more efficiently when standardized software and databases are used for performing the approximate 20,000 LPA bridge analyses.

Tools that Iowa Department of Transportation (IADOT) currently licenses, LARS and AASHTOWare Bridge Rating, could be used to model and store most local agency inventory load ratings. The local agencies and consultants could manage most of the load ratings within this software; the software can evaluate most of the common and standard plan bridges that have been used by local agency bridge owners in Iowa. There may be other software, such as BRASS, that could be used as a standardized tool.

AASHTOWare offers a ‘Supersite’ license that would allow access to the software with no direct costs to the county engineers and consultants, as well as other licensing options for agency sponsored consultants. Once implemented the entire network of bridges can be analyzed at the push of a button when considering future legislation changes.

To implement a statewide rating system, a research project is desired to develop standard files to be used by all users of the AASHTOWare rating software. These standard files will represent the standard bridges in Iowa. All agencies in Iowa, IADOT, County, and Cities will be included in the implementation of the AASHTOWare software.]]></description>
      <pubDate>Mon, 30 Dec 2024 12:26:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2484651</guid>
    </item>
    <item>
      <title>How Effective are Marker Variables at Predicting Attitudinal Factor Scores? An Out-of-sample Evaluation</title>
      <link>https://rip.trb.org/View/2440257</link>
      <description><![CDATA[Despite the fact that existing models are not up to the job of predicting travel behavior in today’s rapidly changing landscape, and despite considerable evidence that attitudes help explain behavior more completely and more meaningfully, attitudes are nowhere to be found in practice-oriented travel demand forecasting models.  Two main objections have been raised to their inclusion:  they are too cumbersome to measure, and difficult-if-not-impossible to forecast.  This project would continue a line of research that focuses on overcoming the first objection.  Specifically, the plan is to use machine learning methods to train a prediction function on one survey dataset (the “donor sample”, and then apply that function to impute attitudes into another dataset (the “recipient sample”).  This keeps the recipient survey less burdensome on the respondent, while allowing the dataset to receive attitudinal information that would otherwise be absent.]]></description>
      <pubDate>Thu, 10 Oct 2024 15:50:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440257</guid>
    </item>
    <item>
      <title>SPR-4927: Bridge Data Virtual Validator</title>
      <link>https://rip.trb.org/View/2410439</link>
      <description><![CDATA[This project has the following objectives/deliverables:
• A list of bridge data items, from where/whom/how each item is collected and used/consumed.
• Tools to cross validate bridge data items between Final Tracings documents; the interface should enable a website-based drag and drop operation similar to the Utah Department of Transportation (UDOT) Validation and Conversion Tools.
• A manual of the tool with example illustrations of input, output, interface, screenshots, etc.
• Piloting results on a project as demo to show the use of the tool, most importantly, the validated results from the tool (bridge birth certificate) could be uploaded into an information technology asset management system (iTAMS) to start the bridge inspection file.
]]></description>
      <pubDate>Tue, 30 Jul 2024 11:19:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2410439</guid>
    </item>
    <item>
      <title>Developing a Data Repository to Help with TSMO Strategies Evaluations



</title>
      <link>https://rip.trb.org/View/2381698</link>
      <description><![CDATA[As agencies seek to improve transportation safety and mobility, effectively operating transportation facilities and networks is critically important. There are dozens of Transportation Systems Management and Operations (TSMO) strategies that a facility or network could use. Knowing which TSMO strategies are more likely to be effective at addressing safety and operations issues enables agencies and practitioners to make data-driven decisions to effectively use limited funding. 

The evaluation of TSMO strategies is often complicated by unique characteristics of TSMO strategies, such as deployment of two or more strategies at once, intermittent or flexible use based on prevailing conditions, and widespread effects across a network. Access to better evaluation methods will support agencies in assessing their own use of TSMO strategies. A lack of a central repository that enables sharing of strategy effectiveness data and information impedes the efficiency of agencies and their decision-making for more effective and efficient investments in TSMO strategies. 

Research is needed to help state departments of transportation (DOTs) develop tools to evaluate TSMO strategies, compile results, and make them available to practitioners.

OBJECTIVES: The objectives of this project are (1) to develop evaluation methods to assess the effectiveness of TSMO strategies and (2) to create a web-based central repository to share information, data, and evaluation methods and results for DOTs and other agencies.  ]]></description>
      <pubDate>Mon, 20 May 2024 21:18:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2381698</guid>
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