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    <title>Research in Progress (RIP)</title>
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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>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>Successful Approaches to Integrating Artificial Intelligence (AI) Into Knowledge Management</title>
      <link>https://rip.trb.org/View/2681237</link>
      <description><![CDATA[As state Departments of Transportation (DOTs) and other transportation agencies expand their knowledge management (KM) programs, interest in incorporating artificial intelligence (AI) is increasing. Agencies are exploring how AI-enabled tools can support knowledge capture, organization, retrieval, and application.

This scan will examine current practices used by state DOTs and other organizations to implement AI in knowledge management. It will identify opportunities as well as common challenges, including data quality, security, governance, and ethical considerations. Careful and responsible integration of AI is essential to ensure effective and sustainable use within KM programs.]]></description>
      <pubDate>Tue, 17 Mar 2026 15:12:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681237</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>Cybersecurity Assurance via AI-Driven Digital Twins for Transportation Safety  </title>
      <link>https://rip.trb.org/View/2663600</link>
      <description><![CDATA[Transportation infrastructure increasingly depends on networked sensor systems for structural health monitoring, yet many operational deployments lack robust data-integrity protections, rendering them vulnerable to cyber-physical attacks. Manipulated sensor readings can misrepresent bridge health, rail conditions, or load limits, thereby creating risks of undetected structural failure, service closures, or catastrophic crashes. Because cyber manipulation directly produces false-safe readings, delays critical maintenance actions, and conceals structural distress, cybersecurity protection constitutes a core safety requirement, not an ancillary concern, for modern monitoring infrastructure.
This project develops a secure, artificial intelligence (AI)-driven digital twin framework that continuously compares real-time sensor data against expected behavioral responses to detect spoofing, tampering, replay, and delay manipulation, and other cyber-physical disruptions. The digital twin is intentionally implemented as a lightweight behavioral model; its purpose is not full structural simulation but rather the generation of expected-response profiles that serve as the ground-truth reference for anomaly detection. Combined with secure sensing hardware, AI-based detection algorithms, and survivability logic, the integrated system maintains reliable monitoring capability even under partial cyber compromise. The framework supports the U.S. Department of Transportation (USDOT) Safe System Approach by preventing cyber-induced safety failures and provides a clear pathway to pilot deployment through a Python-based prototype, agency demonstrations, and structured partner engagement.

Key milestones include the twin baseline model, secure sensing validation, AI detection module completion , and a survivability demonstration with partner input. The resulting system provides transportation agencies with a low-cost cybersecurity layer that protects safety-critical sensing systems from data manipulation and disruption. Deliverables include a Python detection module, interactive dashboard, and validated datasets compatible with existing DOT workflows. By ensuring the trustworthiness of monitoring data, the proposed approach reduces hazard risk, strengthens maintenance decision-making, and scales across bridges, tunnels, and rail systems, offering a realistic and immediate path to pilot adoption within USDOT transportation-cybersecurity priorities.
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:23:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663600</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>Using Linked Data to Explore the Accuracy of Crash Reported Injuries of Minors</title>
      <link>https://rip.trb.org/View/2640191</link>
      <description><![CDATA[Police crash reports often provide the first record of injury severity for minors involved in motor vehicle crashes, yet these reports may not always match clinical assessments. Differences between the reported level of injury and the medically confirmed level can influence emergency response decisions and limit the usefulness of crash databases for safety analysis. This project will link crash data from the Connecticut Crash Data Repository with hospital discharge and Emergency Medical Services (EMS) datasets to compare police reported injury codes with medically derived measures. The analysis will document where inconsistencies occur and examine how factors such as crash location, agency type, passenger protection, and driver behavior relate to reporting accuracy.

The linked dataset will cover crashes involving minors from 2015 through 2024 and will support regression based evaluations of injury classification accuracy across multiple contexts. By identifying sources of error, the study will help improve data quality and support better training and data collection procedures for law enforcement and partner agencies. The resulting insights will strengthen statewide injury surveillance systems and guide the development of safety strategies for children and adolescents.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:50:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640191</guid>
    </item>
    <item>
      <title>Modernization of Borehole Drilling Data</title>
      <link>https://rip.trb.org/View/2560887</link>
      <description><![CDATA[The Idaho Transportation Department (ITD) is enhancing the management of borehole and material site data by
consolidating information currently maintained in multiple formats across offices statewide. This project will
compile all existing geotechnical boring data and develop a centralized, standardized database integrated into
ITD’s ArcGIS environment. ITD staff, contractors, and the public will be able to easily access and visualize
geotechnical data, supporting planning, design, construction, and operation of the highway system. The project
will also establish workflows for incorporating newly collected data, ensuring the system remains current, reliable,
and user-friendly over time.]]></description>
      <pubDate>Tue, 03 Jun 2025 13:29:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2560887</guid>
    </item>
    <item>
      <title>Using Artificial Intelligence to Enhance Transportation Data Quality

</title>
      <link>https://rip.trb.org/View/2558405</link>
      <description><![CDATA[The rapid growth of artificial intelligence (AI) and machine learning (ML) technologies—along with the rapid emergence of generative AI (GenAI) tools—is of strong interest among state departments of transportation (DOTs). These tools have the potential to improve operations, analytics, and decision-making. However, the effectiveness of these tools depends heavily on the quality of the data used to train large language models (LLMs) and to support retrieval-augmented generation (RAG) for accurate results.

Many state DOTs are exploring how AI can automate routine tasks, allowing staff to focus on higher-value analytical work. High-quality data are essential to realizing the full benefits of AI integration across transportation systems. Yet many DOTs are facing challenges with data that are incomplete, inconsistent, or not aligned with established business rules and standards.

As AI technologies advance, a key question arises: Can AI itself be used to assess, identify, and correct data quality issues? If so, these tools could not only improve the accuracy of the data but also enhance the performance of the AI systems that rely on it—while reducing the effort needed for data maintenance.

Research is needed to explore what is feasible today, what advances may be possible, and what resources or strategies are required to apply AI effectively to data quality improvement. Such research will help state DOTs make informed decisions and set realistic expectations about how AI can be used to enhance transportation data quality.

OBJECTIVE: The objective of this research is to develop a guide for state DOTs on how to use AI to enhance transportation data quality. The research will explore and demonstrate how AI can detect, correct, and prevent data errors—automating key aspects of data management. It will identify effective strategies for integrating AI into existing data management practices to enhance the accuracy, consistency, and reliability of transportation and business data systems.]]></description>
      <pubDate>Wed, 28 May 2025 09:42:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558405</guid>
    </item>
    <item>
      <title>TRC2503: Feasibility of Vehicle Probe Data for Origin-Destination Estimation</title>
      <link>https://rip.trb.org/View/2353410</link>
      <description><![CDATA[Origin-Destination (O-D) estimation is an important step for travel demand forecasting. Traditional approaches to O-D estimation involve either survey-based trip diaries or traffic counts. Both methods have limitations.  With the emergence of “Big Data” sources in the form of third-party probe data gathered from Global Positioning System (GPS) and cell-phone sources, approaches to O-D estimation have broadened. The objective of this project is to evaluate Probe data accuracy (or bias) by context (location, region, time of day, etc., and measure the feasibility of extracting the trip attributes such as vehicle type, vehicle occupancy, trip purpose, and mode of transport from the data, if available. Due to the unique characteristics of Arkansas' Interstate and National Highway System (NHS) routes, the accuracy of the probe data measured in locations not in Arkansas may not apply to Arkansas. Penetration rates and adjustment factors estimated at the national level may not accurately represent the characteristics for Interstate and NHS long-distance trips seen in Arkansas. In this regard, it is essential to understand the opportunities and limitations of vehicle probe data for O-D estimation in the context of Arkansas. The results of this study will provide a decision-making tool to guide data purchase decisions based on project specific needs.]]></description>
      <pubDate>Wed, 15 Jan 2025 12:32:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2353410</guid>
    </item>
    <item>
      <title>Low-cost Real-Time Learning-based Localization for Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2440024</link>
      <description><![CDATA[A major operational expense for an autonomous vehicle (AV) is with capturing, processing, and updating high-definition maps to localize itself when driving. To safely navigate, AVs need to know where they are on a given map to determine their trajectory to the next waypoint. Precise localization is a challenge in Global Positioning System (GPS)-denied areas such as dense urban corridors and motion tracking experiences dropouts in large open spaces such as rural highways. Classic localization algorithms are iterative, and their performance relies on direct feature matching between the stored map and the current sensor observations. This makes them prone to errors in large open spaces which have few distinguishing surface features. They are expensive to run in the vehicle as they account for a large share of the computation cost and power consumption. Better accuracy and faster localization directly improve AV safety as they navigate around people and cluttered environments.
 
This project will develop an AV localization service that is low-cost, accurate, and can operate in real-time in any AV at a fraction of the computation and power budget of current approaches. In 2023-24 the research team developed the preliminary version of this localization approach using a specific type of neural networks (i.e. invertible neural networks) to compress the map and lookup the vehicle’s pose efficiently. The team demonstrated the accuracy and cost to operate on 1/10th-scale vehicles and benchmarked the performance using localization datasets to benchmark the performance. In 2024-25, the team will undertake the real-world evaluation on real AVs with their deployment partner, The Autoware Foundation. The team will focus on localization of an electric autonomous goods and person cart for intralogistics for indoor and outdoor navigation. The outcome of this work will result in a portable and easy-to-use localization system for Safety21 projects.
 
Technical details: AV localization is the problem of finding a robot’s pose using a map and sensor measurements, like LiDAR scans and camera images. However, finding injective mappings between measurements and poses is difficult because sensor measurements from multiple distant poses can be similar. To solve this ambiguity, Monte Carlo Localization, the widely adopted method, uses random hypothesis sampling and sensor measurement updates to infer the pose. Other common approaches are to use Bayesian filtering or to find better distinguishable global descriptors on the map. Recent developments in localization research usually propose better measurement models or feature extractors within these frameworks. In this project, the team proposes a radically new approach to frame the localization problem as an ambiguous inverse problem and solve it with an invertible neural network (INN). The team has recently demonstrated that INNs are naturally suitable for the localization problem with many benefits, in terms of high accuracy (within 0.25m for city-scale maps), high-speed operation (>150Hz) and operate on low-cost embedded system hardware. The team will demonstrate this on point-cloud and camera datasets with evaluation on indoor and outdoor localization benchmarks, and also deploy it on real autonomous vehicles around the 23-acre Pennovation campus to show real-time and scalable operation. ]]></description>
      <pubDate>Sun, 13 Oct 2024 09:38:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440024</guid>
    </item>
    <item>
      <title>Prototyping a Low-cost Roadside Device System for Cooperative Automated Driving</title>
      <link>https://rip.trb.org/View/2425404</link>
      <description><![CDATA[Although significant progress has been made in automated driving technologies, technical challenges still exist, especially for complex Operational Design Domains (ODDs). A low-cost roadside device system, the Connected Reference Marker (CRM) System, has been developed to support CAVs in those ODDs. The CRM system can facilitate CAV localization by providing real-time distance measurement and road geometry changes (i.e., work zones). Therefore, the CRM system has the potential to serve as a gateway system for infrastructure-based cooperative driving automation (CDA) due to its low cost and easy deployment. This project will evaluate the performance regarding localization and road geometry data provision in field experiments. Specifically, this project will build a prototype system and evaluate the localization accuracy in various scenarios; in addition, the prototype system will be used to detect the boundaries of work zones, as improving access to work zone data is one of the top needs identified through the USDOT Data for Automated Vehicle Integration (DAVI) effort. The detected boundaries of work zones will be later translated into a data feed following the Work Zone Data Exchange (WZDx) specification, a national work zone data standard pioneered by USDOT to meet the DAVI requirement and a critical part of the Roadway Digital Infrastructure (RDI) strategy.]]></description>
      <pubDate>Thu, 05 Sep 2024 16:58:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425404</guid>
    </item>
    <item>
      <title>Improving the Quality of Highway Profile Measurement</title>
      <link>https://rip.trb.org/View/2423019</link>
      <description><![CDATA[The goal of the proposed pooled fund study “is to continue and extend the work of TPF-5(063) and TPF-5(354)”, which was led by the Federal Highway Administration (FHWA) and South Dakota Department of Transportation (SDDOT), respectively. The project will enable states and FHWA to: identify data integrity and quality issues associated with measuring and analyzing pavement profiles; suggest approaches to addressing identified problems; initiate and monitor projects to address identified problems; disseminate results; and assist in solution deployment. OBJECTIVES: (1) Deliver sample procurement specifications and maintenance guidelines; (2) Direct and support development and maintenance of pavement profile analysis software; (3) Implement criteria for profile verification that include emerging technologies (e.g., low-speed profilers, start and stop profilers, and non-inertial profilers); (4) Verify pavement profile reference devices; (5) Develop and deliver profiler operation and profile analysis training; (6) Implement methods for maximizing the use of pavement profiles for network, project, and forensic analysis, with a focus on cutting-edge methodologies; (7) Provide technical support for the Road Profile Users’ Group and conduct annual face-to-face meetings in conjunction with the group.]]></description>
      <pubDate>Sat, 31 Aug 2024 11:44:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2423019</guid>
    </item>
    <item>
      <title>WITNESS: Washington Integrated Transportation Networks Evaluation System and Security</title>
      <link>https://rip.trb.org/View/2414043</link>
      <description><![CDATA[The objective of WITNESS (Washington Integrated Transportation Networks Evaluation System and Security) platform is to develop a systematic method to assess different datasets comprising heterogeneous data generated by vehicles, sensors or mobile devices deployed by internal as well as external third-party sources.

The specific goals for the WITNESS project are as follows: (1) define data quality attributes (accuracy, completeness, timeliness, relevance) and measurable criteria based on Washington State Department of Transportation (WSDOT) priorities while assessing the constraints and tradeoffs (cost, delays, etc.) regarding each attribute; and (2) collect and evaluate evidence on the data quality meeting the defined criteria along developing machine learning techniques to identify discrepancies, strength and weaknesses of the available data as well as date gaps for future needs.]]></description>
      <pubDate>Wed, 07 Aug 2024 16:35:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2414043</guid>
    </item>
    <item>
      <title>Third-Party Origin-Destination Data Validation for Transportation Planning Applications</title>
      <link>https://rip.trb.org/View/2381702</link>
      <description><![CDATA[Private-sector travel origin-destination (O/D) data has become increasingly popular in a variety of transportation planning applications, including travel demand modeling. But evolving privacy regulations, computational algorithms, and data sources may introduce uncertainties and biases in data quality and stability. The rise of new and big data sources for trip flows adds further complexity, raising concerns about sample size and data representativeness. Each provider’s O/D data has unique characteristics, making direct comparisons and validation challenging. 

State departments of transportation (DOTs) and other agencies need to assess/validate the quality of passenger and freight crowdsourced O/D data. Currently, limited standard guidance exists for such a validation process, leading to potential inconsistencies or biases. Additionally, discrepancies in spatial and temporal granularity, as well as in inferred trip characteristics among vendors, require adaptable guidance. Research is needed to develop comprehensive O/D data accuracy assessment and validation frameworks to support transportation planning agencies in setting data standards and adapting to evolving data sources. 

The objective of this research is to develop a guide for crowdsourced vehicle O/D data assessment and validation for transportation planning applications.   ]]></description>
      <pubDate>Mon, 20 May 2024 20:52:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2381702</guid>
    </item>
    <item>
      <title>Consultant Support for IC and PMTP Projects in 2024-2025</title>
      <link>https://rip.trb.org/View/2362129</link>
      <description><![CDATA[The MoDOT Intelligent Compaction and Paver-Mounted Thermal Profiling (IC-PMTP) projects since 2017 have demonstrated paving quality improvements on numerous field projects. Therefore, MoDOT has included IC-PMTP projects yearly, intending to use IC-PMTP data for acceptance in the future. To ensure the continued success of the MoDOT IC-PMTP projects in 2024 and beyond, MoDOT has procured Consulting Support for the designated IC-PMTP projects in the 2024 and 2025 construction seasons and the implementation of data quality assurance (QA) and future acceptance with IC-PMTP data.]]></description>
      <pubDate>Thu, 04 Apr 2024 11:20:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2362129</guid>
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