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    <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>Statistical Evaluation of Illinois Modified AASHTO T161 Freeze–Thaw Testing Following Laboratory Relocation</title>
      <link>https://rip.trb.org/View/2686616</link>
      <description><![CDATA[A critical way to build high-performing pavements and bridges is to evaluate a mixture’s freeze-thaw performance in the lab to ensure it meets performance parameters. The aim of this project is to calibrate and validate new equipment for freeze-thaw testing at the Illinois Department of Transportation’s (IDOT's) Central Bureau of Materials. Researchers will test aggregate samples using IDOT’s new and existing freeze-thaw equipment, ensuring the new equipment produces consistent and replicable results. They will also create calibration guidelines that will help to establish a repeatable framework when replacing future freeze-thaw testing equipment.]]></description>
      <pubDate>Wed, 01 Apr 2026 09:41:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2686616</guid>
    </item>
    <item>
      <title>Communicable disease preparedness: Aircraft cabin disease dispersion study for model validation</title>
      <link>https://rip.trb.org/View/2675923</link>
      <description><![CDATA[This research supports the Federal Aviation Administration's (FAA’s) Aviation Safety Research Strategy Public Health Preparedness thrust and depends on access to National Research Council Canada’s Centre for Air Travel Research facility. To strengthen public health preparedness, the Office of Aerospace Medicine must quantitatively model disease transmission risk in commercial aviation and evaluate mitigation strategies. Building on preliminary work under prior work, risk analysis models have been developed for interagency Safety Risk Management (SRM) use, with broader dissemination planned to public health planners, industry, and academia.

This project directly responds to the final recommendation of GAO-22-104579, which highlighted critical gaps in prior models. The project will publish key human behavior and ventilation datasets, enabling peer review, independent replication, and expanded application.]]></description>
      <pubDate>Mon, 02 Mar 2026 10:19:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2675923</guid>
    </item>
    <item>
      <title>Using Large Language Models to Generate Synthetic Data for Proactive
Pedestrian Safety Prediction: Overcoming Data Collection Barriers in Surrogate Safety Analysis</title>
      <link>https://rip.trb.org/View/2663604</link>
      <description><![CDATA[Pedestrian fatalities remain a persistent and growing safety crisis, with over 7,500 pedestrians killed on U.S. roads annually. Effective countermeasure deployment requires identifying high-risk locations before crashes occur, yet traditional crash-based analyses are insufficient due to the rarity of pedestrian crashes at individual intersections. Surrogate safety analysis using pedestrian–vehicle close calls offer a proactive alternative, but comprehensive observational data collection is prohibitively expensive and time-intensive. Video monitoring requires specialized equipment, extended deployment periods, and substantial manual processing. These practical constraints severely limit the geographic coverage, temporal scope, and contextual diversity of available datasets, ultimately hindering agencies' ability to develop reliable predictive tools that generalize across diverse intersection types and support evidence-based
safety interventions statewide. This project addresses these fundamental challenges by introducing Large Language Models (LLMs) as a novel tool to generate high-quality synthetic pedestrian–vehicle interaction data. LLMs possess extensive pre-trained knowledge spanning transportation systems, human behavior, and urban
environments, successfully demonstrated in healthcare and climate science for data augmentation. Building upon the Minnesota Traffic Observatory (MTO) dataset, where 18 intersections with 3,314 interactions involving 4,941 pedestrians, the research team will develop a validated methodology to generate contextually realistic scenarios incorporating roadway geometry, traffic control, land use, pedestrian demographics, and temporal patterns. This approach directly tackles the data scarcity problem that prevents agencies from conducting comprehensive pedestrian safety analyses across their jurisdictions.
The project has three objectives: (1) develop a transparent LLM-based synthetic transportation-targeted data generation methodology with validation protocols ensuring realism and quality; (2) evaluate whether synthetic data-augmented models improve prediction accuracy and transferability across intersections compared to observational data alone, using precision-recall AUC, calibration diagnostics, leave-one-site-out validation and other appropriate approaches; and (3) determine the mechanisms driving performance improvements: whether from introducing realistic scenario diversity or addressing rare-event limitations, to guide best practices. The framework will incorporate probability calibration, split-conformal risk control, and decision-curve analysis to deliver deployment-ready tools with quantified uncertainty for operational use.]]></description>
      <pubDate>Tue, 03 Feb 2026 15:34:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663604</guid>
    </item>
    <item>
      <title>Accelerating IFC Adoption by Advancing IFC Validation Service and Software Certification Program</title>
      <link>https://rip.trb.org/View/2652044</link>
      <description><![CDATA[This proposed Pooled Fund Study would look at the viability and best means to significantly enhance the scale and maturity of services (i.e., IFC Validation Service and Global IFC Software Certification), as well as recommend any additional technical and procedural efforts (such as Use Case-based Certification), needed to support software implementation and United States industry adoption and deployment. The following two primary business objectives would be achieved: Enabling state departments of transportation (DOTs) to specify certified (IFC and US industry standard exchange requirement compliant) software for road and bridge projects; Enabling state DOTs to validate deliverables from consultants and contractors to enhance project delivery and management quality. This work would be separate but complimentary to the ongoing work of TPF-5(523) BIM for Bridges & Structures Pooled Fund and TPF-5(480) BIM for Infrastructure Pooled Fund.]]></description>
      <pubDate>Sat, 10 Jan 2026 11:59:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652044</guid>
    </item>
    <item>
      <title>Aeromedical HFACS Nanocode Review and Validation</title>
      <link>https://rip.trb.org/View/2646975</link>
      <description><![CDATA[The Office of Aerospace Medicine (AAM) has developed a specialized Human Factors Analysis and Classification System (HFACS) nanocode framework designed to systematically capture medical contributors to aviation accidents. This innovative taxonomy aims to link latent or undetected pilot health issues to unsafe acts and broader systemic oversight deficiencies, thereby enhancing the Federal Aviation Administration's (FAA’s) ability to understand and mitigate medically related accident risks. However, before this framework can be operationalized within FAA safety programs, it requires rigorous, independent validation to ensure its reliability, usability, and overall effectiveness in real-world applications. The core objective of this research is to evaluate whether the nanocode system accurately identifies causal medical factors in aviation accidents and supports improved aeromedical decision-making. To achieve this, the study will address key questions: How consistently can trained analysts apply the nanocode framework to actual accident cases? Does the framework clearly capture essential medical and supervisory contributors to unsafe acts? And, what refinements are necessary to enhance its clarity, usability, and integration with other FAA safety analysis systems? The answers to these questions will determine the readiness of the framework for widespread implementation and inform future training, oversight protocols, and policy guidance within the FAA’s aeromedical and safety assurance ecosystems.]]></description>
      <pubDate>Thu, 08 Jan 2026 08:56:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646975</guid>
    </item>
    <item>
      <title>Safe and Personalized Control of Autonomous Vehicles with On-Board Vision Language
Models: System Design and Real-World Validation
</title>
      <link>https://rip.trb.org/View/2625313</link>
      <description><![CDATA[This project focuses on enhancing autonomous vehicle control systems by integrating on-board Vision-Language Models (VLMs) for safe and personalized driving experiences. Building on the previously awarded Center for Connected and Automated Transportation
(CCAT) project on “CAV Pilot Development and Deployment in Midwest Winter,” this research addresses critical challenges in autonomous vehicle development regarding limited on-board computational resources by implementing lightweight VLM frameworks and Retrieval-Augmented Generation (RAG)-based memory modules. The project will validate the system’s ability to handle challenging urban scenarios, reduce human takeover rates, and adapt to diverse environmental conditions.
]]></description>
      <pubDate>Thu, 13 Nov 2025 15:43:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625313</guid>
    </item>
    <item>
      <title>Enhancing Flexible Pavement System 23 (FPS23) by Incorporating a Top-Down Cracking Model in Texas Mechanistic-Empirical Flexible Pavement Design System (TxME)</title>
      <link>https://rip.trb.org/View/2604524</link>
      <description><![CDATA[Several Texas Department of Transportation (TxDOT) districts have reported early top-down cracking issues linked to the use of Reclaimed Asphalt Pavement (RAP) materials, which can make the surface layer excessively stiff. Unlike bottom-up fatigue cracking—where distress originates at the bottom of the hot mix asphalt (HMA) layer—top-down cracking begins at the surface and propagates downward. While bottom-up fatigue cracking and the negative effects of RAP in lower asphalt layers have been well addressed, top-down cracking remains unaccounted for in Texas Mechanistic-Empirical Flexible Pavement Design System (TxME). As a result, premature top-down cracking cannot currently be predicted at the design stage. With growing economic and environmental incentives for RAP use—and current specifications allowing it in surface layers—integrating a top-down cracking model into FPS23/TxME is essential to assess its impact properly. The research team will: (1) Evaluate and develop an appropriate mechanistic-empirical (ME) top-down cracking model, (2) Implement it in TxME, and (3) Calibrate/validate the model. The research team will review the literature, identify the ME model, integrate it into TxME, and collect test section data—including mixture properties, structure, and field performance—for calibration and validation.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:12:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604524</guid>
    </item>
    <item>
      <title>Validation of HSM Crash Prediction Methods for Specific Intersection Types in Oregon</title>
      <link>https://rip.trb.org/View/2593954</link>
      <description><![CDATA[The Highway Safety Manual (HSM) is the national guidance of quantitative safety analysis used in highway transportation planning, alternatives development, highway design, operations, and maintenance. However, some crash prediction models and crash modification factors in the Highway Safety Manual were developed using data from other states, not Oregon. Therefore, it is necessary to validate these models and crash modification factors for the implementation in Oregon.
Recently the National Cooperative Highway Research Program (NCHRP) project 17-68 “Intersection Crash Prediction Methods for the Highway Safety Manual” has developed crash prediction models of more intersection types for inclusion in the HSM. The types of intersections include all-way stop control, three-leg intersections with signal control on rural highways, intersections on high-speed urban and suburban arterials, five-leg intersections, etc. Currently, there is no guideline for how to use these new crash prediction models particularly in Oregon. It is necessary to validate these models and crash modification factors in Oregon to guide the statewide implementation.
This research proposes to focus on intersections on urban and suburban arterials, which are common intersection types.]]></description>
      <pubDate>Thu, 28 Aug 2025 12:53:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593954</guid>
    </item>
    <item>
      <title>KYTC Geotechnical Data 
Transition Support and 
Application Development</title>
      <link>https://rip.trb.org/View/2593940</link>
      <description><![CDATA[With the planned discontinuation of gINT, the Kentucky Transportation Cabinet (KYTC) must transition to a new geotechnical data management system. This project will facilitate that transition by ensuring geotechnical data can be accurately transferred, validated, and integrated across existing and proposed systems. Researchers will investigate current geotechnical data transfer protocols and data validation tools before developing Cabinet-specific tools that can maintain data quality and enable efficient data sharing within the agency and with external partners.]]></description>
      <pubDate>Thu, 28 Aug 2025 11:32:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593940</guid>
    </item>
    <item>
      <title>Balance Mix Design Data 
Validation</title>
      <link>https://rip.trb.org/View/2593939</link>
      <description><![CDATA[Balanced mix design (BMD) focuses on optimizing an asphalt mixture’s cracking and rutting performance rather than relying on traditional volumetric properties in its formulation. Over the past five years, Kentucky Transportation Cabinet (KYTC) asphalt contractors have implemented BMD practices. However, no research has looked at the relationship between asphalt performance test results submitted by contractors and the real-world performance of asphalt mixtures.]]></description>
      <pubDate>Thu, 28 Aug 2025 11:32:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593939</guid>
    </item>
    <item>
      <title>Evaluating and Implementing Ground Penetrating Radar (GPR) for Continuous and Rapid Monitoring of Moisture Fluctuations in In-Service Roads</title>
      <link>https://rip.trb.org/View/2487309</link>
      <description><![CDATA[Traditional methods for measuring pavement moisture, including in-place sensors and indirect assessments like FWD, can be costly, invasive, slow, offer limited spatial coverage, and disrupt traffic. In contrast, ground penetrating radar (GPR) offers a non-invasive, portable solution for swiftly evaluating extensive road segments, detecting subsurface moisture levels with reasonable cost, thereby supporting local road authorities in promptly assessing moisture conditions in critical pavement areas. The aim of this research study is to advance the validation and implementation of GPR-based pavement moisture assessments on actual low-volume roads.]]></description>
      <pubDate>Fri, 18 Jul 2025 09:49:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487309</guid>
    </item>
    <item>
      <title>Innovations Deserving Exploratory Analysis--The Transit IDEA Program. IDEA 109. Simulation-based Decision-Making Tool for Microtransit Service Evaluation and Optimization</title>
      <link>https://rip.trb.org/View/2572333</link>
      <description><![CDATA[Microtransit refers to an on-demand, dynamically routed, mobile-app-powered shuttle service with rider walking exchange. Microtransit is an emerging transit service that seems to improve riders’ experience by operating small-sized shuttles that can offer flexible routes and on-demand scheduling services. It is essentially a “smart bus service,” similar to the service of transportation network companies such as Uber and Lyft in which riders are required to book a trip using a smartphone app and get picked up in minutes at a corner by exchanging a short walk distance (walking exchange) or just waiting at the original address (door-to-door). The user may be matched with other passengers heading in the same direction to share their trips, defined as “ridesharing.” An increasing number of places are deploying microtransit to serve their residents and visitors, such as L.A. Metro in Los Angeles, California and King County Metro in Seattle, Washington. Most microtransit services integrate into existing public transit systems, such as city buses, to enhance transit service where running fixed-route buses is rather challenging.

This project is aimed at developing a simulation-based decision-making toolkit to help transit agencies evaluate microtransit service performance, optimize its operation, and make evidence-based decisions on resource distribution. Existing simulation models do not adequately represent on-demand microtransit with ride walking exchange, which makes it difficult to optimize the system and make decision with some assurance. The proposed open source microtransit simulation toolkit will be flexible and can be easily modified to apply to any place or community in any scenario. 

The proposed microtransit simulation toolkit will be based on the Simulation of Urban Mobility (SUMO) package. The flexibility and compatibility of the toolkit as a platform will enable its integration with rider walking exchange and vehicle operations, such as pick up and drop off (PUDO) strategy, or as “plugin-and-play” functional modules. A greedy heuristic shuttle routing approach will be developed for efficiently solving large scale shuttle-rider matching and routing problem, which is a major road blocker for simulating real world microtransit systems. Partnering with the city of Wilson, North Carolina, the proposed microtransit simulation toolkit will be calibrated and validated with real-world traffic and microtransit operation data. In addition, a simulation-based recursive framework will be proposed to quantitively describe the interplay between microtransit’s ride demand and behavior, supply (e.g., system operation and design) and service (e.g., waiting time) and provide a complete and systematic solution for optimizing microtransit systems. Finally, a webpage interface will be designed to take the system design inputs and display simulation and optimization results through a graphical user interface (GUI) on a website. The toolkit can be easily applied by transit agencies interested in testing different operation scenarios for their microtransit systems. 

The benefits of deploying on-demand microtransit are expected to be significant in the light of socio-economy and mobility while enhancing transportation equity, mobility convenience, job access, and reducing traffic congestion, energy consumption, and greenhouse gas emissions.]]></description>
      <pubDate>Tue, 08 Jul 2025 17:11:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2572333</guid>
    </item>
    <item>
      <title>GraphSecure Security Enhancement for Autonomous Vehicle Networks with Knowledge Graph</title>
      <link>https://rip.trb.org/View/2559306</link>
      <description><![CDATA[The project aims to address cybersecurity challenges in connected autonomous vehicles (CAVs), particularly the risk of malicious actors disseminating false information in vehicular networks. The project will develop a scalable, knowledge-graph-based framework, GraphSecure, designed to enhance message authentication and anomaly detection in CAV environments. Objectives: (1)  Real-time Event Detection and Knowledge Graph Construction: Develop a real-time event detection system that integrates diverse data streams from vehicle sensors and traffic systems to dynamically construct distributed knowledge graphs. (2) Privacy-Preserving Data Sharing in Knowledge Graphs: Implement privacy-preserving frameworks that ensure secure data sharing within knowledge graphs, utilizing cryptographic and anonymization techniques. (3) Graph-Based Authentication Protocols: Create protocols that leverage relational and contextual knowledge within graphs for fast, accurate message authentication. (4) Validation and Prototyping: Conduct real-world validation and simulation tests to evaluate the reliability, effectiveness, and practical scalability of GraphSecure.
]]></description>
      <pubDate>Thu, 29 May 2025 21:34:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2559306</guid>
    </item>
    <item>
      <title>AI-Based Tool for Fixing Inconsistencies in Traffic Crash Reports</title>
      <link>https://rip.trb.org/View/2505727</link>
      <description><![CDATA[This project will develop an artificial intelligence (AI)-based system to identify inconsistencies in traffic crash reports by analyzing crash narratives. Work in Stage 1 will focus on developing the proposed system. To maximize the developed system’s potential for practical applications, identification of crash factors essential for analysis and decision making will be prioritized, as well as those that frequently suffer from inconsistencies through literature review and traffic safety practitioners from state DOTs. A literature review of recent AI-NLP techniques will be conducted to ensure that the proposed tool incorporates the most recent and significant developments in the field. To analyze crash narratives, most promising NLP techniques will be implemented. The computational complexity of each technique/model will be examined to determine which techniques offer a convenient balance in capabilities and complexity for practical application. Work in Stage 2 will focus on the validation of the developed system as well as conducting the transfer to practice activities. A validation dataset will be developed based on manual human annotations. The validation will be two-fold. First, the system’s output for multiple crash factors will be compared against the results of manual identification performed by human annotators. Second, collaborating highway agencies will be asked to examine the system output and provide feedback on the relevance of results and functionalities that could enhance the system’s value for practical application. A series of transfer to practice activities will be initiated aimed at making the project findings known to a large audience of traffic safety practitioners. These activities include seeking commercialization opportunities by identifying potential licensees, hosting webinar for traffic safety professionals, disseminating the proposed system at transportation and traffic safety conferences. The final report will present research results, guidelines on the use of the system, and all relevant information, including the development of the NLP models, the data annotation process, the evaluation of the models, and the validation results. ]]></description>
      <pubDate>Mon, 03 Feb 2025 22:31:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2505727</guid>
    </item>
    <item>
      <title>Validation and Correlation of Multi-Speed Friction Data Testing
</title>
      <link>https://rip.trb.org/View/2475944</link>
      <description><![CDATA[Pavement friction testing is conducted by the Ohio Department of Transportation (ODOT) in accordance with ASTM E-274, "Standard Test Method for Skid Resistance of Paved Surfaces, using a Full-Scale Tire". The standard speed of testing in Ohio is 40mph. Due to safety concerns related to testing on cloverleaf ramps, roundabouts, curves, interstate, and divided highways ODOT would like to have the capability to collect friction data at a variety of speeds (i.e., 20mph, 40mph and 60mph). Research is needed to develop a repeatable methodology for collecting friction data while traveling at different speeds and correlating the new data to historical data that was collected at 40mph. 

The goal of this study is to provide for a safer, multi-speed (20MPH,40MPH, and 60MPH) friction collection and correlation process.                      ]]></description>
      <pubDate>Fri, 13 Dec 2024 09:18:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2475944</guid>
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