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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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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>Generative AI-based Framework for Modeling Longitudinal Travel Behavior Adaptation Under Transportation Interventions</title>
      <link>https://rip.trb.org/View/2775063</link>
      <description><![CDATA[Transportation agencies deploy a wide range of interventions to influence travel behavior and manage demand. While a broad range of approaches have been used to model travel behavior dynamics, modeling how behavioral responses manifest, strengthen, or decay over repeated exposure to transportation interventions remains relatively underexplored. This project will address this gap by developing a novel framework that integrates a Generative Artificial Intelligence (AI)-powered behavioral simulation engine with a longitudinal stated preference (SP) study to model, calibrate, and validate the temporal evolution of travel behavior in response to transportation interventions.

Leveraging large language models' capability to reason through open-ended behavioral scenarios, the engine will construct heterogeneous traveler agents with persona profiles that vary across sociodemographic characteristics, mode preference, trip purpose, risk tolerance, and sensitivity to intervention type. These agents will simulate the full range of behavioral adaptation over time, capturing dynamics such as habit formation, inertia, and resistance to change. A longitudinal stated preference study, deploying scenario-based stimuli via Qualtrics to a panel recruited through Prolific, will provide the empirical basis for calibrating, fine-tuning where appropriate, and validating the engine against observed traveler responses. Safety-focused interventions will serve as the instantiation domain. The resulting framework will be intervention-agnostic and transferable, providing transportation planners an evidence-based tool for evaluating long-term behavioral implications of interventions prior to their deployment in the real world.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:40:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775063</guid>
    </item>
    <item>
      <title>Performance Evaluation of Valmont Dampers for Traffic Signal and Sign Structures</title>
      <link>https://rip.trb.org/View/2772643</link>
      <description><![CDATA[Problem Statement and Research Objectives: The pending updates to the
American Association of State Highway and Transportation Officials Load and Resistance Factor Design (AASHTO LRFD) SLTS (Specifications for Structural Supports for Highway Signs, Luminaires, and Traffic Signals) Specifications allow for the quantification of effective vibration mitigation devices to provide fatigue protection for sign and traffic signal structures. Applying the AASHTO fatigue loading criteria to traffic signal and sign support structures in conjunction with an effective mitigation device, will result in a more efficient design that reduces the cost of new structures, improve safety to the traveling public, extend the life of new and existing structures, and lower maintenance, inspection and repair cost. Valmont Industries has vibration mitigation devices available for these types of structures. While Valmont vibration mitigation devices have been successfully deployed in many states throughout the country, there is little/no validated performance in Massachusetts. For the State of Massachusetts to fully adopt these vibration mitigation devices, there needs to be validation on the common types of traffic signal and sign structures deployed by Massachusetts Department of Transportation (MassDOT). This project will validate the performance of the Valmont vibration mitigation devices over the range of traffic signals and sign structures in Massachusetts. Successful completion of this project will provide definitive guidance on the specific sign and traffic signal structures on which to deploy vibration mitigation devices.
Anticipated Products: The deliverables will be documented field test performance of Valmont dampers on four (4) cantilever sign structures, including two (2) monotubes and two (2) dual tube (F-style) and two (2) traffic signal structures; validation of the Valmont models to predict performance on these six (6) structures; and predicted performance, using the validated numerical models, of the Valmont dampers on six (6) additional sign and six (6) additional traffic signal support structures. Documentation material will be generated as needed to satisfy the code for use of the Valmont vibration mitigation devices.]]></description>
      <pubDate>Wed, 02 Sep 2026 13:31:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772643</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Transit Practices. Topic SG-24. Transit Agency Practices for Automating NTD Operational Data Reporting</title>
      <link>https://rip.trb.org/View/2772561</link>
      <description><![CDATA[Transit agencies dedicate substantial staff time and resources to preparing operational data for the National Transit Database (NTD). Although most agencies follow similar reporting requirements, the methods used to prepare NTD operational data vary considerably. Many agencies continue to rely on spreadsheet-based processes and manual quality assurance procedures, while others have implemented automated workflows using enterprise data systems, business intelligence tools, or standardized data formats. Differences in agency size, available technology, staffing, and service types, particularly for demand-response and rural services contribute to a wide range of reporting practices.

Recent initiatives, including the Transit Integrated Data Exchange Specification (TIDES), expanded use of General Transit Feed Specification (GTFS) and other transit data standards, provide new opportunities to improve the efficiency, consistency, and quality of NTD reporting. There has been limited documentation of how transit agencies currently prepare operational data, the degree to which reporting has been automated, and the lessons learned from agencies that have implemented more automated approaches. A synthesis is appropriate because virtually all transit agencies prepare NTD operational reports but use different tools, workflows, staffing models, and levels of automation.

OBJECTIVE: The objective of this synthesis is to document current transit agency practices for preparing, validating, and reporting operational data to the NTD. Documenting current practices would provide agencies with practical information on approaches that improve reporting efficiency, data quality, and organizational resilience.

]]></description>
      <pubDate>Tue, 01 Sep 2026 09:25:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772561</guid>
    </item>
    <item>
      <title>Enhancing The Wyoming Pavement Quality Rating Estimation</title>
      <link>https://rip.trb.org/View/2772465</link>
      <description><![CDATA[The Wyoming Department of Transportation (WYDOT) currently relies on the Pavement Quality Rating (PQR) as the primary performance indicator within its pavement management system. Although PQR is conceptually bounded between 0 and 5, the existing formulations occasionally produce negative values due to nonlinear distress transformations, reducing interpretability and limiting long-term forecasting reliability. This research aims to refine, calibrate, and validate an improved PQR model that addresses these deficiencies while aligning with national performance metrics. The study aims to document existing pavement performance practices from state agencies and research institutions, evaluate and modify the current PQR equations to ensure bounded and explainable outputs, incorporate a layered cracking taxonomy, quantify the influence of traffic loading and pavement structural characteristics, and validate predictive capability using historical distress datasets. Expected outcomes include a robust, bounded PQR equation, decision-support thresholds, and associated implementation tools for integration into WYDOT's pavement management workflows. The resulting model will improve data-driven decision-making, support cost-effective rehabilitation planning, and enhance long-term asset management capability. The project will also provide educational and technology transfer benefits through student engagement, conference presentations, and peer-reviewed publications.]]></description>
      <pubDate>Tue, 01 Sep 2026 08:55:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772465</guid>
    </item>
    <item>
      <title>Evaluating Real-World Multi-View Vulnerabilities in Autonomous Driving Perception and Planning for Improved Safety and Regulation</title>
      <link>https://rip.trb.org/View/2742144</link>
      <description><![CDATA[Autonomous driving systems (ADS) increasingly rely on vision-based perception and learning-based decision modules, yet real-world safety depends on whether these systems remain stable as the ego vehicle continuously changes distance and viewpoint relative to roadway objects. Current evaluation practices often emphasize single images or limited viewpoints, which can mask trajectory-dependent failure modes that emerge during real driving under changing illumination, partial occlusion, and motion blur.

This project studies the limitations of existing ADS through a measurement-driven, multi-view robustness evaluation framework designed to produce actionable engineering insights and evidence-based inputs for transportation safety policy. Building on a differentiable, view-consistent scene representation (3D Gaussian Splatting with view-dependent appearance modeling), the team will generate controlled, physically plausible appearance variations across realistic approach trajectories and use them as a diagnostic tool to quantify perception instability and downstream planning sensitivity.

The project will deliver a reproducible set of safety-relevant scenarios and ego-vehicle approach trajectories representing how a vehicle observes the same object over time; a controllable multi-view rendering and perturbation engine built on 3D Gaussian Splatting that synthesizes viewpoint-consistent observations under bounded, physically plausible appearance variations; and a multi-view robustness evaluation methodology and benchmark protocol using trajectory-based sampling. It will produce quantitative robustness indicators summarizing perception stability and planning sensitivity, a structured taxonomy of observed failure modes, and a reproducible reporting package of metrics definitions, evaluation scripts, and documentation templates. Validation will be performed on representative research ADS models, with black-box evaluation of commercial systems where feasible and safe.]]></description>
      <pubDate>Sat, 01 Aug 2026 10:27:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742144</guid>
    </item>
    <item>
      <title>GenAI-Enabled Automated Traffic Simulation Management</title>
      <link>https://rip.trb.org/View/2742141</link>
      <description><![CDATA[Microscopic traffic simulation software allows users to model traffic flow, assess traffic management strategies, and optimize transportation systems for efficiency. However, building a simulation model for a real-world road network is a complex, manual, time-consuming, and error-prone task. GenAI-Enabled Automated Traffic Simulation Management uses Generative Artificial Intelligence (AI) (GenAI) to automate the preparation of input data, the running of simulations, and the extraction of output results, enabling traffic engineers to focus on the purpose of the simulation rather than the tedious manual work of building the model.

The project maps user text-based scenario descriptions to actual simulation scenarios, with step-by-step validation from the user before execution, by wrapping the INTEGRATION simulation software with a callable API via the Model Context Protocol (MCP) and a web-based interface to a large language model (LLM).

The project will deliver a baseline simulation model built with the INTEGRATION microscopic simulation software for a selected freeway corridor or road network; an INTEGRATION API that wraps the simulation software using the Model Context Protocol so it can be called by any large language model; and a web-based graphical user interface that guides users in prompting an LLM to build simulation models and answer questions using Retrieval-Augmented Generation from the INTEGRATION manual. The GenAI system will be validated by comparing GenAI-generated model files against the baseline model, through human-in-the-loop validation, and through real-world deployment with the City of Alexandria.]]></description>
      <pubDate>Sat, 01 Aug 2026 09:48:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742141</guid>
    </item>
    <item>
      <title>StreetsAPI: A Connected-Vehicle Interface for Planning-Informed Street Management</title>
      <link>https://rip.trb.org/View/2739299</link>
      <description><![CDATA[One of the promises of connected vehicle (CV) technologies, such as vehicle-to-infrastructure (V2I) and vehicle-to-everything (V2X), is the ability to manage streets more nimbly using real-time and continuous data, changing street operating profiles based on data-informed conditions, a promise that, with few exceptions, has yet to be realized. Most streets remain static, using CV data only indirectly through traditional planning processes.

StreetsAPI creates an interface and an extensible markup language (XML) for managing streets with real-time and long-run CV data. Building on empirically grounded models for street and mobility management, such as variable pricing of congestion or parking, it implements a system that uses CV data to inform parametrically modifiable street operating profiles. The resulting interface, the project’s primary distributable product, will enable localities, state departments of transportation (DOTs), and other stakeholders to establish parameters for how streets respond to CV-informed flows across a range of use cases.]]></description>
      <pubDate>Thu, 30 Jul 2026 16:08:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2739299</guid>
    </item>
    <item>
      <title>Risk-Based and Cost-Effective Agency Verification of Contractor-Collected Pavement and Bridge Profiles</title>
      <link>https://rip.trb.org/View/2712193</link>
      <description><![CDATA[State departments of transportation (DOTs) recognize that pavement and bridge smoothness is a key indicator of performance and public satisfaction. As state DOT staffing levels have declined, contractors have become increasingly responsible for collecting profile data, calculating smoothness indices, and sometimes determining pay factors. While federal regulations require independent verification of contractor data used for acceptance decisions, agencies remain uncertain about the level of verification needed to ensure accuracy and judicious allocation of public funds.

Current practices for validation and verification vary widely across state DOTs. Some agencies collect independent profiles on a subset of projects, while others rely on partial sampling, comparisons with contractor data, or limited review processes. The statistical reliability and risk implications of these approaches are not well understood. Additionally, advances in data collection technologies, such as high-speed profilers, have increased the volume of data, challenging traditional verification approaches. There is a need for research that helps state DOTs accurately determine pavement life through the potential use of emerging technologies and improved verification of contractor-collected pavement and bridge profile data.

The objective of this research is to develop a guide and supporting tool to assist state DOTs in conducting cost-effective, risk-based verification of contractor-collected pavement and bridge profiles.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:10:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712193</guid>
    </item>
    <item>
      <title>Deep Learning–Based Digital Image Correlation for Fatigue Crack  Characterization in Steel Structures
</title>
      <link>https://rip.trb.org/View/2703927</link>
      <description><![CDATA[This proposal presents a strategic approach to improving transportation safety through the advancement of deep learning–based Digital Image Correlation (DIC) for fatigue crack characterization in steel structural components. With aging transportation infrastructure and increasing cumulative traffic loading, fatigue-related deterioration in steel bridges and related systems presents ongoing safety risks. Accurate measurement of crack-induced displacement fields is critical for reliable structural assessment and informed maintenance decisions. The primary objectives of this proposal are to advance artificial intelligence (AI)-driven DIC methods beyond the limitations of conventional correlation-based approaches by enabling sub-pixel displacement learning through synthetic data generation, incorporating physics-informed modeling of crack-induced displacement discontinuities, and supporting high-resolution analysis of large image regions without loss of spatial detail. The methodology involves grayscale synthetic speckle data generation for sub-pixel displacement learning, mechanics-based displacement field modeling using finite element simulations, and development of an attention-enhanced deep learning architecture for full-field displacement prediction. Experimental validation against commercial DIC systems will establish a transferable methodology supporting safer fatigue crack evaluation practices.
]]></description>
      <pubDate>Tue, 19 May 2026 13:48:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703927</guid>
    </item>
    <item>
      <title>Hydrologic and Hydraulic Software Enhancements (SMS, WMS, Hydraulic Toolbox, and HY-8)</title>
      <link>https://rip.trb.org/View/2698362</link>
      <description><![CDATA[The Federal Highway Administration (FHWA) sponsors ongoing development of four computer programs that perform both routine and complex hydrologic and hydraulic analyses of watersheds, river and stream systems, and transportation infrastructure. 
This Transportation Pooled Fund (TPF) project will: 1. Enhance the capabilities of the four FHWA sponsored software programs and ensure they remain consistent with the latest FHWA technical reference documents. 2. Update the software user manual documentation. 3. Make new software versions publicly available. 4. Develop and deploy technology transfer materials and workshops to test and demonstrate new software content and features. 5. Inform users of the availability of new software versions and features through website postings, email notifications, newsletter articles, conference presentations, and other avenues.]]></description>
      <pubDate>Fri, 01 May 2026 19:48:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2698362</guid>
    </item>
    <item>
      <title>Resilient Software-Defined Vehicle Platform Architectures with Secure Live Migration</title>
      <link>https://rip.trb.org/View/2696966</link>
      <description><![CDATA[Modern vehicles use software-defined vehicle (SDV) platforms that integrate functions via virtualization, but current designs lack resiliency against security incidents or hardware obsolescence. This project aims to enhance vehicle security by developing and evaluating secure live migration techniques for virtual machine (VM)-based workloads. By allowing actively running services to move between electronic control units (ECUs) without interruption, the project enables real-time upgrades and mitigation of cyberattacks within next-generation zonal architectures.

]]></description>
      <pubDate>Wed, 29 Apr 2026 16:36:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696966</guid>
    </item>
    <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>Aviation Infectious Risk and Safety: Dissemination and benchmarking of human motion for disease modeling</title>
      <link>https://rip.trb.org/View/2675923</link>
      <description><![CDATA[The research will strengthen the agent-based movement component of the TRIP-X communicable disease transmission model through structured benchmarking against publicly available modeling approaches and dissemination of previously collected human behavior datasets. Benchmarking will compare model performance to identify strengths, limitations, and opportunities for model refinement. Human behavior datasets developed under a related project (Communicable disease preparedness: M&S framework for analyzing cabin health hazards) will be prepared for public dissemination to encourage independent replication, facilitate peer review, and support broader adoption by government, industry, and academic researchers. Results will be documented in publications and used to improve confidence in the TRIP-X modeling framework for potential future FAA SRM applications. ]]></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>Advancement of Tools for Transportation Asset Management</title>
      <link>https://rip.trb.org/View/2658112</link>
      <description><![CDATA[Transportation agencies across the United States rely on asset management systems to plan and manage pavements, bridges, and other assets in a cost-effective and performance-based manner. State departments of transportation (DOTs) are federally required to maintain asset management systems for pavements and bridges on the National Highway System (NHS) to support their risk-based Transportation Asset Management Plans (TAMPs).
The Pennsylvania Department of Transportation (PennDOT) has developed an open-source asset management system known as AssetFox (AF) to assist agencies in implementing asset management practices and improve decision-making. Recognizing the need for collaboration, shared technical expertise, and system enhancement, the Advancement of Tools for Transportation Asset Management pooled fund has been established.
This pooled fund brings together State DOTs, the Federal Highway Administration (FHWA), and other transportation agencies to share tools, knowledge, and cross-DOT support for advancing asset management practice. The pooled fund will maintain and enhance AF and related open-source tools, prioritize new features, provide technical support in the implementation and configuration of AF across participating agencies, and promote data-driven decision-making for infrastructure investment.
OBJECTIVES: The objectives of this pooled fund are to: Advance the practice of transportation asset management through collaboration and shared development; Provide technical support in the installation, configuration, and maintenance of the AF asset management system; Identify, prioritize, and implement enhancements to AF and other supporting tools based on needs of member states; Share technical knowledge, tools, and best practices among participating agencies; Provide technical assistance and training to support consistent implementation of asset management systems.

]]></description>
      <pubDate>Fri, 23 Jan 2026 20:17:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2658112</guid>
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