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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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzk0IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Impact of Charging Infrastructure on Electric Vehicle Adoption: A Synthetic Population Approach</title>
      <link>https://rip.trb.org/View/2742793</link>
      <description><![CDATA[This project will employ a synthetic population approach to examine the influence of public charging infrastructure on electric vehicle (EV) adoption in Maryland. By simulating increased access to charging stations across different income levels and regions. Using a Bayesian network model, the project will evaluate how charging availability affects EV ownership patterns, contributing to the development of a cleaner transportation system.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:07:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742793</guid>
    </item>
    <item>
      <title>Development of a CAV Testbed-enhanced Smart Campus at Morgan State University – Phase IV: Smart Corridor Expansion and Data-Driven Intersection Safety Monitoring</title>
      <link>https://rip.trb.org/View/2742148</link>
      <description><![CDATA[Phase IV builds upon the infrastructure and algorithms developed in Phases I through III by expanding the SMART corridor testbed and introducing a data-driven framework for continuous intersection safety monitoring. While earlier phases focused on LiDAR-enabled signal control and connected vehicle messaging, this phase emphasizes corridor expansion, safety analytics, and statewide V2X readiness.

The project analyzes pedestrian safety near the pedestrian bridge within the SMART corridor to understand unsafe crossing behavior and vehicle-pedestrian conflicts, and develops a new Intersection Safety Score framework using LiDAR trajectory data and signal-violation metrics, integrated into a Power BI dashboard for real-time monitoring. The SMART corridor testbed will be expanded to the Hillen Road and Harford Road intersection, a statewide inventory and operational assessment of Roadside Units (RSUs) across Maryland will be conducted, and the team will collaborate with Baltimore City DOT to integrate advanced V2X signal applications including SPaT broadcasting and LiDAR-based signal actuation.

The project will deliver a comprehensive analysis of pedestrian safety risks near the SMART corridor pedestrian bridge; an Intersection Safety Score methodology that quantifies intersection risk using trajectory-based conflict analysis of red- and yellow-light violations, vehicle-vehicle and vehicle-pedestrian conflicts, and near-miss events; and a Power BI safety-monitoring dashboard enabling agencies to visualize and track intersection safety performance. It will expand the SMART corridor testbed to the Hillen Road and Harford Road intersection through LiDAR installation, sensor calibration, and integration with existing connected vehicle systems. A statewide inventory and operational readiness assessment of Maryland’s RSUs will evaluate device status, firmware compatibility, and SAE J2735 messaging capability, and the project will conduct field validation of V2X-enabled signal applications, including SPaT broadcasting, LiDAR-based signal actuation, and trajectory-based interventions such as dynamic all-red extensions.

]]></description>
      <pubDate>Sat, 01 Aug 2026 10:38:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742148</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>Advancing Safe System Implementation in Rural and Small Urban Networks: Practical Screening Solutions</title>
      <link>https://rip.trb.org/View/2742138</link>
      <description><![CDATA[Roadway safety remains one of the most persistent transportation challenges across the Mid-Atlantic region, particularly in rural and small urban communities, which consistently experience disproportionately high rates of fatal and serious injury crashes. Federal, state, and local agencies have adopted the Safe System Approach (SSA), which emphasizes proactive, system-level strategies to prevent severe crashes, yet operational implementation gaps remain in rural environments characterized by low traffic volumes, dispersed crash patterns, and resource constraints. Safety screening and project prioritization in these contexts continue to rely heavily on historical crash concentrations, which may fail to capture latent risk in low-volume corridors or rare but severe crash types.

This project advances the operationalization of the Safe System Approach through the development and pilot application of a Safe System-aligned Risk Screening Tool for rural and small urban networks, supplementing traditional crash-based screening with structured systemic roadway risk factors and selected technology-enabled surrogate indicators, applied within a selected West Virginia network.]]></description>
      <pubDate>Sat, 01 Aug 2026 09:41:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742138</guid>
    </item>
    <item>
      <title>Physics-Informed AI-Enhanced Multimodal Modeling and Governance: Improving Safety and Resilience for Data-Limited Transit Corridors</title>
      <link>https://rip.trb.org/View/2739297</link>
      <description><![CDATA[Limited sensor coverage and fragmented, mode-specific modeling infrastructure hinder the holistic monitoring of modern transportation networks. The resulting data blind spots prevent current models from capturing dynamic, cross-modal dependencies, where a disruption in one mode, such as a metro closure, triggers cascading surges in others, forcing planners and Traffic Management Centers to rely on reactive, siloed strategies.

To improve the state of the art, this project proposes a Virtual Sensor paradigm driven by physics-informed generative artificial intelligence (AI). By integrating fundamental transportation physics with generative deep learning, the framework synthesizes high-fidelity data for sensor-sparse regions by inferring correlations from existing sensing infrastructure, creating cost-effective virtual data streams that simulate physical sensors and provide more complete multimodal network data for real-time operations and long-term planning. The project also evaluates the policy and governance dimensions of integrating emerging AI use cases, such as AI-generated data, into the Delaware Department of Transportation (DelDOT)’s planning, design, and operations.]]></description>
      <pubDate>Thu, 30 Jul 2026 16:41:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2739297</guid>
    </item>
    <item>
      <title>Development and Evaluation of a Large Language Model and Virtual Reality Framework for Improving Flagger Training
</title>
      <link>https://rip.trb.org/View/2739298</link>
      <description><![CDATA[Flaggers are essential for maintaining traffic safety in work zones, serving as human traffic controllers who coordinate alternating traffic through the work zone, yet they work under extremely hazardous conditions in close proximity to high-speed traffic and heavy equipment. Traditional classroom-based training is often insufficient for developing situational awareness, hazard recognition, and communication skills, while real-world training exposes trainees to significant risk. Virtual reality (VR) offers immersive, hands-on practice without danger, but current VR systems rely on preprogrammed scenarios and require instructors to manually identify trainee errors.

This project develops a large language model (LLM)-based virtual flagger that provides dynamic, realistic interactions within a VR training environment rather than rigid, pre-scripted scenarios. The virtual flagger engages in natural radio communication, responds contextually to trainees’ actions, asks clarifying questions when communication is unclear, and adapts its behavior to create diverse, progressively challenging training experiences.

]]></description>
      <pubDate>Thu, 30 Jul 2026 16:26:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2739298</guid>
    </item>
    <item>
      <title>Impact of Facial Hair on Pilot Oxygen Mask Efficacy</title>
      <link>https://rip.trb.org/View/2736587</link>
      <description><![CDATA[The Federal Aviation Administration (FAA) currently lacks modern empirical evidence to determine whether facial hair degrades the performance of contemporary pilot oxygen masks under operational emergency conditions. This creates uncertainty in FAA advisory guidance, operator policies, and potential regulatory decision-making regarding mask use, emergency procedures, and facial hair allowances.

This research will evaluate whether pilot oxygenation (SpO2) is maintained during high altitude airframe decompression events. Human subjects will complete controlled hypobaric hypoxia condition testing in the Civil Aerospace Medical Institute (CAMI) hypobaric chamber while first bearded and subsequently clean-shaven during quick-don oxygen mask use as tethered to a civilian aircraft oxygen system. The results will determine the impacts of facial hair on pilot oxygen mask efficacy with focus upon adequate pilot oxygenation at pressure-altitude (45,000’) that demands positive pressure gas supply and. Additionally, mask seal function will be tested at the normal cruising cabin pressure-altitude of 8,000’ to assess if smoke/fume/odor ingress is present with facial hair with focus upon preservation of oxygen gas rate of usage not to exceed the regulation that requires a minimum 15 minute supply. This research will directly inform potential revisions to 14 CFR §§, 91.211, 121.333, 121.337, 135.89, and AC120-43 as well as Technical Standard Orders TSO-C78, TSO-C89, and TSO-C99, and associated SAE oxygen system standards.]]></description>
      <pubDate>Mon, 27 Jul 2026 12:27:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2736587</guid>
    </item>
    <item>
      <title>AI-Enabled Spatio-Temporal Risk Assessment and Decision Support for Pipeline Infrastructure Preservation</title>
      <link>https://rip.trb.org/View/2732468</link>
      <description><![CDATA[The safe and efficient operation of pipeline systems is essential to the reliability of the United States’ energy supply chain and its integration with maritime and multimodal transportation networks. Pipeline failures can lead to significant disruptions, economic losses, and safety risks, particularly under the influence of aging infrastructure, human factors, and extreme environmental conditions. Building upon prior research, this project proposes to develop an integrated, artificial intelligence (AI)-enabled framework to support the preservation and resilience of pipeline infrastructure within maritime and multimodal transportation systems. The proposed research will have model development, but focuses on validation, system integration, and deployment of decision-support tools. The project will enhance existing spatio-temporal models by incorporating machine learning and explainable artificial intelligence techniques to improve predictive accuracy and interpretability of pipeline system failure risk under varying environmental and operational conditions. Multi-source data will be integrated into a unified analytical platform, including pipeline incident records and environmental datasets. A key innovation of this research is the development of a multimodal infrastructure risk framework that links pipeline systems with maritime transportation components such as ports, inland waterways, and freight corridors. Multi-layer network modeling and scenario-based simulations will be used to evaluate the impacts of infrastructure disruptions on system performance, including energy distribution, freight movement, and resilience under hazardous events. Through the integration of advanced analytics and multimodal system modeling, this project will deliver scalable, interpretable analytical solutions to enhance the safety, reliability, and resilience of pipeline and maritime transportation infrastructure systems.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:53:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732468</guid>
    </item>
    <item>
      <title>Large Language Model-Driven Crash Risk Analysis System for Rural and Tribal Roadways</title>
      <link>https://rip.trb.org/View/2731923</link>
      <description><![CDATA[Rural and tribal roadways in the United States experience disproportionately high crash incidents due to a combination of infrastructure challenges, limited resources, and incomplete crash reporting. Traditional statistical, machine learning (ML), and deep learning (DL) models have struggled to address these issues because they rely heavily on structured data, perform poorly with incomplete or imbalanced datasets, and often fail to leverage the rich information contained in crash narratives. Large language models (LLMs) offer an alternative by reframing crash risk analysis as a text reasoning problem, enabling the extraction of contextual insights from narratives, the imputation of missing or inconsistent fields, and the integration of structured and unstructured data into unified predictive frameworks. This proposal aims to develop an LLM-based crash risk analysis system utilizing North Carolina’s statewide police-reported crash data as a foundation, with the goal of enhancing the accuracy, interpretability, and robustness of rural crash risk assessment. The project will proceed through four main tasks: crash data enhancement and model adaptation, predictive model development, model validation, and the design of an implementation plan for real-time crash risk warnings in connected vehicle (CV) environments. ]]></description>
      <pubDate>Fri, 17 Jul 2026 16:08:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731923</guid>
    </item>
    <item>
      <title>Evaluate Traffic Control Devices Viable Use to Reduce Speeding Behavior at High-Low Speed Zone Locations in Michigan</title>
      <link>https://rip.trb.org/View/2731925</link>
      <description><![CDATA[Determine the effectiveness of various traffic control devices currently in use to reduce driver speeding behavior, when traveling from
a high-speed zone to low-speed zone. Site locations of interest in Michigan are where the speed limit is reduced due to a change in
roadway context, such as locations with increased pedestrian traffic, within school vicinity, and where bicycle traffic exist. Devices
proposed for study under this research project are signs of various sizes, dynamic signs, and experimental type devices.]]></description>
      <pubDate>Fri, 17 Jul 2026 14:50:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731925</guid>
    </item>
    <item>
      <title>High Load Hit Prevention</title>
      <link>https://rip.trb.org/View/2562331</link>
      <description><![CDATA[Many department owned bridges are impacted with over height vehicles every year, however, there are many bridges that
have been struck repeatedly. Bridges being repeatedly struck by over height vehicles leads to structural damage to
department infrastructure as well as impedance to the traveled roadway(s). This damage can cause immediate lane or road
closures while the damage is inspected and repaired, shortened service life of the structure, and potential bridge component or
structure replacement. In order to address this problem, the department seeks to identify methods to locate and inform over
height drivers prior to striking the structure in addition to detecting impacts. Not having these methods will allow over height
vehicles to continue to damage department structures and for damage to go unreported. Michigan Department of Transportation (MDOT) has interest in identifying
which method(s) of over height vehicle impact prevention is best suitable for structures susceptible to damage. The
department is expecting this research to yield the implementation of technology to reduce damage of bridges caused by over
height vehicles impacts. Additionally, Michigan has dozens of bridges located within navigable waterways and following the
collapse of the Francis Scott Key bridge in Baltimore, MDOT would like to evaluate the inherent risk of damage from vessel
allision at those bridges with substructure units within the waterway.]]></description>
      <pubDate>Fri, 17 Jul 2026 10:22:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2562331</guid>
    </item>
    <item>
      <title>SPR-5133: Evaluation of In-CAB Virtual Sign Network on CMV Safety and Operations</title>
      <link>https://rip.trb.org/View/2727696</link>
      <description><![CDATA[Construction work zones often have reductions in lane widths, reductions in shoulder widths, soft shoulders, and uneven pavement profiles that can present challenges for Class 9 vehicles. Although roadside signs can be used, it is believed that targeted incab alerts can perhaps be more effective at alerting commercial drivers of these issues and improve safety.]]></description>
      <pubDate>Wed, 15 Jul 2026 11:42:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727696</guid>
    </item>
    <item>
      <title>SPR-5132: Implementation of Proven CV Applications on the Local System LRS</title>
      <link>https://rip.trb.org/View/2727695</link>
      <description><![CDATA[Adapting Indiana Department of Transportation (INDOT) connected vehicle safety and mobility metrics onto the local road network will provide a more uniform mechanism for identifying and assessing the anticipated benefits of projects on the local road system.
]]></description>
      <pubDate>Wed, 15 Jul 2026 11:40:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727695</guid>
    </item>
    <item>
      <title>Leveraging Telematics Data for Enhanced Traffic Safety: Unveiling Crash-Prone Hotspots and Mitigating Incidents - Phase 2</title>
      <link>https://rip.trb.org/View/2727388</link>
      <description><![CDATA[Cutting-edge connected-vehicle (CV) telematics now stream billions of instantaneous speed, heading, and hard- maneuver records across Texas roadways—an untapped resource for proactive safety management. Phase I of Project 0-7200 capitalized on this opportunity by (1) surveying and vetting statewide CV data sources, (2) building rigorous preprocessing pipelines and a strategic data-archiving scheme with the Receiving Agency, (3) defining data-driven “near-crash” events, and (4) creating proof-of-concept analytics that locate and rank high-risk corridors. Two single-user prototype web tools—TTI’s near-crash explorer and UTA’s multi-criteria hotspot-ranking dashboard—proved the approach valid, with results aligning closely with the Crash Records Information System (CRIS). Phase II will transform those prototypes into a secure, cloud-based, multi-user platform capable of statewide, high-volume ingestion and real-time analytics—advancing the solution to TRL 8 (actual system completed and “TxDOT-pilot ready”). The research teams will, optimize the data-processing engine for scalability, integrate interactive visualizations with enterprise authentication, automate continuous data refresh and long-term archiving, and embed crash- prediction models that fuse telematics with CRIS and roadway inventory. The research teams will develop a decision-support tool that lets TxDOT’s districts quickly pinpoint emerging crash-prone hotspots and deploy targeted countermeasures.]]></description>
      <pubDate>Fri, 10 Jul 2026 17:07:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727388</guid>
    </item>
    <item>
      <title>Examination of Light-Based Directed Vehicle to Everything Communications Systems for Bridge Strike Detection (Using ImpLi-Fi)</title>
      <link>https://rip.trb.org/View/2727317</link>
      <description><![CDATA[In this proposed project, the ImpLi-Fi team - consisting of the University of Michigan-Dearborn and SpectraLux, LLC - will deploy a reliable and directed light-based wireless infrastructure-to-vehicle communication technology to warn at-risk trucks of imminent bridge strikes. Once shown to be feasible, the same concept can also be extended to flash flood warning, wrong-way driving, etc. Unlike wireless communications using radio-frequency (RF), which are always omni-directional, ImpLi-Fi uses light, allowing transmissions to be focused so that they only target specific impacted vehicles, thereby avoiding the risk of annoying/desensitizing other parallel road users.]]></description>
      <pubDate>Fri, 10 Jul 2026 15:22:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727317</guid>
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