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    <title>Research in Progress (RIP)</title>
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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>
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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>SPR-5042: Performance and Safety Evaluation of Truck Mounted Debris Clearing Systems</title>
      <link>https://rip.trb.org/View/2709430</link>
      <description><![CDATA[The principal investigators will help the Indiana Department of Transportation (INDOT) evaluate truck-mounted debris clearing systems by achieving the following three main objectives: 1) Development of an event-triggered, multi-sensor data collection framework integrating multi-camera video and Global Positioning System (GPS) to enable automated, machine vision-based performance assessment. 2) Quantitative evaluation of system performance through field testing to measure debris removal effectiveness, roadway interaction, and operational efficiency across real-world conditions. 3) Assessment of safety and traffic impacts by analyzing worker exposure, operational risks, and vehicle interactions to quantify how these systems influence roadway safety and deployment practices.]]></description>
      <pubDate>Wed, 03 Jun 2026 13:31:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709430</guid>
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    <item>
      <title>Damage Progression of Highway Bridges and Operational Vibration-Waveforms-Phase-2</title>
      <link>https://rip.trb.org/View/2706038</link>
      <description><![CDATA[Aging highway bridges are increasingly subjected to heavy truck traffic that can exceed design load expectations and accelerate structural deterioration. Undetected overload events may contribute to localized stress concentrations, fatigue damage, and reduced service life. Current bridge monitoring approaches typically rely on periodic inspection rather than continuous operational detection of extreme loading events.
This project advances a vibration-based monitoring methodology to detect, identify, and predict the weight of heavy vehicles causing extreme loading on highway bridges. Building on Phase 1 results, the research integrates multi-sensor data—including accelerometers, six-dimensional inertial sensors, strain sensors, gyroscopes, and radar-video systems—to identify overload events and correlate them with structural response and potential damage hot spots. Finite element modeling and moving-load simulations will be used to support weight estimation and validate field measurements. The methodology will be tested on single- and multi-span steel and concrete girder bridges in Iowa. The resulting system is designed to provide a practical, portable, and cost-effective approach for bridge overload detection and condition-informed decision-making.

]]></description>
      <pubDate>Sat, 23 May 2026 18:06:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706038</guid>
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    <item>
      <title>Prototype Development and Pilot Deployment of Ground-Based Intelligent Infrastructure for Resilient Positioning, Navigation, and Timing</title>
      <link>https://rip.trb.org/View/2696990</link>
      <description><![CDATA[Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), form the backbone of modern positioning, navigation, and timing (PNT) services. However, these space-based systems are inherently vulnerable to cyberattacks such as jamming, spoofing, as well as unintentional interference, including signal blockage, particularly in dense urban areas, indoor environments, and adversarial environments. The growing dependence on GNSS, driven by the rapid adoption of autonomous and connected systems, has exposed a single point of failure in the global PNT infrastructure. GPS signals are extremely weak at the Earth’s surface, enabling low-cost jammers or spoofers to easily disrupt receivers. In response to the 2020 Executive Order on strengthening national resilience through responsible use of PNT services signed by President Donald J. Trump, US DOT, the Department of War (DoW), and the Department of Homeland Security (DHS) have jointly emphasized the need for complementary and backup PNT capabilities that are interoperable and independently capable of sustaining precision timing and navigation for critical infrastructure during GNSS outages or cyberattacks. The research goal is to develop and demonstrate a prototype ground-based, GPS-compatible, cyber-secure PNT architecture that can generate, synchronize, and broadcast authenticatable GPS-like signals from a network of ground-based nodes, allowing existing GPS receivers to obtain valid PNT solutions without hardware modification. This goal will be achieved through the following specific research objectives: (1) Design and generate authenticable GPS-compatible terrestrial signals that replicate the L1 C/A (coarse/acquisition) waveform while embedding virtual ephemeris and adjusted clock-offset parameters to enable accurate and PNT computation from ground transmitters. (2) Develop intelligent terrestrial nodes (at least four nodes) equipped with chip-scale atomic clocks, edge computer, and transmitters to establish a distributed ground-based PNT architecture. (3) Synchronize terrestrial nodes with a master clock using precision timing distribution techniques to maintain consistent and reliable time alignment across the network. Real-Time Kinematic (RTK) positioning and differential methods will also be explored using the GEODNET hub within the UA network. (4) Demonstrate that an off-the-shelf GPS receiver can deliver a valid PNT solution using terrestrial signals through software-only modifications, thereby validating the practicality, backward compatibility, and deployment readiness of the proposed system.
]]></description>
      <pubDate>Wed, 29 Apr 2026 16:45:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696990</guid>
    </item>
    <item>
      <title>Successful Strategies to Integrate Digital Technologies to Achieve Data Interoperability Across the Lifecycle of Transportation Assets</title>
      <link>https://rip.trb.org/View/2681238</link>
      <description><![CDATA[State Departments of Transportation (DOTs) are adopting digital processes to improve project delivery and asset management. Federal initiatives such as the Federal Highway Administration’s (FHWA) Everyday Counts (EDC) program—including EDC-4 and EDC-6—have supported these efforts.
The Advanced Digital Construction Management Systems (ADCMS) grant program (FY 2022–2024) provided funding for DOTs to implement and pilot digital approaches that connect data from planning through maintenance. These efforts aim to maintain accurate, consistent asset information throughout the asset lifecycle.
DOTs are at varying stages of implementation and use different tools and approaches, but share a common goal of improving data interoperability. A scan of current practices—including data workflows, system integration, and data repository management—would provide useful insights for agencies nationwide.
OBJECTIVE: The scan is expected to identify key insights in areas such as: Digital technologies and lifecycle processes across the asset lifecycle; Integration with enterprise systems; Core data elements and IT requirements; Change management and workforce development; Data visualization and performance dashboards; Incentives and challenges related to technology adoption; Implementation approaches and alignment with agency policies
Findings will provide practical guidance for construction and maintenance staff, engineering managers, executive leaders, and other decision-makers. The scan will compare successful strategies, identify approaches that support efficient digital adoption, and promote consistent, high-quality data practices across projects and agencies.
]]></description>
      <pubDate>Tue, 17 Mar 2026 14:44:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681238</guid>
    </item>
    <item>
      <title>Context Aware Optimal Information Selection for Reliable, Resilient, Secure, and Efficient
Cooperative Perception</title>
      <link>https://rip.trb.org/View/2676000</link>
      <description><![CDATA[Cooperative perception significantly enhances a vehicle's local field of view by leveraging shared information from nearby vehicles, thus improving overall situational awareness. However, in densely populated environments, cooperative perception can place substantial strain on both communication band-width and computational resources. Such scenarios often result in excessive redundant information, where multiple vehicles repeatedly report the same objects, provide data at unnecessarily high frequencies, or share information irrelevant to the ego vehicle's current context. These issues cumulatively increase computational overhead prior to data fusion and lead to prolonged decision-making times.
Therefore, an effective filtering mechanism is necessary to selectively retain only the most informative objects. Higuchi et al. proposed a value anticipation-based Vehicle-to-Vehicle (V2V) communication approach. In their method, the sender evaluates the potential informational value to receivers and, based on real-time network conditions, either defers or cancels transmissions. This ensures that primarily essential information is disseminated to neighboring vehicles. In another related study, Zhou et al. introduced the Augmented Informative Cooperative Perception (AICP) algorithm, which incorporates both a routing mechanism and message filtering at the receiver side. Their algorithm utilizes an informative-ness measure to assess and select messages, optimizing resource use while ensuring relevant data is received.

While redundant messaging is typically seen as a problem due to its computational demands, it can also provide significant benefits in enhancing security within V2X communications. Specifically, redundancy can enhance detection of malicious behavior through corroborative data from trustworthy vehicles, thereby improving the security of V2X communications. Lie et al. proposed Misbehavior Detection for Collective Perception Services in Vehicular Communications (MISO-V), which leverages redundancy from received V2X messages to validate incoming perception information. Upon verifying a new message against redundant data, the receiver updates the sender’s trust score based on whether the information is classified as benign or potentially malicious. This updated trust score subsequently guides down-stream tasks in determining whether to integrate or discard information provided by that sender.

Balancing redundancy is thus crucial - maintaining an optimal level of redundancy can simultaneously enhance security and sustain computational efficiency. A suitable approach involves dynamically adjusting redundancy based on multiple factors, including source reliability (assessed via trust mechanisms), the planned route of the ego vehicle, prevailing network conditions, and the Age of Information (AoI). This strategy ensures that cooperative perception remains robust, secure, and scalable, supporting accurate and timely decision-making within cooperative vehicle networks.

The aim is to establish a balance between purposeful and efficient redundancy and safety against potential attack scenarios, optimizing the use of communicated data and the reliability of data fusion necessary for downstream tasks such as planning and control. The research team will explore information redundancy, perception inconsistencies, context aware fusion, spoofing and other attack scenarios, and the detection of attack patterns and will employ optimization strategies and reinforcement learning techniques. The focus will include intersection scenarios with varying traffic densities and connectivity levels. In addition to using the VeReMi dataset, the team will explore extensions to more realistic collaborative perception message attach scenarios for evaluation and validation.
]]></description>
      <pubDate>Mon, 02 Mar 2026 19:08:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676000</guid>
    </item>
    <item>
      <title>AI-Enabled Vision System for Intersection Analytics </title>
      <link>https://rip.trb.org/View/2673053</link>
      <description><![CDATA[Phase I of this project revealed limitations of using a single camera per intersection to automatically extract key traffic performance and safety information from video feeds. To overcome these limitations and enhance data accuracy, the Phase II approach will deploy a second camera at selected high-impact intersections. By fusing the views from two different camera angles, the system can establish a true spatial relationship of objects in the intersection, essentially achieving a more complete 3D understanding of vehicle and pedestrian trajectories.]]></description>
      <pubDate>Tue, 24 Feb 2026 15:00:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2673053</guid>
    </item>
    <item>
      <title>Data Integration to Support Digital Infrastructure and Efficient Mobility Insights</title>
      <link>https://rip.trb.org/View/2669548</link>
      <description><![CDATA[Data is at the core of understanding mobility trends, modeling and optimizing transportation systems and informing policy and decision-making. Understanding data is also key to advancing digital infrastructure for transportation, providing technological systems and frameworks to complement physical infrastructure. This project aims to support two other initiatives (the CEM Innovation Accelerator and the Advanced Transportation Optimization and Modeling project) by serving as the “data engine” to support both modeling as well as potential innovation projects. However, this project also stands alone as a data integration effort that will support the expansion and improvement of the previously-developed CEM Data Hub and serve to provide mobility data insights and digital infrastructure frameworks geared towards understanding transportation system efficiency.   

This project will develop a robust data integration framework, collect, assemble, and harmonize diverse transportation datasets to support the development of actionable mobility insights. By integrating data from traffic sensors, transit systems, GPS traces, and mobile applications, the project will create a comprehensive data ecosystem that reflects real-world travel behavior, congestion patterns, and modal interactions. This foundation will enable the development of analytical tools and decision-support systems that help agencies and planners optimize transportation networks for efficiency. The integrated data platform will support advanced analytics, visualization tools, and decision-support systems that can be used by planners, engineers, and policymakers to evaluate mobility strategies.   

Key components of the project include:  

Data Ecosystem Development – Establishing a scalable and secure data architecture that supports integration of diverse transportation datasets   

Collaboration – Engaging with ongoing projects across the CEM consortium to ensure collaborative use of available data   

Analytical Tools and Insights – Developing dashboards, predictive models, and scenario planning tools to support efficient and healthy mobility decisions.  

Ultimately, this effort will position data as a central asset in advancing efficient mobility across urban and rural contexts.  ]]></description>
      <pubDate>Thu, 12 Feb 2026 15:35:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669548</guid>
    </item>
    <item>
      <title>Developing a Data Fusion Tool for Improved Traffic Crash Exposure
Analysis and Modeling</title>
      <link>https://rip.trb.org/View/2663603</link>
      <description><![CDATA[Accurate measurement of exposure is critical for understanding and preventing traffic crashes, as crash frequency is directly related to how much road users are exposed to risk. However, current exposure estimates rely on data sources with complementary but individually insufficient characteristics. Traditional traffic counts and Annual Average Daily Traffic (AADT) offer high accuracy but limited spatial and temporal coverage, while emerging Location-Based Services (LBS) data provide high-resolution mobility patterns but are often biased and less reliable. This fundamental mismatch between accuracy and coverage prevents agencies from developing the complete and reliable exposure estimates needed for effective safety analysis and planning.
This project develops a data fusion tool that integrates traffic counts and AADT, LBS data, and socio-demographically representative survey data from the National Household Travel Survey (NHTS) into a unified measure of exposure. Unlike previous efforts that focused on a single travel mode or low temporal resolution, the proposed framework generates exposure estimates for motor vehicles, pedestrians, bicyclists, and scooters at fine spatial scales (intersection and mid-block) and temporal scales (daily and monthly). The tool is evaluated in Washington, D.C., using three alternative fusion paradigms: Bayesian fusion through hierarchical or state-space modeling, Dempster–Shafer theory for explicit uncertainty representation and accommodation of LBS coverage gaps, and model-based fusion employing structured error modeling with NHTS socio-demographics to correct LBS data bias.
The fusion methods are compared through crash prediction models estimated with fused exposure measures against models using individual data sources, evaluated via pseudo-R², AIC, BIC, and out-of-sample prediction error, with a target improvement of at least 10% in predictive performance. Fused exposure patterns are further validated against Washington, D.C.’s High Injury Network and independent ground-truth count data where available. The final tool is delivered as an open-source Python package with documentation and secure coding practices. Agency outreach, including engagement with D.C. stakeholders managing the High Injury Network, informs tool refinement and supports preparation for future pilot deployment. This research supports USDOT’s Safety priority by generating more accurate and complete multimodal exposure measures that enable better identification of high-risk locations, improved crash prediction, and targeted safety interventions
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:31:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663603</guid>
    </item>
    <item>
      <title>An AI-Based Reasoning Framework for Proactive Infrastructure Monitoring and Preservation Using Connected Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2655750</link>
      <description><![CDATA[This research proposes the development of a Connected Autonomous Vehicles (CAV)-based Proactive Infrastructure Preserving (CAV-PIP) system to enhance the safety, resilience, and operational efficiency of transportation infrastructure. The system leverages the sensing and communication capabilities of CAVs to enable continuous, real-time detection and reporting of roadway anomalies, such as pavement distress and damaged traffic signage. By fusing multi-modal sensor data and incorporating a retrieval-augmented generation (RAG) framework with large language models (LLMs), the system constructs a dynamic prior knowledge base to reason about infrastructure conditions and recommend context-aware maintenance actions. The project aims to transform current reactive maintenance practices into a data-driven, proactive framework that improves decision-making for transportation agencies. The system will be validated through simulation in the CARLA (Car Learning to Act) environment and supported by curated real-world datasets. Expected outcomes include an integrated detection and reasoning framework, structured maintenance reporting tools, and publicly shareable datasets and software packages. The project's broader impact lies in advancing intelligent infrastructure monitoring technologies, reducing long-term maintenance costs, and contributing to safer and more sustainable transportation systems.]]></description>
      <pubDate>Mon, 19 Jan 2026 17:01:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655750</guid>
    </item>
    <item>
      <title>Digital Twin and Automation of Pipeline Data for Predictive Maintenance and Risk Analysis incorporating Bayesian network</title>
      <link>https://rip.trb.org/View/2655704</link>
      <description><![CDATA[Pipeline infrastructure is critical for global energy transportation, yet aging systems face increasing degradation risks from corrosion, fatigue, and environmental factors. Recent catastrophic failures have demonstrated severe safety, environmental, and economic consequences of inadequate integrity management, with annual losses reaching billions of dollars. Despite significant advances in inspection technologies, including intelligent inspection tools and sensor networks, three fundamental challenges remain unresolved: lack of interpretability in machine learning approaches, difficulty quantifying uncertainties in defect growth predictions, and the challenge of optimizing maintenance decisions with incomplete information.

This research develops a Valuation Bayesian Network (VBN) integrated with Digital Twin technology as an automated decision-support tool for pipeline integrity management. The VBN approach provides a principled foundation for uncertainty quantification, data fusion, state estimation, prediction, and maintenance planning while maintaining interpretability. The framework incorporates probabilistic degradation and failure models, logic and relational models, and surrogate models to address key performance indicators, failure risks, and remaining life estimation.

The proposed Digital Twin architecture comprises four interconnected layers. The physical layer encompasses pipeline infrastructure with distributed sensors and expert knowledge. The simulation layer employs VBN-based probabilistic models capturing time-dependent degradation processes, where state variables, including defect depth and material properties, are modeled through Markov transition models with parameters learned from historical inspection records, pipeline failure databases, and expert elicitation. The data fusion layer performs Bayesian updating by integrating inspection data, operational history, and expert knowledge, enabling adaptive parameter calibration to capture site-specific degradation patterns. The decision layer implements maintenance optimization using value of information analysis and supports counterfactual reasoning for intervention scenarios.

This framework achieves full uncertainty propagation from measurement noise through remaining life predictions, ultimately enhancing operational safety by predicting high-risk defect development and enabling timely maintenance interventions.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:27:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655704</guid>
    </item>
    <item>
      <title>Data-Driven Resilience Planning for Transportation Infrastructure: Pilot Study in Texas</title>
      <link>https://rip.trb.org/View/2646954</link>
      <description><![CDATA[This one-year pilot proposes marrying three rich but rarely combined data streams—high-resolution weather data (freeze/thaw, temperature, rainfall, snow/ice, etc.) supplied by the Southern Regional Climate Center (SRCC), Connected-Vehicle Data (movements, windshield wiper events, delay, etc.) that capture real-time operating conditions, and Texas Department of Transportation's (TxDOT’s) own asset and condition inventories (e.g.  pavement condition data) into a cohesive, decision-ready framework. The research team will begin by geolinking these datasets and mining them for hazard frequency, traffic exposure, and structural vulnerability signals. Machine-learning and stochastic life-cycle cost models will then translate those signals into corridor-level risk profiles and economic damage curves under three strategies: do-nothing, reactive repair, and proactive hardening.  

Over the course of twelve months, the research team will iterate through four tightly coupled phases: (1) data assembly and quality control; (2) vulnerability assessment that fuses hazard intensity with deterioration and delay models; (3) scenario-based economic analysis to identify the most cost-effective resilience options; and finally, (4) delivery of an interactive web geographic information system (GIS)-based platform that maps risks, ranks projects, and lets engineers explore “what-if” funding scenarios. The researchers will ensure that methods align with agency workflows and that results are immediately actionable.  

Tangible pilot products—open-source modeling code, corridor-level risk maps, and a web-based GIS platform with an implementation guide and training workshop—will give Texas a clear blueprint for maximizing every resilience dollar. These outputs will enable TxDOT to pursue proactive adaptation and pave the way for multi-state deployment in the future. Expected benefits include lower lifecycle costs, fewer weather-related disruptions, and safer travel for Texans. Equally important, the modular design allows the Southern Plains Transportation Center to extend the framework to other Region 6 states in a potential follow-on effort, furthering USDOT goals for safety and infrastructure durability. ]]></description>
      <pubDate>Mon, 05 Jan 2026 23:27:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646954</guid>
    </item>
    <item>
      <title>Multi-Sensory System for Railway Track Defect Detection </title>
      <link>https://rip.trb.org/View/2646942</link>
      <description><![CDATA[Railway transportation is essential for moving passengers and freight across the U.S., but accidents continue to pose serious safety and economic risks. In 2022 alone, there were about 950 rail-related fatalities and 6,400 injuries nationwide. While human error and reckless behavior are major contributors, defective track infrastructure is a significant and preventable cause of accidents. Railway tracks are complex systems consisting of steel rails, crossties, fasteners, and ballast, all subject to heavy loads, temperature fluctuations, and environmental impacts. These stresses lead to issues such as broken rails, cracked or spalled crossties, loose or missing fasteners, geometry defects, and cross-level variations. Extreme weather conditions can further cause rail buckling or fracture. Failures in these components can trigger derailments, collisions, hazardous material spills, and major service disruptions. Although manual inspections and specialized vehicles are used, many defects go undetected between inspection cycles. Traditional manual inspections, although reliable for identifying visible rail defects, are labor-intensive and limited in scalability. To improve efficiency, various nondestructive testing (NDT) technologies, such as infrared imaging, acoustic emission, ultrasonic, and electromagnetic techniques, have been used primarily for internal defects. As surface defects become more prevalent, various methods have also been developed for detecting surface-level flaws, which can be broadly categorized into three approaches: static monitoring where sensors at fixed locations provide localized coverage; inspection trolleys which integrate sensors generally in the laboratory setting; and onboard sensing systems which enable real-time detection ahead of moving trains but suffer from high cost with varying imaging quality under different weather and lighting conditions. The primary objective of this project is to develop a comprehensive but low-cost multi-sensory system for railway track defect detection. The system will integrate binocular stereovision cameras, Global Navigation Satellite System / Global Positioning System (GNSS/GPS), and IMU sensors. The scope of this project includes development of a multi-sensory system including controller and field data acquisition, development of real-time data fusion and detection algorithms, and recommendations for system deployment on railway tracks. ]]></description>
      <pubDate>Mon, 05 Jan 2026 23:04:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646942</guid>
    </item>
    <item>
      <title>Obtaining Reliable Pavement Friction Measurements Using Connected Vehicles</title>
      <link>https://rip.trb.org/View/2643034</link>
      <description><![CDATA[Pavement friction is a critical factor influencing vehicle control and stopping distance, particularly under wet conditions, yet current measurement practices rely on infrequent and labor-intensive testing methods. These limitations prevent agencies from identifying hazardous low-friction locations in a timely manner. This project investigates whether connected vehicle sensor data can provide a reliable and continuous alternative for pavement friction monitoring.

The research will validate friction estimates derived from connected passenger vehicles against locked-wheel skid testing and invasive and non-invasive roadway sensors. Data will be collected on multiple pavement types along a test corridor in Massachusetts and analyzed using statistical and machine learning methods to relate vehicle-based friction values to standard skid numbers. The project will develop conversion models and data-integration techniques to enable agencies to incorporate connected vehicle friction data into pavement and safety management systems, supporting proactive maintenance and improved roadway safety.]]></description>
      <pubDate>Thu, 18 Dec 2025 15:09:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643034</guid>
    </item>
    <item>
      <title>Investigating Transit Characteristics and Road Safety Outcomes</title>
      <link>https://rip.trb.org/View/2643028</link>
      <description><![CDATA[Public transit systems influence roadway safety through changes in travel behavior, congestion levels, and modal distribution, yet the specific mechanisms linking transit characteristics to safety outcomes are not well quantified. While prior studies suggest that higher transit use is associated with improved safety, agencies lack clear guidance on which system features contribute most to these effects. This project addresses that gap through large-scale data integration and predictive modeling.

The research will combine crash data, transit system attributes, roadway network characteristics, and demographic indicators from national and regional data sources. Machine learning models will be developed to predict crash rates as a function of transit network size, service intensity, demand, and multimodal shares. Explainable modeling techniques will be used to identify the most influential predictors of safety outcomes at both metropolitan and town levels. Results will provide actionable evidence to support data-driven transit planning and roadway safety strategies.]]></description>
      <pubDate>Thu, 18 Dec 2025 14:54:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643028</guid>
    </item>
    <item>
      <title>Promoting Automated Vehicle Safety Using Multi Modal Data and Large Language Models</title>
      <link>https://rip.trb.org/View/2640188</link>
      <description><![CDATA[Automated vehicles must operate in complex environments that involve interactions among human drivers, cyclists, and pedestrians. Traditional rule based approaches may miss subtle factors that contribute to safety risks in these settings. This project will integrate multi modal datasets, including video, radar, LiDAR, global positioning system (GPS), and text based information from logs and incident reports, to train large language models capable of analyzing and predicting potential risk scenarios. The research will create a unified pipeline that fuses sensor data with language inputs so that the model can generate structured assessments and clear explanations of conditions that may influence safety.

The project will test the system in simulation and controlled real world environments. Additional evaluations will include cybersecurity challenges and human machine interface testing to ensure that the system is robust, practical, and informative for users. The final framework will support transportation agencies, developers, and industry partners in understanding and addressing AV safety challenges by providing tools for improved risk detection, situational reasoning, and communication.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:43:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640188</guid>
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