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    <title>Research in Progress (RIP)</title>
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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>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>Enhancing Traffic Signal Controller Performance using LiDAR data.</title>
      <link>https://rip.trb.org/View/2742142</link>
      <description><![CDATA[Enhancing Traffic Signal Controller Performance Using LiDAR Data applies software-in-the-loop testing to a previously developed approach for optimizing traffic signal timings to reduce vehicle delays and increase throughput at signalized intersections, serving as a first step prior to field deployment. Field data will be gathered at a single signalized intersection using LiDAR equipment to investigate the potential of LiDAR data for the field evaluation of traffic signal controllers.

Using the field data, optimized signal timings will be computed and implemented in a simulation environment along a segment of a major arterial. Conducted in collaboration with the Virginia Department of Transportation (VDOT), the study builds on the team’s prior work demonstrating that traditional methods such as the Webster formulation overestimate cycle lengths and fail to minimize delay under congested conditions, and applies a new Laguna-Du-Rakha (LDR) formulation that minimizes delay while reducing stops, deceleration, and acceleration at signalized intersections.

The project will collect LiDAR field data at the signalized intersection of Cloverdale Road and Lee Highway to evaluate the suitability of these data for the field evaluation of signal control strategies. The team will compute optimum cycle lengths using the LDR formulae, extract vehicle trajectory data for the computation of queue lengths, delays, stops, and fuel and energy consumption, and construct and calibrate a simulation network of the intersection. Four signal timing plans, fixed-time and actuated control based on both the Webster and LDR methods, will be implemented and evaluated across ten traffic demand levels, with emphasis on high-demand conditions where the LDR formulation provides the greatest benefit. Pedestrian data will also be collected and incorporated.]]></description>
      <pubDate>Sat, 01 Aug 2026 09:50:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742142</guid>
    </item>
    <item>
      <title>SPR-5108: Automated Highway Asset Inventory Using High-Resolution Aerial Imagery and LiDAR Data</title>
      <link>https://rip.trb.org/View/2732647</link>
      <description><![CDATA[This project proposes to develop, validate, and scale an automated framework for extracting and mapping key highway assets from 3-inch aerial imagery and QL1 LiDAR data. Target assets include the number of travel lanes in each direction, pavement section areas, guardrails, and sound barrier walls. Hamilton County will serve as the pilot implementation area due to its diverse mix of Interstate, U.S., and State routes across urban and suburban environments.]]></description>
      <pubDate>Wed, 22 Jul 2026 15:22:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732647</guid>
    </item>
    <item>
      <title>Assessing the Value of LiDAR in Detecting Conflicts at Intersections to Enhance Safety
</title>
      <link>https://rip.trb.org/View/2717656</link>
      <description><![CDATA[The goal of this research is to evaluate and compare the effectiveness of camera-based and LiDAR-based systems for detecting traffic conflicts.
]]></description>
      <pubDate>Wed, 24 Jun 2026 14:33:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2717656</guid>
    </item>
    <item>
      <title>Structural Safety Evaluation from Computational Modeling of Unknown Bridges Using LiDAR Point Cloud and Nondestructive Testing Data
</title>
      <link>https://rip.trb.org/View/2703878</link>
      <description><![CDATA[This project aims to convert LiDAR point cloud data into a finite element model of an unknown bridge by integrating steel bars identified from nondestructive testing into structural geometries based on LiDAR point cloud and validating the computational model against a reference model created manually using structural drawings. The aim of this study will be achieved by executing four tasks: (1) Data collection from a bridge using drone-based LiDAR flights and nondestructive testing, such as ground penetrating radar for detection and identification of steel reinforcement grids hidden in concrete members. (2) 	Data processing through registration, noise removal, and down-sampling. (3) Automated finite element model generation by integrating hidden features into structural components with outlining geometry of point cloud and discretizing them. (4) Condition assessment by running the computational model with estimated material properties under overloaded trucks and/or earthquake loads.]]></description>
      <pubDate>Mon, 18 May 2026 17:09:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703878</guid>
    </item>
    <item>
      <title>Advanced InSAR–UAV-LiDAR Flood-Deformation Risk Monitoring for Efficient Mobility</title>
      <link>https://rip.trb.org/View/2669656</link>
      <description><![CDATA[El Paso’s critical transportation corridors face compounding risks from ground deformation and flash flooding that can severely disrupt efficient mobility, impede traffic flow, and challenge infrastructure reliability. Such infrastructure disruptions compromise public safety by delaying emergency response access and increase collision risk on compromised roadways. Despite advances in satellite monitoring and hydrologic modeling, no integrated system currently provides transportation agencies with rapid and actionable, near-real-time alerts for combined flood-deformation hazards. This project is designed to support uninterrupted mobility directly by developing and demonstrating a unified monitoring framework that fuses millimeter-precision Interferometric Synthetic Aperture Radar (InSAR) deformation maps with Unmanned Aerial Vehicle–Light Detection and Ranging (UAV-LiDAR) terrain models and Synthetic Aperture Radar (SAR)-derived soil-moisture indices to deliver actionable risk assessments. The research addresses a core challenge in maintaining efficient mobility: predicting when and where infrastructure vulnerabilities will coincide with flood conditions. Using validated Persistent Scatterer (PS) and Small Baseline Subset (SBAS) InSAR processing chains, high-resolution UAV-LiDAR surveys, and machine learning algorithms trained on historical events, the proposed system will provide transportation agencies with advanced warning, which enables proactive response and traffic management. The project will produce a composite flood-deformation risk index with demonstrated 90% accuracy in hazard detection. An edge-computing prototype will be deployed in partnership with the Texas Department of Transportation (TxDOT) to operationalize the fusion algorithms, enabling 24-hour processing turnaround and secure web-based risk visualization. Through formal partnerships with TxDOT and El Paso Water, the system will integrate real-time flow gauge data and infrastructure databases to enhance model calibration and validation. The project includes comprehensive technology transfer components, such as Docker-containerized software, training workshops for state Department of Transportation (DOT) engineers, and a commercialization brief outlining licensing pathways for rapid deployment across additional corridors.  ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:40:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669656</guid>
    </item>
    <item>
      <title>AI-Enhanced LiDAR Tools for Smart Bridge Clearance Assessment and Obstacle Detection</title>
      <link>https://rip.trb.org/View/2646931</link>
      <description><![CDATA[Over-height vehicle collisions with bridges continue to pose serious safety risks and impose significant infrastructure repair costs, despite the presence of clearance signage and record-based systems. Many current clearance datasets rely on legacy documentation or infrequent manual surveys, failing to reflect changes caused by resurfacing, structural modifications, or evolving roadway geometry. As a result, clearance information is often outdated, incomplete, or unreliable for operational decision-making.

This project develops a smart, artificial intelligence (AI)-enhanced system that integrates mobile LiDAR scanning with machine learning models to automatically extract accurate bridge clearance and obstacle measurements. Neural network architectures will analyze 3D point cloud data to identify bridge geometry, detect vertical obstructions, and calculate clearances with sub-inch precision. The resulting data will be delivered through a user-friendly visualization and archiving platform designed for direct use by state Departments of Transportation. Pilot deployment in Massachusetts, in partnership with MassDOT, will demonstrate the system’s potential to improve clearance monitoring, reduce bridge strike risk, and modernize infrastructure safety workflows.]]></description>
      <pubDate>Mon, 05 Jan 2026 22:14:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646931</guid>
    </item>
    <item>
      <title>Development of Methods for Rapidly and Accurately Processing LiDAR Data for Evaluating Deformations in Bridges and Bridge Elements</title>
      <link>https://rip.trb.org/View/2633315</link>
      <description><![CDATA[Light Detection And Ranging (LiDAR) is a remote sensing method that creates point-clouds defining physical configuration of objects within line of sight by measuring travel time of pulsed lasers. It promises to revolutionize structural testing and health monitoring by allowing for the replacement of a legion of point measurement devices with a single device that provides exceptionally accurate and synchronized data to accurately describe external structural features. The point clouds that are generated are useful in both qualitative visualization and quantitative analysis of structural state and changes. LiDAR produces very large data sets which are typically well-suited to assessment with data science methods. These methods include both classical curve/surface fitting methods, and modern machine learning (ML)-based approaches. However, there must be engineers “in the loop” at the development stage to ensure that models produce engineering quantities of interest. For example, models that automatically identify beam and girder elements, and generate plots of deformation, slope, curvature, or changes in these quantities over time. The proposed work seeks to provide a bridge from the raw data to engineering insights with easy-to-use software tools.]]></description>
      <pubDate>Tue, 02 Dec 2025 16:22:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2633315</guid>
    </item>
    <item>
      <title>AI-Driven Predictive Safety Framework for V2X-Based Road User Protection</title>
      <link>https://rip.trb.org/View/2632833</link>
      <description><![CDATA[The Utah Department of Transportation (UDOT) has made notable progress in deploying LiDAR and Vehicle-to-Everything (V2X) systems at intersections to improve roadway safety for both motorized and vulnerable road users (VRUs), including pedestrians and cyclists. These systems can detect multiple road users and broadcast warnings through connected infrastructure, forming the foundation for proactive safety management. However, two major challenges remain. First, current LiDAR-based systems primarily detect VRUs but lack the predictive capability to anticipate their future movements or interactions with vehicles. Without behavior modeling and trajectory forecasting, V2X systems can only respond to imminent risks rather than prevent conflicts before they occur. Second, although V2X technologies are expanding statewide, there is no standardized framework to evaluate how these systems influence driver behavior or reduce VRU-related near-misses and collisions. The absence of field-based performance measurement limits UDOT’s ability to quantify safety benefits and guide data-driven improvements. This project aims to address these gaps by developing and deploying an AI-powered predictive safety framework that integrates LiDAR sensing and V2X communication to protect VRUs in real traffic environments.]]></description>
      <pubDate>Wed, 26 Nov 2025 19:22:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2632833</guid>
    </item>
    <item>
      <title>Enhancing Intersection Safety through Advanced Planning and AI Integration</title>
      <link>https://rip.trb.org/View/2606411</link>
      <description><![CDATA[This research develops innovative methods for improving intersection safety through three integrated approaches: optimized intersection planning, connected and automated vehicle (CAV) integration, and artificial intelligence (AI)-driven pedestrian safety enhancement. The project addresses the safe accommodation of diverse users including private vehicles, trucks, transit, and pedestrians while incorporating emerging technologies such as sensors, control systems, and CAVs. Using population-based metaheuristic algorithms and VISSIM microsimulation modeling, the research will optimize intersection development through cost minimization that includes construction, maintenance, user costs, delays, accidents, and emissions. The CAV integration component focuses on infrastructure readiness for varying levels of vehicle autonomy through simulation and analysis models, cooperative perception systems, and Vehicle-to-Everything communication. The pedestrian safety advancement leverages multi-modal RGBT sensor data, SAM2 AI tracking models, LiDAR integration, and surrogate safety measures to create predictive safety systems. The methodology builds on extensive University of Maryland experience in transportation network optimization and incorporates real-world sensor deployments at Maryland sites with over 25 hours of interaction data collection and analysis.]]></description>
      <pubDate>Thu, 02 Oct 2025 15:24:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606411</guid>
    </item>
    <item>
      <title>Field Deployment and Testing of Enhanced Fixed- and Actuated-Traffic Signal Control Systems</title>
      <link>https://rip.trb.org/View/2606409</link>
      <description><![CDATA[This research conducts field deployment and testing of enhanced traffic signal control systems using the Laguna-Du-Rakha formulation to optimize signal timings for reduced vehicle delays and fuel consumption at signalized intersections. Building on previous work demonstrating that traditional Webster formulation methods produce cycle lengths nearly three times longer than optimal under congested conditions, the study implements and validates improved signal timing approaches through real-world field testing in collaboration with Virginia Department of Transportation. The methodology involves identifying candidate intersections in the Blacksburg and Salem area, with primary focus on the Beamer Way and Southgate Drive intersection equipped with LiDAR surveillance instrumentation tracking objects within 150 meters. Optimized cycle lengths will be calculated using multi-objective optimization balancing delay minimization and fuel consumption reduction through adjustable weighting factors. Field implementation includes one-week deployment of optimized signal timing plans with LiDAR-based trajectory data collection for performance quantification including queue lengths, vehicle delays, stops, and fuel consumption measurements. VISSIM microsimulation modeling creates digital twins of selected intersections for validation against field data and sensitivity testing across various traffic demand levels and cycle length weight combinations, enabling assessment beyond observed field conditions and identification of optimal control strategies.]]></description>
      <pubDate>Thu, 02 Oct 2025 15:18:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606409</guid>
    </item>
    <item>
      <title>Assessing Quick Builds and Safe Streets for Non-Motorized Safety Using Simulations and Portable Sensing Technology</title>
      <link>https://rip.trb.org/View/2606402</link>
      <description><![CDATA[This research establishes a comprehensive evaluation framework for Quick Build interventions using portable LiDAR technology, driving simulation, and field data to quantify safety impacts for non-motorized road users including pedestrians and cyclists. Building on Phase 1 simulation-based foundations, the study addresses the challenge of limited long-term effectiveness data for quick-build street treatments such as curb extensions, protected bike lanes, and temporary traffic calming features. The methodology combines portable detection systems including LiDAR sensors, edge computing, and 5G connectivity to capture high-resolution vehicle and non-motorized transport trajectories, speeds, conflict points, and crossing patterns before and after intervention implementation. UC Win Roads driving simulator environments will replicate selected corridors to analyze road user behavior and interactions under controlled conditions, while survey questionnaires assess community perceptions of safety improvements. The research develops multimodal safety performance metrics tailored to temporary installations including Post Encroachment Time, Time to Collision, and crash modification factors for non-motorized transport modes. The study produces Standard Operating Procedures enabling Maryland Department of Transportation staff to implement evaluation methods independently for future Quick Build projects, supporting evidence-based decision-making for permanent infrastructure investments.]]></description>
      <pubDate>Thu, 02 Oct 2025 14:57:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606402</guid>
    </item>
    <item>
      <title>Research Project Name: Development of a CAV Testbed-enhanced Smart Campus at Morgan State University - Phase III</title>
      <link>https://rip.trb.org/View/2606401</link>
      <description><![CDATA[This research advances Connected and Automated Vehicle (CAV) infrastructure through Phase III expansion of an established testbed, integrating LiDAR-powered safety applications with signal control systems and conducting comprehensive CAV market penetration analysis in partnership with Maryland Department of Transportation. Building on previous phases, the study coordinates signal phasing and timing across three campus intersections equipped with LiDAR and roadside unit infrastructure, implementing dynamic all-red extensions based on vehicle speed and red-light violation risk detection. The methodology develops pedestrian signal extensions activated by real-time crosswalk occupancy detection and creates Safety Data Sharing Messages compliant with SAE J2735 standards for broadcasting object-level data to vehicles. Portable LiDAR deployments collect trajectory data at additional intersections and work zones for solution validation. The market penetration analysis component catalogues CAV data sources, develops quality assurance frameworks, and compares traditional probe data with connected vehicle information. Collaboration with Maryland Motor Vehicle Administration provides vehicle registration cross-referencing with automation levels, while commercial vendor partnerships supply dynamic usage patterns. The research creates geographic information system (GIS)-based visualizations representing regional CAV penetration and develops interactive dashboards for transportation planning support.]]></description>
      <pubDate>Thu, 02 Oct 2025 14:53:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2606401</guid>
    </item>
    <item>
      <title>Milkweed Presence Detection from High-Resolution Mobile Roadway Photography and
LiDAR</title>
      <link>https://rip.trb.org/View/2601429</link>
      <description><![CDATA[This project builds on previous Idaho Transportation Department (ITD) studies to identify and map milkweed along highway rights-of-way using existing roadway photography and LiDAR data. The effort will produce a geographic information system (GIS)-based inventory that documents route, milepost, area, and density information for each identified milkweed cluster, supporting ITD’s initiatives to prioritize and preserve high-quality Monarch and pollinator habitat. Key objectives include detecting milkweed using LiDAR and photo imagery, mapping occurrences by route and milepost, estimating cluster size and plant density, and analyzing correlations between milkweed area and density.]]></description>
      <pubDate>Wed, 17 Sep 2025 16:30:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2601429</guid>
    </item>
    <item>
      <title>Phase 2: Quantitative Evaluation Process for Improved Rockslope Safety and Reduced Maintenance</title>
      <link>https://rip.trb.org/View/2593928</link>
      <description><![CDATA[Thousands of problematic rock slopes adversely impact Oregon Department of Transportation's (ODOT’s) infrastructure and will soon require substantial mitigation as Oregon’s highway rockcuts near the end of their respective design life. This deterioration is predicted to be further exacerbated with climate change. Many short-term mitigation techniques such as scaling or blasting are very expensive and dangerous as these techniques require personnel to physically scale the cliff, while longer term mitigation solutions (e.g., nailing) are often too cost prohibitive to deploy beyond a few select slopes. Rockfall mitigation efforts are further compounded by the subjective, ad-hoc nature of the mitigation process, which can lead to uncertainty regarding actual slope improvement and predicted improvement duration. It is also uncertain how long—and how effective—mitigation efforts actually are, particularly with respect to the longevity of scaling before the eroding slopes return to a similar or even more precarious state. Further, when rock slopes are not clearly improved with mitigation, clean-up costs for rockfall accumulate for our District maintenance crews. Taken together, the lack of quantitative information regarding how mitigation efforts actually improve the overall stability of rock slopes runs counter to ODOT’s aim for budget efficiency and transparency, with potential negative impacts on an already highly limited rockfall mitigation budget which could then in-turn negatively impact the safety of the traveling public. Quantitative, objective methods that better inform successful rockfall mitigation techniques, maintenance strategies, and overall asset management is needed. Recently, ODOT Research invested in the development of a series of lidar-based tools for quick assessment and monitoring of rockfall events (SPR809). Specifically, SPR809 refined the Rockfall Activity Index (RAI) hazard screening tool, analyzed seismic hazards, and developed a user-friendly Graphical User Interface (GUI) for implementation. RAI is a point cloud-derived, high-resolution, low-cost, morphology-based, objective approach for assessing rock slopes that can objectively and efficiently analyze large geographic regions. RAI may also be used to identify specific locations to target for mitigation by identifying rapidly eroding areas of the slope and precarious overhangs. This proposed research leverages these recently developed tools to further develop a new methodology for verifying rockfall mitigation effectiveness that can also be used to direct future mitigation and maintenance efforts.]]></description>
      <pubDate>Thu, 28 Aug 2025 11:23:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593928</guid>
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