<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" 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>Connected and Automated Vehicle (CAV) Readiness Survey: Are MDOT Roads Machine Readable</title>
      <link>https://rip.trb.org/View/2731918</link>
      <description><![CDATA[Considering Michigan Department of Transportation (MDOT) Mission, Values, and Vision, with the evolving landscape of technologies within the connected and automated
vehicle (CAV) industry, there is a pressing need to investigate the requisite support from DOTs to enable seamless integration of
CAVs with infrastructure. As core sensors and systems defining these technologies become more established, understanding the
precise infrastructure requirements becomes paramount. Therefore, the research aims to address the question: "What specific
support and infrastructure enhancements are necessary from MDOT to facilitate effective detection and interaction of connected and
automated vehicles with the surrounding infrastructure?]]></description>
      <pubDate>Fri, 17 Jul 2026 11:47:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731918</guid>
    </item>
    <item>
      <title>Update Standardized Measurement Systems and Laboratory Test Procedures to Improve Motorist Safety</title>
      <link>https://rip.trb.org/View/2727316</link>
      <description><![CDATA[The growth in construction and maintenance activities throughout the state has prompted an increase in the throughput for standardized measurements, laboratory tests, and similar processes performed by TxDOT on a routine basis. TxDOT, as well as its affiliated stakeholders, must meet this increased demand with a limited availability of trained workforce while ensuring high quality and consistent work products. While many of these processes have been developed and used for several decades, recent advances in technologies such as sensors, computing, logic controllers, machine vision, and actuators have now made it feasible to fully or partially automate several of the existing processes or, where feasible, replace some archaic processes with modern advanced and automated methods. The research teams will conduct one or more scouting tours of divisions and districts to create an inventory of processes and identify their potential to be automated. The research teams will use a scoring system with input from TxDOT to prioritize processes that can be partially or fully automated and benefit TxDOT. The research teams will develop, validate, and facilitate implementation of selected solutions with appropriate workflow integration and training of TxDOT’s personnel.]]></description>
      <pubDate>Fri, 10 Jul 2026 14:52:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727316</guid>
    </item>
    <item>
      <title>SPR 782 Improving Worker Safety in Work Zones with Effective Proximity Sensing Technology</title>
      <link>https://rip.trb.org/View/2719302</link>
      <description><![CDATA[The project will synthesize SWZ technologies and analyze their benefits and challenges —specifically focusing on intrusion alert and motorist alert systems—based on their use by other highway agencies and the construction industry. These findings will then be tailored to fit the needs of the South Carolina Department of Transportation's (SCDOT’s) internal and contract workforce. To meet the research goals, the team will focus on three key objectives: (1) Investigate various intrusion alert technologies to identify those best suited for highway worker safety, motorist alert systems effective in work zones, and solutions that can reduce unsafe driving behaviors in South Carolina. (2) Assess at least three intrusion alert and three motorist alert technologies for suitability to the SCDOT, evaluating their capabilities, feasibility, and providing cost estimates for initial implementation and life cycle comparisons against current practices.
(3) Evaluate compliance by workers in using these technologies and assess motorist response and behavioral changes prompted by driver-facing alert devices.]]></description>
      <pubDate>Thu, 25 Jun 2026 08:57:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719302</guid>
    </item>
    <item>
      <title>Advancing Pollinator Habitat Monitoring through Remote Sensing on Nebraska Roadsides</title>
      <link>https://rip.trb.org/View/2689394</link>
      <description><![CDATA[To meet monitoring and reporting requirements under the Monarch Candidate Conservation Agreement with Assurances (CCAA), Nebraska Department of Transportation (NDOT) must collect consistent data on milkweed stem density and nectar-plant cover across extensive roadside networks. Current field-based approaches, though effective, are resource-intensive, limited in spatial coverage, and require a specialized level of biological expertise. NDOT needs a scalable and cost-effective remote sensing strategy that can meet CCAA requirements. Furthermore, NDOT must understand the costs and benefits to applying this technology in-house or via external contract, and how the products could be applied to offer NDOT versatile imagery and data outputs that can support broader environmental review needs, planning, and maintenance decisions.]]></description>
      <pubDate>Fri, 05 Jun 2026 12:41:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689394</guid>
    </item>
    <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>
    </item>
    <item>
      <title>User-Centered Smart Traffic Sign Implementation Development Study</title>
      <link>https://rip.trb.org/View/2703714</link>
      <description><![CDATA[
Flaggers maintain traffic flow through a work zone area despite a shutdown of lanes by providing temporary traffic control. In terms of occupational safety, flaggers have one of the highest risk jobs in the country, with 41 out of every 100,000 workers killed on the job each year. This project developed and tested technology for automatically detecting and documenting the occurrence of near-intrusions into a flagger-controlled work zone. The project developed a low-cost portable device for automatically tracking vehicle trajectories, detecting potential intrusions, and providing audio-visual alerts to warn any errant drivers who might cause a danger to flaggers and workers in the construction zone. A radar sensor on the device is deployed by using a telescoping pole and collects simultaneous measurements from approaching vehicles in multiple lanes.

Tests were conducted in six real-world traffic scenarios, including work zones at one rural location (Cook County), three urban locations (Saint Paul, White Bear Lake, and Eden Prairie), a synthetic urban zone involving pedestrian crossings (Saint Paul) and one suburban/rural location (Mound). Detailed results and analysis are presented in this report. The results indicate that multiple design iterations have improved the device and enabled it to work reliably – Very few false alarms (if any) are triggered and the intrusion detection curves implemented in the system are verified to work well. The vehicles which were alerted using audio-visual warnings in the last work zone test responded appropriately with a majority of them slowing down in response to the alarms.]]></description>
      <pubDate>Fri, 15 May 2026 14:41:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703714</guid>
    </item>
    <item>
      <title>Driver Takeover and Shared-Control Collaboration in Automated Driving Systems under Rural Conditions: Evaluating Real-Time Cognitive Responses in Field and Simulated Settings</title>
      <link>https://rip.trb.org/View/2703691</link>
      <description><![CDATA[This proposal outlines a multidisciplinary research initiative to assess drivers'
physiological and cognitive workload, stress levels, emotional states, and trust during takeover
performance in human-machine co-driving systems using an automated driving simulator. In specific,
three research objectives are to (1) Designing realistic and validated driving scenarios in simulators;
(2) Validating physiological sensors for measuring driver physiological responses; and (3)
Developing cognitive and situational awareness-based decision-making framework. To achieve these
objectives, simulator-based data will be collected, along with test drivers incorporating physiological
sensors while in the driving tests. Existing open-source data will be applied to expanding traffic
scenarios.
]]></description>
      <pubDate>Fri, 15 May 2026 14:19:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703691</guid>
    </item>
    <item>
      <title>Modernizing Rockfall Assessment</title>
      <link>https://rip.trb.org/View/2698372</link>
      <description><![CDATA[Using targeted remote sensing and other advanced survey techniques, combined with data-driven analysis, this pilot study will evaluate how well these approaches can identify meaningful changes in slope conditions and determine whether rockfall material reaches the roadway or is effectively contained (e.g., within ditches). The results will help 
Montana Department of Transportation (MDT) improve the consistency of slope evaluation and prioritization of mitigation efforts, supporting more efficient use of maintenance resources, improved safety, and reduced traffic disruptions. The study will also provide insight into how repeated observations can be used to track changes in slope condition and performance over time, supporting long-term planning and asset management.]]></description>
      <pubDate>Fri, 01 May 2026 16:56:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2698372</guid>
    </item>
    <item>
      <title>3D SPT - Seismic Imaging for Bartow Project</title>
      <link>https://rip.trb.org/View/2697839</link>
      <description><![CDATA[The 3D SPT -seismic method (Mirzanejad et al. 2020) integrates seismic analysis with invasive SPT testing for volumetric imaging.  During SPT advancement, each hammer blow acts as a seismic source at a known depth.  A surface array of geophones (typically 48 sensors arranged over a 60 ft x 80 ft area) records the wavefields.  Using 3D elastic full - waveform inversion, the recorded data are transformed into a 3D Vs model extending laterally up to 60 ft (18 m) from the borehole and to the full SPT depth.

]]></description>
      <pubDate>Thu, 30 Apr 2026 10:02:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2697839</guid>
    </item>
    <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>Monitoring Active Transportation Demand and Safety with Computer Vision</title>
      <link>https://rip.trb.org/View/2696847</link>
      <description><![CDATA[Monitoring demand for and safety of active transportation has been a challenge for decades. With a history of designing roads for cars and monitoring efforts similarly aimed at the flow of cars, transportation researchers and professionals lack system-level knowledge of active transportation. The current state of bicycle and pedestrian counting practice in most cities deploys a few costly permanent counters using inductive loops and passive radar, combined with a few days of manual peak hour traffic counts at few intersections. This neither monitors system-wide demand nor safety. However, recently several companies have produced video- and LiDAR-based sensors and multiclass tracking technology to monitor active transportation demand and unsafe events. These sensors can be installed permanently, or temporarily, and are generally lower in cost to install than other permanent counting devices. This research will leverage an ongoing Caltrans project with these sensors to validate safety metrics, and a mobile version of the sensors to collect active transportation count data for modeling system level active transportation volume in Davis, California as a pilot for other cities and agencies. It will include the prediction of network-wide travel volumes for planning the intervention purposes, and two safety metric evaluations. The final report is expected to not only provide information on the state-of-the-art in active transportation monitoring, but will have direct policy impacts by informing the Active Transportation Data program within Caltrans Traffic Operations, among other programs such as the Active Transportation Resource Center research-to-practice education elements.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:05:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696847</guid>
    </item>
    <item>
      <title>Sensor-informed Generative Digital Twin: High-fidelity Simulation for Sustainable Transportation and Policy Validation</title>
      <link>https://rip.trb.org/View/2691669</link>
      <description><![CDATA[Understanding the behaviors of vehicles and other traffic participants at busy urban intersections is critical for urban planning, infrastructure development, and policymaking. Unfortunately, such understanding often comes after a huge investment for implementation and deployment. Many complex interactions occur infrequently and are difficult to capture through after-deployment monitoring. This project will develop a sensor-informed generative digital twin that integrates real-world data from the Riverside Innovation Corridor’s sensor network. By continuously integrating real-time sensory inputs, the platform can be used to create high-fidelity scenarios and simulate rare and challenging transportation dynamics. The digital twin will serve as a decision-support tool for policy evaluation, traffic efficiency strategies, and urban mobility planning. Its predictive capabilities will assist in designing infrastructure for autonomous vehicles, optimizing multi-modal travel demand, and enhancing energy efficiency. Through engagement with policymakers and stakeholders, the project will pave the foundation for the digital twin’s application in real-world decision-making. The proposed research will serve as a bridge, connecting data-driven insights with policy implementation towards sustainable transportation systems.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:41:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691669</guid>
    </item>
    <item>
      <title>Study of Real-Time Concrete Strength Measurements and Monitoring Systems that Conform to AASHTO T-412-24 for Use in Materials Testing Which Will Provide Cost Savings and Reduce Waste</title>
      <link>https://rip.trb.org/View/2687354</link>
      <description><![CDATA[Concrete strength testing is essential to Nevada Department of Transportation's (NDOT’s) quality control (QC). Current practice relies on destructive cylinder testing (ASTM C39), a labor-intensive, costly method
providing discrete-age strength data and delaying construction decisions. Maturity-based sensors (ASTM C1074) enable early-age monitoring but require mix-specific
calibration. The newly adopted AASHTO T-412-24 provides a nondestructive
alternative, measuring in place dynamic elastic modulus via acoustical resonance for real-time strength estimation. Field trials in Texas and Indiana achieved strength
estimates within ±15% of cylinder results and reduced testing costs by ~50%.
Sensors embedded at placement continuously log data, enabling immediate form
removal, traffic opening, or corrective actions. Nevada’s mixes, with ~20% pozzolan replacement and variable aggregate quality, may alter the modulus-strength relationship. Sensor performance under Nevada’s climate, data reliability, and integration into NDOT quality assurance (QA)/QC procedures remain untested. This study will (i) verify T-412-24 sensor accuracy with NDOT mixes in the field, (ii) assess field performance under local conditions, (iii) evaluate environmental, economic, and waste-reduction impacts, and (iv) develop protocols for NDOT adoption.

The objective of this research project is to evaluate whether T-412-24–compliant embedded sensors can deliver accurate, reliable real-time strength estimates for NDOT applications. The study will also correlate sensor data with ASTM C39 cylinder results for NDOT mixes in lab and field settings, identify implementation challenges, quantify cost/testing time savings, and produce specification-ready recommendations.

The University of Nevada, Reno team plans to achieve the project goal by: (1) Conducting a comprehensive literature review and identifying a set of 4-6 representative NDOT mix designs for use in lab and field testing. (2) Deploying AASHTO T-412-24-compliant real-time strength sensors in four NDOT pilot placements across regions and applications, with multiple sensors per placement to capture spatial gradients. (3) Analyzing and synthesizing the strength data generated through the field deployment, including sensor readings and companion cylinder tests. (4) Assessing the life cycle for each concrete mix design. (5) Analyzing the life cycle cost for each concrete mix design. (6) Estimating the waste reduction for each concrete mix design. (7) Producing final project deliverables including recommendations, tools, and guidance necessary for NDOT to evaluate and adopt real-time strength monitoring technologies.

This project will deliver validated specifications, installation guidelines, decision tools, and cost/benefit analyses for immediate use on NDOT pilot projects. If proven effective, AASHTO T-412-24–compliant real-time concrete strength sensors could be deployed on a wide range of construction and reconstruction projects, reducing cylinder testing costs, accelerating decisions, and improving durability. The main barrier is sensor cost, about $200 per unit plus a reusable datalogger, though savings from reduced materials, labor, and equipment operation are expected to offset this expense. Integration into NDOT’s QA/QC framework will require minor specification updates, with no significant political or socio-economic obstacles anticipated.]]></description>
      <pubDate>Wed, 01 Apr 2026 17:10:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2687354</guid>
    </item>
    <item>
      <title>Mixed Virtual Reality as an Aid in Advancing the Reliability and Robustness of Connected and Automated Vehicle Applications</title>
      <link>https://rip.trb.org/View/2675998</link>
      <description><![CDATA[The rigorous evaluation of safety critical Connected and Automated Vehicle (CAV) scenarios, faces some significant hurdles. Physical testing of scenarios (including edge-cases) presents risk and cost challenges as it is inherently dangerous, cost-prohibitive, and often non-reproducible. Additionally, purely virtual simulation lacks the real-world complexity of communication latency, interference, sensor noise profiles, and realistic representation of physical vehicle dynamics. To address this, the research team proposes using Mixed Reality (MR) co-simulation on a closed-course test track. This powerful alternative merges the real-world fidelity of a physical test platform (live sensor data, vehicle kinematics, real wireless communication channels) with the reproducible complexity of a virtual environment. This enables the safe and rigorous testing of otherwise impractical edge cases. The MR testbed facilitates comprehensive evaluation, addressing critical challenges for example: (1) Robustness and Reliability: It allows for precise injection of sensor degradation faults and failures and enables V2X reliability stress-testing in real-world communication and interference. (2) Cybersecurity and PNT Resilience: The platform safely simulates False Data Injection (FDI) and Denial of Service (DoS) attacks into the V2X communication channel, testing the Vehicle Under Test's Intrusion Detection Systems. Furthermore, it assesses system reliability when Position, Navigation, and Timing (PNT) data is compromised (e.g., via GNSS spoofing), evaluating the system's ability to use V2X data for positioning correction or safe mode transition. This framework leverages the validated utility of Hardware-in-the-Loop (HiL) platforms to rigorously evaluate the real-time performance and resilience of V2X protocols and sensor data fusion architectures on embedded edge computers. The project will leverage the existing highly-instrumented vehicle platform previously developed through the U.S. DOE ARPA-E NEXTCAR Program, which will serve as the Vehicle Under Test (VUT). Collaboration with TRC will be leveraged to facilitate the setup and validation of the MR testbed.]]></description>
      <pubDate>Mon, 02 Mar 2026 18:57:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2675998</guid>
    </item>
    <item>
      <title>Cybersecurity Assurance via AI-Driven Digital Twins for Transportation Safety  </title>
      <link>https://rip.trb.org/View/2663600</link>
      <description><![CDATA[Transportation infrastructure increasingly depends on networked sensor systems for structural health monitoring, yet many operational deployments lack robust data-integrity protections, rendering them vulnerable to cyber-physical attacks. Manipulated sensor readings can misrepresent bridge health, rail conditions, or load limits, thereby creating risks of undetected structural failure, service closures, or catastrophic crashes. Because cyber manipulation directly produces false-safe readings, delays critical maintenance actions, and conceals structural distress, cybersecurity protection constitutes a core safety requirement, not an ancillary concern, for modern monitoring infrastructure.
This project develops a secure, artificial intelligence (AI)-driven digital twin framework that continuously compares real-time sensor data against expected behavioral responses to detect spoofing, tampering, replay, and delay manipulation, and other cyber-physical disruptions. The digital twin is intentionally implemented as a lightweight behavioral model; its purpose is not full structural simulation but rather the generation of expected-response profiles that serve as the ground-truth reference for anomaly detection. Combined with secure sensing hardware, AI-based detection algorithms, and survivability logic, the integrated system maintains reliable monitoring capability even under partial cyber compromise. The framework supports the U.S. Department of Transportation (USDOT) Safe System Approach by preventing cyber-induced safety failures and provides a clear pathway to pilot deployment through a Python-based prototype, agency demonstrations, and structured partner engagement.

Key milestones include the twin baseline model, secure sensing validation, AI detection module completion , and a survivability demonstration with partner input. The resulting system provides transportation agencies with a low-cost cybersecurity layer that protects safety-critical sensing systems from data manipulation and disruption. Deliverables include a Python detection module, interactive dashboard, and validated datasets compatible with existing DOT workflows. By ensuring the trustworthiness of monitoring data, the proposed approach reduces hazard risk, strengthens maintenance decision-making, and scales across bridges, tunnels, and rail systems, offering a realistic and immediate path to pilot adoption within USDOT transportation-cybersecurity priorities.
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:23:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663600</guid>
    </item>
  </channel>
</rss>