<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>Developing a Balanced Mix Design (BMD) Framework for Low-Volume Surface Mixtures</title>
      <link>https://rip.trb.org/View/2730683</link>
      <description><![CDATA[Full implementation of balanced mix design (BMD) for dense-graded non-polymer-modified maintenance surface mixtures was achieved by the Virginia Department of Transportation (VDOT) in 2024. Most of the mixtures used to develop the BMD criteria incorporated 30% RAP and PG 64S-22 binder, a common combination in Virginia. The criteria developed from these mixtures were based on desired performance characteristics for primary or high-volume secondary routes, which have greater traffic levels and typically thicker pavement structures than low-volume routes. However, as low-volume routes comprise over 90,000 lane-mi of VDOT-maintained roads, there is a need to develop performance criteria to address their specific needs.  
OBJECTIVE: The primary purpose of this study is to develop a BMD framework specifically designed for dense-graded non-polymer-modified asphalt surface mixtures applied to low-volume roads. The performance needs of low volume roads along with mixture properties and performance will be evaluated to determine initial BMD threshold values to ensure adequate mixture performance. Historical information for a variety of routes and mixtures will be assessed to determine common properties indicative of their performance. Benchmark testing will be performed for current mixtures used on low volume routes. Modelling of low-volume routes will be performed to evaluate mixture properties necessary for acceptable performance. A strategy for the adoption of BMD criteria for these mixtures will be developed.  
]]></description>
      <pubDate>Thu, 16 Jul 2026 07:52:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2730683</guid>
    </item>
    <item>
      <title>Effect of Using RAP on Gravel Roads</title>
      <link>https://rip.trb.org/View/2720399</link>
      <description><![CDATA[Recycled Asphalt Pavement (RAP) has been used in several construction applications, including blending of RAP with virgin aggregates in gravel roads. RAP is intended to reduce costs and offer environmental benefits through reduced consumption of natural aggregates, while adding cohesion, which can add strength and bind particles to reduce raveling and loss of aggregate. RAP can also reduce the permeability of the surface course by decreasing the void volume, which may have beneficial effects of reducing dust loss and creating a tighter particle packing that aids stability. However, the beneficial effects of RAP may decrease over time as the oils in the RAP dry out. Furthermore, RAP can make blading operations more difficult as the material adheres to the moldboard in hot weather or becomes hard and brittle in cold weather. The objective of this study is to help agencies better understand the potential advantages and disadvantages of using RAP in gravel roads by synthesizing the existing research, surveying local Minnesota agencies, performing field and laboratory tests on new and existing sections of gravel roads containing RAP, and conducting a life-cycle cost analysis (LCCA). ]]></description>
      <pubDate>Tue, 30 Jun 2026 15:25:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2720399</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>Safety Performance of Safe System Treatments for Corridors and Intersections that May Impact Capacity</title>
      <link>https://rip.trb.org/View/2712178</link>
      <description><![CDATA[Transportation agencies are increasingly adopting the Safe System Approach (SSA), which emphasizes a holistic approach to eliminating all fatal and serious traffic injuries on road segments and at intersections. Historically, efforts to reduce congestion often led to the addition of through lanes, implementation of short auxiliary lanes at intersections to facilitate right turns and through movements, and other treatments. However, agencies are now exploring lane reductions—commonly referred to as right-of-way reallocation, road diets, or reconfigurations—to improve safety. In some cases, roads previously widened to accommodate peak-hour traffic are being reevaluated through the lens of the SSA.

Implementing these changes requires a comprehensive understanding of the associated safety impacts and the ability to predict crash outcomes resulting from modifications to roadway capacity. Safety outcomes associated with capacity modifications carry substantial weight in routine planning and operational decisions. Current evidence, however, is limited, and context-specific effects, especially for vulnerable road users, are not well quantified. Additional research is therefore needed to quantify how short auxiliary lanes and cross-section changes affect exposure, likelihood, and severity.

The objectives of this research are to (1) quantify the safety impacts of SSA treatments that may affect roadway capacity and (2) develop analysis methodologies, such as crash modification factors (CMFs) and crash prediction models, to evaluate how these treatments affect crash exposure, likelihood, and severity across all road users and crash types, including pedestrian and bicyclist crashes. The research results can enable planners, designers, traffic engineers, and other decision-makers to make evidence-based choices that balance safety with operational and capacity needs.]]></description>
      <pubDate>Tue, 09 Jun 2026 15:00:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712178</guid>
    </item>
    <item>
      <title>Highway Safety Manual Crash Prediction Models of Arterial Weaving Segments</title>
      <link>https://rip.trb.org/View/2712176</link>
      <description><![CDATA[More than half of U.S. roadway deaths and nearly two-thirds of pedestrian fatalities occur on non-freeway arterials. Arterial sections with weaving maneuvers are complex for all road users to navigate and traverse without incidents or collisions.

The Code of Federal Regulations requires determination of whether the location, configuration, geometric design, and signing related to a proposed change in access may be reasonably expected to serve the anticipated traffic of the Interstate system in a manner that is conducive to safety, durability, and economy of maintenance. For many existing and proposed alternative designs, the safety of the weave is not quantified between ramps. Examples include cloverleaf designs with adjacent intersections and crossing weaves from ramps to downstream left turns. A better understanding of crash outcomes is needed for a variety of rural and urban speeds and contexts.

As part of NCHRP Project 15-66, “Operational Performance and Safety Effects of Arterial Weaving Sections,” crash data and conflict data obtained in the field and a driving simulator were analyzed to assess the safety performance of several types of arterial weaving sections. The results of the safety analysis did not provide a definitive relationship between the length and vehicle maneuvers of arterial weaving sections and crashes or conflicts; however, sufficient information was found to suggest additional research in this area would yield promising results toward developing a methodology for predicting the safety performance of arterial weaving sections suitable for inclusion in the AASHTO Highway Safety Manual (HSM).

The objective of this research is to develop a crash prediction methodology, safety performance functions (SPFs), to assess different types of arterial weaving sections, suitable for inclusion in the HSM.]]></description>
      <pubDate>Tue, 09 Jun 2026 14:53:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712176</guid>
    </item>
    <item>
      <title>Evaluation of Expanded Uses of Residential Driveway Temporary Signals (RDTS): Turn Lane Volumes and Storage</title>
      <link>https://rip.trb.org/View/2712243</link>
      <description><![CDATA[Traffic management for work zones in rural two-lane two-way highways can be challenging as these segments often intersect with low volume side streets and access points. Residential Driveway Temporary Signal devices (RDTS) (formerly Driveway Assistance Device (DAD)) have provided a solution to improve mobility for traffic entering a one-lane, two-way work zone. These devices have been implemented for several years under experimental evaluations by numerous state and local agencies across the United States. In January 2025, the optional use of RDTS was approved by the Federal Highway Administration (FHWA) under Interim Approval (IA) 23. However, whereas prior experimental use of the DAD allowed the device to be deployed at minor cross-streets and commercial driveways, IA-23 only allows for the RDTS to be used at residential driveways. To that end, this proposed research project will assess the operational effects of RDTS when used at non-residential access points to develop guidance that will inform future policy-making related to the device. The proposed project objectives build upon the work already performed with RDTS/DAD and other temporary work zone signal systems by considering their use at uncontrolled rural low-volume side streets and access points. Of particular interest is the ability of RDTSs to handle traffic on low volume side streets and access points with a focus on turning volumes related specifically to the designed turn lane storage capacity (i.e., the amount of left and right turn storage). The results will include implementation guidance that will delineate appropriate usage cases of the RDTS, including appropriate mainline and access point exiting volumes based on storage capacity of the turn lane(s) departing the access point. Guidance related to future Manual on Uniform Traffic Control Devices (MUTCD) language will also be provided where applicable and appropriate.]]></description>
      <pubDate>Tue, 09 Jun 2026 12:32:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712243</guid>
    </item>
    <item>
      <title>Advanced Technologies and Data Analytics for Safe, Smart, and Efficient Transportation (ASSET)</title>
      <link>https://rip.trb.org/View/2709572</link>
      <description><![CDATA[This project assists the Massachusetts Department of Transportation (MassDOT) with (A) calibrating safety models for urban and suburban arterial intersections and developing artificial intelligence models for (B) detecting sidewalks and (C) counting multimodal trips.  

There are three main goals:

(A) Calibrate the Safety Performance Functions (SPFs) in Chapter 16.6.4 of the Highway Safety Manual, 2nd Edition (HSM2), along with the associated parameters, for the twelve types of urban and suburban intersections in Massachusetts using the most recent data.

(B) Develop an Artificial Intelligence (AI) model to automate the detection and mapping of sidewalks from publicly available aerial imagery. Also, the model will be used to identify changes in sidewalks using aerial imagery from multiple years.

(C) Leverage AI to automate the counting of pedestrians, active transportation modes (such as bicycles and e-scooters), and site-generated trips from new developments. The results of this task will form the basis for developing AI and/or statistical models to estimate multimodal trip counts required for transportation planning purposes.]]></description>
      <pubDate>Wed, 03 Jun 2026 15:27:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709572</guid>
    </item>
    <item>
      <title>SPR-5007: Performance Evaluation and Development of Cost-Effective DWPT Pavements</title>
      <link>https://rip.trb.org/View/2709428</link>
      <description><![CDATA[The proposed research work will address key questions that remain among policymakers, fleet operators, investors, and vehicle manufacturers regarding the cost, performance, and viability of Dynamic Wireless Power Transfer (DWPT) technologies. This effort will lay the groundwork for formalizing largescale public-private partnerships necessary to support the deployment of multi-mile DWPT corridors at both interstate and intrastate levels.]]></description>
      <pubDate>Wed, 03 Jun 2026 13:25:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709428</guid>
    </item>
    <item>
      <title>Investigating Contributing Factors and Potential Countermeasures to Reduce Pedestrian Crashes at Intersections</title>
      <link>https://rip.trb.org/View/2705384</link>
      <description><![CDATA[The Commonwealth of Virginia has experienced an increase in pedestrian related crashes, particularly following the COVID-19 pandemic in 2020. Pedestrian fatalities have increased approximately 19% - from an average of 120 per year to 143 per year - when comparing the three years before (2017-2019) the COVID-19 pandemic business closures to the three years after (2021-2023) the pandemic closures (VDOT, 2025). Anecdotal evidence suggests that certain geometric design elements—including tight right-turns, skewed intersections, and poor crosswalk placement or setback—may reduce sight distance and increase the risk of vehicle-pedestrian collisions. Concurrently, vehicle design changes, particularly the growing prevalence of larger A-pillars in newer vehicles, may exacerbate driver blind spots and further obstruct pedestrian visibility at intersections.

This research seeks to investigate how specific intersection design features and vehicle design characteristics contribute to pedestrian fatalities at intersections. The goal is to identify high-risk design conditions and vehicle configurations that impair visibility or increase the likelihood of fatal pedestrian-vehicle conflicts. A thorough understanding of the factors contributing to the increase in pedestrian fatalities will help 
Virginia Department of Transportation (VDOT) implement countermeasures proactively to improve pedestrian safety at intersections across the state. The findings can be used to inform geometric design standards, identify and prioritize intersections for pedestrian safety improvements, and support efforts to include direct vision obstruction in vehicle safety ratings. 
]]></description>
      <pubDate>Thu, 21 May 2026 08:03:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2705384</guid>
    </item>
    <item>
      <title>Pittsylvania County Road Orders 1767-1783</title>
      <link>https://rip.trb.org/View/2702871</link>
      <description><![CDATA[Road history projects undertaken by VTRC establish the feasibility of studies of early road networks and their use in the environmental review process.  This proposed volume marks the 33rd entry in the Historic Roads of Virginia series, initiated in 1973 by the Virginia Highway & Transportation Research Council (subsequently VTRC). Pittsylvania County Road Orders 1767-1783 will further the coverage of the early southern Virginia transportation records begun in the previously published Brunswick County Road Orders 1732-1749, Lunenburg County Road Orders 1746-1764, Amelia County Road Orders 1735-1753, and Halifax County Road Orders 1752-1767.

This volume covers the period of Pittsylvania County’s greatest extent, from its creation from Halifax County in 1767, through its division to create Henry County in 1777, and extending to the end of the Revolutionary War, which saw significant military contributions, including essential supply centers, in Pittsylvania. By the second half of the 18th century, Pittsylvania County contained important east-west and north-south transportation routes. The county’s early transportation records provide information relating to transportation connections not only with neighboring counties and other counties farther to the north, east and west in Virginia, but also with with neighboring North Carolina. This publication will have particular application to the cultural resource research relating to transportation projects in this area of southern Virginia. 

If questions arise about early roads once a VDOT road improvement project is already underway (or nearly underway), primary historical research of this nature can take 6 to 12 months to complete. Therefore, this volume can be a source of potentially significant cost savings for VDOT, including the avoided costs of project delays and avoided consultant costs for cultural resource studies should questions arise. 
]]></description>
      <pubDate>Thu, 14 May 2026 10:58:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702871</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>Assessing the Impacts of Safety-Focused Design Interventions on Arterial Roadways</title>
      <link>https://rip.trb.org/View/2677552</link>
      <description><![CDATA[Arterial roadways serve as critical connectors in urban transportation networks, yet their design often prioritizes vehicular mobility over safety. Despite the widespread application of safety-focused infrastructure interventions on local and collector streets, similar strategies are rarely implemented on arterials due to concerns over congestion, emergency response, and operational efficiency. However, these design choices have proven to result in unsafe conditions.

This project investigates how infrastructure design interventions can improve safety on arterial roadways while addressing operational and institutional constraints. The research follows a phased approach. First, it examines the historical, regulatory, and policy factors that have limited the adoption of safety-focused interventions on arterials, including the influence of fire codes and emergency response standards. Second, it assesses the real-world impacts of infrastructure changes on speeds, crashes, and emergency response metrics. Finally, it synthesizes findings to develop actionable recommendations and a decision-making framework for arterial design.

By providing an evidence-based understanding of how design choices affect safety, mobility, and community outcomes on arterial corridors, this study aims to inform infrastructure design practices.]]></description>
      <pubDate>Tue, 03 Mar 2026 20:07:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2677552</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>LLM-Orchestrated Multi-Layer Digital Twin Network for Cyber-Resilient Traffic Management</title>
      <link>https://rip.trb.org/View/2663602</link>
      <description><![CDATA[Modern connected traffic systems are increasingly vulnerable to cyberattacks capable of propagating rapidly across networked infrastructure, inducing unsafe signal states, traffic congestion, and emergency response delays. Existing anomaly detection approaches including statistical thresholds, rule-based Automated Traffic Signal Performance Measures (ATSPM) and Signal Phase and Timing (SPaT) flags, and classical machine-learning methods such as Isolation Forest and one-class Support Vector Machines operate on limited data modalities and cannot capture cross-layer cyber-physical interactions or operator intent, leaving critical detection gaps in complex attack scenarios.
This project develops a distributed multi-layer digital twin (DT) network for urban traffic systems, enhanced by a large language model (LLM) for context-aware cyber anomaly detection. The framework mirrors physical traffic behavior, cyber infrastructure status, and operational decision processes across a corridor of 4–6 interconnected intersections, enabling early identification of unsafe and malicious events that threaten roadway safety. Each traffic unit is represented by coordinated Physical, Cyber, and Decision Layers: the Physical Layer models real-time mobility and safety conditions using ATSPM, SPaT/MAP data, and detector activity; the Cyber Layer mirrors controller firmware, communication telemetry, and roadside unit status; and the Decision Layer captures operator actions, timing plan updates, and agency-defined safety constraints. A customized transportation-aware LLM ingests both structured telemetry and unstructured logs to generate semantic feature embeddings that capture cross-layer and cross-node dependencies.
A hybrid neural anomaly detection engine integrates Temporal Convolutional Networks (TCNs) to learn evolving traffic and communication behaviors over time with Graph Neural Networks (GNNs) to capture spatial interactions and coordinated disruptions across interconnected intersections. This TCN–GNN architecture enables accurate recognition of both localized cyber intrusions and distributed corridor-level attacks. Detection performance is validated against controlled cyber-attack scenarios—including SPaT spoofing, firmware manipulation, and malicious timing-plan overrides—executed within the DT environment. Upon anomaly detection, the LLM generates actionable mitigation suggestions, such as isolating compromised controllers or reverting to safe fallback signal plans, which are evaluated within the digital twin to ensure that every recommendation supports operational safety, low latency, and service continuity.
The 12-month effort proceeds in two phases: development and calibration of the distributed multi-layer DTs with LLM integration for context modeling, followed by anomaly detection training, validation, and mitigation evaluation. Target performance metrics include detection accuracy of at least 90%, false-positive rates below 10%, decision-support latency improvements of at least 30%, and safety metric improvements of at least 20%. The project delivers a pilot-ready prototype, detailed deployment guidelines, and an open software repository to accelerate adoption by transportation agencies. 
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:28:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663602</guid>
    </item>
    <item>
      <title>Enhancing Chain-Up Infrastructure and Compliance in Utah's Mountain Corridors: A Data-Driven Evaluation</title>
      <link>https://rip.trb.org/View/2655751</link>
      <description><![CDATA[This project evaluates chain-up infrastructure and traction-device compliance in Utah's mountain corridors, focusing on how roadway geometry, winter operations, and driver behavior affect chain-up performance during storm events. Using geospatial analysis, operational data, and field-informed insights, the study identifies locations where existing chain-up facilities may be undersized, poorly situated, or constrained by topography. The project also develops artificial intelligence (AI)-generated videos that explain operational challenges, noncompliance impacts, and potential improvement strategies to both practitioners and the traveling public. Project findings will inform infrastructure upgrades, policy refinements, and improved communication practices, with methods and products readily transferable to mountain corridors in other western states.]]></description>
      <pubDate>Mon, 19 Jan 2026 17:04:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655751</guid>
    </item>
  </channel>
</rss>