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    <title>Research in Progress (RIP)</title>
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    <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>Assessing the Reliability of Hard Braking and Other Vehicle-Based Metrics as Surrogate Safety Measures</title>
      <link>https://rip.trb.org/View/2712175</link>
      <description><![CDATA[Transportation safety is a critical concern for agencies nationwide, and recent technological advances have enabled the collection of vast amounts of vehicle trajectory and behavioral data. Among these data, hard-braking events have emerged as a surrogate safety measure, which can be used to infer the possibility of near misses and crashes. Agencies and data providers increasingly rely on metrics such as hard braking, excessive acceleration, and high-speed cornering, derived from connected vehicle (CV) data, to identify risky driving behavior and to proactively address safety risks.

Unlike traditional crash data, which are retrospective and often delayed, surrogate safety measures offer real-time insights into roadway conditions and driver behavior. This immediacy allows for earlier identification of emerging safety concerns and the implementation of timely countermeasures. However, the reliability and validity of these surrogate measures are not universally established. Their effectiveness can vary depending on factors such as sight distance, geometric design, speed limits, traffic control devices, work zone configurations, and queue warning locations. For instance, a hard-braking event at a congested urban intersection may indicate different risks than one on a rural freeway.

The objective of this research is to rigorously evaluate the reliability and validity of hard braking and other vehicle-based metrics, such as excessive acceleration and high-speed cornering, as surrogate safety measures across diverse transportation environments. ]]></description>
      <pubDate>Tue, 09 Jun 2026 13:01:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712175</guid>
    </item>
    <item>
      <title>Neurocognitive Validation &amp; Transition Study – Phase 1, Rev B</title>
      <link>https://rip.trb.org/View/2703851</link>
      <description><![CDATA[The Office of Aerospace Medicine is evaluating alternative neurocognitive screening tools to support pilot medical certification and reduce reliance on the Federal Aviation Administration's (FAA’s) current single-vendor, proprietary test, which presents continuity and operational risk if the product becomes unavailable or compromised. The FAA has partnered with multiple developers to produce derivative tests tailored for aviation use; however, an independent expert assessment is required to determine whether these tools are ready for operational deployment or require additional validation. This research will provide that assessment, ensuring that neurocognitive impairment relevant to pilot performance can be reliably identified before it presents safety risk, and will directly inform whether the derivative tools can be adopted as-is or whether further reliability, feasibility, or validation studies are needed to support future implementation decisions.]]></description>
      <pubDate>Mon, 18 May 2026 10:37:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703851</guid>
    </item>
    <item>
      <title>Reliability-Aware Accessibility Measurement and Planning for Rural Transportation Systems</title>
      <link>https://rip.trb.org/View/2703797</link>
      <description><![CDATA[Reliable access to essential destinations is a persistent challenge in rural transportation systems, where long travel distances, limited infrastructure, and exposure to environmental disruptions can significantly affect mobility. Transportation accessibility is widely used in planning to evaluate how well transportation networks connect people to services and opportunities, yet most accessibility measures assume deterministic travel conditions and do not account for travel-time variability, weather disruptions, or infrastructure reliability. As a result, existing accessibility metrics may overestimate the practical ability of rural residents to reach essential destinations and provide limited guidance for transportation planning under uncertain conditions.
This project develops a reliability-aware accessibility measurement and planning framework for rural transportation systems. The research will extend traditional accessibility measures by incorporating transportation network uncertainty through scenario-based modeling of travel-time variability and disruption conditions. Reliability-aware accessibility metrics will be benchmarked against conventional accessibility measures and embedded within an optimization-based planning model that helps identify transportation interventions that improve reliable access under resource constraints. The framework will be demonstrated through a rural transportation case study using publicly available data and implemented as a prototype decision-support workflow for transportation planners.]]></description>
      <pubDate>Sat, 16 May 2026 11:55:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703797</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>Efficient system reliability assessment of shoreline seawalls: Applications to SEAHIVE (UM)</title>
      <link>https://rip.trb.org/View/2663225</link>
      <description><![CDATA[Seawalls play a critical role in protecting coastal transportation systems from erosion, flooding and storm surges. Yet their performance is deteriorating due to changes in structural capacity and increasing external demands, posing growing threats to coastal safety. Evaluating the reliability and risk of seawalls along the shoreline is essential for informed maintenance and repair decisions. However, the large scale of shoreline seawalls and the complex coastal and geotechnical conditions in Miami present significant challenges for system reliability analysis. This is a collaborative research project conducted in partnership with Texas State University. The objective of this research project is to develop an efficient and practical framework that integrates interdisciplinary expertise in geotechnical asset management, seawall design and construction, and reliability analysis to perform system reliability analysis of shoreline seawalls.
The proposed project builds on two lines of prior works. First, an effective and well-defined inspection rating system was developed to evaluate the conditions of mechanically stabilized earth (MSE) walls at Texas State University. Second, SEAHIVE®, a novel seawall composed of concrete perforated hexagonal prisms, was developed at the University of Miami and has been implemented in the Miami area for its ability to dissipate wave energy and protect habitats. Leveraging these advances, the proposed project will establish a unified framework for reliability assessment of shoreline seawalls.
The project consists of two phases: component-level and system-level reliability analysis. At the component level, the research team will develop an efficient and effective method to evaluate the reliability analysis of individual SEAHIVE® components. First, using available analytical models and experimental data, the team will define limit states that specify the conditions under which SEAHIVE® components perform adequately or fail. Second, the inspection rating method originally developed for MSE walls will be recalibrated for SEAHIVE® in the Miami area, following procedures established in prior work. Finally, these calibrated ratings will then serve as inputs to the defined limit states, enabling the calculation of reliability indices. The expected outcome of this phase is a practical guideline for engineers to quickly rate the seawall and determine the component reliability index.
Since seawalls function as interconnected systems rather than isolated units, the next phase is system-level analysis. Specifically, the team will elicit statistical correlations in seawall deterioration and soil conditions across different locations using inspection, measurement, and simulation data. An efficient system reliability analysis will then incorporate these correlations into component-level reliability analysis to compute the overall reliability index of seawalls along the shoreline. Together, the two phases will yield a practical decision support tool to efficiently inspect the shoreline seawalls and estimate the system reliability index in support of risk management and maintenance prioritization for seawalls.
]]></description>
      <pubDate>Sat, 31 Jan 2026 11:03:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663225</guid>
    </item>
    <item>
      <title>Cost-Effectiveness and Service Impacts of Bus Transit Priority Strategies


</title>
      <link>https://rip.trb.org/View/2636146</link>
      <description><![CDATA[Transit agencies across the United States are increasingly implementing transit priority strategies to improve service reliability, travel times, operating efficiency, and customer experience. Common transit priority measures include transit signal priority (TSP), bus-only lanes, queue jumps, stop consolidation, and bulb-outs.

As agencies invest in these strategies, there are increasing expectations to justify expenditures based on measurable outcomes, including travel time savings, reliability improvements, operating cost efficiencies, ridership growth, environmental benefits, safety outcomes, and return on investment. Minimal methods exist to evaluate the costs, benefits, and long-term effectiveness of transit priority treatments across varying service characteristics, roadway conditions, land use contexts, and institutional environments.

TCRP Synthesis 149: Transit Signal Priority: Current State of the Practice (2020) documents current agency practices, deployment approaches, technologies, implementation challenges, and lessons learned associated with TSP. TCRP Research Report 262: Transit Capacity and Quality of Service Manual, 4th edition (2026) advances methods for evaluating bus speed, reliability, and capacity. Research is needed to give transit agencies guidance on evaluating, comparing, prioritizing, and implementing bus transit priority investments.

OBJECTIVE: The objective of this research is to develop a guide, with supporting evaluation frameworks and decision-making tools, to enable transit agencies to assess, compare, prioritize, and communicate the costs, benefits, and effectiveness of bus transit priority strategies.]]></description>
      <pubDate>Mon, 08 Dec 2025 19:58:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2636146</guid>
    </item>
    <item>
      <title>Applying Crash Prediction Models Across Traffic Control and Facility Types



</title>
      <link>https://rip.trb.org/View/2558381</link>
      <description><![CDATA[The Highway Safety Manual (HSM) is used by transportation agencies for decisions on planning, design, and operational safety. The manual includes crash prediction models to estimate the expected safety performance of various functional classifications of roadway segments and intersections. However, crash prediction models for intersections in the HSM can sometimes produce unexpected and difficult-to-interpret results when comparing crash outcomes across different traffic control types. For example, signalization at intersections may not consistently yield the expected crash reductions, particularly for high-severity crashes.

While the HSM is under revision to enhance these models and provide broader guidance in their application, there is a need for clear direction on how to apply the models to conduct comparative safety evaluations across traffic control and facility types. Research is needed to establish and quantify differences in predictive crash outcomes derived from the HSM crash prediction models when comparing the safety performance of different traffic control types. The findings will support improving safety in project concepts proposed by transportation planners and designs by engineers.

OBJECTIVE: The objective of this research is to develop a framework and guide, including application recommendations and tools, to support the use of HSM crash prediction models when analyzing traffic control types for a given location or facility type.]]></description>
      <pubDate>Wed, 28 May 2025 14:12:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558381</guid>
    </item>
    <item>
      <title>Silo Storage Impact on Asphalt Mixture Performance

</title>
      <link>https://rip.trb.org/View/2558389</link>
      <description><![CDATA[As state departments of transportation (DOTs) transition from volumetric-based to performance-based specifications through balanced mix design (BMD), it is essential to evaluate how various plant operations, such as silo storage, affect the performance properties of asphalt mixtures. Although several agencies have implemented BMD during the design phase, volumetric properties are still predominantly used for mixture acceptance. Consequently, any changes in mixture properties that occur during production are not fully captured through volumetric criteria alone. Adopting BMD test results for mixture acceptance could address this limitation; however, several practical concerns, such as aging of asphalt mixtures due to silo storage, must be accounted for prior to full implementation.

Research is needed to evaluate how silo storage affects BMD test results. The findings will help state DOTs, asphalt mixture designers, and producers design and produce asphalt mixtures that achieve the desired performance.

The objective of this research is to develop guidelines to address silo storage impact on asphalt mixture performance using a BMD framework.]]></description>
      <pubDate>Wed, 28 May 2025 13:31:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558389</guid>
    </item>
    <item>
      <title>Functional reliability of tunnels and its impact on transportation network resilience (UTI-UTC 21)
</title>
      <link>https://rip.trb.org/View/2543416</link>
      <description><![CDATA[This project explores how the functional reliability of tunnel systems influences the overall resilience of transportation networks during normal operations and disruptive events. By integrating operational data, structural performance metrics, and network modeling techniques, the research establishes a framework to assess the probability and consequences of tunnel functionality loss due to hazards such as structural failures, natural disasters, or extreme weather events. The project develops simulation tools to evaluate tunnel vulnerability within larger transportation systems and models cascading effects of tunnel outages on traffic flow, connectivity, and recovery timelines. Using case studies and real-world tunnel data, it identifies critical points of failure and proposes strategies for improving design, maintenance, and emergency response planning. The findings support infrastructure owners and public agencies in enhancing tunnel system resilience and ensuring network reliability in the face of growing risks.
]]></description>
      <pubDate>Wed, 07 May 2025 18:05:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543416</guid>
    </item>
    <item>
      <title>Understanding and Communicating Reliability of Crash Prediction Models</title>
      <link>https://rip.trb.org/View/2446864</link>
      <description><![CDATA[The objectives of this research were to develop guidelines for: (1) the quantification of the reliability of crash prediction models including crash modification factors and/or functions (CMFs) and safety performance functions (SPFs) for practitioner use; (2) user interpretation of model reliability; and (3) the application of crash prediction models accounting for, but not limited to assumptions, data ranges, and intended and unintended uses. The guidelines were to address the following, at a minimum: methods to improve the reliability of crash prediction models; implications of assumptions; crash Prediction Model validation; data quality; use and reliability of calibrations; combining CMFs; enhanced accuracy and reliability as a result of increased model complexity; implications of crash prediction model limitations on safety programs and policy; and effective communication of crash prediction model outputs to a variety of audiences. The guidelines were to include a number of case studies or illustrative examples that demonstrate the quantification and user interpretation of crash prediction models reliability. Examples may illustrate the application of crash prediction models accounting for, but not limited to assumptions, data ranges, and intended and unintended uses. The guidelines were intended to assist practitioners and researchers in addressing the application and understanding and communicating the model outcomes. The research results may be incorporated in a future edition of the AASHTO HSM.
]]></description>
      <pubDate>Mon, 28 Oct 2024 17:57:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2446864</guid>
    </item>
    <item>
      <title>Mobility Analysis and System Transportation Efficiency Research (MASTER)</title>
      <link>https://rip.trb.org/View/2397880</link>
      <description><![CDATA[Over the past 25 years, the Minnesota Department of Transportation (MnDOT) has participated in a mobility analysis and research pooled fund along with a combination of state departments of transportation and the Federal Highway Administration.  The Principal Investigator, the Texas A & M Transportation Institute, has been providing valuable research related to performance measures and tools for monitoring mobility conditions to its state department of transportation (DOT) partners. Mobility Analysis and System Transportation Efficiency Research (MASTER) members contribute annually to the pooled fund and are reimbursed for travel to the annual meeting that rotates locations among State DOTs. A work plan is developed and approved annually by the State DOT members. The most recent work plans were funded through TPF-5(440) – Support for Urban Mobility Analyses (SUMA). That project number was closed out on August 31, 2023. To view recent work plans and project results, visit the program website at: https://tti.tamu.edu/documents/umi/data/suma/files.htm. OBJECTIVE: The pooled fund study scope focuses on mobility and reliability performance measures, data and issues. New emphasis areas include emerging data sources, freight movement, arterial street mobility issues, reliability performance measures, and addressing the agency challenges for the Infrastructure Investment and Jobs Act requirements.]]></description>
      <pubDate>Wed, 26 Jun 2024 09:52:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2397880</guid>
    </item>
    <item>
      <title>Analyzing and Predicting Truck Travel Time Reliability</title>
      <link>https://rip.trb.org/View/2394459</link>
      <description><![CDATA[Truck travel time reliability (TTTR) is one of the most important performance measures for assessing freight movement on the interstates. The Virginia Department of Transportation (VDOT) and the Office of Intermodal Planning and Investment (OIPI) have been reporting TTTR as required by the FHWA and using TTTR in various project planning and performance measurement processes. They are interested in data-driven approaches to identify the locations and causes of unreliable truck travel times and to predict TTTR metrics. This study will address the research needs by developing a systematic method to identify the location, time period, and causes of unreliable truck travel times, and develop models to predict TTTR.   ]]></description>
      <pubDate>Tue, 18 Jun 2024 09:51:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2394459</guid>
    </item>
    <item>
      <title>Improved Resiliency of Transportation Networks through Connect Mobility</title>
      <link>https://rip.trb.org/View/2329687</link>
      <description><![CDATA[A significant number of bridges (older bridges in particular) in the Southeastern and Central region of United States have been designed and constructed according to older seismic provisions. Based on an article by Wong et al. (2005), the economic loss from the Charleston region could reach over $14 billion if the 1886 Charleston earthquake were to happen again. Due to outdated seismic design strategies used for older bridges, recent research has investigated potential damage in Charleston. However, most of these investigations do not account for the simultaneous aspects of bridge importance (such as centrality, historical significance, and traffic capacity).  Furthermore, these prior investigations do not consider the actual detailing of critical structural connections, such as the critical pile to bent cap connection. This connection region is depended upon for energy dissipation while simultaneously providing structural integrity during an event. Full-scale experimental studies performed at the University of South Carolina were used to assess projected performance of these connections in a seismic event. This project develops a new tool that is informed with actual structural behavior gained through full-scale experimental investigations and combines centrality, historical significance, and traffic capacity to assess expected damage. The results are useful for informing placement of monitoring systems, identification of potential retrofit strategies, and optimizing network performance.  One goal of the work is technological transfer. The research findings can be used to assist the Department of Transportation in identification of the most critical bridges in the network for purposes of instrumentation, meaning which bridges should be monitored and, for those bridges, which specific regions should be monitored to rapidly assess damage after a seismic event. This information can then be utilized for routing of traffic and for the assessment of potential retrofitting strategies, thereby improving reliability of the transportation system. The tool runs on Matlab and includes transportation network and seismic demand visualization. Results are presented in sets of graphics and tables through a multi-window graphical user interface.]]></description>
      <pubDate>Tue, 30 Jan 2024 10:10:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329687</guid>
    </item>
    <item>
      <title>Sensitivity and Reliability for Cybersecurity of Traffic with Autonomous Vehicle Participation</title>
      <link>https://rip.trb.org/View/2301342</link>
      <description><![CDATA[The use of Sensitivity and Reliability Analysis is not new in transportation studies, where it has been utilized in traffic flow related problems. However, in this project the research team proposes to use it for helping feedback control decisions in real-time. A basic reason for the use of feedback is to render a closed loop system less susceptible to the effects of plant parameter variations than an open-loop system having the same nominal input-output characteristics. The team will be leading to obtaining a method and generating software to identify which components either on the vehicle or infrastructure would need to be changed for the highly autonomous vehicle (HAV) to be more cybersecure. The team proposes to focus on three thrusts: (1) identify emerging cybersecurity threats to Highly Automated Transportation Systems (HATS); (2) analyze threat scenarios; (3) mitigation methods utilizing sensitivity functions. The team shall base this research on both their results in the previous University Transportation Center (UTC) at The Ohio State University and their earlier work on decentralized systems.]]></description>
      <pubDate>Fri, 01 Dec 2023 05:14:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2301342</guid>
    </item>
    <item>
      <title>Multiple-Sensor Weigh-In-Motion Systems to Enhance Data Accuracy and Reliability



</title>
      <link>https://rip.trb.org/View/2286618</link>
      <description><![CDATA[Weigh-in-motion (WIM) systems measure the axle weight of moving vehicles as they traverse WIM measurement sites. WIM data are essential for the design, assessment, and maintenance activities related to pavement and bridge infrastructure and may be used for monitoring and enforcing motor carrier truck weights and dimensions and collecting tolls. 

WIM sensors vary from instrumented metal plates to piezoelectric, quartz, and strain gauge strip sensors. Their accuracy is evaluated with reference to static loads referenced in the American Society for Testing and Materials, Standard Specification for Highway Weigh-In-Motion (WIM) Systems with User Requirements and Test Methods, ASTM E1318-09 (2017) and is affected by the interaction between roadway roughness, vehicle dynamics, and speed. The narrow strip-type WIM sensors that sample a smaller part of the dynamic axle loads applied to the road may be strategically spaced to capture more data points of the dynamic axle load waveforms by using multiple strip sensors (two or more). The use of multiple strip sensors will potentially result in increased data reliability and more accurate estimates of the corresponding static axle loads, reduce measurement error, improve data quality, and reduce maintenance costs.  

State departments of transportation (DOTs) require accurate and cost-effective WIM technology. Research is needed to assess and optimize multiple-sensor spacing and determine its benefits and feasibility.    

OBJECTIVE: The objective of this project is to develop a model to determine the optimal number of WIM strip sensors and array layout, given specified levels of accuracy and reliability considering pavement, environmental, and traffic conditions. ]]></description>
      <pubDate>Mon, 06 Nov 2023 16:33:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2286618</guid>
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