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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzk0IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Corridor Speed Management Strategies Toolbox</title>
      <link>https://rip.trb.org/View/2693730</link>
      <description><![CDATA[This project will develop a repository of speed management countermeasures that are applicable for use in Virginia based on existing evidence-based research of traffic calming and speed management practices in the United States. The scope will be limited to applications for state and regional highways. The toolbox will include countermeasure definitions and a description of the appropriate context for application. The countermeasure details may include contexts with evidence for speed management effectiveness, contexts where countermeasures may be appropriate, and contexts where further research is needed to justify their use. This contextual guidance will provide useful information for practitioners in Virginia.     ]]></description>
      <pubDate>Thu, 16 Apr 2026 10:46:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2693730</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>Optimizing External Human-Machine Interfaces (eHMIs) Designs in Autonomous Vehicles to Improve Communication with Drivers and Bicyclists</title>
      <link>https://rip.trb.org/View/2691668</link>
      <description><![CDATA[Autonomous Vehicles (AVs) will transform road safety and efficiency in the years to come, but achieving this requires large-scale deployment, trust, and understanding from all human road users, including drivers and bicyclists. External Human-Machine Interfaces (eHMIs) are becoming a crucial part of the process, enabling intuitive communication between AVs and other road users. This project aims to develop, assess, and optimize the concept of eHMIs to foster positive perceptions, build trust, and ensure safe interactions in mixed traffic scenarios. This study will involve a test of about 40 participants who will interact with AVs fitted with various eHMI prototypes under controlled conditions using driving and bicycle simulators. Behavioral metrics like the perception-reaction time (PRT), the perceived level of comfort, and the perceived level of trust, as well as transportation metrics like travel time, intersection clearance time, and near-miss incidents, will be assessed for different designs for the eHMI, including visual-based (LED Displays, Symbolic Messages, Color-coded Signals, Animated Indicators, etc.) and multimodal designs. Longitudinal experiments will measure the impact of acclimatization and determine the best eHMI setups, followed by field tests under realistic conditions for verification. User-focused optimization tools will also be designed to adapt enhanced eHMI setups to various demands and scenarios. Expected outcomes will include best-in-class eHMI designs for increased road safety, operational efficiency, and user confidence, providing valuable guidance for city planners, policymakers, and AV manufacturers.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:39:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691668</guid>
    </item>
    <item>
      <title>Evaluating User Acceptance and Effectiveness of Cognitive Measurements and Intervention for Shared Autonomy</title>
      <link>https://rip.trb.org/View/2690985</link>
      <description><![CDATA[Vehicles equipped with automated driving systems (ADS) have become more widespread in the trucking industry. On the one hand, ADS are known to be susceptible to occasional errors in environment perception, but on the other, ADS can demonstrate safer and more efficient behavior in situations where the driver is cognitively impaired. Shared autonomy systems thus have the potential to combine the best of both paradigms. Some early instantiations of such shared autonomy ADS use measurements of the human cognitive state to perform interventions, either in the form of sensory feedback, and/or by actively taking over the driving task. The main objective of this project is to address the gap in research on the effectiveness and acceptance of cognition-aware shared-autonomy methods with respect to the overall system safety. Qualitative data will be collected through semi-structured interviews with truck drivers and systematically encoded into operational design requirements and hypothesis-driven performance metrics that directly inform the design of cognition-aware shared autonomy systems. The research team will perform a driving simulator study that enables a controlled evaluation of adaptive cognition-aware intervention policies, including rule-based and data-driven triggering mechanisms that dynamically adjust system behavior based on real-time cognitive interventions. Researchers will study how specific design choices in cognition-aware intervention policies (e.g., trigger thresholds, modality selection, and intervention persistence) influence system acceptance, misuse, and compliance, enabling actionable design guidance beyond descriptive acceptance analysis. The datasets collected inform policy on the use of ADS in both drayage and long-haul trucking. This project will develop a methodology for designing and evaluating cognition-aware behavioral interventions that couple driver monitoring outputs with explicit control and feedback policies, enabling reproducible comparison across intervention strategies and deployment contexts.]]></description>
      <pubDate>Thu, 09 Apr 2026 14:23:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2690985</guid>
    </item>
    <item>
      <title>Evaluation of Cement-Based Pavement Markings</title>
      <link>https://rip.trb.org/View/2689760</link>
      <description><![CDATA[Pavement markings provide continuous safety information to roadway users related to the roadway alignment, vehicle positioning, and other important driving-related tasks. During nighttime hours on many roadways, pavement markings are the only roadway asset to guide and regulate traffic, and their visibility becomes even more critical during nighttime wet conditions. Additionally, today pavement markings must be visible not only to human drivers but also to the machine vision technology of Advanced Driver Assistance Systems (ADAS) common in many vehicles.
For these reasons, it is important that pavement markings are performing well and are both durable and retroreflective (provide nighttime visibility) to aid with safe roadway navigation. However, there are many factors that can affect pavement marking performance, such as the environment, traffic, and winter maintenance practices. The Vermont Agency of Transportation (VTrans) experiences significant challenges with all of these factors. Vermont’s climate, which has low winter temperatures and harsh freeze-thaw cycles, places considerable strain on a pavement marking material’s capacity to remain bonded to the pavement surface. The repeated freeze-thaw cycles have caused marking materials to crack or peel. The tire abrasion from high traffic volumes, especially heavy truck traffic, accelerates wear on pavement markings, and winter maintenance practices, particularly in northern states like Vermont, significantly impact the longevity and effectiveness of pavement markings. The frequent mechanical abrasion from snowplows accelerates marking degradation, and deicing agents cause chemical degradation such as fading, stripping, and surface damage.
Besides the potential safety risks to roadway travelers, there are other consequences for rapidly deteriorating pavement markings. The financial costs to VTrans for frequently replacing markings can be significant. Costs include not only the material, equipment, and labor for installation, but also the administrative costs for programming and managing striping contracts, as well as indirect economic costs for disruption to traffic. The traffic disruption can also have a negative impact on the traveling public, such as traffic congestion, delays, and driver frustration.
However, a relatively new pavement marking material, a polymer modified cement pavement marking called Enduramark, has a high potential for being more durable than most other marking materials. The cement-based marking has performed well for over three years in heavy snowplow environments. With a longer service life, it also has the potential for having a highly competitive annualized cost. The purpose of this study is to conduct a measured evaluation of the Enduramark’s performance on VTrans roadways, determine an estimated service life, and from the service life calculate the material’s annualized cost. The study will support the Agency’s Strategic Plan Goal 2: Grow Vermont’s economy by providing a safe, reliable, and efficient transportation system in a state of good repair.]]></description>
      <pubDate>Wed, 08 Apr 2026 09:40:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689760</guid>
    </item>
    <item>
      <title>Naïve Subject Testing – Suite Emergency Passage Features</title>
      <link>https://rip.trb.org/View/2686617</link>
      <description><![CDATA[Applicants for type design approval are working to support their airline customers by installing passenger suites that include doors between the passenger and exit.  To install these doors, an exemption to 25.813(e) is required in which one of the conditions of the exemption is that the applicant must show the emergency passage feature (EPF) is simple and obvious to open.  Applicants achieve this showing by completing a naïve subject test.  The test method currently being used combines test parameters from the naïve subject test for evacuation specified in Part 25 Appendix J, the naïve subject test for life vest donning specified in TSO-C13, and the naïve subject test for floor proximity markings outlined in AC 25.812-1 and AC 25.812-2a.  The test method has several variables involved that are debated amongst regulators and applicants on how they should be controlled.  As a result, the test is run inconsistently, and variations in how the test is performed has led to an unlevel playing field amongst applicants, delays in certification testing by seat suppliers, and conflicting design approvals.   ]]></description>
      <pubDate>Wed, 01 Apr 2026 10:17:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2686617</guid>
    </item>
    <item>
      <title>Comparing Traffic Safety Risks Across Transportation Modes</title>
      <link>https://rip.trb.org/View/2685708</link>
      <description><![CDATA[This research project compares traffic safety risks across different transportation modes. Very few studies have quantified crash risk across modes, especially recently in the United States. First, this study will calculate and compare mode-specific crash rates, using data from Utah and potentially elsewhere. Second, these mode-specific crash rates will be adjusted for key traffic safety risk factors, about the locations where and times when crashes take place, and the people who are involved. Finally, this work will propose a research-informed framework and analytical methods for improved comparisons of traffic safety risks across transportation modes, especially considering the contexts, factors, and mechanisms of crash causation. Overall, this research will generate useful traffic safety performance measures to help with prioritizing transportation safety investments and communicating safety risks to the public.]]></description>
      <pubDate>Sun, 29 Mar 2026 18:54:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2685708</guid>
    </item>
    <item>
      <title>Leveraging Emerging Data for Traffic Safety Analyses</title>
      <link>https://rip.trb.org/View/2685697</link>
      <description><![CDATA[This project will leverage emerging large-scale vehicle trajectory data to help identify high-risk roadway segments. The analysis will focus on leveraging surrogate safety indicators extracted directly from vehicle movement patterns. Key indicators such as abrupt speed changes, harsh acceleration or braking, and irregular motion signatures are used as proxies for operational risk. These indicators will be aggregated at the roadway-segment level and compared with the “traditional” crash data and crash outcomes on the KABCO scale. This is to help proactively and more quickly identify roadway locations that pose a higher potential safety risk based on data from driving behaviors.
Project efforts will address technical workflows for handling high-volume trajectory data, including data preparation, event extraction, spatial segmentation, and identification of behavior-based patterns. This work aims to develop a structured approach for highlighting rural segments with surrogate safety risk indicators of elevated operational risk based on trajectory-derived metrics.
As a case study, the efforts will use a dataset obtained a data aggregator for portions of the state of Nevada. The dataset contains over a billion trajectories recorded from millions of unique trips between June 2024 and June 2025. Each record includes spatial, temporal, and motion-related attributes, offering a high-resolution view of driving behavior on roadways. These data can be obtained within days or weeks compared to traditional crash data which typically takes many months to obtain.
The outputs of this project include the illustration of the exploratory use of large-scale vehicle trajectory data to identify high-risk roadway segments, and the development of a structured approach to highlight road segments with surrogate safety risk indicators of elevated operational risk based on trajectory-derived metric.
This work will highlight how high-resolution telematics data can support early identification of potential safety concerns on road networks. These insights can assist transportation and law enforcement agencies to identify parts of the road network for design and operations review considerations, prioritize law enforcement priorities and practices, allocate resources efficiently, and strengthen data-driven safety management practices. This could also help effect changes in policies, programs, procedures, and practices to improve traffic safety outcomes such as reduce fatalities and injuries.
]]></description>
      <pubDate>Sun, 29 Mar 2026 18:53:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2685697</guid>
    </item>
    <item>
      <title>Enhancing Freight Safety and Efficiency for California’s Logging Industry: A Data-Driven Approach</title>
      <link>https://rip.trb.org/View/2684215</link>
      <description><![CDATA[The logging industry plays a vital role in the U.S. economy, particularly in California’s northern regions, where timber production supports local supply chains. However, the safe and efficient movement of logging trucks is increasingly challenged by road curvature, steep grades, aging infrastructure, and seasonal fluctuations in freight demand. These factors create high-risk conditions, exacerbated by overlapping tourist activity and inadequate roadway data. This research aims to develop a comprehensive, data-driven framework to identify and mitigate freight safety risks for logging trucks. By leveraging open-source tools, data collection efforts, 3D road profiling, and advanced statistical and machine-learning models, this study will identify and predict high-risk freight routes for California’s logging industry.

Problem: The terrain, road curvature, seasonal harvest demands, and aging infrastructure pose significant challenges to both roadway safety and freight efficiency. Certain high-risk locations - such as roads with sharp curves, steep grades, or deteriorating bridges - may be especially hazardous for large vehicles like logging trucks. Furthermore, the seasonal nature of logging, combined with heightened tourism activity, creates fluctuating traffic patterns and additional stress on key corridors.

Objectives/Goals: This proposal seeks to develop a comprehensive, data-driven framework to identify, analyze, and recommend improvements for critical freight corridors used by logging trucks.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:03:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684215</guid>
    </item>
    <item>
      <title>Enhancing Heavy Vehicle Crash Prevention in North Dakota through Machine Learning and Weather Data Integration</title>
      <link>https://rip.trb.org/View/2683255</link>
      <description><![CDATA[Heavy vehicle crashes continue to be a persistent safety concern across the Midwest, with several states reporting disproportionately high rates of incidents involving large trucks. According to the National Safety Council, in 2023, North Dakota recorded 18% of its fatal crashes involving large trucks, placing it among the highest in the nation. Neighboring states, such as Nebraska (16%) and Iowa, also face elevated risks. Illinois reported 7,509 truck accidents in 2022, ranking among the top five states nationwide. In North Dakota, the risks are especially pronounced during the winter months. In 2023, 64% of heavy vehicle crashes occurred between October and March, with 81% of these crashes taking place in rural areas. These figures highlight how weather conditions and geography amplify the risk associated with large-truck travel in the region. Further, crashes in rural areas in challenging weather conditions poses immense issues for first responders and their ability to provide timely medical care to crash victims.   

Traditional safety strategies have struggled to account for the dynamic, real-time factors that contribute to crash risk. Static approaches often fall short when adverse weather, road conditions, and traffic volume interact in unpredictable ways. This gap highlights the urgent need for predictive, data-driven solutions.  

This proposal aims to investigate the application of machine learning (ML) models, combined with weather and crash data, to predict high-risk scenarios before accidents occur, to support planning for safety and emergency response needs. By leveraging predictive analytics, North Dakota could enhance resource allocation, deploy preventive interventions, and reduce the frequency and severity of heavy vehicle crashes. The high incidence of winter crashes and the limitations of conventional methods make North Dakota an ideal proving ground for an innovative, ML-driven approach to roadway safety.  

The study will utilize historical crash records for heavy vehicles in North Dakota, including crash type, severity, date, and time, combined with corresponding weather data such as temperature, precipitation, snowfall, and visibility. Feature engineering will create representations of temporal and weather conditions relevant to crash severity. Machine learning models, including Random Forest, XGBoost, and Neural Networks, will be trained to predict crash severity. To ensure interpretability, SHAP (SHapley Additive exPlanations) will be applied to quantify the contribution of each feature to individual predictions and overall model behavior. This analysis will reveal which weather or temporal factors most strongly influence severe crashes, both globally across the dataset and locally for specific incidents. High-risk periods and conditions identified by the model, along with explanations provided via SHAP, will be visualized both temporally and geographically, offering actionable insights to support targeted preventive measures and inform DOT decision-making.  ]]></description>
      <pubDate>Tue, 24 Mar 2026 14:09:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683255</guid>
    </item>
    <item>
      <title>Improving the Quality and Useability of Planned and Active Work Zone Data</title>
      <link>https://rip.trb.org/View/2683244</link>
      <description><![CDATA[Work zone data may be used to support efforts ranging from internal operational and safety analysis to public communications and connected vehicle navigation. Ensuring the quality and consistency of this data is vital to its usability. The Virginia Department of Transportation (VDOT)’s current systems,  VaTraffic and the Lane Closure Advisory Management System (LCAMS), require double entry of data, and the other data sets they feed into all display the data differently. This project will review data quality standards and create guidance that can be applied in LaneAware to ensure quality moving forward. In November 2024, the Federal Highway Administration (FHWA) updated its Work Zone Safety and Mobility Final Rule (23CFR630 Subpart J), which in part requires state departments of transportation (DOTs) to identify mobility and work-zone-exposure performance metrics that will be used to track performance and the statewide level and for specific major projects.  Best practices used by other DOTs will be gathered and recommended for adoption. Tools and scripts for data cleaning and analysis will improve the application of these data to operational and safety analysis, which is currently hampered by issues such as identifying data from planned work zones from active ones. By consulting with a wide range of stakeholders, these recommendations will consider the wide-ranging needs of both data producers and consumers in this system.     ]]></description>
      <pubDate>Tue, 24 Mar 2026 10:53:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683244</guid>
    </item>
    <item>
      <title>Innovations to Enhance Employee Safety in the Field</title>
      <link>https://rip.trb.org/View/2681235</link>
      <description><![CDATA[The nation’s aging transportation infrastructure is increasing the need for maintenance and reconstruction activities conducted under traffic. While work zones are essential to address these needs, changing traffic patterns, narrowed lane widths, and the presence of workers and work vehicles create safety challenges for all road users traveling through work zones, including motorists, bicyclists, and pedestrians. Continued traffic growth further complicates work zone operations and often pushes agencies to schedule work at night, adding additional risk factors and operational constraints.

In response, agencies are deploying a range of innovations intended to reduce worker exposure and improve safety outcomes. These include technologies that remove employees from hazardous situations (e.g., unmanned aerial systems, automated truck-mounted attenuators, positive protective systems, dynamic signing, and remote monitoring), improved engineering controls (e.g., enhanced advance warning systems, intrusion alerts, and in-vehicle notifications), and administrative approaches (e.g., speed management, move-over laws, and targeted law enforcement support). Agencies are also adopting advancements in personal protective equipment and using virtual and augmented reality for immersive safety training that allows workers to practice procedures in controlled environments. Documenting and sharing these practices can support broader adoption of safety-enhancing innovations across the highway community.

OBJECTIVE: This scan will identify and document proven innovations that highway transportation agencies are using to improve the safety of field personnel. Agencies to be examined may include state departments of transportation (DOTs), counties, municipalities, and toll agencies that have implemented effective safety innovations.

The scan will compile lessons learned and effective practices from participating agencies to inform a practical “toolbox” of resources that other agencies can adapt to their needs. The scan will also identify gaps, challenges, and opportunities to improve current approaches and technologies.]]></description>
      <pubDate>Tue, 17 Mar 2026 15:01:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681235</guid>
    </item>
    <item>
      <title>SMARTER Center CAV Testbed Digital Twin</title>
      <link>https://rip.trb.org/View/2676080</link>
      <description><![CDATA[This project advances transportation safety and mobility by developing a high-fidelity digital twin of the SMARTER Center’s Connected and Automated Vehicle (CAV) testbed at Morgan State University. The proposed platform synchronizes key infrastructure states, sensor observations, and traffic dynamics with a virtual environment in near real time, enabling safety and mobility interventions to be evaluated in a controlled, repeatable setting without exposing road users to risk. Currently, CAV safety validation faces a well-documented gap: physical testing is costly, slow, and may introduce safety concerns, while purely virtual simulations often lack real-world calibration. This project addresses that gap by integrating live testbed data—including LiDAR, CCTV cameras, roadside units, and V2X messages—with simulation-based scenario testing using CARLA, sensor fusion methods, and validated data pipelines. The system targets low latency and high spatial accuracy suitable for behavioral and safety analysis under representative traffic conditions. The platform demonstrates multi-modal capability through two application scenarios: (1) pedestrian crossing conflict analysis at signalized intersections under varying speeds, visibility, and occlusion conditions, and (2) transit signal priority evaluation using U.S. DOT bus trajectory data to assess potential operational impacts, including delay reduction. Validation is conducted using RTK-GPS probe vehicles and annotated video data, with trajectory similarity and time-to-collision metrics quantitatively assessed. Key outcomes include a functional digital twin system, evaluation of safety-critical scenarios with agreement between digital and physical testbed behavior on key performance indicators, a 5-hour annotated dataset with DCAT-US metadata, and three software modules released via GitHub. The extensible platform architecture supports future applications such as emergency vehicle preemption, freight operations, and micromobility, with documented APIs enabling replication across diverse testbeds and agencies.]]></description>
      <pubDate>Wed, 11 Mar 2026 15:33:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676080</guid>
    </item>
    <item>
      <title>Developing Data-Based Recommendations for Pedestrian Hybrid Beacons (PHBs) and Midblock Pedestrian Signals (MPSs) Deployment in Nevada</title>
      <link>https://rip.trb.org/View/2677562</link>
      <description><![CDATA[Current Pedestrian Hybrid Beacon (PHB) and Midblock Pedestrian Signal (MPS) deployment decisions often lack state-specific data-driven criteria, resulting in inconsistent implementation, potential safety risks, and operational inefficiencies.  Moreover, land use considerations—including proximity to school zones, commercial areas, and transit stops—play a crucial role in determining the most effective crossing treatment. Without comprehensive, localized guidelines, agencies struggle to deploy PHBs and MPSs optimally, leading to variability in effectiveness across different contexts.

The primary objective of this research is to develop robust, data-driven guidelines for the deployment of PHBs and MPSs in Nevada, thereby improving pedestrian safety and mobility statewide. These guidelines will provide a structured approach to identifying optimal locations, ensuring compliance, reducing delays, and enhancing safety and mobility at midblock crossings.

The University of Nevada, Reno (UNR) research team will complete this project in multiple phases: (1) Literature review including information from peer-reviewed studies, Federal Highway Administration (FHWA) and Manual on Uniform Traffic Control Devices (MUTCD) guidance, state/ local reports, and stakeholder interviews. (2) The research team will partner with local agencies to deploy UNR’s LiDAR and fisheye-camera data collection units to collect in-field data at each location. (3) The research team will process and analyze all collected data to conduct a comprehensive safety and compliance study alongside evaluations of operational efficiencies. (4) The research team will develop implementation recommendations for PHBs and MPSs in Nevada.

The development of implementation recommendations will identify the most effective PHB and MPS treatments based on compliance, operational considerations, and local context. This task will also address barriers to adoption, such as regulatory gaps or policy misalignment. Second, the research team will create a detailed implementation plan tailored to Nevada Department of Transportation's (NDOT’s) operational structure. This plan will include step-by-step guidance for integrating recommendations into NDOT’s planning and design workflows, a roadmap for updating internal policies and procedures, and a strategy for stakeholder engagement and training.

Following the development of recommendations, the final report and stakeholder workshop will consolidate all findings and present them to NDOT leadership and regional partners. This workshop will facilitate feedback, promote adoption, and ensure that the implementation plan is aligned with agency needs and priorities. Potential barriers to implementation include institutional challenges, such as the absence of existing NDOT guidelines for MPSs, which may delay formal adoption of recommendations.]]></description>
      <pubDate>Wed, 04 Mar 2026 14:48:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2677562</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>
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