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    <title>Research in Progress (RIP)</title>
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    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Systemic Safety Analysis and Assessment of Bicycle and Pedestrian Crash Risk: Developing Risk Factors using the Multimodal Inventory Project Data</title>
      <link>https://rip.trb.org/View/2725665</link>
      <description><![CDATA[Inconsistent and incomplete data on multimodal infrastructure and operations limits 
Oregon Department of Transportation's (ODOT’s) ability to develop data-driven risk factors. Accurate and up-to-date bicycle and pedestrian risk factors are necessary inputs for ODOT programs aiming to proactively address active transportation safety, as they can help identify locations with geometric and operational characteristics that lead to increased crash risk for active transportation users. The Multimodal Inventory Project offers new data and a unique opportunity to develop more rigorous, data-driven bicycle and pedestrian risk factors. Leveraging these new data and methodologies, in addition to crash data and exposure data, will enable analysis that can identify roadway and operational characteristics most strongly associated with bicycle and pedestrian crash risk.
OBJECTIVES: This research will provide ODOT with up-to-date, high-quality bicycle and pedestrian risk factors to be used for proactive safety analysis. The anticipated outcome of this research is to develop these new bicycle and pedestrian risk factors by leveraging new multimodal data from the Multimodal Inventory Project and by applying more rigorous risk factor methodologies. The latter will be accomplished by developing a Risk Factor Tool that relies on data inputs and analysis results to provide site-specific bicycle and pedestrian risk assessments. Objectives of this research include: (1) A comprehensive review of studies that develop bicycle and pedestrian risk factors and/or apply them, methods used to derive bicycle and pedestrian risk factors, and current policies and practices implemented through Active Transportation Safety Plans and Vulnerable Road User Safety Assessments; (2) A data collection and fusion process that combines existing and new Multimodal Inventory Project data; and (3) Development of bicycle and pedestrian risk factors using an integrated approach that leverages descriptive statistics and safety modeling techniques, resulting in a Risk Factor Tool to conduct site-specific risk assessments.
This research will provide ODOT with up-to-date data and risk factors to improve bicycle and pedestrian safety, addressing Transportation Plan Safety Objectives, Social Equity Objectives, and Mobility Objectives.]]></description>
      <pubDate>Wed, 08 Jul 2026 16:52:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725665</guid>
    </item>
    <item>
      <title>Two-Lane to Three-Lane Roadway Conversions: Active Transportation Safety Implications</title>
      <link>https://rip.trb.org/View/2712207</link>
      <description><![CDATA[Land development patterns often shift two-lane collector roadways from serving primarily through traffic to accommodating a greater mix of through and local access traffic. In other cases, local destinations remain while downstream development increases through-traffic demand. In both scenarios, turning movements and through traffic increasingly conflict with each other. Where no alternative corridor options exist, through traffic remains on the road along with increased traffic volumes generated by new land uses. Prior to development, these roads may have included paved shoulders occasionally used by pedestrians and bicyclists, though active transportation activity was generally limited because fewer nearby destinations existed. As development intensifies, pedestrian and bicycle activity increases alongside turning conflicts, creating pressure on agencies to widen the roadway cross-section by adding center turn lanes, bicycle lanes, sidewalks, or shared-use paths.

While active transportation users benefit from dedicated facilities, they may not benefit from roadway changes that increase vehicle speeds, traffic volumes, or unprotected crossing distances. Meanwhile, motorists on two-lane roadways with frequent left-turning vehicles may experience more rear-end crashes, driver frustration, and risky drive-around maneuvers. Drivers making left turns under these conditions may also accept smaller traffic gaps or perform less thorough scans for pedestrians and bicyclists. When agencies determine that adding pedestrian and bicycle facilities alone is insufficient to meet operational needs, an important question remains: how can transportation agencies improve traffic operations while optimizing safety for active transportation users?

The objective of this research is to evaluate and compare the safety performance of two-lane roadway cross-sections without dedicated turn lanes and three-lane cross-sections with center two-way left-turn lanes along corridors that include bicycle lanes, sidewalks, and effective pedestrian crossing treatments. The research will identify the traffic, land use, and operational conditions under which each cross-section is more effective at improving pedestrian and bicyclist safety while balancing multimodal operational performance.

A secondary objective is to develop a guide with evidence-based recommendations, design considerations, and implementation strategies for improving multimodal safety along suburban and urbanizing corridors with current or projected high through-motorist travel and left-turn motorist demand.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:36:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712207</guid>
    </item>
    <item>
      <title>Guidance for Selecting Pedestrian Safety Treatments at Signalized Intersections to Address Permitted Turn Conflicts</title>
      <link>https://rip.trb.org/View/2712202</link>
      <description><![CDATA[State departments of transportation (DOTs) and local agencies work to improve pedestrian safety at signalized intersections while maintaining efficient vehicle operations. Traditional signal timing practices often allow pedestrians to cross concurrently with permitted turning vehicles, which can create unsafe or stressful conditions for pedestrians, particularly at intersections with high turning volumes and speed or complex geometries. Agencies have implemented treatments such as leading pedestrian intervals (LPIs), delayed-turn strategies, and protected-only turn phases, but these applications are often applied without nationally consistent, data-driven guidance.

Existing resources identify available pedestrian safety treatments but provide limited guidance on when specific strategies are most appropriate. This can lead to inconsistent practices and difficulty balancing pedestrian safety improvements with operational impacts to vehicles.

The objective of this research is to develop a data-driven guide for selecting pedestrian safety treatments at signalized intersections to address conflicts with permitted turning vehicles. Using field-collected data, the research will evaluate treatments under varying traffic, geometric, and signal timing conditions, with results to help agencies identify appropriate strategies.]]></description>
      <pubDate>Wed, 10 Jun 2026 11:11:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712202</guid>
    </item>
    <item>
      <title>Crash Evaluation of Roadsides in Urban and Suburban Contexts</title>
      <link>https://rip.trb.org/View/2712180</link>
      <description><![CDATA[The American Association of State Highway and Transportation Officials (AASHTO) places strong emphasis on multimodal design, flexibility, and addressing bicyclist and pedestrian serious injuries and fatalities. Pedestrian and bicyclist fatalities account for approximately 20% of all roadway fatalities. Research is needed to help state departments of transportation identify roadside design or in-roadway or cross-section treatments that reduce vehicle speeds and that will significantly reduce vulnerable road user fatal and serious injury crashes.

The 7th edition of A Policy on Geometric Design of Highways and Streets (the AASHTO Green Book) provides some qualitative guidance on roadside design within urban or restricted environments, but there also is a need for quantitative data on the benefits and drawbacks associated with lateral offsets and roadside treatments. Roadway designers currently lack the information needed to make related data-driven decisions on roadside and in-roadway design.

The objective of this research is to develop a guide to support designers’ efforts to quantitatively and qualitatively evaluate potential safety impacts of various roadside design configurations. The guide will include measures to determine safety impacts on each type of user (drivers, pedestrians, bicyclists, motorcyclists, etc.) for each configuration. The research will provide greater insight into how roadside design decisions—primarily lateral offsets—affect the safety of all road users in different urban and suburban roadway contexts. The research will explore how future Manual for Assessing Safety Hardware (MASH) specifications could offer new guidance for evaluating safety systems for vulnerable road users. ]]></description>
      <pubDate>Tue, 09 Jun 2026 15:10:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712180</guid>
    </item>
    <item>
      <title>Improving Crash Data for Active Transportation Users</title>
      <link>https://rip.trb.org/View/2701260</link>
      <description><![CDATA[In recent years, the United States has experienced sharp, inexplicable increases in the number of pedestrian fatalities. In response to this disturbing trend, the Governors Highway Safety Association, state highway safety offices (SHSOs), state departments of transportation (DOTs), and local transportation agencies have been conducting safety analyses to better understand the problem and develop remediation plans.  

Crash data is the primary source of information used for safety analysis. This critical data source, however, has many limitations, including inconsistencies in reporting, inaccurate or incomplete coding of crashes, and underreporting, especially for active transportation/non-motorized users (herein after referred to as active transportation users). Also, crash typing (used to describe events and movements prior to a crash) can lack details for pedestrian- and bicycle-involved crashes, and in some cases must be constructed using multiple variables.

Improving pedestrian and bicyclist injury and fatality data, adopting consistent typing methods at the national and local levels, improving data storage, sharing and accessibility, and integrating police and hospital crash data would help practitioners understand risk factors and potential countermeasures. It is important to understand the reasons for crash data limitations, related implications, and measures that can be taken to improve the completeness, consistency, and accuracy of crash data for active transportation users.

Research is needed to improve the current state of the practice for collecting injury and fatality data for active transportation users.

OBJECTIVE: The objective of this research was to develop recommendations to improve the completeness, consistency, and accuracy of crash data for active transportation users. The project sought to (1) document current shortcomings related to existing data for crashes involving active transportation users (including crashes that do not involve motor vehicles in transport), and (2) consider non-motorist victim characteristics. ]]></description>
      <pubDate>Tue, 12 May 2026 15:48:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701260</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>Real-time Fleet Composition via Machine Vision and AI for use in Pedestrian Safety and Risk Exposure Studies
</title>
      <link>https://rip.trb.org/View/2669652</link>
      <description><![CDATA[In 2024, Georgia Tech researchers developed automated procedures to capture very consistent vehicle images using portable high-resolution video cameras positioned on Interstate overpasses.  The team collected and processed more than one-million vehicle images from four locations in the Atlanta Metro Area for a State Road and Tollway Administration research project, in which a large subset of vehicle images were coded by make-and-model.  The team subsequently developed machine-vision models and AI tools to identify vehicle make-and-model combinations as part of a 2024 CHEM research project, for use in a variety of pedestrian safety and risk exposure studies.  In the initial model development work, the team worked in Georgia Tech’s PACE distributed computing system.  However, the resulting models are so fast, the team has concluded that the AI fleet composition models can run in real-time.

In this follow-on project, the research team will refine the current models to further reduce computational requirements so that the AI models can be used in edge-computing, which will process vehicle fleet composition on site, without transmitting video data to a data center.  The team’s second challenge is to design and package an efficient portable computing system with a high-end graphics card that can operate under year-round temperature and humidity conditions.  The team will balance system performance with power- draw and heating/cooling requirements.  Overpass video is very consistent, providing elevated and unobscured rear views of vehicles.  In the third phase of the project, the team will develop protocols for collecting video from major arterials and will develop machine-vision models from a wider variety of camera views (as constrained by intersection design and safe placement of equipment).  The team anticipates that arterial corridor implementation will be much more complicated and that strict camera placement protocols may be needed to reach the accuracy of overpass-image-derived models.

The team anticipates that equipment development (downsizing, enclosure design, heat dissipation, power consideration, etc.) and machine vision model implementation may lead to patentable inventions or licensable software.  If successful equipment deployments are afforded patent protection, the team will work with Tech’s commercialization office (commercialization.gatech.edu) to develop license agreements for the manufacture of equipment and deployment of portable edge-computing systems and/or will create a GT Create-X business startup.  If the USPTO rejects the patent claims, the team will release equipment specifications, software code, and technology transfer reports under open-source licensing that will allow state DOTs and their consultants to implement the systems.
]]></description>
      <pubDate>Sun, 15 Feb 2026 16:27:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669652</guid>
    </item>
    <item>
      <title>Toward Smarter Mobility: AI-Powered Safety Insights for AVs and Vulnerable Road Users</title>
      <link>https://rip.trb.org/View/2669549</link>
      <description><![CDATA[This project investigates the safety dynamics between autonomous vehicles (AVs) and vulnerable road users (VRUs)—including pedestrians, cyclists, and e-scooter riders—by applying advanced artificial intelligence (AI) and data fusion (DF) methods to high-resolution, real-world datasets. By understanding how AVs interact with diverse VRUs in complex urban environments, this project will generate critical insights into where and how conflicts occur, what environmental factors contribute to unsafe conditions, and how different VRUs respond to perceived threats. These findings will inform safety improvements that reduce crashes and injuries, leading to significant public health benefits such as fewer hospitalizations, reduced long-term disabilities, and lower healthcare costs.  Moreover, safer streets will encourage more pedestrian and active transportation activity, promoting healthier lifestyles and improving community well-being. As AVs become more integrated into urban mobility systems, their potential to provide efficient, reliable, and low-stress transportation will further enhance public health by reducing traffic congestion, energy use, and travel-related stress.  

The first dataset used for this study  Argoverse 2 3D Tracking, captures interactions between AVs and pedestrians/cyclists in Austin, Texas. The second dataset includes sensor data collected from e-scooters in San Antonio, Texas, by ScooterLab at the University of Texas at San Antonio.  

The project will begin by training an AI model on the Argoverse data to identify close encounters (e.g., within 2 meters), spatially aggregate them to locate dense near-miss zones, and analyze built environment features and vehicle movement characteristics. Next, the e-scooter data will be used to detect abrupt rider responses—such as hard braking, sudden acceleration, or sharp turning—using anomaly detection algorithms, which signal perceived or actual hazards. These data streams will be fused to perform a comparative analysis across different VRU types, enabling researchers to identify common risk patterns and mode-specific vulnerabilities.   

This project will advance the scientific understanding of AV-VRU safety interactions, and discuss how mobility and efficiency can be co-optimized with safety. It will lay the groundwork for future research and transportation interventions that support healthier, safer, and more efficient communities.  ]]></description>
      <pubDate>Thu, 12 Feb 2026 15:28:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669549</guid>
    </item>
    <item>
      <title>Immersive AR/VR learning to enhance pedestrian safety</title>
      <link>https://rip.trb.org/View/2663606</link>
      <description><![CDATA[Pedestrian injuries remain one of the leading causes of death among children in the United States and globally. Safe crossing behavior depends on cognitive and perceptual skills such as attention, hazard recognition, and gap judgment that are still maturing in younger populations. Traditional classroom instruction offers limited opportunities to practice these skills in realistic traffic contexts, highlighting the need for controlled, repeatable, and engaging training environments that can bridge the gap between knowledge and real-world decision making.
This project develops, tests, and disseminates an integrated Augmented Reality (AR) and Virtual Reality (VR) learning platform designed to improve pedestrian safety among children. The immersive simulator replicates crosswalks, intersections, and near-miss zones identified through the District Department of Transportation (DDOT) crash database and the DC Traffic Safety Data Portal. VR modules enable users to experience controlled crossings at high-risk intersections, while AR modules project digital traffic cues and guidance into real environments through tablets or mobile devices. Three-dimensional environments are constructed in the Unity or Unreal Engine platform and configured for both mobile devices and VR headsets to support flexible deployment across educational settings.
The methodology proceeds through four tasks: scenario design using DDOT crash data and Vision Zero reports to identify high-risk child pedestrian corridors, prototype development of immersive environments with realistic vehicle motion, environmental conditions, and compliant signal timing, controlled evaluation sessions with K-12 and university participants to assess realism, usability, and learning effectiveness, and dissemination including open-source release, a collaborative workshop with DDOT and school partners, and preparation of a deployment-ready package with simulation files, user manuals, and integration guides. Success is measured by improvements in hazard detection, gap judgment, and safe crossing decisions, with a target of at least 30 percent gain relative to baseline performance. The project also provides initial estimates of the number of crashes and injuries that could potentially be avoided if the toolkit were adopted more broadly in Washington, D.C.
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:36:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663606</guid>
    </item>
    <item>
      <title>Using Large Language Models to Generate Synthetic Data for Proactive
Pedestrian Safety Prediction: Overcoming Data Collection Barriers in Surrogate Safety Analysis</title>
      <link>https://rip.trb.org/View/2663604</link>
      <description><![CDATA[Pedestrian fatalities remain a persistent and growing safety crisis, with over 7,500 pedestrians killed on U.S. roads annually. Effective countermeasure deployment requires identifying high-risk locations before crashes occur, yet traditional crash-based analyses are insufficient due to the rarity of pedestrian crashes at individual intersections. Surrogate safety analysis using pedestrian–vehicle close calls offer a proactive alternative, but comprehensive observational data collection is prohibitively expensive and time-intensive. Video monitoring requires specialized equipment, extended deployment periods, and substantial manual processing. These practical constraints severely limit the geographic coverage, temporal scope, and contextual diversity of available datasets, ultimately hindering agencies' ability to develop reliable predictive tools that generalize across diverse intersection types and support evidence-based
safety interventions statewide. This project addresses these fundamental challenges by introducing Large Language Models (LLMs) as a novel tool to generate high-quality synthetic pedestrian–vehicle interaction data. LLMs possess extensive pre-trained knowledge spanning transportation systems, human behavior, and urban
environments, successfully demonstrated in healthcare and climate science for data augmentation. Building upon the Minnesota Traffic Observatory (MTO) dataset, where 18 intersections with 3,314 interactions involving 4,941 pedestrians, the research team will develop a validated methodology to generate contextually realistic scenarios incorporating roadway geometry, traffic control, land use, pedestrian demographics, and temporal patterns. This approach directly tackles the data scarcity problem that prevents agencies from conducting comprehensive pedestrian safety analyses across their jurisdictions.
The project has three objectives: (1) develop a transparent LLM-based synthetic transportation-targeted data generation methodology with validation protocols ensuring realism and quality; (2) evaluate whether synthetic data-augmented models improve prediction accuracy and transferability across intersections compared to observational data alone, using precision-recall AUC, calibration diagnostics, leave-one-site-out validation and other appropriate approaches; and (3) determine the mechanisms driving performance improvements: whether from introducing realistic scenario diversity or addressing rare-event limitations, to guide best practices. The framework will incorporate probability calibration, split-conformal risk control, and decision-curve analysis to deliver deployment-ready tools with quantified uncertainty for operational use.]]></description>
      <pubDate>Tue, 03 Feb 2026 15:34:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663604</guid>
    </item>
    <item>
      <title>Designing Safer Streets</title>
      <link>https://rip.trb.org/View/2662686</link>
      <description><![CDATA[Designing Safer Streets is an implementation strategy in which the transportation network is planned, designed, built, operated, and maintained to enable safe mobility within the transportation system. The pooled fund will be established to conduct research on innovative strategies to design and implement a safe streets.  

OBJECTIVES: To assemble a consortium composed of State Departments of Transportation; County, regional, local, or tribal transportation agencies; additional interested entities or organizations; and Federal Highway Administration (FHWA) program offices to meet national needs in support of safer streets. Activities of the consortium include: Identify planning, roadway design, human factors, safety, and operational issues related to safe streets elements and projects; Select new and existing safe Streets elements and/or projects for evaluation; Initiate and monitor research projects; Disseminate results; and Facilitate collaboration and information sharing among members.]]></description>
      <pubDate>Thu, 29 Jan 2026 16:30:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2662686</guid>
    </item>
    <item>
      <title>Spatio Temporal Graph Learning for Real Time Pedestrian Exposure Estimation</title>
      <link>https://rip.trb.org/View/2640189</link>
      <description><![CDATA[Pedestrian crashes occur infrequently and are often underreported, which makes it difficult for agencies to rely only on crash records when assessing safety. Traditional Safety Performance Functions do not capture short term patterns or local context, and therefore cannot fully represent changes in pedestrian activity. This project will create a new framework that uses spatio temporal graph neural networks combined with statistical modeling to estimate pedestrian exposure across different locations and time periods. The research will draw from computer vision systems, Streetlight data, manual counts, roadway characteristics, land use, and travel related factors to produce high resolution exposure estimates.

The modeling framework will include two tiers. The first tier will use generalized linear mixed models to build a baseline exposure structure, while the second tier will apply deep learning methods to capture spatial spillover effects and temporal variation such as peak periods and seasonal changes. The results will help agencies identify areas with elevated pedestrian activity and evaluate how different roadway or land use conditions influence exposure. These data will support improved pedestrian safety analysis and guide the development of timely, evidence based interventions.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:45:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640189</guid>
    </item>
    <item>
      <title>Are Automonous Vehicles Safer Drivers than Humans? Comparing performance in San Francisco</title>
      <link>https://rip.trb.org/View/2625584</link>
      <description><![CDATA[This research project seeks to determine if automated vehicles (AVs) are safer drivers than humans by comparing their pedestrian interaction behaviors and yielding performance in real-world conditions in San Francisco. The study will be framed by the city's "Focus on Five" strategy, which targets the five moving violations most commonly associated with traffic fatalities. Researchers will conduct evaluations of two focus violations, with the first being a comparison of the compliance of AVs and human drivers in yielding to pedestrians in a crosswalk. To gather data, the team will install high-resolution video cameras at two or more crosswalks with no traffic control for a period of one to three weeks to passively record vehicle-pedestrian interactions. Machine learning-based computer vision methods will then be used to automatically classify vehicles as either automated or human-driven. Following this classification, researchers will review the footage to code each interaction, noting if the vehicle yielded to the pedestrian. Finally, the performance of the two groups will be compared using two-sample t-tests to determine if any observed differences are statistically significant. A parallel analysis will be conducted for a second violation, to be determined.]]></description>
      <pubDate>Tue, 18 Nov 2025 15:14:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625584</guid>
    </item>
    <item>
      <title>Hotspot Stability of Freight Vehicle Crashes Involving Vulnerable Road Users: A Spatio-Temporal Perspective</title>
      <link>https://rip.trb.org/View/2625586</link>
      <description><![CDATA[This research will analyze the interaction between two of the most different transportation road users that interact on roads—freight vehicles and vulnerable road users (VRU), i.e., pedestrians and bicyclists. The research objective of this project is to identify the temporal stability of hotspots in (1) non-fatal crashes, (2) fatal crashes, and (3) all crashes (non-fatal and fatal) between freight vehicles and VRU in two U.S. States. This research proposes a novel spatiotemporal analysis to answer whether crash hotspots intensify over time (i.e., the number of crashes increases over time at the same location) or if it stays the same over time.  In terms of processes, the first one is collecting the data on fatal, non-fatal, and all crashes of both States into a single file, cleaning it, and ensuring its validity/accuracy/consistency. Once the data collection is ready, the second process focuses on merging the panel data into a space-time cube. This arrangement will host on a single data array geographical and temporal data of the total number of (1) non-fatal crashes, (2) fatal crashes, and (3) all crashes between freight vehicles and VRU for each State. The third process is calculating a Local Indicator of Spatial Association Statistic (the Gettis Ord*) to identify crash hotspot locations for each year of analysis for each State, and estimate emerging hotspot patterns based on the panel data results. The fourth process will use crash hotspot locations (identified in process three) and data from the County Business Pattern data, the Census Tract Data, and the American Community Survey to compare urban economic and built environment characteristics between different types of hotspots (e.g., recent versus consecutive hotspots), and identify common factors and differences. Specifically, the research team will compute an ANOVA and a post hoc test to identify statistical differences between crash hotspot locations. The last process focuses on visualizing the results on a geographic information system (GIS) software or tables for statistical analysis.  The results of the spatiotemporal analysis will be correlated with urban economic and built environment features to identify common factors in hotspot locations that could have influenced road crashes in both States. These factors include built environment attributes and the number of establishments by industry sector, among others.]]></description>
      <pubDate>Tue, 18 Nov 2025 14:19:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625586</guid>
    </item>
    <item>
      <title>Evaluating Safety Impacts of Permissive Green Signal Phasing on Observed Conflicts Between Left-Turning Vehicles and Pedestrians</title>
      <link>https://rip.trb.org/View/2625837</link>
      <description><![CDATA[Permissive left-turn signal phasing allows vehicles to make left turns while pedestrians may concurrently receive a “walk” signal on parallel crosswalks. These simultaneous movements create elevated risks for pedestrians, as drivers often focus solely on identifying safe gaps in opposing through traffic. Currently, there are no clear recommendations directing state departments of transportation (DOTs) regarding permissive left turns and pedestrians. As current Nevada Department of Transportation (NDOT) practices lack evidence-based, clear, context-sensitive treatments that can translate into consistent pedestrian safety improvement, an integrated study is essential to mitigate pedestrian safety risks.]]></description>
      <pubDate>Mon, 17 Nov 2025 18:34:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625837</guid>
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