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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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <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>Secure Multi-Modal Transportation Artificial Intelligence (AI) at Run-Time</title>
      <link>https://rip.trb.org/View/2689389</link>
      <description><![CDATA[This research develops robust and secure artificial intelligence (AI) systems for smart transportation that could defend against novel adversarial attacks with high performance. The project addresses critical vulnerabilities in Test-Time Adaptation (TTA) mechanisms and Multimodal Large Language Models deployed in transportation systems. Through new attack discovery, effective defense framework development, and deployable prototype systems, this work ensures that AI technologies can be safely deployed in safety-critical transportation applications. The research delivers practical solutions including natural scene adversarial attack frameworks, sharpness-aware minimization based TTA defenses, event-conditioned representation compression for efficient multimodal AI, and adversarially robust multimodal fusion architectures.]]></description>
      <pubDate>Tue, 28 Apr 2026 15:43:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689389</guid>
    </item>
    <item>
      <title>Monitoring Active Transportation Demand and Safety with Computer Vision</title>
      <link>https://rip.trb.org/View/2696847</link>
      <description><![CDATA[Monitoring demand for and safety of active transportation has been a challenge for decades. With a history of designing roads for cars and monitoring efforts similarly aimed at the flow of cars, transportation researchers and professionals lack system-level knowledge of active transportation. The current state of bicycle and pedestrian counting practice in most cities deploys a few costly permanent counters using inductive loops and passive radar, combined with a few days of manual peak hour traffic counts at few intersections. This neither monitors system-wide demand nor safety. However, recently several companies have produced video- and LiDAR-based sensors and multiclass tracking technology to monitor active transportation demand and unsafe events. These sensors can be installed permanently, or temporarily, and are generally lower in cost to install than other permanent counting devices. This research will leverage an ongoing Caltrans project with these sensors to validate safety metrics, and a mobile version of the sensors to collect active transportation count data for modeling system level active transportation volume in Davis, California as a pilot for other cities and agencies. It will include the prediction of network-wide travel volumes for planning the intervention purposes, and two safety metric evaluations. The final report is expected to not only provide information on the state-of-the-art in active transportation monitoring, but will have direct policy impacts by informing the Active Transportation Data program within Caltrans Traffic Operations, among other programs such as the Active Transportation Resource Center research-to-practice education elements.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:05:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696847</guid>
    </item>
    <item>
      <title>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>Modeling Bicyclist Behavioral Patterns and Multi-Faceted Decision-Making Strategies in Urban Settings with Limited Infrastructure: Guidance for Future Development</title>
      <link>https://rip.trb.org/View/2691667</link>
      <description><![CDATA[While bicycling is an essential mode of urban transportation, most parts of U.S. cities lack adequate infrastructure to keep bicyclists safe and allow them to travel efficiently. This research aims to model how psychological, street, and infrastructure characteristics influence bicyclist behavior in urban settings with inadequate bicycling infrastructure, such as in the Greater Houston area (Houston-The Woodlands-Sugar Land), Texas. This research will integrate quantitative and qualitative methods to develop a model that supports adaptive decision-making for bicycling in urban areas with limited infrastructure. The project will recruit 40 adult bicyclists to participate in surveys and bicycle simulator testing. A realistic urban network will be simulated in the bicycle simulator to replicate bicycling conditions under varying (infrastructure quality, traffic volume, visibility, etc.), psychological (risk perception, motivation, and attitudes, etc.), and operational (route choice, adaptation, and interaction with other modes of transportation, etc.) scenarios. Various techniques, including both qualitative and quantitative methods, can be used to identify key drivers of route choice and to develop optimal strategies for efficiency and safety. The findings will inform action-oriented urban planning and policy recommendations for enhancing bicycle infrastructure and safety. The outcomes have the potential to offer a replicable methodology for implementation in similarly challenged cities, providing active urban transportation and improved public health.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:34:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691667</guid>
    </item>
    <item>
      <title>What Makes Complete Streets Projects Work?</title>
      <link>https://rip.trb.org/View/2690983</link>
      <description><![CDATA[This project will assess community reactions to complete streets projects that repurpose vehicular travel lanes or parking spaces for bicycle lanes, sidewalks or other pedestrian amenities, and/or transit-only or transit-priority lanes. The primary research goal is to better understand the decision-making processes – how and why cities have developed complete streets project proposals and engaged with stakeholders who may have competing interests and perspectives, such as local business owners, homeowner and community groups, bicycle and active transport groups, and public transportation agencies including Caltrans and relevant transit agencies. The project will explore whether and how stakeholder concerns overlap and align and where they do not, and how conflicts are addressed and resolved, when possible. The project will also explore whether stakeholder perspectives change over time (including after project completion). The primary research method will be to conduct case study research in a sample of communities which will vary by regional location and community type. The case studies will involve interviews of key stakeholders, a survey of local business owners, analysis of business revenue data, and document review. The findings will help planners and policymakers understand the political stakes and practical challenges involved in implementing complete streets projects, and how they can successfully be managed.]]></description>
      <pubDate>Thu, 09 Apr 2026 14:32:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2690983</guid>
    </item>
    <item>
      <title>Utility of Improving Nonmotorized Volume Forecasts for Bike Infrastructure</title>
      <link>https://rip.trb.org/View/2681257</link>
      <description><![CDATA[Virginia Department of Transportation (VDOT) lacks clarity on several foundational questions for bicycle and pedestrian demand forecasting: (1) the accuracy of the current forecasting method(s), (2) the full range of decisions that would benefit from more precise demand estimates, (3) the availability and reliability of existing bicycle and pedestrian count data, and (4) whether a more advanced forecasting method could be effectively adapted for Virginia. Given these four unknowns and the anticipated large expense of a Virginia-specific model, it is unclear whether VDOT should spend substantial time and resources creating a better approach for estimating nonmotorized demand. Through a literature review, survey and potentially follow-up interviews, assessment of alternative methods, evaluation of existing count data, and data analysis to evaluate the utility of improving forecasts, this research will determine if VDOT should develop a better method or continue the current approach.  ]]></description>
      <pubDate>Tue, 17 Mar 2026 09:48:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681257</guid>
    </item>
    <item>
      <title>VISIAM (Visual Street Index for Active Mobility): An AI-Based Tool for Assessing Bikeability and Walkability of Streets</title>
      <link>https://rip.trb.org/View/2680127</link>
      <description><![CDATA[This project proposes the Visual Street Index for Active Mobility (VISIAM)—an AI-driven framework that integrates computer vision, self-supervised deep learning, and human-perception data to systematically assess bikeability and walkability at the street segment level. Current tools for assessing bikeability and walkability are limited because they rely on subjective audits or basic metrics. VISIAM addresses this gap through a multi-stage framework: computer vision models extract and classify streetscape features from Google Street View imagery, human-perception evaluations from diverse stakeholder groups rate street imagery across multiple dimensions, and the AI-derived classifications and human ratings are integrated to produce a composite bikeability and walkability score for each street segment. The final output is a citywide, street-level index visualized through interactive maps and dashboards, enabling policymakers to identify gaps, prioritize investments, and explore how infrastructure improvements could improve street quality.
]]></description>
      <pubDate>Wed, 11 Mar 2026 15:15:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2680127</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>AI-Enabled Vision System for Intersection Analytics </title>
      <link>https://rip.trb.org/View/2673053</link>
      <description><![CDATA[Phase I of this project revealed limitations of using a single camera per intersection to automatically extract key traffic performance and safety information from video feeds. To overcome these limitations and enhance data accuracy, the Phase II approach will deploy a second camera at selected high-impact intersections. By fusing the views from two different camera angles, the system can establish a true spatial relationship of objects in the intersection, essentially achieving a more complete 3D understanding of vehicle and pedestrian trajectories.]]></description>
      <pubDate>Tue, 24 Feb 2026 15:00:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2673053</guid>
    </item>
    <item>
      <title>Enhancing Utah's Rest Area Safety Through AI-Based Near-Miss Detection and Risk Mapping</title>
      <link>https://rip.trb.org/View/2672765</link>
      <description><![CDATA[State-maintained rest areas are facing increasing safety risks as infrastructure ages and traveler demand grows, raising concerns about both user well-being and operational efficiency. Utilizing open-source data and video from surveillance cameras in rest areas managed by the Utah Department of Transportation (UDOT), this project proposes to incorporate a safety-focused analysis layer into rest area management in Utah. The research team will develop computer vision pipelines for multi-object tracking and scene calibration to extract vehicle and pedestrian precise trajectories and speeds, then calculate surrogate safety measures to identify near-miss interactions in parking areas and walkways. These metrics will be integrated into spatiotemporal hotspot maps and a site-level Safety Performance Index (SPI) that ranks locations and time periods with the highest safety risks. The SPI will guide a risk-based shortlist of targeted countermeasures, including signage enhancements, speed-calming features, bollards, and lighting upgrades, each accompanied by projected risk reduction and cost estimates to support informed decision-making.]]></description>
      <pubDate>Sun, 22 Feb 2026 10:38:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2672765</guid>
    </item>
    <item>
      <title>Driver to Non-Driver Transitions: Related Health, Mobility and Safety Outcomes</title>
      <link>https://rip.trb.org/View/2671991</link>
      <description><![CDATA[This project involves analyzing the impacts of becoming a non-driver (suddenly or gradually) in Wisconsin and nationally and effects on health, mobility, and safety outcomes. The project will analyze health, quality of life and mobility outcomes for drivers who are no longer able to drive. The researchers will analyze the safety, mobility, and quality of life outcomes for those who have suddenly or gradually become non-drivers. Analysis should focus on adult non-drivers of all ages and demographics, with particular emphasis on adults aging in place and urban versus rural areas. Once the analyses are conducted and complete, the researchers will report findings and provide recommendations for policies that lead to improved outcomes—namely increases in mobility and safety benefits for the entire state. Recommendations will help Wisconsin Department of Transportation (WisDOT) understand how to best offset impacts to mobility for individuals suddenly or gradually transitioning from being drivers to non-drivers.]]></description>
      <pubDate>Wed, 18 Feb 2026 11:39:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2671991</guid>
    </item>
    <item>
      <title>Micromobility Decision-Making Atlas</title>
      <link>https://rip.trb.org/View/2669653</link>
      <description><![CDATA[This work will examine how U.S. micromobility users make everyday travel and safety decisions. Participants will be identified from two experience groups: riders who integrate e-scooters or e-bikes with public transit and those who substitute them for car trips. Situated within the broader mixed-methods design, this project builds directly on the “Healthy Micromobility: Moving from Crisis to Opportunity” pilot project. It will provide explanatory depth on the psychosocial and contextual mechanisms that shape micromobility use and user safety. These findings will also inform the system-level analyses by clarifying how user experiences and perceptions translate into behavioral, safety, operations, and other relevant outcomes.   

A micromobility decision-making atlas will be designed to serve as a current, comprehensive database of local micromobility regulations and policy environments across U.S. jurisdictions, providing an updated and more detailed successor to existing resources such as the Shared-Use Mobility Center’s Policy Atlas. The atlas would compile and standardize policy data from the environmental scans, allowing users to explore and compare domains such as fleet management, parking, speed limits, and accessibility provisions. An optional infrastructure layer could incorporate indicators of supportive design conditions, such as protected lane coverage or PeopleForBikes Bicycle Network Analysis scores, to contextualize how local infrastructure aligns with policy intent.  ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:30:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669653</guid>
    </item>
    <item>
      <title>Real-time Pedestrian Safety and Risk Exposure using Real-time Vehicle Activity and Fleet Composition</title>
      <link>https://rip.trb.org/View/2669652</link>
      <description><![CDATA[Pedestrians face significant a risk crossing roadways as they interact with vehicle traffic (more than 7,000 pedestrians were killed in traffic crashes in 2023).  New tools that assess risk exposure can improve the safety of routes delivered by navigation apps.  In 2025, the research team generated a complete-paths pedestrian network for Downtown Atlanta and inspected the condition of all sidewalk surfaces.  In 2024, Georgia Tech researchers also began collecting very consistent vehicle images using portable high-resolution video cameras positioned on Interstate overpasses (more than one-million vehicle images) for the State Road and Tollway Authority.  A large subset of vehicle images were coded by vehicle make-and-model and used in a prior research project to develop machine-vision artificial intelligence (AI) models to generate fleet composition profiles, for use in energy and safety research.  

In this new project, the researchers will integrate traffic operations data and assess pedestrian exposure to high traffic volumes, high vehicle speeds, and turn movements that cross pedestrian paths.  The team will enhance SidewalkSim and G-MAP (www.its.dot.gov/research-areas/ITS4US/), models that find the “shortest path” (i.e., lowest impedance path) for pedestrian trips between any origin-destination, by integrating traffic exposure into routing impedance factors.  This will allow the apps to route pedestrians around high-risk-exposure crossings.  The team will also map and integrate pedestrian safety countermeasures (bollards, barriers, pedestrian fences, extended crossing times, leading pedestrian intervals, no-crossing zones, no-right-turn-on-red, etc.) in the study area, so that these countermeasures can also be used in impedance-based pedestrian routing along safer paths.  The project culminates by integrating traffic conditions, fleet composition, and risk exposure into SidewalkSim and G-MAP pedestrian routing app and demonstrating the system in downtown Atlanta.  


Another finding from past research was that the resulting machine vision models are so fast, they can run in real-time.  In this new project, the research team will further refine the AI models so that they can be used in edge-computing, processing vehicle fleet composition in the field, without transmitting video data to a data center.  In this project, the team aims to design and package an efficient portable computing system with a high-end graphics card that can operate under year-round Atlanta outdoor temperature and humidity conditions, balancing system performance with power-draw and heating/cooling requirements.  This equipment research (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 Georgia 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>
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