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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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    <item>
      <title>New Travel Insights from Cell Phone GPS Data</title>
      <link>https://rip.trb.org/View/2691672</link>
      <description><![CDATA[Building and operating an effective and efficient transportation system requires deep insights into how people move from place to place. These insights include long term behaviors to understand trips that happen infrequently, determining which routes people choose, and understanding how different demographics use their transit options. This project proposes to build an open-source software application programming interface (API) that will produce new travel insights from cell phone global positioning system (GPS) data. Currently available transportation data analytics packages omit important travel insights that are important for transportation research, such as long-term patterns of life, route selection, and demographically stratified travel behavior. These deeper analyses are important for planning more efficient transit. Abundant cell phone GPS data serves to replace costly travel surveys with better coverage and currency. However, GPS data is challenging due to noise, sporadic sampling, and privacy. Building on the research team lab’s extensive experience with GPS data, the researchers will create an open-source API to robustly deliver the new insights. Unlike commercially available packages, the API will be transparent and extensible. The API can serve as a platform for new algorithms and expanded insights. The project team will demonstrate the API on three National Center for Sustainable Transportation (NCST)-relevant mobility insights that are not possible with commercial packages: (1) Determining long term patterns of individual travel behavior, (2) detailed route selection for individuals, and (3) variations in travel behavior among different demographic groups.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:48:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691672</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>
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    <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> Simulating Accessibility from CAVs and ICTs (SACI)</title>
      <link>https://rip.trb.org/View/2680126</link>
      <description><![CDATA[Simulating Accessibility from CAVs and ICTs (SACI) develops a simulation tool that helps transportation agencies understand and plan for the transformative impacts of connected and automated vehicles (CAVs) and information and communication technologies (ICTs) on travel behavior and network demand. As CAVs and ICTs reshape how people choose destinations and routes, new models are needed to predict future demand and usage. The project develops a framework that captures cognitive, perceptual, and behavioral effects of CAVs and ICTs, implements it in an agent-based model using SILO, MITO, and MATSim simulation components, and packages the result as a software tool for use by state, regional, and local DOTs. The model uses multimodal transportation network data from the DC, Maryland, and Virginia region to assess how CAV-ICT deployment affects travel patterns and land use across the region.]]></description>
      <pubDate>Wed, 11 Mar 2026 15:25:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2680126</guid>
    </item>
    <item>
      <title>Advanced Transportation Optimization and Modeling (ATOM)</title>
      <link>https://rip.trb.org/View/2676009</link>
      <description><![CDATA[The U.S. transportation system is experiencing increasing complexity driven by evolving infrastructure, land-use patterns, travel demand, demographic shifts, and rapid advances in vehicle and mobility technologies. Emerging behaviors such as telecommuting, ridesharing, and micromobility, along with changing attitudes toward public transit and vehicle ownership, are reshaping how people and goods move across regions. To ensure that transportation investments remain efficient, resilient, and cost-effective, transportation agencies require advanced, data-driven tools to anticipate and evaluate the system-level impacts of these changes.  

This project develops an advanced transportation modeling and optimization pipeline in Austin, Texas, to evaluate the impacts of alternative strategies and technologies through scenario-based analysis. The system will be built around the Behavior, Energy, Autonomy, and Mobility (BEAM) model. BEAM is an open-source, agent-based regional transportation model that enables realistic simulation of travel behavior, mode choice, fuel consumption, and system performance, and associated community-level impacts under different “what-if” scenarios.  

By leveraging BEAM’s scalable, modular architecture, the project will address key limitations of conventional four-step and activity-based transportation models, providing a robust framework for testing strategies such as emerging technologies, infrastructure enhancements, and new mobility services before deployment. The pipeline will be developed and extended to assess additional impacts (via coupling to additional models) and therefore to serve as a decision-support tool for engineers, planners, and service providers, allowing them to evaluate performance outcomes and trade-offs across multiple metrics relevant to both economic productivity and community outcomes. Model calibration and validation of the Austin BEAM Core pipelines will utilize highly resolved local datasets on traffic flows, speeds, and network performance. These data will enable precise representation of real-world operating conditions in the Austin region and ensure the model’s reliability for planning and investment analysis.  

Scenario development will be coordinated with implementation partners regional stakeholders identified through a stakeholder mapping exercise. These scenarios will reflect practical policy and technology options under active consideration in Texas, ensuring alignment with state and regional priorities. The resulting pipeline will be structured for extensibility, allowing future integration with additional datasets and modeling components for use in other applications. Project outcomes will be shared broadly through technical reports, workshops, and data portals to facilitate adoption by other agencies, research institutions, and industry partners.  

Ultimately, this project supports goals of enhancing efficiency, safety, and reliability, while strengthening economic competitiveness and enabling informed, data-driven investment decisions. By combining open-source modeling innovation with public–private collaboration, the project will provide a replicable framework for modern, performance-based transportation system management.  

Moreover, the pipeline embraces and deploys advanced and transformative research: using an open-source, agent-based framework (BEAM) exceeds conventional planning methods. The stakeholder-co-development model (with public and industry partners) ensures that this research is not only theoretically innovative but also rooted in real-world deployment potential. This initiative empowers decision-makers to implement policies that enhance safety, the economy, and with various co-benefits to communities.  ]]></description>
      <pubDate>Tue, 03 Mar 2026 16:42:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676009</guid>
    </item>
    <item>
      <title>Modeling Event Travel Dynamics for the 2028 Los Angeles Olympics Using Large-Scale Mobility Data</title>
      <link>https://rip.trb.org/View/2676006</link>
      <description><![CDATA[The 2028 Los Angeles Olympics will require innovative transportation strategies to move hundreds of thousands of travelers reliably to and from events. The challenge is that mega-events, such as the 2028 Los Angeles Olympics, produce travel behaviors that differ from routine patterns and which exceed the scope of existing planning models. Traditional data sources, such as travel surveys and long-term regional transportation models, cannot capture short-term behavioral changes in response to major events. Large-scale human mobility data from smartphones, which continuously updates, now enable direct observation of how millions of people adjust their travel in and around major events in high spatial and temporal resolution, and can form the basis for forecasting models for future events. This project will leverage large-scale mobile data to build a foundation for modeling mega-event travel. The 2028 Olympics present an obvious application, and this research can inform the work of the White House Task Force on the 2028 Summer Olympics (established by Executive Order 14328). This project’s results will also inform innovations in transportation planning models well beyond the Olympics, pioneering adaptations of mobile data that with follow up work could model travel patterns from novel events such as evacuations or changes to infrastructure to accommodate safety, health, economic, or seasonal needs.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:26:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676006</guid>
    </item>
    <item>
      <title>Data Integration to Support Digital Infrastructure and Efficient Mobility Insights</title>
      <link>https://rip.trb.org/View/2669548</link>
      <description><![CDATA[Data is at the core of understanding mobility trends, modeling and optimizing transportation systems and informing policy and decision-making. Understanding data is also key to advancing digital infrastructure for transportation, providing technological systems and frameworks to complement physical infrastructure. This project aims to support two other initiatives (the CEM Innovation Accelerator and the Advanced Transportation Optimization and Modeling project) by serving as the “data engine” to support both modeling as well as potential innovation projects. However, this project also stands alone as a data integration effort that will support the expansion and improvement of the previously-developed CEM Data Hub and serve to provide mobility data insights and digital infrastructure frameworks geared towards understanding transportation system efficiency.   

This project will develop a robust data integration framework, collect, assemble, and harmonize diverse transportation datasets to support the development of actionable mobility insights. By integrating data from traffic sensors, transit systems, GPS traces, and mobile applications, the project will create a comprehensive data ecosystem that reflects real-world travel behavior, congestion patterns, and modal interactions. This foundation will enable the development of analytical tools and decision-support systems that help agencies and planners optimize transportation networks for efficiency. The integrated data platform will support advanced analytics, visualization tools, and decision-support systems that can be used by planners, engineers, and policymakers to evaluate mobility strategies.   

Key components of the project include:  

Data Ecosystem Development – Establishing a scalable and secure data architecture that supports integration of diverse transportation datasets   

Collaboration – Engaging with ongoing projects across the CEM consortium to ensure collaborative use of available data   

Analytical Tools and Insights – Developing dashboards, predictive models, and scenario planning tools to support efficient and healthy mobility decisions.  

Ultimately, this effort will position data as a central asset in advancing efficient mobility across urban and rural contexts.  ]]></description>
      <pubDate>Thu, 12 Feb 2026 15:35:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669548</guid>
    </item>
    <item>
      <title>Enabling Mobility for Older Adults in the US</title>
      <link>https://rip.trb.org/View/2669552</link>
      <description><![CDATA[Driving is essential for the preservation of functional independence for older adults, yet there is a growing number of older adult drivers with comorbid health conditions that might impair their ability to drive safely. Older adult drivers are overrepresented in motor vehicle crash deaths and injuries, which is a major public health concern. The purpose of this project is to (1) develop a comprehensive understanding of the mobility needs and challenges of older adults in the United States, and (2) develop an innovative tool to extend their safety while they drive.  

Aim 1: Develop a comprehensive understanding of the mobility needs and challenges of older adults  

To develop a comprehensive understanding of the mobility needs of older adults, the research team will analyze data from a nationally representative survey of U.S. adults aged 65 and older. The survey contains a comprehensive set of questions that explore driving behavior, transportation options, mobility limitations, and attitudes toward future transportation technologies and policies among adults aged 65 and older.   

Aim 2: Develop an innovative tool to extend their safety while they drive.  

The goal of this project is to understand older adults’ perceptions of an app (StreetCoach) that provides a driving score based on actual driving behavior. A number of apps exist for older adult drivers but the perceptions of older drivers towards their driving score is poorly understood. This study will use a sequential explanatory research design by asking 10 older adults to download and use the app for 60 days. Following this, the research team will conduct in-depth interviews with the participants to gain an understanding of their perception and interpretation of their telematics score, and what factors might motivate them to change their driving to improve the score.   ]]></description>
      <pubDate>Thu, 12 Feb 2026 15:16:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669552</guid>
    </item>
    <item>
      <title>From perception to preparedness: Virtual reality simulations of flooded roadways in coastal communities (UPRM)</title>
      <link>https://rip.trb.org/View/2663232</link>
      <description><![CDATA[Project Description: Coastal flooding regularly disrupts transportation networks, damages infrastructure, and limits access to essential services through storm surge, tidal inundation, and extreme precipitation. These events result in vehicle failures, stranded motorists, pavement damage, and delays in emergency response and daily mobility. Communities with aging infrastructure, limited resources, or constrained evacuation options face heightened vulnerability. The total annual economic burden of flooding in the U.S. ranges from $179.8 to $496.0 billion (US Congress JEC, 2024). In addition, the National Weather Service and the Centers for Disease Control and Prevention report that over half of all flood-related drownings occur when a vehicle is driven into hazardous floodwater. Understanding how drivers decide whether to cross or avoid flooded roads is essential for designing warnings, signage, and roadway treatments that reduce risky behavior and improve outcomes. The use of virtual reality (VR) and immersive 360° scenarios can let residents experience rising water, blocked routes, and mitigation measures without real-world risk, increasing realism and emotional stimulus. Scenario-based VR visualizations can help translate technical flood data into intuitive, actionable information for nontechnical audiences. Local resilience depends not only on infrastructure but also on household-level preparedness and decision-making, including how individuals interpret alerts and respond to flood risks. Chacon-Hurtado (2013) advocates for embedding community preferences and preparedness considerations directly into transportation decision-making frameworks, arguing that investments should be evaluated not only on engineering metrics but also on how they advance local capacity to act under hazard conditions. 
This project will employ virtual reality (VR) simulations of flooded highways that are being developed by the University of Puerto Rico at Mayagüez (UPRM) team to study human behavior and perception in flood scenarios, with three main goals: (1) Enhance public understanding of flood risks by immersing participants in realistic coastal flooding scenarios, (2) Evaluate driver decision-making when encountering flooded roadways, analyzing how variables such as water depth, roadway conditions, and alert systems (e.g., signage, ADAS, in-vehicle alerts) influence choices, and 
(3) Assess community preferences for flood mitigation strategies, using immersive experiences to gather feedback on potential interventions. 
Two VR approaches will be implemented. The first involves a driver simulator with 24–36 participants navigating flooded roadway scenarios to assess behavioral responses under controlled conditions. The second approach will engage community members from coastal municipalities like Isabela, Puerto Rico, in immersive 360° simulations to explore perceptions of flood risk and mitigation strategies. Pre- and post-tests will measure changes in knowledge, perception, and behavioral intent. Insights from both simulations will inform the design of more effective alert systems and flood mitigation strategies that reflect community preferences and improve safety. The findings will support transportation and emergency planning professionals in developing human-centered solutions for flood-prone coastal areas.

]]></description>
      <pubDate>Sat, 31 Jan 2026 12:03:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663232</guid>
    </item>
    <item>
      <title>Enhancing Chain-Up Infrastructure and Compliance in Utah's Mountain Corridors: A Data-Driven Evaluation</title>
      <link>https://rip.trb.org/View/2655751</link>
      <description><![CDATA[This project evaluates chain-up infrastructure and traction-device compliance in Utah's mountain corridors, focusing on how roadway geometry, winter operations, and driver behavior affect chain-up performance during storm events. Using geospatial analysis, operational data, and field-informed insights, the study identifies locations where existing chain-up facilities may be undersized, poorly situated, or constrained by topography. The project also develops artificial intelligence (AI)-generated videos that explain operational challenges, noncompliance impacts, and potential improvement strategies to both practitioners and the traveling public. Project findings will inform infrastructure upgrades, policy refinements, and improved communication practices, with methods and products readily transferable to mountain corridors in other western states.]]></description>
      <pubDate>Mon, 19 Jan 2026 17:04:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655751</guid>
    </item>
    <item>
      <title>Using Artificial Intelligence to Uncover How Safety Perception Influences Travel Behavior Shifts: Comparative &amp; Longitudinal Analysis for the Future of Autonomous Vehicle, Transit and Ride-hailing Services</title>
      <link>https://rip.trb.org/View/2655700</link>
      <description><![CDATA[Transit agencies and cities are increasingly overwhelmed by large volumes of unstructured data; yet they lack methodical, validated tools to turn safety narratives into operational indicators. This project addresses that gap by measuring and comparing public safety perception for autonomous-vehicle services (robotaxis), public transit, and ride-hailing services. It will assess how these perceptions relate to traveler profiles and mode choice in San Francisco and San Jose over a six-month period. San Francisco as a mature setting where robotaxis may compete with ride-hailing and transit, and San Jose as a newer coming deployment that provides a baseline for comparison and forward-looking extrapolation.
The research team will use artificial intelligence with human-audited classification to analyze public discourse drawn from news-comment threads and social-media posts, for example, discussions of disengagements, curb conflicts, yielding behavior, and interpersonal harm such as unwanted contact, theft, or assault. Validation will include human audit with inter-rater reliability (aiming for Cohen’s kappa of at least 0.60), time- and city-based cross-validation, and an error taxonomy with documented adjustments. The project will deliver (1) a transparent safety-perception taxonomy, (2) traveler-persona profiles linked to safety perceptions, (3) a lightweight dashboard for agencies and cities to explore time, place, and topic trends, and (4) operational and policy frameworks for improvements across all modes, organized into vehicle-level safety measures, station and hub operating practices, reporting and response mechanisms, and rider communication standards. The approach and workflow are replicable and can be extended to additional cities. The innovation lies in a reusable tool bridging research and practice providing concrete, methodical steps to turn qualitative narratives into consistent indicators they can trust. Agencies can adopt it to sort and prioritize incoming signals, rerun it with new data, and compare results across time and places to support day-to-day decisions and longer-term planning.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:09:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655700</guid>
    </item>
    <item>
      <title>Impact of Passing Lane Guidance through Angled Markings on Two-Lane Rural Highways</title>
      <link>https://rip.trb.org/View/2655580</link>
      <description><![CDATA[Previous research has shown that the provision of low-cost measures, such as passing lanes, can be highly cost-effective in improving the level of service of two-lane highways, by increasing passing opportunities and safety. The passing lanes, such as Super 2 highways adopted by Texas Department of Transportation (TxDOT) are beneficial in dispersing platoons at locations where passing sight distance is shorter than the designated passing zones.
However, drivers should be informed, educated, and receptive to such design changes to have any positive impact on driving behavior and safety on these highways. In addition, the adopted pavement markings or design features used along passing lanes should be intuitive and considerate of human factors. Therefore, it is essential to investigate drivers’ perception and behavioral response to design changes in the passing lanes, such as any transitional lane markings, to ensure the desired safety and operational benefits prior to the installation at selected sites.]]></description>
      <pubDate>Thu, 15 Jan 2026 13:01:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655580</guid>
    </item>
    <item>
      <title>Impact of Double Centerline Rumble Strips (CLRS) on Driver Behavior</title>
      <link>https://rip.trb.org/View/2655579</link>
      <description><![CDATA[According to the NCHRP report 641 (2005) Centerline rumble strips (CLRS) effectively reduce head-on, sideswipe, and crossing-the-centerline crashes on two-lane rural highways by 44-46%. Benefits of using CLRS include alerting the inattentive drivers, offering visibility of lane markings, and providing a low-cost treatment to avoid crashes. Previous research suggests that a separation of 6” between the double rumble strips along the center of the road can prevent joint deterioration. In addition, the gap may alert the driver to allow sufficient time to correct lane encroachment. However, the effectiveness of such a gap between double CLRS to enforce safety has not been investigated. Furthermore, other factors may affect the effectiveness of double CLRS on surrogate safety measures, such as traffic density, visibility conditions, geometric design (crest curves), presence of shoulders (paved/unpaved), passing zones, etc. Therefore, the objective of this project is to investigate the impact of different double CLRS installation patterns on the behavior and safety of drivers. To achieve this objective, a driving simulator-based study will be designed to investigate the effect of the pattern of double CLRS on driving behavior using various surrogate safety measures such as lane position, speed, post lane encroachment time, and time-to-collision (TTC). These measures will be collected across segments installed with and without the double CLRS in a control condition without any distraction/inattentiveness and compared with the test conditions including a distracted driver using a driving simulator experiment. Based on the study findings, the final report will include recommendations for the systematic installation of double CLRS for safety enhancement.]]></description>
      <pubDate>Thu, 15 Jan 2026 12:52:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655579</guid>
    </item>
    <item>
      <title>Healthy Micromobility: Moving From Crisis to Opportunity</title>
      <link>https://rip.trb.org/View/2652680</link>
      <description><![CDATA[Micromobility, including e-scooters and e-bikes, is an emerging transportation mode with the potential to alleviate congestion and improve urban mobility. However, prior research has primarily focused on safety risks and injury rates, with less attention given to its potential benefits, such as improved accessibility, reduced vehicle miles traveled (VMT), and enhanced health through active transportation. This project aims to provide a more comprehensive assessment of both the risks and benefits of electric micromobility within the U.S. transportation system using a combination of literature review, survey research, and systems dynamic modeling. The study examines how electric micromobility reduces VMT while also evaluating the health trade-offs related to safety risks and active transportation benefits. The project consists of three main aims: (1) a targeted literature review to synthesize existing evidence on electrified micromobility’s health impacts, (2) a nationally representative survey to capture user behavior, trip substitution patterns, and safety concerns, and (3) the development of a system dynamics simulation model to quantify the net health effects across diverse urban settings.     ]]></description>
      <pubDate>Tue, 13 Jan 2026 16:27:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652680</guid>
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