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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>Analyzing Trends in Micromobility Safety to Inform ODOT’s Safety Programming</title>
      <link>https://rip.trb.org/View/2726155</link>
      <description><![CDATA[The use of electric micromobility devices, including e-bikes, e-scooters, e-unicycles, and other emerging devices, has been steadily increasing as modes of transportation in Oregon over the past several years, generating many questions about how best to integrate these devices into the transportation system. Safety concerns, as the rate of injuries sustained while riding an electric micromobility devices that necessitated an ER visit or hospitalization has significantly increased between 2021 and 2024, rising from 414 potential injuries in 2021 to 1,229 in 2024, according to Oregon Health Authority (OHA) data. These numbers probably underrepresent total crashes. Oregon Department of Transportation's (ODOT’s) Transportation Safety Office (TSO) oversees safety education and training programs, including bicycle and motorcycle safety, but they currently do not have a clear picture of the crash data and magnitude of risk associated with e-micromobility devices given the relative nascency of the mode. Analyzing the available sources of data and developing a deeper understanding of the safety concerns of local agencies and community organizations are critical first steps. This research will help the agency and their transportation safety partners to be data driven in their development of e-micromobility safety materials, safety training programs, strategic project planning and funding investment decisions to reduce the crash and injury risk related to these devices.
OBJECTIVES: This research will help answer the following questions: What’s the extent and magnitude of injuries? What’s the rate of injury for youth compared to adults?  Through a safe system approach, what are the primary causes or factors of crashes involving people riding e-micromobility devices?  What types of devices are most involved in crashes? What are the current concerns for transportation partners and law enforcement related to e-micromobility safety and how are these concerns compared to what is seeing in the data?  
The findings of this important research project will help ODOT identify the core strategies to better tailor ODOT safety programming and partnerships. The results will also be used to educate policymakers, interested partners, and the public. ODOT is seeking a deeper understanding of safety issues and concerns related to the emerging field of small devices that have varying amounts of assisted power beyond human propulsion, such as e-bikes, e-scooters, and e-unicycles. While usage of these devices has emerged in the last decade—and in higher numbers in the last five years—safety research and recommendations have been slow to catch up. Analyzing the available data will help inform the development of data-driven strategies and investments within ODOT, as well as the external partners ODOT works with.]]></description>
      <pubDate>Wed, 08 Jul 2026 17:27:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726155</guid>
    </item>
    <item>
      <title>Establishing Operating Characteristics for Non-Motorized Road Users</title>
      <link>https://rip.trb.org/View/2712196</link>
      <description><![CDATA[State departments of transportation (DOT) have qualitative design guidance available to them for walkable and bikeable transportation system improvements. However, the specific design of non-motorized transportation facilities is often selected based on the amount of space available, rather than the physical and operational characteristics of their users and equipment.

Bicycle-related research into operational characteristics is limited, and more information about bicycles and their riders is needed. Recently completed research has improved our understanding of bicyclist acceleration and speed on conventional bicycles, but more information is needed related to reaction time, deceleration, braking, lean angle, coefficients of friction, and lateral shy distance. Further, the research does not capture the full range of users, such as those using e-bikes and other micromobility devices.

Pedestrian traits such as walking speed and space requirements have been well-studied, but only in certain contexts. Pedestrian walking speed influences traffic signal timings, and walking speed information has been collected through a variety of methods. Sophisticated modeling of pedestrian flow is available to apply toward the design of infrastructure such as transit stations. However, available guidance does not fully capture how pedestrians, including those using mobility devices, operate in a typical transportation context.

 The objective of this research is to collect information about the basic operating characteristics of a wide range of pedestrians, bicyclists, and other micromobility users to better understand their spatial requirements along sidewalks, bikeways, and roadways. This research will be useful to transportation planners and designers seeking to develop safe and effective infrastructure for non-motorized users.]]></description>
      <pubDate>Tue, 09 Jun 2026 17:35:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712196</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>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>
    </item>
    <item>
      <title>Operational Characteristics of Conventional and Electric-Assisted Bicycles and Their Riders</title>
      <link>https://rip.trb.org/View/2487318</link>
      <description><![CDATA[The Minnesota Department of Transportation (MnDOT) Bicycle Facility Design Manual and the American Association of State Highway and Transportation Officials (AASHTO) Guide for the Development of Bicycle Facilities are the go-to resources for the design of bicycle facilities. The values in these manuals are used to make decisions on bicycle projects every day; however, some of the values and calculations presented in these guides are based on information collected 30 or more years ago. The objective of this research is to collect information about basic operating characteristic of conventional bicycles, electric assist bicycles (e-bikes) and their riders. Research will identify the characteristics most relevant to bicycle facility design guidance.]]></description>
      <pubDate>Wed, 08 Oct 2025 10:21:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487318</guid>
    </item>
    <item>
      <title>Tools to Support Enforcement of Micromobility Traffic Laws and Crash Reporting



</title>
      <link>https://rip.trb.org/View/2570610</link>
      <description><![CDATA[The rise of micromobility devices, including e-scooters, e-bikes, and other personal transportation technologies, has transformed urban transportation. The proliferation of these devices has introduced new challenges for law enforcement. The characteristics of micromobility devices—small size, high maneuverability, lack of registration, and shared usage models—create complexities for traffic enforcement and crash reporting. Additionally, traffic safety laws in many states are often unclear with regard to micromobility, providing limited guidance to law enforcement officers in regulating these devices.

Crashes involving micromobility devices are often underreported. Furthermore, legal ambiguity and limited police training exacerbate the difficulty of enforcing traffic laws and properly reporting crashes involving micromobility devices. These gaps hinder efforts to ensure the safety of all road users. Research is needed to assess these challenges and develop actionable recommendations to improve enforcement practices, reporting protocols, and interagency collaboration.

The objective of this research is to develop a toolkit to improve the state of the practice related to enforcement of traffic safety laws for micromobility users and crash reporting.]]></description>
      <pubDate>Tue, 01 Jul 2025 14:24:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2570610</guid>
    </item>
    <item>
      <title>Impact of Weather Variables on Emerging Micromoblity options (Bikeshare and E-Scooter) in the South-Central United States</title>
      <link>https://rip.trb.org/View/2480351</link>
      <description><![CDATA[As transportation infrastructure evolves, bikeshare programs, e-bikes, and e-scooters have become vital components. These systems require substantial infrastructure, including docking stations, pavements, sensors, and other elements. Cities invest in this infrastructure with the expectation of significant returns, such as reduced environmental impacts, decreased traffic congestion, and improved health outcomes for riders. Therefore, increasing the usage of these transportation modes is crucial for cities. However, weather extremes including hotter temperatures and more intense rainfall, may impact on ridership patterns. The aim of this pilot study is to examine how extreme temperatures and precipitation affect bikeshare system usage, including travel time and ridership, and then to use the relationships built to project how these extremes are expected to affect future ridership. The study will focus on cities within the Southern Plains Transportation Center (SPTC) region. The results will provide community and city planners with essential insights into the challenges and opportunities for enhancing or modifying infrastructure to support these emerging transportation modes in the face of extreme weather events. 
The objective is to develop relationships between bikeshare usage and both temperature and precipitation and apply those relationships to downscaled projections to project how bikeshare usage is expected to change with increasing temperatures and heavy precipitation events. Although micromobility systems are viewed as transformative for transportation infrastructure, it is possible that their usage will be limited by extreme temperatures and rainfall events Therefore, this study attempts to synthesize the results of this analysis and past literature to develop policy recommendations to optimize micromobility infrastructure in the SPTC region. For instance, policy options may include better shelters for micromobility infrastructure or increased tree canopy for bike lanes and sidewalks. 
The study will be carried out through the following detailed tasks. Task 1: Conduct a comprehensive review of existing literature on micromobility, temperature and precipitation, and urban transportation. Task 2: Identify gaps in the current research as related to micromobility usage and weather conditions. Task 3: Gather bikeshare and e-scooter usage data from service providers and municipal transportation departments in selected cities in SPTC region. Task 4: Collect historical weather data from Daymet Version 4 and downscaled weather projections from the South Central CASC. Task 5: Quality assure and integrate the collected datasets to ensure consistency and accuracy and conduct data aggregation for and re-gridding to a 5-km grid with daily data. Task 6: Identify and select appropriate statistical, AI, or machine learning methods for developing relationships during the historical period on the impact of temperature and precipitation on travel ridership, trip duration, travel type and time. Task 7: Develop the predictive model and apply it to projected data for midcentury and end-of-century. Task 8: Compare how micromobility transportation systems have been used during the historical period with that projected for mid-century and end-of-century. Task 9: Based on the study results, develop policy recommendations for optimizing micromobility programs for each study location. Task 10: Share research findings through academic publications, conferences, and workshops. Task 11: Engage with stakeholders, including city officials and transportation agencies, to discuss the implementation of recommendations.

]]></description>
      <pubDate>Wed, 01 Jan 2025 16:04:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2480351</guid>
    </item>
    <item>
      <title>Investigating safety and risk disparity between personally owned and shared micromobility modes</title>
      <link>https://rip.trb.org/View/2401752</link>
      <description><![CDATA[This research examines the safety implications of the increasing popularity of micromobility, particularly focusing on shared e-bikes and bicycles. The study has three main goals: comparing crashes involving shared e-bikes and bicycles, understanding how safety trends for personally owned e-bikes are changing, and identifying differences in safety between personally owned and shared e-scooters. The research seeks to uncover patterns, risk factors, and disparities in micromobility-related accidents by analyzing existing data and collaborating with industry partners to conduct surveys. By analyzing detailed crash reports and new injury codes related to micromobility, the project aims to provide evidence to inform policies and improve infrastructure, ultimately enhancing overall transportation safety. By fostering collaboration between academia and industry, the project enhances our understanding of micromobility safety and provides invaluable learning experiences. Ultimately, the research endeavors to inform policymakers, practitioners, and the public on strategies to mitigate safety risks associated with the proliferation of micromobility modes in urban environments.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401752</guid>
    </item>
    <item>
      <title>Safety and Health Impacts of Mobility Alternatives Technology Transfer</title>
      <link>https://rip.trb.org/View/2335147</link>
      <description><![CDATA[Conveying new concepts and research results to the general public and professionals can help positively transform our practice and generally our future. This project will center its efforts on extending the technology transfer initiatives from three previously concluded 
Center for Connected Multimodal Mobility (C2M2) projects. These projects include the exploration of potential reductions in fatal crashes in South Carolina attributed to Automated Vehicles, the assessment of the potential of Bike Share Networks and Active Transportation to enhance urban mobility and public health outcomes, and the development of a Cloud-based Quantum Artificial Intelligence-supported Automated Truck Platooning Strategy aimed at reducing energy consumption and improving mobility. The ongoing technology transfer endeavors will be broadened, with the outcomes disseminated in national and international forums, as well as within academic circles. This dissemination aims to share valuable insights and findings derived from the completed research projects, fostering broader understanding and application in relevant fields.]]></description>
      <pubDate>Tue, 06 Feb 2024 17:14:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2335147</guid>
    </item>
    <item>
      <title>Implementing and Evaluating Machine Learning Algorithms for Bikeshare System Demand Prediction
</title>
      <link>https://rip.trb.org/View/2315311</link>
      <description><![CDATA[A bikeshare (public bicycle, or bicycle-sharing) system is a service in which bicycles are made available for shared use to individuals on a short-term basis for a price or free. Bikeshare systems have increased from operating in a few European cities to expanding in the United States at an increasing pace. Many bikeshare systems allow users to borrow a bike from a station and return it at another station belonging to the same system. The goal is to encourage cycling as a mode of transportation as well as recreation. Nevertheless, the flexibility to pick up and return bicycles at any station can lead to inventory imbalances in the system. To enhance the effectiveness of the system, bikeshare operators should implement suitable methods to realign resources, guided by precise forecasts of bicycle demand. This research endeavors to develop models for Houston bikeshare system demand prediction at the station level by leveraging data on station activities. Accurate prediction of bikeshare demand has the potential to transform the way these systems are managed and integrated into urban transportation networks, leading to improved efficiency, customer satisfaction, and sustainability.]]></description>
      <pubDate>Wed, 27 Dec 2023 17:50:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2315311</guid>
    </item>
    <item>
      <title>Bike Lending in North America: Understanding Business Models, User Acceptance, Social Equity, and Public Safety</title>
      <link>https://rip.trb.org/View/2292798</link>
      <description><![CDATA[Bicycle lending is a growing phenomenon within cities and towns across the country. Bike lending libraries allow people to check out bicycles, much like checking out a library book, for a set period of time and return it after the term is up or they have finished with it. Bike lending is different from bike rental or bike sharing in that most lending arrangements do not involve an exchange of money. Bike lending libraries exist to serve several different use cases and purposes. One of the main purposes of bike lending libraries is to allow people to use certain types of bicycles that they do not need all the time. To better understand the role and potential that bike lending libraries may have on the growth of bicycling as well as on the safety and social equity of access to riders, the research team first need an understanding of the scope and scale of bike lending initiatives across North America. This study will explore the topic by (1) conducting a literature review, (2) building an online census of bike lending operations in North America, (3) conducting expert interviews with operators, (4) conducting a survey of operators, and (5) conducting a focus group of users/lendees. The results will be synthesized in a final report.]]></description>
      <pubDate>Tue, 21 Nov 2023 16:44:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292798</guid>
    </item>
    <item>
      <title>Shared Micromobility as a Last-Mile Complement to Public Transit</title>
      <link>https://rip.trb.org/View/2292729</link>
      <description><![CDATA[Shared micromobility services, especially dockless e-scooters and e-bike, have
experienced explosive growth across cities in recent years. People in the US took 136 million trips on shared bikes, e-bikes, and e-scooters (86 million trips) in 2019, 60% more than in 2018. As this trend continues, it becomes increasingly important for transit agencies to understand and respond to the impacts of micromobility on transit ridership and operations; given that shared micromobility is best used for short trips, a strategic response is to promote shared micromobility as a last-mile complement to public transit. Integrating micromobility with transit can not only enhance customer experience but also promote transit ridership. Despite a growing research interest in this topic, several major knowledge gaps remain. First, we lack knowledge of the spatiotemporal patterns of integrated transit and shared micromobility. That is, we do not know when, where, and how the transit-micromobility integration happened at high levels of spatial and temporal resolution, which limits vendors’ and local administrations’ ability to monitor and intervene. Second, little work has examined the equity aspects of transit-micromobility integration. Since public transit disproportionately serves low-income communities and minority populations in the U.S., enhancing the integration between transit and shared micromobility may deliver significant equity benefits. However, there has been little empirical work on identifying strategies to promote this potential. The availability of high-resolution micromobility data (e.g., GBFS data) and transit data (e.g., GTFS static and real-time data) provides a great potential to fill in these research gaps and generate important insights to inform planning and policy decisions in both private and public sectors.
The overall objective of this project is to explore strategies that can maximize the
potential of leveraging shared micromobility to complement public transit. We will work
with transportation agencies (e.g., District Department of Transportation and Washington
Metropolitan Area Transit Authority) and industry partners (e.g., Spin, Lime, and Lyft) to
examine three research questions: 1) Where are first-mile/last-mile transit-connecting shared e-scooters trips happening and what factors shape this use? 2) To what extent does transitmicromobility integration benefit traditionally marginalized communities and advance equity?
To address the two research questions, we will leverage existing datasets such as GPS
data and survey data and obtain high-resolution trip-level data from our collaborators from industry and local governments. UF has previously collaborated with Spin/Ford Mobility to explore bundled pricing strategies to promote shared e-scooters and transit integration. Specifically, we plan to undertake the following research tasks:
(1) Conduct a literature review regarding the state of knowledge on transit and
micromobility integration.
(2) Collect relevant data. The PI has previously conducted a behavioral survey to analyze
who are shared micromobility riders, who are using shared micromobility to connect
with transit as well as what factors shape the frequency of this use. Our team has also
obtained a unique and novel dataset from micromobility operator, Spin, that indicates
the time and location of Spin shared e-scooter trips as well as whether the trip was a
transit-connecting trip. The two datasets are complementary to each other as the
former can shed light on who and why people are using shared micromobility to
connect with transit and the latter can shed light on spatiotemporal patterns of transitintegrated shared micromobility trips.
(3) Conduct exploratory analysis. We will analyze the survey data with univariate
analysis and cross-tabulation analysis. We will also conduct geospatial analyses of the
transit-connecting shared micromobility trip patterns, focusing on how they differ
across neighborhoods of distinct sociodemographic characteristics. For example, we
will examine if transit-connecting shared micromobility trips disproportionately
happen in advantageous neighborhoods compared to traditionally underserved
neighborhoods.
(4) Perform statistical modeling. We expect to build two sets of statistical models. One is
a set of travel behavior models that analyze which population groups are more
inclined to adopt shared micromobility as a first-mile/last-mile transit solution and the
factors that shape usage frequency. The other is a regression model (e.g., Possion or
negative binomial) that examines which geospatial factors are associated with the
frequency of transit-connecting shared micromobility trips.
(5) Summarize findings. Results from Tasks 4) and 5) will be summarized into a final
report which we expect to include actionable insights and policy recommendations
for public agencies (DOTs, transit agencies) and private vendors regarding promoting
transit-micromoblity integration. We plan to develop two journal manuscripts based
on the proposed work.]]></description>
      <pubDate>Mon, 20 Nov 2023 16:31:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292729</guid>
    </item>
    <item>
      <title>Tompkins Mobility-as-a-Service (MaaS) Phase 1</title>
      <link>https://rip.trb.org/View/2077906</link>
      <description><![CDATA[Tompkins County will receive funding to develop a multi-modal trip planning platform that integrates information on bus services, demand-response service, taxis, volunteer transportation, car-share and bike-share services. The platform will enable riders in rural upstate New York to plan multi-modal trips through a mobile app and web platform.]]></description>
      <pubDate>Tue, 06 Dec 2022 09:48:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2077906</guid>
    </item>
    <item>
      <title>Exploring E-bikes in the Era of Electrification: Towards a More Sustainable and Equitable Transportation System</title>
      <link>https://rip.trb.org/View/2004399</link>
      <description><![CDATA[Innovative technologies have entered our lives, promising a safer, greener, and more affordable future of transportation, with electrification at the heart of this transformation. While increasing electrification is vital, further steps are needed to expand its benefits in terms of reducing congestion and promoting sustainable, accessible, and equitable transportation for all. In this respect, the primary objective of this study is to deepen our understanding of the barriers, concerns and needs associated with electric bikes (e-bikes). Apart from being a sustainable and active mode of transportation, bicycling has quickly emerged as one of the safest transportation modes in the face of the pandemic. The popularity of bicycling has grown even more when it comes to e-bikes, owing to their potential to improve a person’s ability to ride a bike over longer distances, on steeper grades, and/or despite physical disabilities or limitations that might otherwise be a barrier to using a bicycle. On the other hand, bicycling has faced with significant challenges in terms its equitable and safe access. For instance, many bike-share services have limited types of bikes, which may not be suitable for individuals with disabilities. Studies also noted the lack of diversity in the demographic distribution of bikeshare users, which has raised equity concerns. Using qualitative and quantitative methods, this study will explore challenges associated with the access to and use of e-bikes whether they are part of shared micromobility services or personally owned vehicles. A specific focus will be given to marginalized populations—particularly those with disabilities and elderly people—while also capturing racial/social inequities. Case studies will be conducted to examine differences in travel environments, distinctions between driver and non-driver populations as well as users and non-users. The research will help identify strategies to enhance safe and equitable access to new mobilities while reducing congestion.]]></description>
      <pubDate>Sun, 07 Aug 2022 15:34:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2004399</guid>
    </item>
    <item>
      <title>Safety of Two-Wheeled Scooters</title>
      <link>https://rip.trb.org/View/1887433</link>
      <description><![CDATA[This project examines the sources of data available to learn about the safety facts of motorcycles with small engines including two-wheeled scooters (also referred to as motor scooters), mopeds, mini-bikes, pocket motorcycles, and e-bikes. The project will characterize the use and safety of these vehicles. The scope of work includes a literature scan, data acquisition and analysis, and report writing. The project will scan the literature for information on two-wheeled scooters, mopeds, mini-bikes, pocket motorcycles, and e-bikes to document the extent and type of use, crash risk and crash characteristics, and data needs (for example, crash reporting by the vehicle type and exposure metrics). The outcome of this project will be a report that (1) documents existing data sources on these types of vehicles, (2) reports on the extent of use and trends in use, (3) analyzes the type and frequency of crashes and crash injuries of the focus vehicles, including looking at use and safety data as a function of race, ethnicity, and SES, and (4) identifies data needs and ways to improve the collection of safety data. The results will provide a foundation of knowledge to improve safety programs for these road users who are a part of daily transportation, and whose presence will likely increase in coming years.]]></description>
      <pubDate>Fri, 22 Oct 2021 18:34:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/1887433</guid>
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