<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnM+PGZpbHRlciBmaWVsZD0iaW5kZXh0ZXJtcyIgdmFsdWU9IiZxdW90O1Njb290ZXJzJnF1b3Q7IiBvcmlnaW5hbF92YWx1ZT0iJnF1b3Q7U2Nvb3RlcnMmcXVvdDsiIC8+PC9maWx0ZXJzPjxyYW5nZXMgLz48c29ydHM+PHNvcnQgZmllbGQ9InB1Ymxpc2hlZCIgb3JkZXI9ImRlc2MiIC8+PC9zb3J0cz48cGVyc2lzdHM+PHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg==" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <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>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>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>Assessing User Perceptions and Needs for AI-Assisted Micro-Mobility and Teleoperation</title>
      <link>https://rip.trb.org/View/2505752</link>
      <description><![CDATA[This research project aims to enhance the safety and usability of micro-mobility and autonomous vehicle systems through the development of advanced human-machine interfaces (HMIs). Artificial Intelligence (AI)-assisted visual, auditory, and tactile interfaces that provide real-time road and environmental information will be evaluated in both direct operation of micro-mobility devices (e.g., e-scooters) and remote operation scenarios. This work aims to contribute to the advancement of inclusive and adaptive transportation technologies for safer, smarter, and more efficient mobility systems. This project aligns with USDOT priorities by advancing equitable, safe, and sustainable transportation through the development of AI-assisted multimodal HMIs for micro-mobility and autonomous systems.]]></description>
      <pubDate>Tue, 04 Feb 2025 16:32:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2505752</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>Scooter-Share Travel Demand Forecast: A Context-Aware LSTM Recurrent Neural Network Approach</title>
      <link>https://rip.trb.org/View/2459122</link>
      <description><![CDATA[Shared micromobility has been popular in many cities in the U.S. The rise of shared micromobility brings significant operational challenges such as fleet management and demand forecasting. This project develops a Context-Aware Long Short-Term Memory (CALSTM) recurrent neural network to enhance the prediction of daily travel demand for scooter-sharing in Austin, Texas. The CALSTM model boosts prediction accuracy by integrating the impact of nearby points-of-interest (POIs) and daily weather conditions on scooter usage. It processes historical scooter-sharing demand and weather information through separate LSTM modules to extract temporal information. The outputs from these modules are combined through element-wise multiplication to establish temporal dependencies. Additionally, POI information is analyzed using a Multi-Layer Perceptron (MLP) to capture spatial dependencies. These spatial and temporal dependencies are then integrated by another MLP module to produce the forecast outputs. Case study experiments in Austin, TX, demonstrated that the CALSTM model significantly outperformed benchmark models, achieving improvements of 28% in Mean Absolute Error (MAE) and 19% in Root Mean Squared Error (RMSE) over traditional LSTM models. These results offer valuable insights for transportation planning and the enhancement of shared micromobility in urban settings.]]></description>
      <pubDate>Sat, 23 Nov 2024 11:10:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2459122</guid>
    </item>
    <item>
      <title>Enhancing Urban Micromobility Safety and Adoption through Biometrics and Mobile Sensing Technologies in El Paso, TX and New Brunswick, NJ</title>
      <link>https://rip.trb.org/View/2459052</link>
      <description><![CDATA[This project will explore how street-level infrastructure design factors affect the perceived safety of e-scooters in El Paso, Texas, and New Brunswick, New Jersey, using advanced biometric sensing technologies like eye-tracking glasses, galvanic skin response sensors, heart rate trackers, and video cameras. Researchers at the University of Texas El Paso (UTEP) and Rutgers will conduct e-scooter riding experiments with varied environments, infrastructure, demographics, and micromobility policies. This analysis aims to identify stress patterns, safety issues, and congestion challenges faced by micromobility users. Thirty participants will be recruited in each city, with diverse demographics, to ride e-scooters on pre-defined paths featuring different travel environments, such as bike lanes, varied land uses, shaded areas, and topographies. The post-trip questionnaire survey data will be collected to calibrate/validate sensor results. The sensor data on the perceived travel environment will be digitized using advanced segmentation and object detection algorithms applied to video and gaze data. Statistical and machine learning models will analyze gaze behavior and perceived stress levels by environment, providing insights on improved infrastructure design and transportation policies for e-scooters.]]></description>
      <pubDate>Thu, 21 Nov 2024 16:37:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2459052</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>Making Micromobilty Work: Exploring Public Opinion to Inform Policy, Infrastructure, Technology, and Sharing Services</title>
      <link>https://rip.trb.org/View/2296530</link>
      <description><![CDATA[In a few short years, micromobility has attracted intense interest from the public, policymakers, researchers, device manufacturers, mobility-as-a-service companies, and investors. Micromobility as a class encompasses lightweight devices like e-scooters and skateboards propelled by either human or battery power, though the recent storm of interest was triggered in 2017 by the emergence of one type of micromobility, shared e-scooter systems.

This project will explore public opinion around micromobility, notably questions of particular relevance to local policymakers. Survey topics will include perceptions of safety for both micromobility riders and pedestrians, the potential for micromobility as a first/last mile solution for public transit riders, and road management issues (e.g. “rules of the road” for riders and government policy on shared mobility companies).

USDOT Priorities:

The proposed project aligns with US DOT Notice of Funding Opportunity Challenge 1 (Improving Mobility of People and Goods), particularly with respect to Mobility Innovation. E-scooters have been one of the most successful innovative personal mobility technologies in recent years and is arguably still emerging, with later-Covid rebounds in travel and increasing use of privately-owned scooters. Technological innovation is also still ongoing with companies working on safer and more durable e-scooters, adaptive e-scooters for people with disabilities, and the development of new micromobility form factors.]]></description>
      <pubDate>Mon, 27 Nov 2023 19:18:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2296530</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>Impact of WIM-based Direct Enforcement on the Service Life of Bridges
</title>
      <link>https://rip.trb.org/View/2283487</link>
      <description><![CDATA[The Brooklyn-Queens Expressway (BQE) in New York City is a crucial corridor connecting the two boroughs of Brooklyn and Queens with other counties. Given its substantial daily traffic for transporting goods and services, the longevity of BQE is crucial to enhance public safety and alleviate congestion. The team has collaborated with the New York City Department of Transportation (NYCDOT) to implement (1) an integrated weigh-in-motion (WIM) systems on the northern part of the BQE corridor for a direct enforcement of the high percentage of overweight (OW) trucks, and (2) a structural health monitoring (SHM) system to estimate the remaining service life of the BQE structures. This project synthesizes WIM and SHM data to study the impact of the reduction in OW percentage over time resulting from direct OW enforcement on extending the service life of the BQE. Furthermore, the team will conduct a life-cycle cost analysis (LCCA) of the network of bridges in NYC based on the established correlation. The output of this project will be a new framework to evaluate the effect of reduced OW percentage on the service life prediction. This framework will help introduce new legislation(s) for direct OW enforcement to mitigate the number of OW trucks and their OW tonnages; thus, improving bridge service life and preserving highway infrastructure. Another aspect of this proposal is to expand the potential uses of physical testbeds to investigate the feasibility of utilizing biometric sensors, including eye tracking glasses, galvanic skin response sensors, and heart rate trackers, to assess the perceived safety of micro-mobility users, encompassing both cyclists and e-scooter riders. The primary objective is to collect pilot data to develop well-structured semi-naturalistic experiment protocols, allowing for the acquisition of reliable psychological data concerning the safety perceptions of micromobility users. The acquired sensor data will be cross-referenced with qualitative survey responses to analyze the advantages and disadvantages of various methods for collecting safety perception data among micromobility users. The data collected from these experiments will play a crucial role in providing insights into the types of infrastructure designs that are well-received by micromobility users and identifying built environments considered unsafe for travel. These findings will be invaluable for informing infrastructure design improvements aimed at enhancing the travel experiences of micromobility users, supporting mode shifts, and mitigating congestion.]]></description>
      <pubDate>Mon, 30 Oct 2023 22:45:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2283487</guid>
    </item>
    <item>
      <title>Enhancing Transit Access and Safety Through Equitable Micromobility Solution</title>
      <link>https://rip.trb.org/View/2283488</link>
      <description><![CDATA[Micromobility refers to transportation enabled by small, low-speed, human- or electric-powered transportation devices, such as bicycles and scooters. Micromobility may be organized and deployed as a shared vehicle system, as the first and last-mile transportation mode to supplement transit. This project will investigate two major issues associated with the use of micromobility as a solution to improve the accessibility to transit in underserved communities. The first issue is related to the identification of micromobility stations in areas that are underserved by the fixed-route transit system. The second issue is the safety impacts of implementing micromobility in the abovementioned neighborhoods, and the related infrastructure improvements. A Concept of Operations (ConOps) of micromobility will be proposed to address the needs of the first and last-mile travel in consideration of traffic safety and infrastructure needs. The research team will collaborate with the City of El Paso’s bus service operator (Sun Metro), Street and Maintenance Department, the El Paso Metropolitan Planning Organization (MPO) to use El Paso, Texas as the case study, with safety data from the Texas Department of Transportation (TxDOT), and operational experience of GLIDE Scooter.]]></description>
      <pubDate>Mon, 30 Oct 2023 22:40:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2283488</guid>
    </item>
    <item>
      <title>Quantifying the Impact of New Mobility on Transit Ridership </title>
      <link>https://rip.trb.org/View/1938499</link>
      <description><![CDATA[The objective of this research project is to quantify the impact of shared micromobility on transit ridership at the metropolitan level. The analysis would build upon the team’s existing TCRP study, which includes analysis of a small-sized city (Louisville, KY) that has shared scooters. In this project, a larger metropolitan area (Nashville, TN) is the focus of study. The project assesses disaggregate micromobility trips that include time, origin, destination, and route-level data against route-level transit data.  
The research is divided into two primary parts: 
Part 1: The first part of the research includes an econometric analysis of shared electric scooter trips and route-level bus ridership for Nashville, Tennessee to assess the overall impact of e-scooters on bus ridership. 
Part 2: The second part aims to identify locations for new shared electric scooter corrals in Nashville that can complement transit service and help to increase bus ridership. 
The results of Part 2 will be provided to local stakeholders and research partners (e.g., Tennessee DOT and Nashville Public Works) in the form of a list of proposed locations for new scooter corrals to complement transit for consideration for installation.  ]]></description>
      <pubDate>Wed, 06 Apr 2022 14:23:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1938499</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>
    </item>
  </channel>
</rss>