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    <title>Research in Progress (RIP)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>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>Which Way Forward? Learning from Global Informal Transport Networks to Inform Microtransit Services in California</title>
      <link>https://rip.trb.org/View/2695811</link>
      <description><![CDATA[This proposed 12-month study seeks to draw upon lessons learned from informal transit systems, particularly from the developing world, to inform the development and implementation of demand-responsive transit (often referred to microtransit) strategies in California. Through a comprehensive review of existing literature, case studies (n= up to 5), and expert interviews (n=15-20), this study aims to identify lessons learned, challenges, and opportunities associated with informal transit operations. Leveraging this understanding, the research will assess how such lessons can be applied to the design, deployment, and evaluation of microtransit and other demand-responsive services in California communities, including transportation network companies (TNC) and taxi models. Key areas of focus include business and operational models, fare affordability and financial sustainability (including operational costs), and potential policy frameworks. By synthesizing insights from informal transit experiences internationally, this proposed study seeks to contribute to the development of efficient and sustainable microtransit and demand-responsive strategies tailored to the diverse needs of all travelers.]]></description>
      <pubDate>Thu, 23 Apr 2026 18:05:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2695811</guid>
    </item>
    <item>
      <title>Optimizing Last-Mile Delivery Using Micromobility and Autonomous Technologies: A
Scalable Framework for Future Logistics Solutions</title>
      <link>https://rip.trb.org/View/2684210</link>
      <description><![CDATA[Freight delivery is essential to urban mobility and the economy but contributes to congestion, emissions, and infrastructure wear. With e-commerce growth, it is vital
to improve last-mile delivery (LMD), which can comprise up to 51% of logistics costs. This proposal introduces a framework combining micromobility and autonomous technologies to optimize LMD. These solutions offer flexible and labor-efficient alternatives for dense, high-traffic environments. The goal is to ease congestion and enhance delivery efficiency. Real-world case studies in urban and semi-urban settings will assess the framework’s feasibility, scalability, and overall impact.
OBJECTIVES/GOALS:
• Develop a scalable framework for LMD that integrates various micromobility and
autonomous technologies to optimize routes and identify the best delivery options for
policymakers.
• Design algorithms for efficient route planning that enhance operational efficiency, reduce fuel/electricity costs, and improve delivery speed.
• Evaluate the feasibility of different delivery methods, taking into account constraints and
practical considerations to ensure real-world applicability.
• Improve system resilience by enabling real-time route adjustments to address real-world obstacles, such as road repairs and traffic disruptions.
• Validate the proposed framework through agent-based simulation using Amazon’s last-mile data  to demonstrate its effectiveness.
• Leverage AI-powered technology to analyze historical data and predict demand to enable dynamic adaptation of delivery methods, such as deploying more drones on weekdays and fewer on weekends in specific areas, to optimize operational performance.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:38:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684210</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>Evaluating the Cumulative Impact of Environmental Conditions on Stress Levels in Micromobility Users: An AI-Driven Multimodal Approach</title>
      <link>https://rip.trb.org/View/2652172</link>
      <description><![CDATA[Micromobility solutions, such as e-scooters and bicycles, are increasingly utilized in urban transportation, providing flexible and sustainable mobility options. However, micromobility users face significant exposure to environmental stressors, including air pollutants emitted by motorized traffic. While prior studies have explored the physiological effects of transportation emissions, the psychological impacts, particularly stress, remain underexplored. This study aims to bridge this gap by developing an AI-driven predictive model that evaluates the cumulative impact of transportation-related air pollutants on stress levels in micromobility users. By integrating wearable sensor data (e.g., electrodermal activity, heart rate variability, and skin temperature), air pollutant concentration data (e.g., PM2.5, NOx, and CO), and spatial context data (e.g., GPS and accelerometer readings), this research will leverage Temporal Fusion Transformer (TFT) models to predict real-time stress levels and generate stress heatmaps. The results will inform policymakers, transportation planners, and public health officials, contributing to more sustainable and inclusive urban transportation systems. Additionally, the project will provide hands-on research opportunities for students, fostering workforce development in AI-driven transportation health studies. ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:55:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652172</guid>
    </item>
    <item>
      <title>Making CAV Deployments Compatible with Complete Streets Objectives for Safe and Efficient Operations - Phase III</title>
      <link>https://rip.trb.org/View/2639855</link>
      <description><![CDATA[This proposal is for the continuation of a multi-year effort initiated in the first year of the current Center for Connected and Automated Transportation (CCAT) program, Making CAV Deployments Compatible with Complete Streets Objectives for Safe and Efficient Operation.  Phase I was initiated in 2023 and Phase II in 2024.  The primary motivation is the safety, mobility and accessibility implications of potentially conflicting forces in the progressive deployment of CAV capabilities and intelligent mobility in urban city streets. As planners and engineers focus on the next generation of disruptive technologies through connectivity and automation, a counter movement is seeking accessible, walkable, sustainable neighborhoods with easier bike and micromobility access for all residents.  Overlayed on the urban fabric is increasing reliance on delivery vehicles of all sizes associated with on-demand eCommerce. The primary question motivating this research is how to design and operate complete streets that accommodate both the requirement of flow efficiency achievable through connectivity, automation and shared autonomous mobility services with the aspirations for access to micromobility and human-scale urban spaces. For the coming year, the main objectives include (1) Complete data analysis for the interactions especially for the under-represented user categories, especially bicycling and micromobility; (2) Extend the  simulation framework to consider various arrangements and hierarchies of shared road space; and (3) Develop design framework for allocating roadway space to the various user classes that recognizes the dual needs of safe and efficient flow on one hand, and access to micromobility in urban spaces on the other. ]]></description>
      <pubDate>Wed, 10 Dec 2025 16:12:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2639855</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>Solving first mile – last mile through micromobility connections</title>
      <link>https://rip.trb.org/View/2495000</link>
      <description><![CDATA[The integration of active and public transport is a well-known synergy for sustainable transportation, yet in the US the two are often planned in isolation. The increasing use of shared micromobility services offers an opportunity for the research team to test the role of integrated payment and pricing to improve first mile – last mile transit access, especially in fixed-asset rail corridors decimated by post-COVID ridership. This project will use partnerships with local transit agencies and shared mobility providers to implement such strategies in a controlled experiment.]]></description>
      <pubDate>Fri, 31 Jan 2025 16:36:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495000</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>Envisioning Micromobility as Public Transit: Two intervention studies in the living lab of Davis, California</title>
      <link>https://rip.trb.org/View/2431628</link>
      <description><![CDATA[Mode share in public transit in the United States traditionally lags behind other developed countries. One promising strategy to encourage public transit use involves enhancing access and egress from transit stops. Given the potential of shared micromobility services to address the "last mile" challenge, there is growing interest in integrating these services into public transit services. However, achieving affordability and access in micromobility services poses challenges for operators in ensuring sustainable operations at appropriate pricing levels. The cost of using micromobility services has sharply increased in recent years, making it unaffordable for many people. Concurrently, the researchers’ recent research suggests there is consensus across private industry, government, and advocates that micromobility can best serve the public if it is viewed as a public transit option. To begin to envision micromobility as serving existing public transit and acting as public transportation itself, the researchers will examine the role of pricing on micromobility demand. In this project, the researchers will conduct two pricing-focused field experiments, partnered with the micromobility operator, SPIN, and a railway operator, Capitol Corridor. The first experiment will use the railway station of the Capitol Corridor in Davis, California as a living lab to assess the effectiveness of increasing rail usage by subsidizing micromobility services. The second experiment will focus on micromobility services operated by SPIN in Davis, aiming to understand the general price elasticity of demand for micromobility. Through these experiments, the researchers will analyze the causal effects of the interventions on increasing railway and micromobility use. The insights gained from this analysis will provide valuable guidance on the potential of micromobility and regional rail partnerships to enhance transit use in other corridors throughout the state as well as pricing mechanisms to understand the potential for micromobility services to satisfy the travel demand of communities. ]]></description>
      <pubDate>Wed, 18 Sep 2024 19:12:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431628</guid>
    </item>
    <item>
      <title>Making CAV Deployments Compatible with Complete Streets Objectives for Safe and Efficient Operation — Phase II</title>
      <link>https://rip.trb.org/View/2425224</link>
      <description><![CDATA[The primary question motivating this research effort is how to design and
operate complete streets that accommodate both the requirement of flow efficiency achievable through connectivity, automation and shared autonomous mobility services with the aspirations for equitable access to micromobility and human-scale urban spaces. The main objectives include (1) develop component models that comprise the multi-dimensional interactions taking place in a Complete Street environment with added CAVs; (2) obtain data on the more vulnerable (and under-represented) user categories, especially bicycling and micromobility; and (3) integrate components in simulation framework and perform initial prototype testing.]]></description>
      <pubDate>Thu, 05 Sep 2024 11:11:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425224</guid>
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