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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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <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>Potential Impact of Autonomous Vehicles on Reducing Congestion</title>
      <link>https://rip.trb.org/View/2676004</link>
      <description><![CDATA[Traffic congestion is a major problem in large metropolitan areas in the United States. In 2022, on average, a commuter lost about $1,259 in monetary terms annually due to congestion nationwide, which amounts to 8.7 billion lost hours in total. The lack of coordination among individual users, who make routing decisions independently based on current traffic information without anticipating that others may follow similar decision-making patterns, contributes significantly to the high cost of congestion.

The behavior of drivers optimizing their individual routes leads to a state known as the User Equilibrium (UE), leading to travel times that can be significantly higher than travel times from the System Optimal (SO), particularly in congested urban networks where the effects of individual decisions cascade throughout the system. With the future emergence of autonomous vehicles, it is possible that organizations may now own more of the fleet of vehicles and control their routing, providing the organization more options for balancing route selections and thus making it possible to find routing solutions closer to the system optimal. Driverless ride-hailing companies such as Waymo have already begun their service in five major cities across the United States and Tesla has started to test their Robotaxi service in Austin, Texas. The centralized routing capabilities of these autonomous services have the potential to reduce congestion. This first phase of this research project will develop centralized optimization models to quantify the impact of using autonomous vehicles on ride-hailing platforms in reducing congestion.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:17:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676004</guid>
    </item>
    <item>
      <title>Using Artificial Intelligence to Uncover How Safety Perception Influences Travel Behavior Shifts: Comparative &amp; Longitudinal Analysis for the Future of Autonomous Vehicle, Transit and Ride-hailing Services</title>
      <link>https://rip.trb.org/View/2655700</link>
      <description><![CDATA[Transit agencies and cities are increasingly overwhelmed by large volumes of unstructured data; yet they lack methodical, validated tools to turn safety narratives into operational indicators. This project addresses that gap by measuring and comparing public safety perception for autonomous-vehicle services (robotaxis), public transit, and ride-hailing services. It will assess how these perceptions relate to traveler profiles and mode choice in San Francisco and San Jose over a six-month period. San Francisco as a mature setting where robotaxis may compete with ride-hailing and transit, and San Jose as a newer coming deployment that provides a baseline for comparison and forward-looking extrapolation.
The research team will use artificial intelligence with human-audited classification to analyze public discourse drawn from news-comment threads and social-media posts, for example, discussions of disengagements, curb conflicts, yielding behavior, and interpersonal harm such as unwanted contact, theft, or assault. Validation will include human audit with inter-rater reliability (aiming for Cohen’s kappa of at least 0.60), time- and city-based cross-validation, and an error taxonomy with documented adjustments. The project will deliver (1) a transparent safety-perception taxonomy, (2) traveler-persona profiles linked to safety perceptions, (3) a lightweight dashboard for agencies and cities to explore time, place, and topic trends, and (4) operational and policy frameworks for improvements across all modes, organized into vehicle-level safety measures, station and hub operating practices, reporting and response mechanisms, and rider communication standards. The approach and workflow are replicable and can be extended to additional cities. The innovation lies in a reusable tool bridging research and practice providing concrete, methodical steps to turn qualitative narratives into consistent indicators they can trust. Agencies can adopt it to sort and prioritize incoming signals, rerun it with new data, and compare results across time and places to support day-to-day decisions and longer-term planning.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:09:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655700</guid>
    </item>
    <item>
      <title>Operational Innovations for Efficiency and Accessibility of On-demand Mobility in Rural Areas</title>
      <link>https://rip.trb.org/View/2509042</link>
      <description><![CDATA[Unlike the fixed-route transit service, microtransit can thrive in rural and suburban areas with low demand density, because its operation is tailored to individual travel plans. Therefore, microtransit has great potential in improving mobility and accessibility for certain individuals in rural areas. Although many researchers have investigated how microtransit vehicle schedules and routes can be optimized, it is widely assumed that travel requests submitted by individual riders are accommodated independently, without exploring any coordination among riders. The proposed project aims to relax this assumption and test the hypothesis that a microtransit operator can significantly improve the operational efficiency (measured by the vehicle occupancy) by merely adjusting the requested pickup time windows through a process named rider schedule coordination. The following research tasks are proposed under this project: conducting a comprehensive review of individual decision-making coordination in microtransit, formulating and solving a mathematical program for schedule coordination, and deriving insights from extensive numerical studies and case studies. This project, once completed, can improve the microtransit accessibility for residents in rural communities and reduce the service delivery cost for the operator, both of which are well aligned with the center’s themes.    ]]></description>
      <pubDate>Wed, 12 Feb 2025 17:36:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2509042</guid>
    </item>
    <item>
      <title>Legal Considerations for Transit Planning and Managing Large-Scale Events</title>
      <link>https://rip.trb.org/View/2487296</link>
      <description><![CDATA[Large events can significantly impact cities for days or even weeks, requiring careful planning by transit providers. Key metrics such as service reliability, headways, and staffing levels must be evaluated. Additionally, considerations must include the integration of paratransit, ride-sharing, charter services, and microtransit to meet diverse transportation needs. Addressing the risk of human trafficking is also a critical priority in event planning.
Over the next decade, major global events like the 2026 FIFA World Cup, the 2028 Summer Olympic Games, two Rugby World Cups, and new Formula One races will place considerable demands on urban transit systems. Effective transit strategies will be essential for moving millions of spectators and athletes efficiently while minimizing disruptions to local communities.
OBJECTIVE: The objective of this research is to examine and summarize the legal frameworks that influence decision-making for transit systems supporting large-scale sports events. This includes addressing human trafficking prevention and ensuring the efficient use of paratransit, ride-sharing, charter services, and microtransit. Specifically, the research will analyze the legal implications of implementing multimodal transportation solutions to meet the demands of these events effectively.
]]></description>
      <pubDate>Wed, 08 Jan 2025 15:54:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487296</guid>
    </item>
    <item>
      <title>Compliance of Alternate Transit Options with State and Federal Requirements</title>
      <link>https://rip.trb.org/View/2487297</link>
      <description><![CDATA[The public transit landscape has expanded beyond traditional systems, with various agencies and private companies now providing transportation options to the public. These include transportation network companies (TNCs), taxis, and paratransit service providers. When these service providers establish contractual partnerships with federally funded transit agencies, they become subject to federal requirements.
In June 2018, TCRP LRD 53: Legal Considerations in Evaluating Relationships Between Transit Agencies and Ridesourcing Service Providers was published. This digest examined whether such providers were eligible for federal financial assistance and analyzed the legal implications of their partnerships with transit agencies. At the time, ride-sourcing services were relatively new to the transit industry. TCRP LRD 53 identified significant challenges these providers faced in meeting state and federal requirements.
Updating TCRP LRD 53 is now essential due to the significant changes that have occurred in this field. Since 2018, the number of companies entering the market has grown considerably. Additionally, federal and state governments have introduced new regulations and prioritized the enforcement of existing requirements, such as those under the Americans with Disabilities Act (ADA). The legal landscape has also evolved, with increased litigation addressing key issues related to these partnerships. An updated report should reflect these changes and provide transit agencies with up-to-date legal guidance.
OBJECTIVE:
The objective of this research is to develop practical guidance and summarize relevant laws, litigation, and policy developments to support public transportation agencies, their legal counsel, and policymakers in decision-making related to contracting with service providers and ensuring compliance.]]></description>
      <pubDate>Tue, 07 Jan 2025 18:56:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2487297</guid>
    </item>
    <item>
      <title>Ride-Sourcing Demand and Supply Estimation Based on Coarse Public Data</title>
      <link>https://rip.trb.org/View/2472694</link>
      <description><![CDATA[Ride-sourcing platforms like Uber and Lyft have transformed urban transportation, yet their impacts on transit ridership, congestion, and emissions remain challenging to assess due to limited data access. This research addresses the lack of detailed ride-sourcing data by developing regression, gravity, and network equilibrium models to estimate trip demand and supply distribution at finer spatial resolutions. Using Massachusetts town-level data as a baseline, the project incorporates high-resolution land-use, transportation, and demographic data to predict trip patterns and driver availability. Outputs include a practical tool for government agencies and driver organizations to analyze ride-sourcing markets, improving operational decisions and supporting safety and system efficiency goals.
]]></description>
      <pubDate>Mon, 09 Dec 2024 09:57:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2472694</guid>
    </item>
    <item>
      <title> Guidelines for Managing Current and Future Transportation Network Companies (TNCs) Operations at Airports







</title>
      <link>https://rip.trb.org/View/2413910</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Mon, 05 Aug 2024 19:37:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2413910</guid>
    </item>
    <item>
      <title>Impacts of Shared Autonomous Vehicles on Traffic Operations (4.17)</title>
      <link>https://rip.trb.org/View/2378075</link>
      <description><![CDATA[As global demand for ride-hailing services rises, there is an increased urgency to study shared autonomous vehicles (SAV) fleets and their impacts on regional travel. According to Schaller (2018), the number of ride-sourcing vehicles and trips in New York City from 2013 to 2017 increased by 59% and 15%, respectively. In the same period, the number of idle vehicles increased by 81% and ride-sourcing drivers spent more than 40% of their time empty and cruising for passengers, which increased vehicle-miles travelled (VMT) by 36%. The same trends are expected to happen for ridesharing using SAVs if appropriate policies are not used to manage the empty VMT. For this reason, this proposed project aims to understand the impacts of ride-sharing, especially through shared autonomous vehicles on traffic operations and infrastructure durability (including the wear and tear of these vehicles on asphalt) in Connecticut. Investigating this impact requires the simulation of traffic for the entire population in the state under different ride-sharing scenarios. Traffic simulation tools require multiple datasets and calibrated models, which are different for each region. The research team plans to use a traffic
simulator, such as POLARIS, which is an agent-based traffic simulation tool developed by Argonne National Laboratory, for SAV simulations. These tools allow for simulating multimodal traffic over large-scale transportation networks and requires multiple inputs and models calibrated for each specific region. Therefore, the research team will collect the required data and estimate models, including but not limited to activity generation, mode choice, and destination choice models, specific to Connecticut. The expected findings of this study could provide valuable insights into the impacts of autonomous vehicles and ride-sharing options provided by these vehicles on traffic operations including but not limited to VMT, empty VMT, and total travel time, as well as travel patterns in the state of Connecticut. These traffic operations and travel patterns will impact the deterioration of asphalt, which will be investigated in this study through the surface damage index.]]></description>
      <pubDate>Thu, 09 May 2024 15:15:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2378075</guid>
    </item>
    <item>
      <title>Emerging Vehicle Technologies and Operating Strategies for Tribal and Rural Communities</title>
      <link>https://rip.trb.org/View/2335158</link>
      <description><![CDATA[Often, new technologies and advances in transportation are not readily accessible to all, resulting in their benefits being unavailable to several underserved communities, such as in rural and tribal areas. Technological advancements in transportation, including emerging vehicle technologies and the development of “new mobility” options and other advancements, are generally geared toward serving urban areas. This project will investigate how rural and tribal communities can optimally use new technologies and operating strategies. The general objective is to investigate the feasibility of adopting different vehicle technologies or operating strategies by rural and tribal transit operators. Specific objectives are to measure current adoption rates and identify challenges and opportunities for new vehicle technologies in rural and tribal areas; research strategies for on-demand rural ridesharing and the potential for improved efficiencies. The study, which will incorporate a mixed-methods approach, will provide information about adoption rates, deterrents, challenges, and successes in rural and tribal areas. It will examine the cost, fuel efficiency, and feasibility of operating on-demand rideshare programs in rural areas with different types of vehicles. This project will apply CEM’s 14 Pathways to Health framework to rural and tribal communities. Results will help to ensure an equitable distribution of benefits and disbenefits of these new technologies for rural and tribal communities. ]]></description>
      <pubDate>Tue, 06 Feb 2024 16:56:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2335158</guid>
    </item>
    <item>
      <title>Towards a Safe, Healthy and Efficient Gig Transportation Workforce</title>
      <link>https://rip.trb.org/View/2325390</link>
      <description><![CDATA["Towards a Safe, Healthy and Efficient Gig Transportation Workforce" is a research initiative that addresses the heightened safety and health risks faced by the gig transportation workforce, particularly in Massachusetts. The gig economy, characterized by independent contractor roles and piece-rate payments, poses unique challenges to drivers/riders, including higher anxiety, fatigue, risk-taking behaviors, and lack of federal regulation protections. This project aims to enhance the overall well-being of gig drivers/riders by focusing on three main objectives: understanding their economic, safety, and health status; developing a decision support system (DSS) prototype to aid in making informed scheduling decisions; and engaging disadvantaged communities in the design and testing of this DSS.

The research involves a collaborative effort between faculty members from Civil and Environmental Engineering and the School of Public Policy, combining expertise in driver behavior analysis, operation optimization, community outreach, and socio-technical ecosystem studies. The research plan includes conducting a literature review, focus groups/interviews with gig drivers/riders, designing and testing a DSS prototype, and final reporting.

The project methodology combines qualitative and quantitative research methods, including focus groups and surveys. The DSS prototype will be designed to help gig workers balance economic gains with safety and health considerations, potentially transforming how gig work is conducted and perceived. The community engagement aspect ensures that the DSS is user-friendly and effective for the target audience.]]></description>
      <pubDate>Fri, 19 Jan 2024 10:38:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325390</guid>
    </item>
    <item>
      <title>Real-time Information Dissemination for Efficiency in a Robo-taxi System (RIDERS)</title>
      <link>https://rip.trb.org/View/2321642</link>
      <description><![CDATA[The Real-time Information Dissemination for Efficiency in a Robo-taxi System (RIDERS)
project addresses the emerging challenges in shared-use mobility, particularly the congestion and efficiency issues posed by ride-hail services like Uber and Lyft, and the rise of autonomous ride-hailing or robo-taxis. It explores how a robo-taxi fleet, equipped with connected and automated vehicles (CAVs), can mitigate traffic congestion and improve safety by collecting and sharing real-time traffic information in urban networks. The project's goal is twofold: first, to enable continuous, widespread traffic data collection and system monitoring without hindering passenger service; and second, to enhance overall transportation system efficiency and safety through informed operational and routing decisions. This interdisciplinary effort involves collaboration between transportation engineering and computer science, with a focus on developing smart infrastructure and connected systems. Using simulation-based experiments and real-world data from New York City, the project aims to devise effective strategies for vehicle repositioning and routing that account for traffic conditions and service quality. The outcomes will contribute to smarter, safer urban transportation, with findings shared through academic publications, presentations, and a dedicated project webpage.]]></description>
      <pubDate>Fri, 12 Jan 2024 10:57:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321642</guid>
    </item>
    <item>
      <title>Advancing Equity and Congestion Relief:  An Investigation into On-Demand Shared Rides for Under-served Populations</title>
      <link>https://rip.trb.org/View/2250700</link>
      <description><![CDATA[Transportation network companies (TNCs) and microtransit are changing the way people travel by providing dynamic, on-demand mobility that can supplement public transit and personal vehicle use. Early research suggests that TNCs can expand access and mobility for underserved communities, such as racial minorities and persons with disabilities. However, heavy TNC use among all socio-demographic populations could contribute to increased vehicle miles traveled, congestion, and/or greenhouse gas emissions. Well-designed policy strategies are needed to balance the objectives of increasing mobility and access for underserved communities while simultaneously mitigating the potential adverse impacts of increased TNC usage through policies such as pooling and first-mile and last-mile linkages. However, more research is needed to better understand the mobility gaps and needs of underserved populations to identify potential strategies to mitigate the negative impacts of TNCs and other on-demand transportation services and make the services more equitable. This part of the project proposes to employ a mixed-method approach to examine on-demand transportation services for underserved populations with a focus on shared-ride services. A series of interviews and a literature review will be conducted, identifying individual narratives and lived experiences to put the flesh into quantitative analysis. The study will deploy a national mobility survey and conduct analysis to uncover current shared mobility user patterns and possible relationships to transportation equity. This study will inform why certain socio-demographic populations are more likely to use on-demand transportation services, particularly shared mobilities, factors that contribute to user behavior, and potential strategies to maximize equitable access and mobility offered through these services while mitigating potential adverse impacts.]]></description>
      <pubDate>Thu, 21 Sep 2023 15:17:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2250700</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Transit Practices. Topic SB-41. Microtransit Solutions in Rural Communities: On-Demand Alternatives to Dial-A-Ride Services and Unproductive Coverage Routes

</title>
      <link>https://rip.trb.org/View/2190462</link>
      <description><![CDATA[Dial-a-ride and fixed-route transit options are some of the ways that microtransit services have traditionally been implemented in rural areas. App-based booking and dynamic routing are among the newer offerings transit agencies are exploring to provide greater flexibility and more spontaneous options for users.

TCRP Synthesis 178: Microtransit Solutions in Rural Communities: On-Demand Alternatives to Dial-a-Ride Services and Unproductive Coverage Routes, from TRB's Transit Cooperative Research Program, provides a comprehensive overview of rural microtransit operations through a literature review, surveys of 19 transit providers, and case examples of seven agencies. High customer satisfaction and improved service efficiency were frequently cited as benefits of microtransit solutions.]]></description>
      <pubDate>Fri, 09 Jun 2023 19:17:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2190462</guid>
    </item>
    <item>
      <title>Investigating the Contributing Factors to Willingness to Share Automated Vehicles with Gender Focus</title>
      <link>https://rip.trb.org/View/2142128</link>
      <description><![CDATA[This study uses a survey collected in four metropolitan areas in the United States (Phoenix, Atlanta, Austin, and Tampa) to understand the attitudinal factors underlying men and women’s willingness to share rides on ridehailing services that use automated vehicles (AVs). The study uses a measurement model to classify the attitudinal measures into unobserved latent constructs, and preferences towards owning and driving a vehicle. A Structural Equation Model is then used to measure the effects of gender upon the willingness to share rides in autonomous vehicles, controlling for respondents’ attitudes (latent constructs), current use of mobility-on-demand services, and socioeconomic characteristics. The results of this study are key to ensure that the future of transportation reaches all, regardless of gender. Understanding women’s willingness to engage in autonomous shared rides will enlighten the process of including them in the automated, shared, and electric future. By identifying the different attitudinal traits motivating different groups to engage in shared ridehailing rides, ridehailing service providers can better accommodate their needs, and promote a more egalitarian transportation service. Preliminary results indicate that men’s environmental motivations to use AV shared rides are stronger than women’s, while women’s perception of autonomous vehicles is a stronger predictor of AV ridesharing adoption.]]></description>
      <pubDate>Fri, 24 Mar 2023 10:57:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2142128</guid>
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