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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>
    <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>Efficient Mobility for Rural Communities</title>
      <link>https://rip.trb.org/View/2683240</link>
      <description><![CDATA[Rural communities face unique transportation challenges due to low population density, dispersed destinations, and limited resources. Efficient mobility requires strategies that both optimize existing transit systems and embrace innovative service models. This project integrates two lines of research that can support efficient mobility and access to active modes and destinations in rural areas. The first focuses on right-sizing rural transit fleets and the second, on analyzing modal shifts from shared-use mobility services.  

On the vehicle procurement side, rural agencies must balance capital and operating costs, service quality, and reliability while meeting the capacity needs of their riders. When procuring vehicles, rural transit operators choose between transit buses, cutaways, vans, and minivans of various sizes. Each has its own advantages and disadvantages regarding capital costs, operating costs, performance, service quality, and ability to meet the needs of their users. This study builds upon previous research to develop spreadsheet-based user tools that can help rural agencies and state departments of transportation (DOTs) make these decisions. This research improves upon the decision tools developed in a previous study by incorporating new models for the capacity needs of rural transit. Previous decision tools provide guidance on the types and sizes of vehicles to procure but they lack the ability to estimate capacity needs for individual agencies. This study develops a vehicle procurement decision model that incorporates a more sophisticated capacity needs analysis. The result will be a tool that estimates capacity needs of an agency and provides guidance on the number, types, and sizes of vehicles that can best meet that capacity need while improving efficiency and meeting the unique needs of the transit agency.  

A second tool to be developed by the study is an optimization tool. The study will improve upon a previously developed optimization model by incorporating more detailed estimates for lifecycle costs of different types and sizes of vehicles. The study will consider transit buses, cutaways, vans, and minivans of different sizes and seating capacities and include more detailed analysis of the operating costs for each vehicle type and the overall lifecycle costs. This will be incorporated into an optimization model that will minimize total costs, including capital and operating costs, for an agency while meeting capacity needs and service requirements and considering the impact of the fleet configuration on service quality.  

At the same time, emerging technology-enabled shared-use services—such as ridesourcing, microtransit, bikesharing, and carsharing—are transforming mobility in rural areas. By analyzing National Household Travel Survey data and documenting real-world deployments, the study will evaluate adoption patterns, service models, and performance outcomes of rural shared-use systems. This analysis will identify factors contributing to the success or failure of shared-use mobility initiatives and provide actionable strategies for rural agencies and policymakers . ]]></description>
      <pubDate>Tue, 24 Mar 2026 14:22:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683240</guid>
    </item>
    <item>
      <title>Leveraging Vehicle Camera Data for Road Condition Monitoring: A Crowdsourcing and Machine Learning Approach</title>
      <link>https://rip.trb.org/View/2408289</link>
      <description><![CDATA[One key part of pavement management is to assess the road condition and identify pavement distresses such as cracks and potholes. These road distresses, if not identified and repaired timely, could compromise road safety, cause expensive damage claims, and also lead to more expensive later repairs. To assess pavement condition, pavement condition data need to be collected first. However, traditional pavement data collection still relies on manual or specialized vehicles equipped with expensive sensors and requires personnel driving along each road in the road networks. Therefore, traditional road inspection methods are often costly, labor-intensive, and sporadic with limited coverage, leading to delayed maintenance and compromised safety. Recent advancements in machine learning (ML) and the proliferation of  vehicles equipped with various cameras (built-in or dashacams) and sensors offer a promising avenue for revolutionizing road condition assessment practices. This project will establish a framework for collecting and processing crowdsourcing vehicle camera data, and develop machine learning algorithms that uses such data to automatically assess road conditions and identify road damages such as cracks and potholes. The project has the potential to offer a more efficient, cost-effective, and real-time approach to road condition monitoring over large road networks and provide critical information for timely maintenance.]]></description>
      <pubDate>Fri, 26 Jul 2024 21:32:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2408289</guid>
    </item>
    <item>
      <title>Assessing Safety and Mobility Benefits of Autonomous Ride Sharing Services Among Older Adults</title>
      <link>https://rip.trb.org/View/2366293</link>
      <description><![CDATA[	The primary objective of this project is completion of the Final Report for work conducted under BED31-977-02.]]></description>
      <pubDate>Mon, 03 Jun 2024 14:43:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2366293</guid>
    </item>
    <item>
      <title>Community-Assisted Rideshare Service (CAReS) for Rural Communities</title>
      <link>https://rip.trb.org/View/2383707</link>
      <description><![CDATA[This project focuses on assessing the feasibility of community-driven rideshare services in underserved rural areas of Alabama, particularly in the Black Belt region. Rural communities often lack access to essential services. The ultimate goal of the project is to create a community-operated rideshare service to address the local transportation needs of transport-poor communities. The project envisions a community-participatory ridesharing service led by local leaders and operated by volunteers, seeking to minimize transportation disparities. The immediate goal of this project requires understanding community needs and support, identifying volunteering needs, assessing technological needs, and exploring revenue models. 
This is an exploratory study and does not involve actual implementation. The project focuses on the feasibility of creating a local community-based rideshare service. It will identify and understand the resources within the community and will gauge their willingness to support such an initiative.]]></description>
      <pubDate>Wed, 29 May 2024 09:45:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2383707</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>Strategic Development of GUI Tools for Enhancing Transportation Mobility Among Vulnerable Groups During Pandemics</title>
      <link>https://rip.trb.org/View/2362126</link>
      <description><![CDATA[This project aims to improve transportation equality and life quality of voluntary groups with disabilities and the elderly by addressing social exclusion, accessibility, and mobility issues. The project team proposes to develop a strategic GUI application to facilitate the planning, establishment, and life-cycle maintenance and management of ride-sharing vehicle facilities, effectively accommodating their transportation needs. The objective is to develop a user-friendly GUI application using Python Tkinter library to simplify the adoption of the developed algorithms of the car-sharing system for vulnerable groups into practice. Within the GUI application, to streamline all core functions derived from the developed algorithms, encompassing interactive data input processes (such as retrieving city network data and user-defined points of interest (POI) like retirement communities, healthcare centers, grocery stores, entertainment clubs, etc.), automated data processing, calculations, decision-making, and result reporting.]]></description>
      <pubDate>Thu, 04 Apr 2024 12:26:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2362126</guid>
    </item>
    <item>
      <title>Competitiveness, User Preference, and Willingness-to-Pay for Peer-to-Peer Ridesharing Service</title>
      <link>https://rip.trb.org/View/2343858</link>
      <description><![CDATA[The Peer-to-Peer (P2P) ridesharing model is a cost effective system where transit service is not available. This research explores the competitiveness, user preference, and willingness-to-pay (WTP) for P2P ridesharing services as a sustainable mode of transportation. The study aims to understand the factors influencing users' choices and WTP for P2P ridesharing platforms. The research methodology includes a suggested stable price structure for P2P ridesharing for drivers and users using a game theory and a comprehensive analysis of the competitiveness of P2P ridesharing compared to traditional transportation modes and other alternatives. Moreover, a survey will be conducted to identify user preferences and the key attributes influencing their decision to opt for P2P ridesharing. To estimate users' WTP, the adaptive choice-based conjoint (ACBC) analysis will be employed using Sawtooth Software's SSI Web. The findings of this study will contribute to a deeper understanding of the viability and user acceptance of P2 ridesharing, enabling policymakers and ridesharing platforms to optimize their offerings and pricing strategies for improving P2P ridesharing system.]]></description>
      <pubDate>Thu, 22 Feb 2024 16:11:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2343858</guid>
    </item>
    <item>
      <title>Investigating Transportation Decarbonization through Transit and Rideshare Electrification: A Scenario Analysis with Large-Scale Models</title>
      <link>https://rip.trb.org/View/2312903</link>
      <description><![CDATA[Enhancing the environmental sustainability of the transportation system hinges on the critical aspect of transportation decarbonization. While the concept of rapid electrification across all existing modes has been previously explored, understanding the profound implications of such a transformative shift is paramount. Typically, proposed policies and strategies have been evaluated using traditional four-step models, often lacking a vehicle powertrain model in the analytical loop, which has limited the ability to comprehensively assess their holistic impact. Addressing this gap, this project capitalizes on the Department of Energy's Systems and Modeling for Accelerated Research in Transportation (SMART) workflow to delve into the realm of transportation decarbonization, specifically through the electrification of transit and rideshare systems. The core objective is to evaluate the potential outcomes of this endeavor. This will be accomplished by harnessing a large-scale agent-based activity-based transportation modeling tool meticulously designed for the Houston Metropolitan Area.]]></description>
      <pubDate>Wed, 20 Dec 2023 16:06:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2312903</guid>
    </item>
    <item>
      <title>Smart Rideshare Matching – Feasibility of Utilizing Personalized Preferences </title>
      <link>https://rip.trb.org/View/2251066</link>
      <description><![CDATA[Current transport-share systems or carpooling typically rely on users to actively request or offer a ride and to coordinate the time and pickup location. Services such as Lyft and Uber have addressed this problem by using location to provide ride services that are convenient and on-demand. The on-demand and convenience aspects of transportation might also be the main reason behind using personal cars as they allow to combine commutes with other activities (e.g., picking up kids to and from school, running errands, going to off-campus meetings, etc.). This convenience, however, comes at a great personal and societal cost including traffic congestion, parking demand, stress, and health problems. Despite various agencies' incentives and discounts for ridesharing, this kind of service has not been widely used for obvious reasons mentioned above as well as hassled coordination, scheduling requirements, commitment, and having to actively request or offer rides. In this project, the research team proposes to conduct a case study using a university community to increase engagement in ridesharing in the UVA community by building a proactive context-aware matching and recommendation system that matches the community members based on predicted ride events inferred from their calendars and routines (e.g., shared time and location of events in Outlook calendar).
]]></description>
      <pubDate>Thu, 21 Sep 2023 15:48:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2251066</guid>
    </item>
    <item>
      <title>Developing Connected Micro-transit Vehicles for Efficient Mobility Service for Rural and Underserved Communities</title>
      <link>https://rip.trb.org/View/2239020</link>
      <description><![CDATA[Sporadic transportation demands over large, geographically dispersed rural areas make conventional fixed-schedule, fixed-route solutions inefficient. This project develops technology-driven solutions for development and deployment of micro-transit transportation vehicles to address distributed and low-demand transportation needs in rural areas by providing more efficient, customer-focused autonomous transit services via flexible routing and scheduling while interacting with and integrating into other transportation modes. This integrated systems of systems and technology-driven approach will lead to efficient transportation solutions by reducing transportation costs due to smaller vehicle sizes and the adoption of ride-sharing strategies for transit services in rural and disadvantaged communities.]]></description>
      <pubDate>Fri, 01 Sep 2023 16:43:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2239020</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>
    </item>
    <item>
      <title>A Multidimensional Analysis of Willingness to Share Rides in a Future of Autonomous Vehicles</title>
      <link>https://rip.trb.org/View/2137503</link>
      <description><![CDATA[A sustainable transportation future is one in which people eschew personal car ownership in favor of using automated vehicle (AV) based ridehailing services in a shared mode. However, the traveling public has historically shown a disinclination towards sharing rides and carpooling with strangers. In a future of AV-based ridehailing services, it will be necessary for people to embrace both AVs as well as true ridesharing to fully realize the benefits of automated and shared mobility technologies. This study investigates the factors influencing the willingness to use AV-based ridehailing services in the future in a shared (with strangers) mode. This is done through the estimation of a comprehensive behavioral model system on a comprehensive survey data set that includes rich information about attitudes, perceptions, and preferences regarding the adoption of automated vehicles and shared mobility modes. Model results show that current ridehailing experiences strongly influence the likelihood of being willing to ride AV-based services in a shared mode. Campaigns that provide opportunities for individuals to experience such services firsthand would potentially go a long way in enabling a shared mobility future at scale. In addition, a number of attitudinal variables are found to strongly influence the adoption of future mobility services; these findings provide insights on likely early adopters of shared automated mobility services and the types of educational awareness campaigns that may effect change in the prospects for such services.]]></description>
      <pubDate>Tue, 14 Mar 2023 12:34:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2137503</guid>
    </item>
    <item>
      <title>Examining On-Demand Transportation Services with a Focus on Shared Rides: Use and Users, Attitudes and Perceptions, Barriers and Solutions</title>
      <link>https://rip.trb.org/View/2118672</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>Fri, 17 Feb 2023 14:03:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2118672</guid>
    </item>
    <item>
      <title>Mobility On Demand (MOD)</title>
      <link>https://rip.trb.org/View/2077902</link>
      <description><![CDATA[The Grand Gateway Economic Development Association will receive funding to introduce an integrated, on-demand shared-ride service in 21 rural communities in eastern and central Oklahoma. Using intelligent transportation systems, the project will connect four regional rural public transit partners with predictive scheduling and routing technology that helps riders tailor trips to meet their needs.]]></description>
      <pubDate>Tue, 06 Dec 2022 09:48:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2077902</guid>
    </item>
    <item>
      <title>Travel Rewards Research Pilot</title>
      <link>https://rip.trb.org/View/2062457</link>
      <description><![CDATA[The Los Angeles County Metropolitan Transportation Authority (LA METRO) will receive funding to work with researchers and technology firms to encourage new ways to travel, including transit, rideshare and vanpooling under the Federal Transit Administration's (FTA’s) Accelerating Innovative Mobility (AIM) program.]]></description>
      <pubDate>Tue, 15 Nov 2022 16:17:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2062457</guid>
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