<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=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" 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>Evaluating the Willingness to Pay for Managed Lanes (MLs)</title>
      <link>https://rip.trb.org/View/2563659</link>
      <description><![CDATA[This research project will investigate users’ willingness to pay to use managed lane (ML) facilities in light of the recent and rapidly shifting demographic trends and develop a better understanding on how recent mobility options, shifts in telework, online shopping adoption, and demographic and societal trends may have affected the preferences and choices toward using ML facilities.]]></description>
      <pubDate>Wed, 11 Jun 2025 13:19:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2563659</guid>
    </item>
    <item>
      <title>Telemedicine Adoption Before, During, and After COVID-19: The Role of Socioeconomic and Built Environment Variables</title>
      <link>https://rip.trb.org/View/2519198</link>
      <description><![CDATA[In this research, the research team focuses their investigation on the telemedicine adoption preferences of patients/consumers. This comprehensive approach contributes to advancing the existing body of knowledge in five distinct ways. First, the team uses rigorous multivariate econometric models that accommodate multiple sociodemographic and built environment (BE) variables at once rather than simple bivariate correlations of determinant factors with telemedicine adoption. Second, the framework is structured to discern the shifts in the effects of the factors affecting telemedicine adoption between the before- and after-COVID periods. This helps gain a deeper understanding of how socioeconomic and BE variables influenced telemedicine adoption before the pandemic and how the willingness of different segments of society to engage in telemedicine shifted as a result of the pandemic. Third, proposed multivariate model system recognizes that unobserved individual factors (such as technology savviness) that elevate telemedicine adoption before the pandemic may also affect adoption during the pandemic, and collectively influence an individual’s intention to use telemedicine in the post-pandemic period. Not accounting for such intra-individual correlation effects due to unobserved individual-level factors variables will, in general, provide biased estimates of the evolution pattern of telemedicine adoption over time. In this study, the longitudinal data comprises responses from the same individuals across three specific time periods, offering a unique advantage in quantifying the causal effect of the pandemic on telemedicine use. Fourth, the study explores the reasons for using or not using telemedicine in the after-COVID period from the patient’s viewpoint. The team conducts a consumer-focused analysis that provides unique insights into the motivations, preferences, and concerns of different patient segments regarding telemedicine. Specifically, in the after-COVID period, for telemedicine adopters, the team jointly models the reasons for adoption using multivariate binary probit models. Similarly, in the after-COVID period, for non-adopters, the team uses multivariate binary probit models to jointly analyze cited reasons for not adopting telehealth. This can inform healthcare providers, policymakers, and other stakeholders seeking to sustain telemedicine adoption post-COVID. Fifth, the study is the first that the team is aware of in the travel behavior literature that focuses on telemedicine adoption. Earlier studies related to virtual participations have investigated tele-adoption in the context of work, grocery shopping, and non-grocery shopping, but have not considered telemedicine adoption. However, telemedicine adoption can also have transportation ramifications, just as virtual participation in other types of activities can (including individuals potentially appropriating the freed-up time for pursuing other activities). In this regard, the team hopes that their study will open up additional research in studying the travel implications of tele-participation in medical-related activities. This should be of particular interest in the context of medical accessibility for the increasingly aging population of many countries, including the United States.]]></description>
      <pubDate>Sat, 08 Mar 2025 11:26:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2519198</guid>
    </item>
    <item>
      <title>Evaluating Housing Dissonance and the Potential for Smart Growth in Rural America</title>
      <link>https://rip.trb.org/View/2495006</link>
      <description><![CDATA[The potential to adopt more sustainable travel behavior and the ability to meet travel needs in small and rural communities is strongly linked with land use. Some evidence points to a large unmet demand in rural US areas for more compact and mixed-use development that could help create more livable and sustainable communities. This project involves a national study to evaluate the types of communities where people in rural areas currently live and how those align with their preferences. This project will focus on understanding neighborhood-level attributes and transportation factors that explain housing location preferences and provide insights into the potential for land-use strategies to address rural transportation needs while reducing emissions.]]></description>
      <pubDate>Fri, 31 Jan 2025 16:35:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495006</guid>
    </item>
    <item>
      <title>Planning and Policy for Safer Roads with Autonomous Vehicles: Moral Decision Making Behavior in Dilemma-inducing Situations</title>
      <link>https://rip.trb.org/View/2440031</link>
      <description><![CDATA[The unparalleled technological advances in vehicle automation and artificial intelligence have made autonomous vehicle (AV) technically available for extensive road tests and, even recently, for limited commercial mobility services. Notwithstanding these advances, a critical challenge to integration of AV into the real-world transportation systems as well as our personal and professional lives is establishing ethical regulations for AV. In particular, such ethical principles would determine how an AV makes moral decisions in dilemma-inducing situations, for instance, whether it should hit a teenager pedestrian to spare two senior passengers onboard.

This research project looks at this problem not from the philosophical perspective, which prescribes moral behavior of an AV. Instead, this project describes the public expectation and perception of a moral AV, which is the perspective of econom(etr)ics, psychology, and cognitive science disciplines. To do so, the studies in economics and psychology explore the process of human decision making focusing on the morality dimension of decisions, since many of decisions humans routinely make can have a moral aspect. A recent example is the decision of receiving vaccination at a cost (e.g., side effects for the receiver) to immune the community and the society. This research project aims at understanding the public expectations of moral AVs by unravelling the cognitive process of human decisions making considering the decisions’ morality aspect. This objective will be accomplished in two consecutive tasks explained below.

Task 1: Designing and Conducting a Survey Using Stated Preferences (SP) Experiment. For the purpose of analyzing consumers’ choice behavior (e.g., travel behavior), the SP experimental design method provides a rigorous and efficient tool, which is extensively applied in the relevant literature. Applying this tool, this task designs a survey to collect an empirical dataset on human subjects. The PI plans to accomplish the required IRB certificate for data collection.

Task 2: Developing a Modeling Framework on Humans’ Decision Making. This task focuses on developing methods built on econom(etr)ics, psychology, and cognitive science disciplines, to be capable of capturing morality dimension of decisions. One of such methods is choice theory-based model of latent class choice, which is capable of capturing “reason-based” morality. Another example is random regret model, which can capture “emotion-based” morality (since regret is an emotion). The models are then empirically estimated on the dataset collected in Task 1.

]]></description>
      <pubDate>Sun, 13 Oct 2024 10:57:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440031</guid>
    </item>
    <item>
      <title>Exploring the Changing Dynamics of Household Vehicle Ownership and Use in the U.S.</title>
      <link>https://rip.trb.org/View/2440045</link>
      <description><![CDATA[This project is driven by a pressing need to understand the rapidly evolving landscape of household vehicle dynamics amidst technological advancements and significant societal changes. It focuses on the growing urgency of climate change mitigation and adaptation, a push for equitable mobility for all, and the transition towards vehicle electrification. Aiming to fill the knowledge gap in how households are adapting to these transformative forces, the project will design and deploy a comprehensive nationwide survey, called Evolving Vehicle Ownership Preferences and Use Survey (EVOPUS). This survey seeks to collect data on vehicle ownership, use, and preferences in the context of societal and environmental changes as well as related changes in household energy use (e.g. the adoption of residential solar photovoltaics and battery storage). The major contributions of the project are the following: 1) a nationwide dataset including data on travel behavior, household characteristics, vehicle ownership/transactions and use, mobility patterns as well as attitudes, perceptions, preferences, and lifestyles, made available to other researchers; 2) enhanced understanding of key barriers and drivers of electric vehicle adoption in distinct population segments; 3) a basis for new policies and programs and improvements to existing policies and programs to enable an equitable transition to sustainable mobility across heterogeneous population segments throughout the country.]]></description>
      <pubDate>Thu, 10 Oct 2024 17:16:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440045</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>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>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>ACRP Web Resource for Airport User Choice Modeling and Analysis</title>
      <link>https://rip.trb.org/View/2007981</link>
      <description><![CDATA[Airport users’ ground access and egress transportation mode choice and airport choice models provide essential information for airport planning studies. An airport user's preference is one consideration for airport operators when evaluating investments in ground transportation access and egress infrastructure. This research will provide publicly available data for airport user choice modeling and analysis.

OBJECTIVE: The objective of this research is to develop an Airport Cooperative Research Program (ACRP) web resource to analyze and model the users’ choice of an airport based on ground access and egress transportation modes and air transportation decision drivers. ]]></description>
      <pubDate>Tue, 16 Aug 2022 14:11:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2007981</guid>
    </item>
    <item>
      <title>Attitudes Towards Emerging Mobility Options and Technologies – Phase 3: Survey Data Compilation and Analysis for Tampa, FL</title>
      <link>https://rip.trb.org/View/1983979</link>
      <description><![CDATA[Emerging transportation technologies including electric and autonomous vehicles and emerging mobility services such as ride-hailing and vehicle sharing are bringing about transformative changes in the transportation landscape. With the emergence of new transportation technologies and services, it is critical that transportation forecasting models be enhanced to account for behavioral dynamics that will result from the increasing penetration of disruptive forces in the transportation marketplace. To enhance transportation forecasting models, people’s attitudes towards and perceptions of emerging technologies and services need to be measured and understood. Armed with such an understanding, it will be possible to specify and develop behavioral models that account for attitudes and perceptions, adoption cycles, and adaptation patterns. It is envisioned that such models will help decision-makers better plan transportation infrastructure systems and design marketing and policy strategies that maximize the benefits of these disruptive technologies. This project aims to collect survey data from a sample of 1000 residents in the Tampa Bay metro area to understand how the market perceives, adopts, and adapts to transformative transportation technologies. The third phase of this research project focuses on the compilation and analysis of survey data in order to better understand people’s preferences and choices for future mobility options and technologies in the Tampa Bay metropolitan area. A comprehensive description of all the steps taken to full deployment, data cleaning, and weighting is provided, in addition to a descriptive weighted univariate illustration of the findings from the Tampa Bay survey sample.]]></description>
      <pubDate>Tue, 21 Jun 2022 09:35:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/1983979</guid>
    </item>
    <item>
      <title>An Exploratory Analysis to Estimate the Value of Free Charging Bundle in Electric Vehicle Purchases </title>
      <link>https://rip.trb.org/View/1983977</link>
      <description><![CDATA[This research establishes a national estimate of the willingness-to-pay (WTP) for a free charging bundle in the United States electric vehicle market. Using a stated choice experiment conducted using a probability-based sample from an internet panel, 36 choice scenarios were generated with 9 scenarios received per respondent. Individuals chose between three vehicles (two EVs and a comparable gasoline vehicle) with varying vehicle attributes: purchase price, driving range, annual fuel cost, and years of free charging. For EVs, the free charging bundle was offered at four levels: zero, one, two, and three years. Results from the mixed logit and latent class analysis showed heterogeneity in the sensitivity to the free charging time scale with a significant share of the population showing no sensitivity to a single year of free charging. All population segments experienced some WTP for free charging at the two- and three-year time frames.]]></description>
      <pubDate>Tue, 21 Jun 2022 09:30:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/1983977</guid>
    </item>
    <item>
      <title>Investigating the Role of Attitudinal Factors on Adoption of Emerging Automated Vehicle and Vehicle Safety Technologies</title>
      <link>https://rip.trb.org/View/1983972</link>
      <description><![CDATA[Emerging automotive and transportation technologies have created revolutionary possibilities in the way we might travel and drive in the future. These technologies include advanced vehicle safety technologies that are aimed at keeping the vehicle occupants safe and automated vehicles that can drive by themselves with little to no need for a human driver, and have the potential to revolutionize travel behavior. However, it is important to understand the effect that consumers’ attitudes will have on the adoption of these technologies and their ultimate impact on travel. The proposed project will result in the review and development of modeling methods that incorporate information about people’s attitudes and perceptions with socioeconomic data and unobserved heterogeneity. These modeling efforts will provide researchers and practitioners with documents to help familiarize them with the incorporation of attitudes and perceptions in the adoption of new technologies and their impact on travel behavior. The models developed and their accompanying software will be available for use by the public to help disseminate these methods into the larger transportation modeling community. Additionally, the project will collect data that will enable future analysis and development of models to forecast changes in perceptions due to social learning and social influence processes. The data collection effort will aid in providing guidance on how respondents are handling the task of relaying perceptions and travel choices. This will be important in understanding possible sources of survey error related to ​biases due to question structure and question ordering. This will be important for improving the efficiency and accuracy of TOMNET’s year 3 collective survey effort. ​]]></description>
      <pubDate>Tue, 21 Jun 2022 09:14:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/1983972</guid>
    </item>
    <item>
      <title>Synthesis of Information Related to Airport Practices. Topic S03-17. Airport Centric Advanced Air Mobility Market Study</title>
      <link>https://rip.trb.org/View/1897247</link>
      <description><![CDATA[ACRP Synthesis 130: Airport-Centric Advanced Air Mobility Market Study, from TRB's Airport Cooperative Research Program, is designed to help airports and other stakeholders as they plan for AAM. Many of the initial use cases for advanced air mobility (AAM) will be integrated into existing airports of all sizes, including airport ground access, connecting passenger service between regional and hub airports, and cargo operations.]]></description>
      <pubDate>Tue, 14 Dec 2021 14:20:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/1897247</guid>
    </item>
    <item>
      <title>Y3R8 - Incorporating Freight in Regional Land Use
Planning Models</title>
      <link>https://rip.trb.org/View/1868908</link>
      <description><![CDATA[The tremendous potential of technology-driven innovations to address the inefficiencies in lastmile deliveries has prompted e-commerce companies, retail chains, logistic providers and
technology start-ups to invest in sidewalk autonomous delivery robots (SADRs) and road
autonomous delivery vehicles (RADRs). The growing appeal for utilizing SADR and RADR
technologies arises from the increased demand for same-day deliveries in business to consumer
(B2C) e-commerce and the associated challenges for logistics providers (Cárdenas et al., 2017;
Jennings and Figliozzi, 2019; Wang et al., 2016). Apart from improving the delivery efficiency,
autonomous vehicles have the potential for initiating a more sustainable, and customer focused
delivery practice with limited externalities on road congestion, noise and CO₂  emissions (Hardi
and Wagner, 2019; Stolaroff et al., 2018). Due to the rapid advancements sensing technology and
artificial intelligence algorithms, large-scale deployments of autonomous delivery vehicles are on
the verge of becoming a reality in some delivery scenarios with known and repeatable routes.
SADRs and RADRs developed by Amazon, FedEx, Starship, and Nuro are already deployed and
being tested in multiple U.S. cities.
The ongoing COVID-19 pandemic and the need for contactless deliveries that avoid the risk
of person-to-person infection has made it clear that autonomous robot deliveries have many
advantages. Consumers, businesses, and governments have switched from cautious beta testers
into eager early adopters. Despite this unprecedented requirement necessitated by the pandemic,
SADRs and RADRs need to be deployed by logistics service providers and Government agencies
in a way that is generally accepted by the public. In fact, if not widely accepted by the public, the
development and introduction of autonomous delivery vehicles can be a substantial waste of
resources for logistics service providers and vehicle developers alike. Therefore, it is imperative
to conduct micro-level behavioral research on user acceptance early in the deployment roadmap
of delivery robots to be able to design, develop and promote them as an accepted alternative to its
conventional delivery practices (i.e., van-based human delivery). One of the contributions of the
proposed project is to address this urgent research gap by investigating the psychological factors
that determine public acceptance of ADRs (Autonomous Delivery Robots) from an end-consumer
perspective.
To evaluate delivery robot adoption rates and tradeoffs it is necessary to model how vehicles are
likely to evolve. Technology is evolving rapidly and new players like Tesla are entering the heavy
and light truck electric vehicle (EV) market pushing up freight EV efficiency and capabilities
(Ulrich, 2020). Delivery cost is an important variable that is changing as technologies and vehicles
are evolving. In addition, cities are reevaluating how to assign and prioritize roadway and curb
infrastructure during and after COVID-19 (Davies, 2020). Autonomous delivery vehicles can also
interact with smart curb and parking management technology in ways that may increase delivery
efficiency and resource utilization by reducing double parking and congestion (Jennings and
Figliozzi, 2019). ADRs are also likely to affect not just the last mile but also the last echelons of
supply chains.]]></description>
      <pubDate>Wed, 28 Jul 2021 12:23:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1868908</guid>
    </item>
  </channel>
</rss>