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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>A Model of EV Adoption and Rank-Based Contributing Factors</title>
      <link>https://rip.trb.org/View/2553164</link>
      <description><![CDATA[Electric vehicles (EVs) have the potential to significantly reduce greenhouse gas emissions associated with the current widespread use of internal combustion engine vehicles. EV adoption has grown significantly in the past several years, due to improvements in battery technologies and EV ranges, increasing availability of public charging infrastructure and government incentives, and growing awareness and concern regarding climate change. However, EV adoption rates in the US remain relatively low, and more study is needed to understand the behavioral factors that influence EV adoption. Specifically, most existing EV adoption studies use either aggregate market share analysis or stated intentions for future adoptions rather than revealed adoption behavior at an individual level. The current study examines EV adoption using a survey of California households that includes revealed adoption behaviors as well as a ranked set of factors that led to adoption for existing EV owners. To the research team's knowledge, this is the first comprehensive modeling effort that examines the factors that lead to adoption for existing EV owners. A Generalized Heterogeneous Data Model is used to estimate these outcomes as a function of sociodemographic characteristics, local land-use characteristics, individual-level lifestyle preferences, and perceptions of EVs compared with ICEVs, while capturing jointness caused by unobserved factors. The results have important implications for transportation planners and policymakers by informing EV incentive policies, revealing the impacts of EV charging infrastructure, and identifying potential future adopters. ]]></description>
      <pubDate>Thu, 15 May 2025 14:48:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553164</guid>
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      <title>Developing a Transportation Demand Management Tool for DRPT to Understand the Factors Affecting Transit Ridership</title>
      <link>https://rip.trb.org/View/2499025</link>
      <description><![CDATA[This research aims to develop a Transportation Demand Management (TDM) evaluation tool for the Virginia Department of Rail and Public Transportation (DRPT). TDM strategies aim to reduce dependence on single-occupant vehicles and promote more sustainable modes such as public transportation. One traditional way to understand the impact of a single TDM strategy on mode choice would be to conduct before and after surveys. Without implementing the TDM strategy, forecasting the effect of that specific TDM strategy on transit ridership is not possible. To address this issue, this research will develop a tool to assess the effect of a single TDM strategy or a combination of multiple TDM strategies on individuals' mode choices. A joint revealed preference (RP) and stated preference (SP) survey will help understand individuals' preferences towards certain TDM strategies in Virginia. An advanced joint RP-SP model will be estimated based on the collected data. This model will be integrated into a flexible tool, so staff from Virginia Department of Transportation (VDOT), DRPT, transit agencies, and TDM organizations could use the tool to estimate the effect of various TDM policies on transit ridership and mode choice behavior. ]]></description>
      <pubDate>Tue, 28 Jan 2025 11:44:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2499025</guid>
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      <title>Exploring Older Adults’ Mobility Challenges and Preferences Before and After COVID-19 Pandemic</title>
      <link>https://rip.trb.org/View/1948615</link>
      <description><![CDATA[The world-wide pandemic of COVID-19 have influenced older adults (65+ years) more than any other age groups. Older adults are more likely to have health problems and/or weak immune systems. Hence, they have been at higher risk for severe illness. In addition, the statistics show that older adults have a disproportionate number of deaths in the United States and elsewhere due to the coronavirus. Considering the life threats of this infectious disease among older adults, transportation plays a critical role in maintaining older adults’ safety while providing mobility to meet their essential needs such as access to health care.
Beyond driving, older adults have few mobility options (e.g., using taxi, public transit, Uber/Lyft). During pandemic period such as COVID 19, safe mobility options for older adults become even more limited. In 2019, the share of senior Americans (with 65 years old and above) from the total US population was about 16.5% and is predicted to reach 22% by 2050. It should be noted that this is a significant increase in senior Americans’ share of the total US population compared to 1950 in which merely 8% of the whole population was 65 or above (US Census Bureau, 2020). Since the population in USA is aging (like other developed countries), it is very crucial to examine older road users’ opinions, preferences and needs regarding the necessary transportation options that should be provided (during normal times as well as pandemic periods like COVID-19) to maintain their mobility and quality of life.
Fortunately, autonomous vehicles (AV) technology can provide a safe mode of transportation for older adults and persons with disabilities (especially those who no longer can drive safely) during infectious disease epidemics. Although previous research showed a lack of trust and willingness among older population to use AVs, there is no sufficient evidence about the changes in their attitudes, concerns, and willingness to use AVs including shared automated vehicles (e.g., driverless taxi) after experiencing COVID-19 pandemic.
Considering these gaps in the literature, the primary objectives of this study are to:
(1) Examine older Americans’ mobility challenges to use current transportation modes (e.g., driving private automobile, using public transit, taxi, shared rides such as Uber) before and during the COVID-19 pandemic.
(2) Explore the changes in concerns, preferences, and willingness of older Americans to use autonomous vehicles before and after the pandemic.
(3) Identify and quantify the contributing factors affecting older adults’ willingness and concerns to use different levels of autonomous vehicle technology including shared autonomous vehicles (shared taxi).
It is expected that the results of this study will provide actionable countermeasures to mitigate older adults’ mobility challenges especially during epidemic conditions such as COVID 19. The findings of this study are believed to shed light on key mobility challenges and preferences of older adults, which provide transportation authorities and car manufacturers with valuable insight about the future direction of autonomous vehicles in terms of technological needs, policy, design, and planning.]]></description>
      <pubDate>Fri, 06 May 2022 11:27:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/1948615</guid>
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      <title>New Fare Payment Technology and Pricing Strategies for Mobility-as-a-Service (MaaS) </title>
      <link>https://rip.trb.org/View/1938497</link>
      <description><![CDATA[The objective of this project is to evaluate new fare payment technologies and emerging pricing strategies in a step toward integrating transit into a Mobility-as-a-Service (MaaS) ecosystem. The first part of this project includes a nationwide policy analysis and best practice review of fare capping, which is an emerging type of transit fare policy enabled by new payment technologies. This synthesis of fare capping practices will inform the second part of this project, which aims to evaluate technological aspects and potential pricing bundles of MaaS using surveys of transit riders. Surveys will be conducted in one or more city(ies) that have already made transit fare payment available through mobile phones and have some level of integration with shared and/or micromobility services. It is envisioned that the survey will have two primary sections. The first will be a revealed preference questionnaire evaluating existing payment technology options and existing pricing options for local shared and/or micromobility services. The second part of the survey will utilize stated preference methods to envision payment technology options and potential pricing bundles in the future. 
The results of the fare capping analysis will provide a comprehensive review of nationwide practices that can inform other transit agencies considering or planning for fare capping. The results of this survey will provide insight into the current and potential future behavior of transit riders in a MaaS ecosystem. ]]></description>
      <pubDate>Wed, 06 Apr 2022 14:30:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/1938497</guid>
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