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    <title>Research in Progress (RIP)</title>
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    <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>
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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>Rural Vehicle Markets and Consumer Affordability</title>
      <link>https://rip.trb.org/View/2691725</link>
      <description><![CDATA[There is a need to better understand rural vehicle consumer choice and transportation affordability to inform efforts to support economic vitality in rural communities. Access to adequate vehicle choices at affordable price points may be limited in rural contexts due to the spatial location of vehicle purchase options. At the same time, access to affordable vehicle options has important implications for transportation affordability, mobility, and economic opportunity in rural areas. Prior research suggests that people living in rural areas are more vehicle dependent, and that vehicle affordability and access is related to mobility and economic opportunity. Recent research indicates that rural vehicle consumers face more limited options and higher prices for a small subset of vehicle options, however, little is known about the implications for consumer choice and vehicle affordability for the overall vehicle market. This project uses detailed vehicle data and vehicle dealership listings in Colorado, Maine, and Vermont to evaluate the relationship between vehicle options, distances people travel to purchase a vehicle, and the price paid for the vehicle in both urban and rural contexts. Findings from this research can inform policies that seek to expand access to affordable transportation options in rural communities.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:55:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691725</guid>
    </item>
    <item>
      <title>Personal Vehicle Ownership and Operating Cost Calculator (Version 2.0) for Quantifying On-road Vehicle Operating Costs</title>
      <link>https://rip.trb.org/View/2691663</link>
      <description><![CDATA[In 2018, the Georgia Tech National Center for Sustainable Transportation (NCST) research team developed the Vehicle Ownership and Operating Cost Calculator (VCC) Version 1.0, allowing users to calculate and understand total vehicle ownership costs over the lifespan of the vehicle. Traditional resources typically found on automotive websites offer five-year cost projections, but often overlook or simplify long-term expenses such as financing, maintenance, energy use, and depreciation, which vary widely based on region, vehicle type, and individual driving habits. By allowing users to input personalized data, the calculator provides a tailored, detailed analysis of ownership costs, helping users make more informed decisions about vehicle purchases. The VCC is designed to serve as an educational resource (highlighting the cost categories associated with vehicle ownership) and as an instructional aid in courses that examine transportation planning and economic assessments. The VCC allows users to input data specific to their circumstances, including vehicle purchase price, loan details, annual mileage, insurance, energy costs, maintenance, and other costs like parking and tolls. Using data from sources such as the Georgia Department of Revenue’s vehicle pricing database and the U.S. Department of Energy’s Fuel Economy Database, the calculator provides customized cost estimates. The tool provides users (students and the public) with a thorough understanding of the full costs associated with lifetime vehicle ownership, by offering a comprehensive breakdown of ownership costs, including hidden expenses often overlooked in purchase decisions. The original model became dated, because the tool did not have the ability to automatically ingest and update vehicle ownership cost data. This project will update the tool with new data, develop data ingestion procedures, and modify output formats to support economic assessments of roadway design alternatives. To make the VCC accessible and support technology transfer, this project will update the calculator to accommodate the latest vehicle technologies (2018-2025) and to generate an online model presence. The research team will update fuel prices, maintenance, insurance costs, and depreciation rates to capture recent market changes. The team will also assess and implement enhanced reporting features to provide users with more detailed breakdowns and visualizations of ownership costs. Finally, the team will modify the structure of the model so that the tool can compile operating costs per vehicle-mile for observed and modeled on-road fleet compositions and operating conditions. The deliverables will include an updated version of the calculator accessible as both an Excel tool and a web interface.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:22:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691663</guid>
    </item>
    <item>
      <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>
    </item>
    <item>
      <title>Understanding Drivers of Change in Vehicle Availability and Ownership in North Carolina</title>
      <link>https://rip.trb.org/View/2452920</link>
      <description><![CDATA[Access to a safe, reliable car is, for many North Carolina households, a prerequisite for accessing employment, education, and meaningful social interaction. Without regular access to one or more working cars, households may find themselves ‘transportation-disadvantaged’ if suitable alternative travel options are not available. However, the relationship between car access—regular, unlimited access to a reliable car through lease or ownership—and transportation disadvantage (TD) is not straightforward.

Whether a household has access to a car is based on a unique set of factors, including characteristics of the household and of the cultural norms, built environment, and transportation system in which the household is situated. For some households, a lack of access to a car may indicate disadvantage, while for others it may be a sign of relative advantage and a statement of preference. The relationship between car access (CA) and TD is further complicated by the growing availability of ride shares and micromobility options, increased overall costs of driving, and increased demand for location-efficient 
housing.

NCDOT’s Transportation Disadvantage Index maps various sociodemographic attributes to identify communities in which households’ ability to access critical opportunities may be constrained. The index includes CA as an indicator of TD, while recognizing the increasing challenges in interpreting CA with respect to TD. Understanding the role of CA—its causes and its impacts—is critically important to ensuring that NCDOT’s policies and investments meet the needs of the state’s constituents, today and in the future.
Thus, this research proposes to develop a knowledge base to support creation of updated metrics of household-level TD that capture the complex, dynamic relationships among household mobility pressures, CA decisions, and TD. The transdisciplinary, multi-institutional team will build this knowledge base through (1) a detailed review of the literature on and methods used to assess the relationship between CA and TD, (2) targeted community-based listening sessions with community stakeholders to collect exploratory data on the factors underlying CA and TD, (3) thematic content analysis and interpretation of exploratory data, (4) validation of findings with community stakeholders, and (5) dissemination of findings through a report and slide deck.

This work will uncover critical new information about the forces behind household CA and of how CA relates to mobility and accessibility options for people across North Carolina’s various geographic and socio-demographic contexts. This new knowledge—and the recommendations that will accompany it—can then be used to develop longitudinal survey instruments and geospatial indicators that can be used to enhance NCDOT’s Transportation Disadvantage Index, the Complete Streets Policy, and other policies and tools.]]></description>
      <pubDate>Fri, 15 Nov 2024 16:18:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2452920</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>Modeling the impacts of California electric vehicle policies with emphasis on
the used vehicle market</title>
      <link>https://rip.trb.org/View/2420214</link>
      <description><![CDATA[There are now a wide variety of California and U.S. policies designed to support the goal of
decarbonizing light-duty vehicles. Existing models of the California vehicle market are not capable
of analyzing the impact of all these policies. A key shortcoming is the lack of accurate modeling of
the used vehicle market. Low-income households primarily purchase used vehicles, but the supply
of used vehicles is largely determined by the decisions of new vehicle owners (primarily high-income
households). The price of used vehicles is determined by the interaction between supply and
demand. The research team will build a new model of the California personal vehicle market that accurately models
the interactions between the new and used vehicle markets. The team will use this model to evaluate a
wide range of current and proposed policies designed to increase the penetration of electric vehicles.
This model will allow California policymakers to craft policies that reach decarbonization goals at
lowest cost while alleviating (or at least not worsening) existing transportation inequities.]]></description>
      <pubDate>Sat, 24 Aug 2024 10:46:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420214</guid>
    </item>
    <item>
      <title>Evaluating Accessibility Changes of Electric Vehicle (EV) Supported
Projects through an Agent-based Simulation Approach
</title>
      <link>https://rip.trb.org/View/2420038</link>
      <description><![CDATA[As the transition towards transportation electrification accelerates in California, the integration
of electric vehicles (EVs) into transportation systems holds significant promise for reducing greenhouse gas emissions and improving air quality. However, the implications of EV adoption on transportation accessibility remain underexplored, particularly in the context of diverse demographic and socioeconomic communities. This research proposes a comprehensive framework to evaluate the impacts of EV-supported projects on transportation accessibility, with a focus on the State of California. The framework encompasses four key components:
(1) a review of existing EV-supported projects to identify potential scenarios analysis;
(2) the design and implementation of an EV survey to collect data on purchasing behaviors and associated travel choices; (3) the development of an agent-based simulation model with an integrated EV module to simulate the effects of EV integration on travel behavior and system dynamics; and (4) the creation of an accessibility calculator to quantify changes in accessibility metrics, such as travel time and distance to charging stations, resulting from EV-supported projects. The agent-based simulation model captures dynamic travel behaviors among millions of individuals, generating detailed travel trajectories at the individual level and serving as a robust platform for accessibility evaluation. Additionally, spatial analysis techniques will be employed to identify areas with improved or reduced accessibility, and policy recommendations will be formulated to guide future EV deployment strategies and infrastructure investments. By bridging the gap between research and practice, this study aims to inform evidence-based decision-making and advance the development of inclusive and equitable urban transportation systems in the era of electric vehicles. 
]]></description>
      <pubDate>Thu, 22 Aug 2024 15:24:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420038</guid>
    </item>
    <item>
      <title>Charging station investments’ impact on electric vehicle accessibility
and adoption
</title>
      <link>https://rip.trb.org/View/2420033</link>
      <description><![CDATA[Accessibility to daily destinations for electric and internal combustion engine vehicles is not fundamentally different except for one important dimension. Anxiety range, the fear of running out of battery power before reaching a charging station, significantly affects the ease with which people can access different destinations and, therefore, their willingness to buy or drive an electric vehicle (EV). This project focuses on how people might evaluate the charging infrastructure within the areas where they drive most often, the set of places they drive to routinely like workplaces and stores. The research team will create travel zones based on the observed patterns of driving established under a system where gas-powered cars dominate to assess the ability of EVs with different ranges to reach the same destinations. The team will then use these zones to analyze how different measures of charging station accessibility are associated with higher EV ownership and whether charging infrastructure buildup leads to faster EV adoption rates. Finally, the team will focus on residents of multi-unit dwellings, who have a greater need for charging stations near their homes, by creating a fine-grained measure of accessibility based on road network distance.]]></description>
      <pubDate>Thu, 22 Aug 2024 15:18:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420033</guid>
    </item>
    <item>
      <title>Investigating safety and risk disparity between personally owned and shared micromobility modes</title>
      <link>https://rip.trb.org/View/2401752</link>
      <description><![CDATA[This research examines the safety implications of the increasing popularity of micromobility, particularly focusing on shared e-bikes and bicycles. The study has three main goals: comparing crashes involving shared e-bikes and bicycles, understanding how safety trends for personally owned e-bikes are changing, and identifying differences in safety between personally owned and shared e-scooters. The research seeks to uncover patterns, risk factors, and disparities in micromobility-related accidents by analyzing existing data and collaborating with industry partners to conduct surveys. By analyzing detailed crash reports and new injury codes related to micromobility, the project aims to provide evidence to inform policies and improve infrastructure, ultimately enhancing overall transportation safety. By fostering collaboration between academia and industry, the project enhances our understanding of micromobility safety and provides invaluable learning experiences. Ultimately, the research endeavors to inform policymakers, practitioners, and the public on strategies to mitigate safety risks associated with the proliferation of micromobility modes in urban environments.]]></description>
      <pubDate>Mon, 08 Jul 2024 14:54:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2401752</guid>
    </item>
    <item>
      <title>Guide for Conducting Right-of-Way Research in the Project Delivery Process</title>
      <link>https://rip.trb.org/View/2381758</link>
      <description><![CDATA[The responsibility for conducting the right-of-way (ROW) and utility research activities to confirm ownership of the ROW and identify the impacts to adjacent properties is essential for state departments of transportation (DOTs) during project delivery. However, the extent, type, level, and timing of these research activities varies, due to limited information and guidelines for conducting ROW research.

The timing and depth of ROW research can differ as some state DOTs will initiate ROW and adjacent property research during the planning phase while others may not coordinate the research until later in the design stages. Also, the scope of research can vary as some state DOTs will investigate all parcels indicated in plans, whereas others focus solely on those directly impacted. Furthermore, title searches with inaccurate or incomplete information could lead to delays when ROW research is conducted later in project delivery. Research is needed to study state DOTs current ROW research practices and develop effective methods and procedures to implement in the project delivery process.

OBJECTIVE: The objective of this research is to develop a guide for state DOTs to conduct ROW and adjacent property research activities throughout the project delivery process.]]></description>
      <pubDate>Thu, 23 May 2024 09:54:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2381758</guid>
    </item>
    <item>
      <title>Modernizing Fuel Tax Revenue Forecasting</title>
      <link>https://rip.trb.org/View/2307248</link>
      <description><![CDATA[State departments of transportation (DOTs) are facing funding challenges because state and federal fuel tax revenues are changing and becoming harder to accurately forecast. One of the factors responsible for changes is improvements in vehicle fuel economy. For example, there are increases to the National Highway Traffic Safety Administration (NHTSA)’s Corporate Average Fuel Economy (CAFE) standards, fleet economy changes, electric and alternative fuel vehicles, and changes in vehicle miles traveled (VMT). Some state legislation has inadvertently decreased fuel revenues as a side effect. For instance, more than a dozen states have adopted regulations through legislation or other government actions to rapidly scale down emissions of light-duty passenger cars, pickup trucks, and sport utility vehicles and require an increased number of zero-emission vehicles to meet air quality and climate change emissions goals.

Six separate excise taxes are imposed to finance the federal Highway Trust Fund (HTF) program. Three of these taxes are imposed on highway motor fuels (gasoline, diesel fuel and kerosene, and alternative fuels) and generate the majority of the revenues dedicated to the HTF. The FHWA’s Highway Revenue Forecasting Model (HRFM) provides projections for a 20-year time horizon for HTF and new revenue sources. The model uses VMT and fuel economy projections, as well as changes in composition of vehicles over the forecasting period. The fuel efficiency projection incorporates anticipated penetration of fuel-efficient vehicles, including electric vehicles (EVs). The model provides revenue projections, contribution of the 21 different vehicle classes to revenues, and costs (tax burdens) to households by income group and other demographics. Outputs from this model are primarily used for conducting highway cost allocation (HCA) studies (https://www.fhwa.dot.gov/policy/hcas/final/).

Research is needed to help state DOTs develop improved models to accurately forecast motor fuel transportation revenue in the near and long term for operational and planning needs. Further, these forecasts are necessary to quantify and understand potential shortfalls in revenue that need to be replaced by alternative sources of revenue.

The objective of this research is to develop a method and model(s) to help states forecast motor fuel transportation revenues in light of increased fuel efficiency and alternative fuels.]]></description>
      <pubDate>Mon, 11 Dec 2023 21:33:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2307248</guid>
    </item>
    <item>
      <title>The Induced Demand Implications of Alternative Adoption Modalities of Automated Vehicles</title>
      <link>https://rip.trb.org/View/2274313</link>
      <description><![CDATA[There is considerable concern about the induced demand implications of the advent of automated vehicles. In an automated vehicle future, drivers and passengers are relieved of the driving task, thus rendering car travel more convenient and less onerous. As such, there is the possibility that people will undertake more trips in an automated vehicle future, raising the specter of induced demand. Induced demand may also arise from mode shifts, changes in trip lengths, and residential relocations. This research project recognizes that induced demand resulting from the adoption of automated vehicles is inter-related to the adoption modality. Automated vehicles may be purchased and owned personally or used as a mobility-on-demand service (or both). This project aims to shed light on the relationship between automated vehicle adoption modality and likelihood of making additional trips in an automated vehicle future. A joint model of these two outcome variables, wherein automated vehicle adoption modality affects likelihood of making additional trips, is estimated. The results show that any adoption modality increases the likelihood of making additional trips, with private ownership contributing more to induced demand than a service-based adoption modality. Efforts should be aimed at curbing private ownership of automated vehicles to prevent unintended consequences. ]]></description>
      <pubDate>Tue, 24 Oct 2023 14:34:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2274313</guid>
    </item>
    <item>
      <title>Does Ridehailing Use Affect Vehicle Ownership or Vice Versa? An Exploratory Investigation of the Relationship Using a Latent Market Segmentation Approach</title>
      <link>https://rip.trb.org/View/2087514</link>
      <description><![CDATA[This research project presents an examination of the inter-relationship between household vehicle ownership and ridehailing use frequency. Both variables constitute important mobility choices with significant implications for the future of transport. While it is generally known that these two behavioral phenomena are inversely related to one another, the direction of causality is rather ambiguous. Do vehicle ownership levels affect ridehailing use frequency, or does the adoption and use of ridehailing services affect vehicle ownership? If ridehailing services affect vehicle ownership, then it is plausible that a future of mobility-as-a-service would be characterized by lower levels of vehicle ownership. To explore the degree to which these causal relationships are prevalent in the population, a joint latent segmentation model system is formulated and estimated on a survey data set collected in four automobile-oriented metropolitan areas of the United States. The latent segmentation model system recognizes that the causal structures driving mobility choices of individuals are not directly observed. Model estimation results show that 58 percent of the survey sample follow the causal structure in which ridehailing use frequency affects vehicle ownership. This finding suggests that there is considerable structural heterogeneity in the population with respect to causal structures, and that ridehailing use does indeed hold considerable promise to effect changes in private vehicle ownership in the future.]]></description>
      <pubDate>Wed, 21 Dec 2022 12:12:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2087514</guid>
    </item>
    <item>
      <title>How Will Use of Autonomous Vehicles for Running Errands Affect Future Autonomous Vehicle Adoption and Ownership?</title>
      <link>https://rip.trb.org/View/1984236</link>
      <description><![CDATA[Concerned with the potentially deleterious effects of having personal autonomous vehicles (AVs) running errands autonomously, this research project aims to shed light on the level of interest in sending AVs to run errands and how that variable affects the intent to own an AV. Using data from the T4 Survey, the relationship is explored through a joint model system estimated using the Generalized Heterogeneous Data Model (GHDM) methodology. Results show that even after accounting for socio-economic and demographic variables as well as latent attitudinal constructs, the level of interest in having AVs run errands has a positive and significant effect on AV ownership intent. The findings point to the need for policies that would steer the entry and use of AVs in the marketplace in ways that avoid a dystopian future where personally owned AVs would be personally owned by households – enabling people to live farther away from destinations, inducing additional travel, and roaming roadways with zero occupants. The project has been fully completed in the preceding reporting period.]]></description>
      <pubDate>Wed, 22 Jun 2022 09:35:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/1984236</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>
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