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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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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>
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      <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>Establishing Performance Measures for Transportation and Quality of Life</title>
      <link>https://rip.trb.org/View/2786772</link>
      <description><![CDATA[Evaluate the performance measures used in Utah Department of Transportation's (UDOT's) transportation planning process by contextualizing the Utah Well-Being survey and ensuring that travel demand models produce relevant performance measures that are sensitive to transportation policies and alternatives.]]></description>
      <pubDate>Tue, 06 Oct 2026 17:22:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786772</guid>
    </item>
    <item>
      <title>Transportation Satisfaction and Life Satisfaction: Examining the Roles of Attitudes and Personality</title>
      <link>https://rip.trb.org/View/2780204</link>
      <description><![CDATA[Life satisfaction has emerged as an important indicator of how individuals evaluate their overall life circumstances, with increasing relevance for understanding how public infrastructure systems contribute to quality of life. Transportation systems play a central role in enabling access to employment, services, and social interaction, yet their contribution to overall life evaluation remains insufficiently understood within transportation planning and policy analysis. This gap limits the ability of agencies to fully account for how infrastructure and service decisions affect user experience, system utilization, and broader societal outcomes.

Life satisfaction, as used here, refers to a self-evaluation of one's life as a whole, according to metrics that individuals themselves value rather than externally imposed circumstantial measures. Two broad psychological frameworks explain variation in life satisfaction. The top-down framework holds that personality plays a dominant role, with more sociable, less neurotic, and more positively oriented individuals predisposed to higher satisfaction regardless of circumstances. The bottom-up framework, by contrast, argues that actual life circumstances across key domains (most commonly job satisfaction and relational satisfaction) are the primary drivers. These frameworks are not mutually exclusive, and this research accommodates both by simultaneously examining domain-specific transportation circumstances alongside personality and attitudinal factors as determinants of both transportation satisfaction and overall life satisfaction.

Transportation infrastructure is a particularly promising domain through which to examine life satisfaction, as it intersects with several fundamental human needs. But, given the importance of attitudes and personality in determining life satisfaction, the research team focuses on the ways that transportation-related attitudes and perceptions influence satisfaction as well as on the ways that transportation circumstances interact with personality characteristics to influence satisfaction. Specifically, this research focuses on four attitudinal/personality-related latent constructs. The first construct is desire for autonomy and control. A sense of freedom and control is strongly associated with life satisfaction and has also been closely connected to transportation options/abilities, potentially revealing an important avenue that transportation provision influences overall life satisfaction. The second construct is desire for socialization, which has also been closely associated with overall life satisfaction in the existing literature. However, here the focus is also on the interaction between transportation context and socialization preferences. These interaction effects allow us to determine if more socially-inclined individuals exhibit elevated levels of life satisfaction when using select modes of transport (such as walking and public transit that may facilitate incidental social interactions). The third latent construct focuses more specifically on transportation, encompassing perceptions of travel time and the productivity/enjoyment of travel itself. Intuitively, this type of domain-specific personality alignment is closely related with transportation-specific satisfaction as well as with broader life satisfaction. Finally, the last latent construct is somewhat broader, focusing on values alignment. In this case, rather than focusing on any specific values (such as sustainability and safety), the team examines the extent to which individuals consider secondary (value-oriented) effects (such as, status associated with a particular mode) when considering their transportation options. This value alignment is an important aspect of how individuals consider their own transportation decisions as well as how they make decisions in general, and is closely connected to their own self-image and their own evaluation of their life circumstances. From a transportation planning and policy perspective, this last latent construct has important policy implications, for instance by helping agencies better frame and communicate infrastructure investments and service offerings in ways that resonate with the values of different user segments.

The study will use data from the Transportation Heartbeat of America survey, a national survey that captures respondents' demographic characteristics, transportation habits, and transportation-related attitudes. As noted above, the team focuses on two main outcomes: self-reported satisfaction with transportation options and self-reported life satisfaction, each measured on a five-point agreement scale. The modeling framework employs a generalized heterogeneous data model (GHDM) that jointly estimates the two satisfaction outcomes as a function of observed sociodemographic characteristics, transportation context attributes, and the four latent constructs described above. Overall, the project is designed to produce insights that are directly relevant to transportation agencies. The findings will support more effective prioritization of investments, better system performance, and improved evaluation of transportation projects in terms of their broader impacts on user experience and system effectiveness.]]></description>
      <pubDate>Fri, 18 Sep 2026 10:22:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780204</guid>
    </item>
    <item>
      <title>National Access Evaluation Pooled Fund - Phase III</title>
      <link>https://rip.trb.org/View/2777536</link>
      <description><![CDATA[The National Access Evaluation project has two main objectives. The first is to calculate national Census block-level Access datasets that can be used by partners in state and local transportation system evaluation, performance management, planning, and research efforts. The second is to add value to these datasets through research into questions of interest to Minnesota Department of Transportation (MnDOT) and the pooled fund Technical Advisory Panel (TAP), including research into the variability and change in access, as well as exploring access data in combination with other datasets. Access conditions will be described for 2025, 2026, 2027, 2028, and 2029.
Accessibility evaluation has applications in a variety of areas: Strengthening Cost-Benefit Analysis - Understanding the impacts of transportation investments requires quantification of benefits. These benefits include economic opportunities, such as increased access to jobs, healthcare facilities, recreational activities, commercial activity, or other ways to participate fully in the economy. Access measures these opportunities in a way that can be applied in the context of a given project, or used to prioritize among transportation investments based on the expected benefits to different groups. Transportation and Land Use Research - Access calculations can provide a valuable data source for transportation and land use research. Researchers have employed access in models of mode choice and other aspects of travel behavior, linked access to residential property values, and used access to explore the spatial relationship between jobs and worker locations. Study partners can share the datasets produced by this study with consultants and researchers as a component of contracted projects. Performance Management [AD1] - By tracking access over time, transportation agencies at all levels of government can better understand how well their transportation networks support the goal of providing opportunity. Access evaluation can be applied to federal, state, and local performance goals. The reports produced by this study will track access performance each year, and over time as the study progresses. Study partners can share the datasets produced by this study without restriction, including (for example) with municipal and county transportation departments.

]]></description>
      <pubDate>Fri, 11 Sep 2026 19:56:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2777536</guid>
    </item>
    <item>
      <title>Understanding How Heterogeneity in Travel Needs and Perceived Mode Availability for Non-Commute Trips Are Addressed in Transportation Planning in California</title>
      <link>https://rip.trb.org/View/2775777</link>
      <description><![CDATA[This research investigates how heterogeneity in travel needs and perceived availability of transportation modes for non-commute trips are addressed within transportation planning processes in California. While transportation research and planning have traditionally focused on commute travel, non-commute trips constitute a substantial and complex share of daily mobility. These trips, including those for shopping, exercise, healthcare, and social participation, are essential for household maintenance and well-being, yet remain comparatively understudied and insufficiently addressed in planning and service provision. Using a qualitative approach, this study evaluates transportation planning practices through semi-structured interviews with agency representatives across multiple levels of governance in California. It examines how non-commute travel needs and perceived mode availability are incorporated into data collection, modeling, and decision-making processes. The analysis identifies key limitations, highlights emerging practices, and assesses opportunities for improving transportation planning frameworks. By integrating behavioral insights with planning analysis, this research provides empirical evidence on how non-commute travel is currently represented in practice and where gaps persist. It also considers how evolving work arrangements and technology-enabled activities are reshaping travel patterns and how the planning process is adapting to these evolving trends. The findings offer actionable recommendations to enhance planning processes and better support non-commute travel within transportation systems.]]></description>
      <pubDate>Wed, 09 Sep 2026 15:57:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775777</guid>
    </item>
    <item>
      <title>Rural access to healthcare: Building a longitudinal panel dataset to inform policy</title>
      <link>https://rip.trb.org/View/2775485</link>
      <description><![CDATA[Healthcare consolidation is a growing trend across the United States, particularly in rural areas, and may come with consequences for healthcare that is routine, recurrent, or time sensitive. Service reductions and closures may act as a barrier to care for rural patients, who already travel longer distances to reach health care services. At the same time, the expanding landscape of virtual healthcare services provides an opportunity for greater access to some types of care. This one-year project will develop a unique individual-level longitudinal panel dataset that can be used to evaluate the connections between spatial access and routine healthcare utilization in the largely rural state of Vermont. The dataset will be built from insurance enrollment and claims data from the Vermont Health Care Uniform Reporting and Evaluation System (VHCURES) that captures approximately 400,000 to 500,000 individuals annually from 2007 to 2027. Using insights from prior literature, the dataset will be built to capture spatial access to healthcare providers, healthcare utilization, and residential location and mobility. An exploratory analysis of the dataset will identify specific types of healthcare services and utilization that present opportunities for deeper evaluation of causal relationships. Based on gaps in prior literature and the exploratory analysis, this project will conduct a preliminary case study to demonstrate the value of the dataset. Expected outcomes include a literature review, a rigorously constructed and documented dataset, descriptive statistics, and a final report detailing dataset design, scope, limitations, and promising areas for future research. This project provides a foundation for future analyses to evaluate long-term causal relationships between spatial healthcare access and healthcare utilization to inform investments in health services in rural settings, including those in other regions/jurisdictions.]]></description>
      <pubDate>Tue, 08 Sep 2026 17:07:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775485</guid>
    </item>
    <item>
      <title>Statewide Multimodal Destination Access Methods and Demographic Analysis</title>
      <link>https://rip.trb.org/View/2725639</link>
      <description><![CDATA[The Oregon Department of Transportation (ODOT) does not currently have a consistent, statewide method to evaluate destination access—whether people can reliably and affordably reach essential destinations such as employment, education, health care, and key services. While agency performance measures and analyses focus primarily on infrastructure conditions and system mobility, they do not answer whether investments are improving people’s ability to access what they need for daily life. Without a standardized destination access methodology, ODOT lacks a clear, data-driven basis for monitoring progress, understanding structural access gaps, or using access outcomes to inform investment decisions. 
OBJECTIVES: (1) Establish a standardized, agency-wide methodology for multimodal destination access analysis. ODOT currently performs destination access analysis on an ad hoc basis and does not have a consistent, documented method for statewide or cross-program use. This project will develop and test a unified approach that can be used across business lines for performance reporting, planning, and investment decision-making. (2) Integrate user profile analysis to identify which populations face transportation access gaps—and to what extent. This research will move beyond single-variable demographic assumptions and instead use data-informed definitions of at-risk populations to better understand who experiences structural access barriers and why. (3) Develop a tool for viewing destination accessibility metrics. The tool can be used to view accessibility by mode and destination type by region. The combination of transportation and land use data will enable planners to understand existing accessibility conditions and needs in specific areas.  This tool would be usable for the ODOT Capital Investment Plan (CIP), local transportation system plans, and other programs where access measures offer utility. 
This research fulfills a need for a destination access methodology that supports ODOT policy, planning, and prioritization. With the results of this research, ODOT will be able to answer critical questions about how the transportation system is serving residents. Access metrics can play a critical role in vehicle miles of travel (VMT) per capita and emissions reduction strategies by informing staff on which areas have feasible multimodal access. ]]></description>
      <pubDate>Wed, 08 Jul 2026 16:48:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2725639</guid>
    </item>
    <item>
      <title>How do perceptions of transportation challenges influence travel behavior for people with disabilities?</title>
      <link>https://rip.trb.org/View/2702517</link>
      <description><![CDATA[Although it has been over thirty years since the Americans with Disabilities Act (ADA) was enacted, people with disabilities—who represent approximately one-quarter of the U.S. population—continue to face significant barriers to mobility and access. They tend to make fewer trips and are more reliant on others, largely due to shortcomings in pedestrian infrastructure, transit, and for-hire vehicle services, and specialized paratransit options, as well as the negative attitude of drivers to them. While substantial research has documented the wide range of mobility and access challenges faced by people with disabilities, there has been limited investigation into how these challenges affect their mode choice decisions. This project will develop and administer a web-based survey, targeting a sample of California residents with disabilities, to explore how disability shapes mode choice, factoring in perceptions of the inaccessibility of transportation infrastructure, mode design-induced challenges, and ableism faced while travelling by different modes. This project will explore how these problems influence their willingness to use paratransit and trip frequency to activity centers. The research team will apply various analytical techniques, including descriptive statistics, basic comparative statistical tests, and multinomial logistic regression, to address the research questions. To ensure the survey is relevant and impactful, the team will collaborate with organizations serving people with disabilities, both to inform the survey design and to disseminate findings that support broader universal access goals shared by these organizations and public agencies.]]></description>
      <pubDate>Thu, 14 May 2026 16:54:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702517</guid>
    </item>
    <item>
      <title>Assessment of Litter Hot Spot Areas for Targeted Reduction in Prince George's County</title>
      <link>https://rip.trb.org/View/2701237</link>
      <description><![CDATA[The frequency and volume of litter and illegal dumping on state and county roadways in Prince George’s County are increasing, despite efforts like scheduled litter blitzes, which have shown limited long-term success. Over the past five years, the Maryland Department of Transportation State Highway Administration (MDOT SHA) spent approximately $42 million removing litter and debris, with last year’s costs alone reaching $15 million—the equivalent of 45 new dump trucks or nearly 60 miles of resurfaced roads (Source WBAL News: https://www.msn.com/en-us/news/us/drivers-watch-out-for-operation-clean-sweep-maryland/ar-BB1jY1rr). These expenditures are unsustainable, especially given recent fiscal shortfalls. This joint research proposal, submitted by District 3 and Prince George’s County Department of Public Works and Transportation (DPW&T), aims to evaluate litter hot spots at the census tract level, as current efforts have not addressed the root causes of the issue. Prince George’s County, a well-resourced and educated area, presents unique challenges, suggesting the problem extends beyond awareness or resource deficits. ]]></description>
      <pubDate>Wed, 13 May 2026 09:15:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701237</guid>
    </item>
    <item>
      <title>Car or Public Transit? Exploring Factors Affecting Mode Choice in the Mobility of Care</title>
      <link>https://rip.trb.org/View/2696849</link>
      <description><![CDATA[This project explores the factors affecting mode choice decisions in the mobility of care. By applying the discrete choice modeling of Nested Logit (NL) and the Multiple Discrete Continuous Extreme Value (MDCEV) to the 2022 National Household Travel Survey (2022 NHTS), this study explores factors regarding where transit service may better accommodate travel needs in serving care trips. Also, it highlights how public transit contributes to reducing household travel generated by care trips. The researchers expect to obtain distinct characteristics of care trips and the socio-demographics of those who travel for care to inform the California Department of Transportation (Caltrans) which transit-related manuals, guidelines, and policies are critical to accommodate travel and their mobility of care. Results are also useful for transit agencies when considering aspects to improve their service in care trips.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:10:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696849</guid>
    </item>
    <item>
      <title>Health-Aware Edge Computing for Durable Autonomous Transportation</title>
      <link>https://rip.trb.org/View/2696026</link>
      <description><![CDATA[Across global markets, transportation systems are rapidly evolving toward automation, pervasive sensing, and intelligent decision-making capabilities. These advancements are often designed primarily around traditional metrics, such as safety, throughput, and cost. Modern autonomous and semi-autonomous systems introduce new types of human exposures (e.g., fatigues, cognitive stress, motion discomfort) and new system constraints (e.g., battery degradation, vibration-induced wear, thermal loads). If left unmanaged, these exposures degrade long-term system performance, reduce user trust and adoption, and impose hidden lifecycle and health costs. This project proposes a new research paradigm for Health-Aware and Durable Transportation Systems, enabling through advanced technologies in autonomous driving, edge computing, and optimized machine learning. We envision that transportation systems can be engineered to actively sense, model, and mitigate human and mechanical exposures, turning transportation into a joint human-machine health ecosystem. The research objectives include: 1) develop joint occupant/vehicle exposure models that quantify health and mechanical burdens, 2) enable adaptive autonomy strategies that mitigate cognitive stress, fatigue, and mechanical wear, and 3) build edge computing framework for efficient inference and control.  ]]></description>
      <pubDate>Thu, 23 Apr 2026 17:32:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696026</guid>
    </item>
    <item>
      <title>Customizing Transportation Services and Technologies Based on Rural Patient Healthcare Needs</title>
      <link>https://rip.trb.org/View/2667211</link>
      <description><![CDATA[The purpose of this project is to build on previous research to better understand the various linkages between specific comorbidities, lifestyle habits and targeted public transportation-related services and technologies. In addition, the project will demonstrate how these services and technologies can be adapted specifically for rural populations to secure better health outcomes. The primary research methods for this project will be as follows: 1) obtain literature about risk factors for specific comorbidities and lifestyle habits and how they interact with healthcare system access ; 2) use data from the University of Kentucky’s Healthcare’s Center for Clinical and Translational Studies and other Southeastern health systems to create a panel analysis of health outcomes based on University of Kentucky’s patient surveys, lifestyle habits, known comorbidities and diagnoses, and patient histories; 3) review materials as needed to determine best practices and synthesize findings for transportation-based support for specific medical conditions for rural residents; and 4) work with technology transfer programs and other stakeholders to develop a tool and/or outreach materials based on project findings for technology transfer professionals to improve transportation efficiency, technology and system innovation in trainings for transit and healthcare providers. The goal is to help both health and transportation providers to implement customizable healthcare mobility strategies based on their logistical capacity and patient needs.]]></description>
      <pubDate>Mon, 23 Feb 2026 14:19:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2667211</guid>
    </item>
    <item>
      <title>Driver to Non-Driver Transitions: Related Health, Mobility and Safety Outcomes</title>
      <link>https://rip.trb.org/View/2671991</link>
      <description><![CDATA[This project involves analyzing the impacts of becoming a non-driver (suddenly or gradually) in Wisconsin and nationally and effects on health, mobility, and safety outcomes. The project will analyze health, quality of life and mobility outcomes for drivers who are no longer able to drive. The researchers will analyze the safety, mobility, and quality of life outcomes for those who have suddenly or gradually become non-drivers. Analysis should focus on adult non-drivers of all ages and demographics, with particular emphasis on adults aging in place and urban versus rural areas. Once the analyses are conducted and complete, the researchers will report findings and provide recommendations for policies that lead to improved outcomes—namely increases in mobility and safety benefits for the entire state. Recommendations will help Wisconsin Department of Transportation (WisDOT) understand how to best offset impacts to mobility for individuals suddenly or gradually transitioning from being drivers to non-drivers.]]></description>
      <pubDate>Wed, 18 Feb 2026 11:39:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2671991</guid>
    </item>
    <item>
      <title>Enabling Mobility for Older Adults in the US</title>
      <link>https://rip.trb.org/View/2669552</link>
      <description><![CDATA[Driving is essential for the preservation of functional independence for older adults, yet there is a growing number of older adult drivers with comorbid health conditions that might impair their ability to drive safely. Older adult drivers are overrepresented in motor vehicle crash deaths and injuries, which is a major public health concern. The purpose of this project is to (1) develop a comprehensive understanding of the mobility needs and challenges of older adults in the United States, and (2) develop an innovative tool to extend their safety while they drive.  

Aim 1: Develop a comprehensive understanding of the mobility needs and challenges of older adults  

To develop a comprehensive understanding of the mobility needs of older adults, the research team will analyze data from a nationally representative survey of U.S. adults aged 65 and older. The survey contains a comprehensive set of questions that explore driving behavior, transportation options, mobility limitations, and attitudes toward future transportation technologies and policies among adults aged 65 and older.   

Aim 2: Develop an innovative tool to extend their safety while they drive.  

The goal of this project is to understand older adults’ perceptions of an app (StreetCoach) that provides a driving score based on actual driving behavior. A number of apps exist for older adult drivers but the perceptions of older drivers towards their driving score is poorly understood. This study will use a sequential explanatory research design by asking 10 older adults to download and use the app for 60 days. Following this, the research team will conduct in-depth interviews with the participants to gain an understanding of their perception and interpretation of their telematics score, and what factors might motivate them to change their driving to improve the score.   ]]></description>
      <pubDate>Thu, 12 Feb 2026 15:16:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669552</guid>
    </item>
    <item>
      <title>Healthy Micromobility: Moving From Crisis to Opportunity</title>
      <link>https://rip.trb.org/View/2652680</link>
      <description><![CDATA[Micromobility, including e-scooters and e-bikes, is an emerging transportation mode with the potential to alleviate congestion and improve urban mobility. However, prior research has primarily focused on safety risks and injury rates, with less attention given to its potential benefits, such as improved accessibility, reduced vehicle miles traveled (VMT), and enhanced health through active transportation. This project aims to provide a more comprehensive assessment of both the risks and benefits of electric micromobility within the U.S. transportation system using a combination of literature review, survey research, and systems dynamic modeling. The study examines how electric micromobility reduces VMT while also evaluating the health trade-offs related to safety risks and active transportation benefits. The project consists of three main aims: (1) a targeted literature review to synthesize existing evidence on electrified micromobility’s health impacts, (2) a nationally representative survey to capture user behavior, trip substitution patterns, and safety concerns, and (3) the development of a system dynamics simulation model to quantify the net health effects across diverse urban settings.     ]]></description>
      <pubDate>Tue, 13 Jan 2026 16:27:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652680</guid>
    </item>
    <item>
      <title>Bridging Data Gaps with Modeled Data from Generative AI: Advancing Health in Transportation Research</title>
      <link>https://rip.trb.org/View/2652171</link>
      <description><![CDATA[Transportation-related factors, such as air quality changes and exposure disparities, have significant impact on health outcome. Communities near high-traffic corridors experience elevated exposure levels, yet efforts to assess these impacts are hindered by the lack of high-resolution health and socio-demographic datasets. Traditional air quality models, such as dispersion and interpolation techniques, estimate pollutant distributions but struggle to capture localized exposure variations and real-world uncertainties due to their reliance on static assumptions. These limitations reduce the precision of transportation health impact assessments. 

This project addresses data gaps in air quality and health outcomes by integrating AI-generated data with  traditional modeling techniques. Bridging the data gap is essential to improving exposure assessments and provide a more comprehensive understanding of transportation-related health effects. The research develops and trains generative AI models for data augmentation, using harmonized datasets to create high-fidelity modeled data that reflects real-world patterns. Furthermore, we integrate the trained AI models with air quality simulation models to estimated transportation-related air quality scenarios and assess potential health impacts.
 
The project produces a validated generative AI model for data augmentation, generating high-resolution datasets that enhance geographic and demographic granularity in transportation health research. The application of scenario-based health impact simulations provides new insights into the relationships between air quality and health outcomes, improving the ability to evaluate transportation-related interventions. By combining AI-driven data synthesis with traditional modeling approaches, this research advances methodologies for transportation and environmental health assessments, providing more reliable data for exposure studies and policy evaluations. 
]]></description>
      <pubDate>Tue, 13 Jan 2026 16:10:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652171</guid>
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