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    <title>Research in Progress (RIP)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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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>Understanding Factors Influencing Truck Crashes with Vulnerable Road Users: A Panel Data Approach</title>
      <link>https://rip.trb.org/View/2625592</link>
      <description><![CDATA[The purpose of this project is to define the spatial, temporal, and socioeconomic factors that most significantly contribute to truck-related accidents involving vulnerable road users (VRU) and to determine how variations in these factors alter the frequency of crashes. VRUs, such as pedestrians and bicyclists, are at the greatest risk when interacting on roadways, and accidents involving trucks and VRUs very frequently result in severe injuries or fatalities. This research will be conducted in New Mexico and Tennessee, both served by major interstate highways and characterized by unique economic patterns. To achieve this, the research will employ panel data regression analysis using crash records from both states, combined with socioeconomic and economic activity indicators at the Zip Code level. The dependent variable will be the frequency of truck-related accidents involving VRUs, while independent variables will include demographic, economic, and contextual factors, with controls such as weather conditions. The models will be tested for robustness, and results from the two states will be compared to identify context-specific patterns and to develop policy recommendations that enhance roadway safety for VRUs.]]></description>
      <pubDate>Mon, 17 Nov 2025 16:15:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625592</guid>
    </item>
    <item>
      <title>Experimental Approaches for Integrating External Factors into Pathway for Planning (P4P) Traffic Volume Forecasting</title>
      <link>https://rip.trb.org/View/2573011</link>
      <description><![CDATA[Traffic volume forecasts are critical for effective transportation planning, helping to assess future roadway demand and allocate resources efficiently.  Virginia’s Department of Transportation (VDOT) uses the Pathway for Planning (P4P) web-based application to provide historical and forecasted traffic volume data.  However, P4P’s current trend-based approach, which relies on linear regression, may either under- or over-predict future traffic volumes due to the lack of consideration for socioeconomic changes.

The purpose of this study is to evaluate alternative methods for forecasting annual traffic volumes by incorporating external socioeconomic factors such as population growth, employment trends, and a forecast change in personal income from reliable sources.  This research focuses on annual average daily traffic volumes (AADT) only and excludes daily, seasonal, and project induced traffic variations. The research will include a literature review, a survey focused on segment-level traffic forecasting, an exploration of available external data, and the development of a pilot forecast model in VDOT Fredericksburg District as a case study.  Statistical analyses will be conducted to ensure region-specific predictions and to evaluate the effectiveness of alternative models for enhancing the P4P platform.  The goal of this work is to incorporate socioeconomic factors into what are currently trend-based traffic volume forecasts.  If this modification improves forecast accuracy, it may also improve infrastructure planning and policy decisions across Virginia.
 
]]></description>
      <pubDate>Thu, 10 Jul 2025 08:17:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2573011</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>Patterns and drivers of urban expansions in the Texas Triangle: Case study of the Austin metropolitan region</title>
      <link>https://rip.trb.org/View/2459117</link>
      <description><![CDATA[Urban expansions result from population and economic growth; yet they often come together with diseconomies, for example, congestion, pollution, and resource depletion. Understanding urban expansion patterns, dynamics, and trends is essential to formulate public interventions aimed at achieving long-term sustainability. This study makes the needed effort through a case study of the Texas Triangle megaregion. Texas Triangle is one of the fastest growing megaregions in the United States. In the past thirty years (1990-2020), the region’s population grew by 89%, from 12.32 million to 23.02 million. Accompanying the fast and large population growth was the urban expansion of matched speed and size. The style of Texas’s past expansion has made it known as one of America’s most sprawling and automobile-dependent regions. Texas Triangle’s population is projected to continue to grow rapidly, reaching 28.71/32.88 million by 2050 in the moderate/fast growth scenario. With the extensive growth in the past and in the forthcoming future, the megaregion is facing many daunting challenges to be addressed urgently. What adds to the urgency is the annual threats from natural disasters. For instance, Hurricanes landed in Houston and the Gulf Coast in recent years have caused billions of dollars of property damages, hundreds of deaths, and thousands of households being displaced. The impacts of the disasters have gone beyond the Houston region, reaching to the rest of the Triangle and other counties and neighboring states. The purpose of the study is to analyze Texas Triangle’s growth patterns and dynamics and simulate future scenarios of urban expansion. A specific interest of the study is in the role that major transportation investments and projects have played in the past in Texas Triangle’s expansion. Findings obtained from analyzing the past and future trends will help planners and policy makers to formulate public interventions to channel future growth towards a sustainable path. The project will utilize SLEUTH, an open source, cellular automaton modeling tool that has been widely applied to model land use/land cover changes. The main datasets for the project include 2001-2016 land use/land cover imageries available from the United States Geological Survey's (USGS’) National Land Cover Database (NLCD). Additional data on the socioeconomic characteristics and transportation networks for the study area will be processed from the U.S. censuses or obtained from Texas Department of Transportation (TxDOT).]]></description>
      <pubDate>Sat, 23 Nov 2024 10:16:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2459117</guid>
    </item>
    <item>
      <title>Effect of Drivers Education on Traffic Safety
</title>
      <link>https://rip.trb.org/View/2437699</link>
      <description><![CDATA[Much of the research on the relationship between young driver training and crash risks has been correlational. What remains unclear is whether driver training has a direct positive impact on reducing crashes among teen drivers. Answering this question will allow planners and policymakers who aspire to enhance teen driving safety to better understand the effectiveness of driver training on safe driving.
In the proposed study, the research team predicts post-licensure traffic crashes among young drivers under 19 years old in Ohio based on whether they received formal driver training before obtaining a driver’s license. The analysis is part of a larger research initiative to study teen driver safety, in collaboration with Children’s Hospital of Philadelphia (CHOP) and the State of Ohio. Data came from a licensing record database maintained by the Ohio Bureau of Motor Vehicles (BMV). The database contains detailed driver demographics, including date of birth, sex, and home address, as well as information on each driver’s interactions with the BMV, including licensing transaction dates and a driver training completion date. In accordance with data privacy agreements between Ohio and CHOP, a data operations team at CHOP will prepare a de-identified dataset that links young driver’s training records and demographics with their crash records and socioeconomic status variables that are associated with their home Census tracts such as median household income. This study is exempt from institutional review board oversight by CHOP owing to use of de-identified data.
Young drivers’ crash risks and whether they choose to take driver training are likely both affected by their safety awareness. Due to the difficulties in measuring safety awareness of young drivers, conventional statistical models such as logistic regression are unable to capture the effect of safety awareness. This shortcoming, known as endogeneity, will lead to inaccurate estimates of the relationship between young driver crash risks and their driver training status. To overcome this issue, the team proposes a two-stage logistic regression modeling framework. In the first stage, the team predicts young drivers’ likelihood of taking driver training using the travel time to the nearest driving school from the drivers’ home Census tracts and the median household income of their home Census tracts. Previous studies from the research team have found that both travel time and home Census tract’s median household income are significant contributors to teens’ likelihood of taking driver training (Dong, Wu, Jensen, et al., 2023; Dong, Wu, Walshe, et al., 2023). In the second stage, the team uses the young driver’s predicted probabilities of taking driver training from the first stage to estimate whether they have been involved in traffic crashes post-licensure. The team hypothesizes that young drivers who took driver training have lower crash risks than those who delayed licensure until 18 years old and forwent driver training.
]]></description>
      <pubDate>Thu, 03 Oct 2024 15:39:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2437699</guid>
    </item>
    <item>
      <title>Socio Economic Impacts of Technology Based Stakeholder Engagement Platforms</title>
      <link>https://rip.trb.org/View/2040366</link>
      <description><![CDATA[Michigan, as well as the nation, has been forever changed as a result of social interaction ripples created by the Covid
pandemic. Isolation and distance have been a few of the tools utilized to slow the virus spread. All walks of life have
adjusted daily interaction with others to alleviate the virus spread. The pandemic health measures mandated technology
play a larger role in closing the communication distance. This approach fosters a couple questions. Has the pandemic
environment of technology-based communication platforms promoted a dichotomy in stakeholder involvement? Cities
and States credit Web based engagement platforms for a significant increase in stakeholder engagement. However, are
socio-economic challenged neighborhoods / census tracts able to utilize new tech platforms? Or, has their voice been
further diminished via socio economic in-equality? Furthermore, at times of heighten stakeholder stress, are there
thresholds at which point socio-economic groups simply ‘shut down’. Hearing stakeholders is the foundation from which
well-designed transportation solutions are built. However, if the abilities and limits of the stakeholders are not
understood, can or are engagement techniques effective?]]></description>
      <pubDate>Mon, 22 Jul 2024 13:06:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2040366</guid>
    </item>
    <item>
      <title>Community-Centered Traffic Safety</title>
      <link>https://rip.trb.org/View/2325713</link>
      <description><![CDATA["Community-Centered Traffic Safety" is a research project focused on addressing the complex issue of traffic safety through a multifaceted, locally informed approach. Recognizing the limitations of uniform strategies, the project explores traffic safety challenges by considering varied cultural, financial, and infrastructural factors that influence different communities. The project includes four research thrusts: evaluating the effectiveness of current citation fine structures across different population groups, developing tailored safety messaging, creating a crowdsourced app to identify local safety concerns, and analyzing pedestrian crashes near transit stops to identify contributing factors.

This comprehensive effort involves collaboration with the Massachusetts Department of Transportation (MassDOT) and interdisciplinary student participation from Engineering, Planning, and Public Policy departments. The project will engage with local communities through surveys, focus groups, and observational studies, and will use the UMassSafe Crash Data Warehouse for analysis. Expected outcomes include a proposed revision to the fine structure, targeted traffic safety campaigns, and a prototype of a community-informed app. The project also emphasizes student involvement in data analysis, stakeholder collaboration, and app testing, contributing to the broader goal of improving traffic safety and strengthening community engagement.
]]></description>
      <pubDate>Mon, 22 Jan 2024 12:27:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325713</guid>
    </item>
    <item>
      <title>Spatiotemporal Trends in Pedestrian Crashes</title>
      <link>https://rip.trb.org/View/2325688</link>
      <description><![CDATA[Spatiotemporal Trends in Pedestrian Crashes is a study conducted by researchers from the University of Maine (UMaine) and the University of Connecticut (UConn), in collaboration with the Maine and Connecticut Departments of Transportation. The project aims to understand the factors contributing to the rising number of pedestrian fatalities and injuries in the United States, including the 7,388 pedestrian fatalities recorded in 2021—a 13% increase from the previous year.

This research focuses on identifying patterns in pedestrian crashes and the role of community and infrastructure-related factors in crash outcomes. The study will utilize econometrics and machine learning tools to analyze pedestrian crash data in Maine and Connecticut, with particular attention to rural and geographically dispersed areas.

The research plan includes defining the appropriate scale for analysis, collecting relevant data on population, community characteristics, and infrastructure variables, and applying clustering algorithms and statistical models to uncover trends and contributing factors in pedestrian collisions. The findings will support efforts to improve pedestrian safety by addressing key risk factors and identifying priority areas for intervention.
]]></description>
      <pubDate>Mon, 22 Jan 2024 09:42:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2325688</guid>
    </item>
    <item>
      <title>Improving Livability: Identifying Operational and Access Barriers to Active Transportation in Connecticut Communities</title>
      <link>https://rip.trb.org/View/2321727</link>
      <description><![CDATA["Improving Livability" is a comprehensive research initiative focused on identifying and addressing disparities in walking and biking conditions across Connecticut communities. The project is motivated by rising pedestrian and cyclist fatalities, often tied to historical development patterns and uneven infrastructure investment. This research will include a literature review and data analysis to examine differences in transportation safety related to factors such as race, ethnicity, and income. It will assess variations in access to safe infrastructure for active transportation and develop methods to evaluate safety outcomes, accounting for both reported and underreported incidents.

The project includes two phases: the first involves gathering and preparing data using crash records, U.S. Census demographic information, and land use data; the second phase, proposed for Year 2, will develop econometric models to identify key influences on pedestrian and cyclist safety and create a risk index to highlight high-risk areas in Connecticut. The project will provide practical insights for planners and other stakeholders, including toolkits and strategies to improve mobility and safety. Graduate research assistants will support the work, gaining experience while contributing to the project’s overall goal of making active transportation safer and more reliable for all communities.
]]></description>
      <pubDate>Tue, 16 Jan 2024 12:18:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321727</guid>
    </item>
    <item>
      <title>Evolution of Mode Use Due to COVID-19 Pandemic in the United States: Implications for the Future of Transit</title>
      <link>https://rip.trb.org/View/2137504</link>
      <description><![CDATA[The COVID-19 pandemic has brought about transformative changes in human activity-travel patterns. These lifestyle changes were naturally accompanied by and associated with changes in transportation mode use and work modalities. In the United States, most transit agencies are still grappling with lower ridership levels, thus signifying the onset of a new normal for the future of transit. This report addresses this challenge using a novel panel survey data set collected for a representative sample of individuals from across the United States. The study involved the estimation of a panel multinomial probit model of mode choice to capture both socio-economic effects and period (pre-, during-, and post-COVID) effects that contribute to changes in mode choice. This work provides rich insights into the evolution of commute mode use as a result of the pandemic, with a particular focus on public transit. Through a rigorous modeling approach, this study provides a deep understanding of how transit use has evolved, how it is likely to evolve into the future, and the socio-economic and demographic characteristics that affect the evolution of (and expected future use of) public transit. Results suggest that transit patronage is likely to remain depressed by about 30 percent for the foreseeable future, in the absence of substantial changes in service configurations. This study also shows that minority groups and those living in higher density regions are more likely to exhibit transit use recovery in the post-pandemic period.]]></description>
      <pubDate>Tue, 14 Mar 2023 12:39:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2137504</guid>
    </item>
    <item>
      <title>Mode Substitutional Patterns of Ridehailing and Micro-Mobility Services</title>
      <link>https://rip.trb.org/View/2137501</link>
      <description><![CDATA[In this study, the research team explores the heterogeneous impacts of ridehailing on the use of other travel modes using survey data (N = 1,438) collected from June to October 2019 (i.e., before the COVID-19 pandemic) across three regions in southern U.S. states: Phoenix, Arizona; Atlanta, Georgia; and Austin, Texas. The research team applies a latent-class cluster analysis to indicators of changes in the use of various travel modes as a result of ridehailing adoption, with covariates of socioeconomics, demographics, a land-use attribute, and individual attitudes. The research team identifies four distinctive latent classes of behavioral changes in response to the use of ridehailing. About half of ridehailing users in the sample (49.7%) are found to behave as Mobility augmenters, who use ridehailing rarely, in addition to other travel modes, and do not change their travel routines much as a result of the adoption of this mobility service. The second largest class includes Exogenous changers (24.5%), whose members report many changes in their use of various travel modes, but which can be largely explained by other reasons. Private car/taxi substituters (15%) frequently hail a ride, and as a result, reduce their use of private vehicles while making more trips by public transit and active modes, as the result of using ridehailing. Interestingly, Transit/active mode substituters (10.8%) often use ridehailing, likely for trips that they previously made by public transit or active modes, and consequently reduce their use of these less-polluting modes while enjoying enhanced mobility. This study reveals substantial heterogeneity in ridehailing impacts, which were masked in previous studies that focused on average impacts, and it suggests that policy responses should be customized by users’ socioeconomics and residential neighborhoods.]]></description>
      <pubDate>Tue, 14 Mar 2023 12:31:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2137501</guid>
    </item>
    <item>
      <title>Examining On-Demand Transportation Services with a Focus on Shared Rides: Use and Users, Attitudes and Perceptions, Barriers and Solutions</title>
      <link>https://rip.trb.org/View/2118672</link>
      <description><![CDATA[Transportation network companies (TNCs) and microtransit are changing the way people travel by providing dynamic, on-demand mobility that can supplement public transit and personal vehicle use. Early research suggests that TNCs can expand access and mobility for underserved communities, such as racial minorities and persons with disabilities. However, heavy TNC use among all socio-demographic populations could contribute to increased vehicle miles traveled, congestion, and/or greenhouse gas emissions. Well-designed policy strategies are needed to balance the objectives of increasing mobility and access for underserved communities while simultaneously mitigating the potential adverse impacts of increased TNC usage through policies such as pooling and first-mile and last-mile linkages. However, more research is needed to better understand the mobility gaps and needs of underserved populations to identify potential strategies to mitigate the negative impacts of TNCs and other on-demand transportation services and make the services more equitable. This part of the project proposes to employ a mixed-method approach to examine on-demand transportation services for underserved populations with a focus on shared-ride services. A series of interviews and a literature review will be conducted, identifying individual narratives and lived experiences to put the flesh into quantitative analysis. The study will deploy a national mobility survey and conduct analysis to uncover current shared mobility user patterns and possible relationships to transportation equity. This study will inform why certain socio-demographic populations are more likely to use on-demand transportation services, particularly shared mobilities, factors that contribute to user behavior, and potential strategies to maximize equitable access and mobility offered through these services while mitigating potential adverse impacts.]]></description>
      <pubDate>Fri, 17 Feb 2023 14:03:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2118672</guid>
    </item>
    <item>
      <title>Grid-Aware Robust Fast-Charging Station Deployment for Electric Buses Under Socioeconomic Considerations</title>
      <link>https://rip.trb.org/View/2087437</link>
      <description><![CDATA[The fast-charging technology enables electric buses to be quickly recharged during trips so that on-route operations can be maintained with modest battery capacity. Where to place the fast-charging stations and how to ensure the availability, efficacy, and efficiency of charging infrastructure become very important issues and have been studied in recent years; however, most of existing literature of this research only focus on the transit system itself and aim to reduce the costs. There are still several critical issues remain to be unsolved for the deployment of electric bus fast-charging stations. The major objective of this project is to develop a comprehensive optimization model to select the optimal location of fast-charging stations of electric buses. It aims at addressing several conceptual and methodological complexities inherited in the interconnected transportation-electricity infrastructure systems and the socioeconomic considerations for installing new charging stations to disadvantaged communities. In order to make the location selection decisions reliable, the optimization will also take into account a variety of uncertainties, such as traffic, local grid conditions, and energy usage between stops. A robust optimization methodology is adapted to guarantee that the selected locations are trustworthy enough to handle any real case scenario of uncertainty without violation of constraints. Benders’ decomposition approach is utilized to effectively solve the resulting two-stage robust optimization problem in an iterative way.]]></description>
      <pubDate>Wed, 21 Dec 2022 11:31:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2087437</guid>
    </item>
    <item>
      <title>Incorporating Mobility on Demand Into Public Transit in Suburban Areas: A Comparative Evaluation of Cost-Effectiveness
</title>
      <link>https://rip.trb.org/View/2087436</link>
      <description><![CDATA[The main objective of this study is to understand the relative cost-effectiveness of using different types of mobility on-demand (MOD) services to supplement transit by filling first/last mile gaps compared to conventional alternatives, especially expanding fixed-route services and supporting driving alone with park-and-ride facilities. To do so, this study further develops a cost-effectiveness evaluation approach from a societal perspective and then applies it to selected geographic contexts. In addition, this study aims to inform transit agencies’ decision-making on establishing partnerships with MOD providers to serve first/last mile trips. Moreover, the research will investigate the conditions under which the socially more cost-effective alternatives are consistent with the individual preferences of users from disadvantaged socioeconomic backgrounds. To achieve these objectives, this research will address the following questions:
(1) From a societal perspective, what factors determine the comparative cost-effectiveness of alternative modes for first/last mile travel? Does lower travel demand density, for example, increase the likelihood for MOD to be more cost-effective? How do socioeconomic factors associated with the riders affect the possibility for fixed-route buses to be a more cost-effective alternative?
(2) Under what conditions and arrangements are transit agencies-private service providers partnerships most cost-effective?
(3) From the user’s perspective, what factors differentiate the comparative cost-effectiveness among alternative modes for first/last mile trips? What are the equity implications of each alternative mode?]]></description>
      <pubDate>Wed, 21 Dec 2022 11:24:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2087436</guid>
    </item>
    <item>
      <title>What Is the New Normal? An Analysis of Post-COVID-19 Commute and Work Patterns</title>
      <link>https://rip.trb.org/View/2087424</link>
      <description><![CDATA[The study is addressing the following questions: (1a) What are the adoption rates and frequencies of working from home in Spring 2022 (representing at least the “back side” of the COVID-19 pandemic, if not yet completely post-COVID), and what are the intentions to continue to work remotely in the future? (1b) What demographic, geographic, and attitudinal characteristics are associated with adoption/non-adoption, higher or lower frequencies? (2a) What is the distribution of one-way commute lengths, and how has that distribution changed since before COVID-19? (2b) Putting one-way commute lengths together with commute frequencies, what is the distribution of total weekly commute distance traveled, and how has that distribution changed since before the pandemic? (2c) What socio-economic and other characteristics are associated with one-way commute lengths and total weekly commute distances? (3) How have the shares of commute modes changed since before the pandemic, and what characteristics are associated with those changes? To address these questions, we have designed, and are in the process of fielding, an online survey of employed Georgia residents. The study team is recruiting approximately 2000 respondents through an online opinion panel vendor (Qualtrics). Ultimately, the team will also develop models of key behavioral indicators, to enable them to control for multiple behavioral influences simultaneously.]]></description>
      <pubDate>Wed, 21 Dec 2022 10:56:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2087424</guid>
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