<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Decision Support for Dynamic Risks: Determinants of Model Adoption</title>
      <link>https://rip.trb.org/View/2703696</link>
      <description><![CDATA[Since the COVID-19 pandemic, significant supply chain disruptions continue to impact the U.S. economy and have negative impact on transportation networks. Sudden changes in demand or freight availability contribute to increased volatility in freight prices. In turn, volatile freight rates impact the management of transportation networks and increase the difficulty of decision making. This research addresses this problem through the development of decision support tools to proactively respond to initial indicators that predict changes in driver availability and freight cost with the goal of supporting enhanced, early actions to mitigate the risk of disruptions and promote safer transportation network operations.
Work on related prior projects has underscored the importance of forecasting sources of risk to improve the management of transportation systems and the need to understand the key decision components to maximize the value of information to the decision maker. The proposed research will rely on this prior work and make advancements towards the design of an implementable system by examining the end-user perception of decision support recommendations for transportation contracting decisions. 
The research will interview transportation professionals to identify factors that influence their current decision-making and factors that would affect their adoption of a decision support tool. The results of these interviews, in conjunction with prior findings in related research, will inform the design of features for a decision support tool. Design features will be identified for an initial prototype that is suitable for conducting future usability testing of the interactive features. This research continues progress towards the development of a dynamic decision support tool that can ultimately improve the quality of transportation management decisions and continue the legacy of leadership in America’s transportation networks. ]]></description>
      <pubDate>Fri, 15 May 2026 14:13:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703696</guid>
    </item>
    <item>
      <title>The Effects of Street Repurposing on Pedestrian, Vehicle and Visitor Patterns</title>
      <link>https://rip.trb.org/View/2702858</link>
      <description><![CDATA[COVID is a crisis that is unanticipated both in its occurrence and also its length of impact. In the early days, many office employers implemented work-from-home policies while retail businesses shuttered, leading to deserted downtowns across the country. Yet crisis is also an opportunity, and municipalities and businesses innovated in response to the fears of infection. In particular, many cities changed transportation infrastructure, including permitting sidewalk cafes that accommodated outdoor dining, reallocating street space from travel or parking to outdoor dining, and redesigning streets to accommodate a wide variety of users etc. What are the effects of these urban infrastructure innovations? How well do they draw visitors and support businesses nearby? What are their effects on the region’s traffic patterns? Are there spillover effects spatially? As cities emerge from COVID and re-imagine the future of our urban cores, answers to these questions are critical. Though the existing literature has a wealth of knowledge on the built environment effect on travel behavior, they are nearly exclusively at much larger scale (e.g., census tracts) and static (comparing different behavioral patterns between places with different built environment characteristics. There is little to no insight on how block-level urban infrastructure innovations lead to changes in visit patterns as well as nearby businesses. And yet, changes at this scale (block-level) are where local policy changes take place. This proposal is to answer these questions.]]></description>
      <pubDate>Thu, 14 May 2026 15:19:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702858</guid>
    </item>
    <item>
      <title>Zero- and Reduced-Fare Transit Policy and Post-Pandemic Recovery: A Multi-Agency Analysis of Ridership, Service Supply, and Access</title>
      <link>https://rip.trb.org/View/2697838</link>
      <description><![CDATA[Despite growing interest in fare reduction as a policy lever, rigorous comparative evidence on its effects, particularly in the post-pandemic context, remains limited. In Virginia some 40 transit agencies eliminated fares for some period of time during the COVID pandemic, leading the Department of Rail and Public Transportation to ask how fare reduction or fare elimination have affected ridership, operations, and access for system users. This research addresses that question by developing a structured analytical framework and applying it to a sample of transit agencies in which Virginia properties are heavily represented. Using longitudinal data spanning years before and after the pandemic “lockdown”, the research compares agencies that adopted zero- and reduced-fare policies or means-tested fare-free programs against matched fare-collecting agencies. The analysis addresses three interrelated outcomes: ridership recovery trajectories, changes in service supply and scheduled speeds and headways, and shifts in access to employment and key destinations. For a representative subset of agencies, the study also conducts a network-level analysis of access to employment using Remix, a transit planning and scheduling software tool, for a selected set of agencies that represent a range of system sizes. Findings are intended to provide evidence-based guidance for Virginia transit agencies and other stakeholders considering fare policy as a tool for ridership recovery and service quality and performance.]]></description>
      <pubDate>Thu, 30 Apr 2026 08:37:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2697838</guid>
    </item>
    <item>
      <title>Covid and Traffic Crashes/Impact on Safety Targets</title>
      <link>https://rip.trb.org/View/2562323</link>
      <description><![CDATA[The public health emergency due to COVID-19 in March of 2020 significantly changed driving patterns and behaviors.
Research is needed to assess how the pandemic has affected mobility patterns and impacted the number of road
fatalities. Travel was decreased for a period of time but speeds and fatalities increased. Determining the potential
explanations for these differences and understanding the characteristics of the drivers, the engagement in high risk
behaviors, and the continued impacts (current fatality estimates are still increasing) needed to be researched to better
understand these safety impacts and how Michigan Department of Transportation (MDOT) and other safety stakeholders may be able to address these underlying
issues with proactive countermeasures, policies, programs and future target setting.]]></description>
      <pubDate>Mon, 09 Jun 2025 07:59:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/2562323</guid>
    </item>
    <item>
      <title>A Multidimensional Analysis for Understanding Walking Habits in Older Adults Post-Pandemic</title>
      <link>https://rip.trb.org/View/2553167</link>
      <description><![CDATA[Physical activity, including walking, is essential for health and well-being. Yet, many adults, particularly those in older age groups, fail to meet recommended activity levels. Additionally, the COVID-19 pandemic has exacerbated this issue, further reducing walking among this vulnerable population. To effectively design walking infrastructure (from a transportation perspective) as well as promote physical activity within this demographic group (from a health/well-being standpoint), there is a need to understand walking habits and determinant factors. Motivated by this need, the proposed study examines three interrelated aspects of walking behavior in older adults in the post-pandemic era, including frequency, duration, and companionship. The research team utilizes data from the "AARP Walking Survey: Attitudes and Habits of Adults Aged 50 and Older" conducted in July 2022, to estimate a multivariate model that jointly evaluates the impacts of sociodemographic, built-environment, and perceptual factors on these three walking dimensions for those aged 50+. The proposed multivariate framework incorporates an ordered response probit model for evaluating walking frequency, an ordered response model to assess walking duration, and another ordered response for exploring social aspects of walking. By integrating frequency, duration, and companionship into a unified analytical framework, the study reveals the interdependencies between different walking dimensions and highlights the importance of encouraging walking among older adults, especially in the aftermath of the COVID-19 pandemic. The findings hold significant policy implications, suggesting targeted interventions to increase walking frequency, facilitate social walking opportunities, and improve walking infrastructure, thereby promoting non-motorized travel and health/well-being among older adults in a post-pandemic context.]]></description>
      <pubDate>Thu, 15 May 2025 14:56:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553167</guid>
    </item>
    <item>
      <title>An Evaluation of the Long-Term Effects of the COVID-19 Pandemic on Public Transportation Use</title>
      <link>https://rip.trb.org/View/2553165</link>
      <description><![CDATA[Public transportation provides many advantages compared to other transportation modes. It offers a cost-effective commuting opportunity, plays an important role in reducing traffic congestion and carbon emissions, and promotes equity among travelers. However, recent changes in transportation behavior, largely influenced by the COVID-19 pandemic, have resulted in a decline in transit ridership, posing challenges for the future of this mode. While there is evidence of significant rebounds in ridership from pandemic lows, transit has not fully recovered. Continued fears of safety, service cuts, new travel habits, evolving work arrangements, and the growth of online activity participation have all contributed to the slow recovery of public transportation. In this context, an in-depth and rigorous study is needed to assess the ongoing and long-term effects of the pandemic on public transportation use. To explore these changing dynamics, the research team examines the changes in individual-level use of public transportation since the onset of the pandemic, as well as the possible return to pre-pandemic behaviors. Using data from the 2022 National Household Travel Survey, the team considers the self-reported impact of the pandemic on public transportation ridership and the expected permanence of this impact. The proposed investigation will allow the team to identify individuals who have altered their public transportation use since the pandemic, and distinguish which groups of individuals may be willing to return to previous ridership levels. In addition, the research will also examine the characteristics of current users of public transportation. Taken together, the results will have important implications for ongoing and future public transportation policies, providing insights into future mobility trends and informing strategies to improve public transportation ridership. ]]></description>
      <pubDate>Thu, 15 May 2025 14:50:22 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553165</guid>
    </item>
    <item>
      <title>The Effect of Urban Infrastructure Change on Movement</title>
      <link>https://rip.trb.org/View/2519203</link>
      <description><![CDATA[COVID is a crisis that is unanticipated both in its occurrence and also its length of impact. In the early days, many office employers implemented work-from-home policies while retail businesses shuttered, leading to deserted downtowns across the country. Yet crisis is also an opportunity, and municipalities and businesses innovated in response to the fears of infection. In particular, many cities changed transportation infrastructure, including permitting sidewalk cafes that accommodated outdoor dining, reallocating street space from travel or parking to outdoor dining, and redesigning streets to accommodate a wide variety of users etc. What are the effects of these urban infrastructure innovations? How well do they draw visitors and support businesses nearby? What are their effects on the region’s traffic patterns? Are there spillover effects spatially? As cities emerge from COVID and re-imagine the future of urban cores, answers to these questions are critical. Though the existing literature has a wealth of knowledge on the built environment effect on travel behavior, they are nearly exclusively at much larger scale (e.g., census tracts) and static (comparing different behavioral patterns between places with different built environment characteristics). There is little to no insight on how block-level urban infrastructure innovations lead to changes in visit patterns as well as nearby businesses. And yet, changes at this scale (block-level) are where local policy changes take place. This proposal is to answer these questions.

This research will leverage a variety of data sources to answer the above questions, including app-based global positioning system (GPS) data, google street view data, business data, and satellite images etc. All data are longitudinal, covering several years from pre- to during and post-COVID (now). More specifically, app-based GPS data will allow the research team to quantify people’s visit patterns as well as traffic flow patterns; Google Street View and satellite images will allow the team to capture changes in urban infrastructure at the block level; and business data will capture business activities over time. The project will develop novel algorithms to clean and explore these data and address issues inherent to their collection, including biases, sparsity, and unrepresentativeness etic. When necessary, data fusion methods integrating different types of data will also be developed. The project will also develop methods and metrics to quantify changes in urban infrastructure. In addition to answering the questions raised above, project deliverables will also include: (1) open-source notebooks that can be used to process the various kinds of data; and (2) visualizations at selected locations to illustrate the changes from before to after.

The study site will be in the City of Seattle, a medium-large city in the Pacific Northwest that has implemented several innovations in urban infrastructure during COVID. Initial sites include Ballard and University District in Seattle. Both have a vibrant business district (though tailoring to different populations), a popular farmers’ market and saw a surge of outdoor restaurants and cafes during the COVID period. In addition to these initial sites, the research team will also screen google street view datasets, satellite images, as well as consult local cities for identification of additional sites in the region. The goal is to have a set of sites with contrasting characteristics in built environment and socio-demographic characteristics.]]></description>
      <pubDate>Sat, 08 Mar 2025 11:33:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2519203</guid>
    </item>
    <item>
      <title>Teleworking to Play or Playing to Telework? A Latent Segmentation Approach to Exploring the Relationship Between Telework and Nonwork Travel</title>
      <link>https://rip.trb.org/View/2519199</link>
      <description><![CDATA[Technology has evolved at a tremendous pace over the past decade, permeating into our everyday existence and affecting literally every aspect of our lives. Our activity-travel choices have been no exception in this regard, as we make continuous and joint decisions about which activities we can and want to undertake (either in-person or virtually). Add to this the pandemic’s upheaval of habits and behaviors, and there emerges a critical and renewed need to understand the activity-travel choices and decisions of individuals within a new landscape of transportation, technology, and pandemic-altered lifestyles. In this study, the research team explores the causal direction/jointness issue underlying the interplay of teleworking choice and nonwork travel, within the context of the telework landscape in the aftermath of the pandemic. In particular, the team models the telework frequency, maintenance stop frequency, and leisure stop frequency decision-making process as a package choice to account for unobserved factors, as well as use a latent segmentation approach to recognize the two possible and distinct causal behavioral directions that may be at play. The methodology combines an ordinal choice model for telework adoption/intensity with weekly count models for the number of maintenance and leisure stops. The data for the analysis is drawn from a 2021-2022 weekly travel diary and survey of Minnesotan workers.]]></description>
      <pubDate>Sat, 08 Mar 2025 11:28:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2519199</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>Investigating the Evolution of Residential Self-Selection in the Post-COVID Era: The Transition to Digital Lifestyles and Changing Travel Behaviors</title>
      <link>https://rip.trb.org/View/2437976</link>
      <description><![CDATA[The COVID-19 pandemic has caused a profound transformation in individuals’ residential location choices and travel patterns. Traditional determinants of where to live, such as housing affordability or proximity to work/amenities, now closely intersect with the convenience of remote work and other online activities. This transition reshapes how individuals choose their living environment and organize their travel during the day. To navigate this evolving landscape, this research will delve into the residential self-selection in the post-COVID era. Central to the researchers’ investigation is understanding individuals’ shifting tendencies towards digital nomadism and the impact of this transition on housing preferences and activity-travel behavior. The researchers seek to uncover to what extent factors like the convenience of remote activities, hybrid work schedules, and tech-savviness influence residential and travel choices. Furthermore, the researchers will explore the equity considerations inherent in these decisions, examining how sociodemographics intersect with the evolving structure of residential self-selection. To achieve these objectives, the researchers will employ a targeted sampling methodology to recruit individuals who have relocated over the last 12 months, as well as those who have not moved since the start of the pandemic in March 2020. Through the analysis of this dataset that consists of movers and non-movers, the researchers will have the opportunity to (1) track the factors impacting relocation decisions and those influencing residential stability, (2) explore the effects of the remote activities both on residential and travel choices, and (3) assess the differences in remote activity participation and residential location choice across various population segments. These findings will not only inform planning and policy decisions but also set a baseline for future studies focusing on the interplay between residential self-selection, digital lifestyles, and housing preferences. ]]></description>
      <pubDate>Wed, 09 Oct 2024 16:11:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2437976</guid>
    </item>
    <item>
      <title>A Time and Space Exploration of Traffic Crash Trends During the Covid
Recovery
</title>
      <link>https://rip.trb.org/View/2420216</link>
      <description><![CDATA[Past research has shown, including studies by UC Berkeley’s SafeTREC, that crashes in congested
conditions differ fundamentally from those in free-flowing traffic. Despite this, traditional traffic crash
monitoring programs often overlook congestion as a factor when identifying high-risk locations. Recently,
state Departments of Transportation have begun focusing on the most serious crashes (Fatal and Serious
Injuries), which tend to occur in free-flow conditions. While this is a better approach to identify high-risk
locations, there is room for further improvement by understanding the role played by congestion.
During the recovery from the COVID-19 pandemic, traffic crashes in California have returned to
prepandemic levels. Fortunately, fatal and serious crashes have not increased significantly, as seen
nationwide (1). A common explanation for this trend is the increase in reckless driving, which tends to
occur in free-flow conditions. However, it is very likely that the reduced congestion during the COVID
recovery has heightened the risk of serious injuries on previously congested roads. This research aims to
explore the extent to which these trends are driven by reduced congestion, providing a clearer picture of
the factors contributing to high-risk road segments.]]></description>
      <pubDate>Sat, 24 Aug 2024 10:48:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420216</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>Comprehensive Analysis of Factors Influencing Pedestrian Injury Severity at Intersection and Non-intersection Locations in Connecticut </title>
      <link>https://rip.trb.org/View/2321729</link>
      <description><![CDATA[Pedestrian safety is a growing concern in the United States, with 7,500 fatalities reported in 2022, marking the highest in three decades. Connecticut followed this trend, recording 69 fatalities in the same year. This study examined factors influencing pedestrian injury severity through a multi-level statistical analysis using Connecticut crash data, NHTSA’s VIN decoder, and Canadian Vehicle Specification data.


Crashes were classified into two categories: intersection and non-intersection, and pedestrian injuries were categorized into three: severe (Fatal/K, serious/A), non-severe (Evident/B, Possible/C), or no-injury/property-damage-only (O). Separate multinomial logistic regression models were developed to identify the factors influencing pedestrian injury severity, and binary logistic regression models were developed to compare fatal and serious injuries, providing a deeper analysis of severe injury outcomes.


At non-intersection locations, pedestrian impairment (OR=3.57), driver speeding (2.85), improper crossing (2.84), driver impairment (1.88), and unlighted roadways (1.55) significantly increased the odds of severe injury. At intersections, pedestrian impairment (4.53), speeding (7.40), roadway downgrade (2.04), and unlighted conditions (1.48) were key contributors.
Binary logistic models revealed, at non-intersections, pedestrian age (3% per year), pedestrian impairment (2.03), driver impairment (1.91), and roadway upgradient (3.18) significantly increased the risks of a fatal injury versus a serious injury. At intersections, speeding (7.39) was especially critical, while passive (0.20) and active (0.61) traffic control devices substantially reduced the risk of fatal injury.


The findings provide detailed, context-specific insights to guide pedestrian safety strategies. Reducing pedestrian impairment, enforcing speed control measures, improving roadway lighting, and implementing effective traffic control devices, particularly at intersections, can substantially reduce the likelihood of pedestrian injury severity.]]></description>
      <pubDate>Tue, 16 Jan 2024 12:31:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2321729</guid>
    </item>
    <item>
      <title>Quick-Response Research on Long-Term Strategic Issues. Task 49. Strategies to Increase Transit Ridership in the Post-Pandemic Era</title>
      <link>https://rip.trb.org/View/2307257</link>
      <description><![CDATA[Public transportation ridership in the United States declined precipitously in 2020 during the COVID-19 pandemic. While transit ridership has recovered nationally to about 70 percent of pre-pandemic levels, recovery has varied across transit agencies by size and mode. 

Transit agencies throughout the United States are reassessing past practices and considering how they might change or adapt to better serve their communities. Many are experimenting with strategies and innovations to increase transit ridership by reshaping transit services in the near- and long-term through changes in current services, new service models, pricing and payment systems, communication, and other approaches.  

OBJECTIVES: The objectives of this project are to (1) present cost-effective and evidence-based strategies and innovations to achieve increases in transit ridership in the post-pandemic era at transit agencies of different sizes and modes located in communities with different attributes throughout the United States and (2) identify research needs and strategies beyond the project budget that will help transit agencies increase transit ridership and the value of transit services to their community. ]]></description>
      <pubDate>Wed, 13 Dec 2023 13:17:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2307257</guid>
    </item>
    <item>
      <title>The Impact of Children's School Format on Women Professionals in STEM</title>
      <link>https://rip.trb.org/View/2244203</link>
      <description><![CDATA[With many school closures during the pandemic resulting in long-term changes (more than just a month) to child education format (e.g., online or hybrid), many women took on increasingly greater home and childcare responsibilities. Even prior to the pandemic, the retention of women in science, technology, engineering, and mathematics (STEM) faced many challenges. The research project described herein tried to capture the experiences of women in STEM with children, as (rather than in retrospect) they navigated various school formats during the coronavirus pandemic (COVID-19). The authors anticipate that the results of this research will highlight the challenges facing women in STEM with children when it comes to the education of their children. Three surveys were administered to women in STEM: one in October of 2020, one in March of 2021, and one in May of 2021. Forty-six, ten and three survey respondents replied to each survey. The results suggest that while overall survey respondents remained concerned about impacts that COVID-19 may have on them and their families, the level of concern seemed to dissipate over the successive surveys. Overall, women in STEM reported very limited options for additional support (e.g., a nanny). The hybrid school format was reported as requiring some of the most significant levels of support followed by online and then in-person. As a whole, women in STEM whose children were attending school in-person reported little to no impacts, often instead remarking on impacts felt during the initial lockdowns. Women in STEM with elementary school-aged children seemed to report the most significant impact. The inability to work uninterrupted was one of the most significant challenges suggested, as there are implications that the work that women in STEM are conducting requires periods of meta focus. Therefore, while the flexibility of allowing women in STEM to work at home can bring some benefits, ultimately, when her children are also at home, the benefits are significantly mitigated. Finally, while the three surveys were expected to be able to capture the oscillation between school formats, at least one survey respondent described many changes between subsequent surveys.]]></description>
      <pubDate>Fri, 08 Sep 2023 19:02:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2244203</guid>
    </item>
  </channel>
</rss>