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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>Implementing an Advanced Open-Source Activity Based Travel Demand Model to Support Rural Transportation Planning and Policy Decisions</title>
      <link>https://rip.trb.org/View/2692312</link>
      <description><![CDATA[Travel demand models (TDMs) are used to support state and regional transportation planning and policy decisions. TDMs were originally developed to forecast passenger traffic volumes with the primary objective of identifying investments to reduce traffic congestion. Today, TDMs are used to support a much broader range of purposes, including multimodal and freight transportation planning, demand management strategies, forecasting accessibility outcomes, evaluating network resiliency to disasters, and modeling air quality and public health impacts. However, the aggregate, trip based TDMs used by most regional and state transportation agencies lack the fidelity and sensitivity to evaluate contemporary planning and policy decisions. Activity based travel demand models (ABMs) offer substantial improvements and their agent-based simulation platforms allow for integration with a wide range of other agent-based modeling including land use simulation, vehicle adoption, population growth simulation models among others. Despite their advantages, the complexity of ABMs has constrained their adoption to all but the largest metropolitan areas, often with support from academic researchers. Smaller urban areas and rural states like Vermont could benefit substantially from adopting ABMs. The goal of this project is to implement an open source and/or free for public use ABM in Vermont. Several ABMs meeting these criteria have been developed by US Department of Energy labs. This project will implement a modeling platform that University of Vermont can use in partnership with regional and state stakeholders to advance rural transportation planning and policy research; perform a case study to demonstrate the unique capabilities of ABMs to inform current transportation policy debates in Vermont; identify implementation barriers; and identify future research directions to address implementation barriers to enable wider ABM adoption outside of large urban areas.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:09:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692312</guid>
    </item>
    <item>
      <title>Implementing an Advanced Open-Source Activity Based Travel Demand Model to Support Rural Transportation Planning and Policy Decisions: Phase 2 – Calibration</title>
      <link>https://rip.trb.org/View/2691726</link>
      <description><![CDATA[Travel demand models (TDMs) are used to support state and regional transportation planning and policy decisions. TDMs were originally developed to forecast passenger traffic volumes with the primary objective of identifying investments to reduce traffic congestion. Today, TDMs are used to support a much broader range of purposes, including multimodal and freight transportation planning, demand management strategies, forecasting transportation access outcomes, evaluating network resiliency to disasters, and modeling air quality and public health impacts. However, the aggregate, trip based TDMs used by most regional and state transportation agencies lack the fidelity and sensitivity to evaluate contemporary planning and policy decisions. Activity based travel demand models (ABMs) offer substantial improvements and their agent-based simulation platforms allow for integration with agent-based population growth and land use simulation tools, among others. Despite their advantages, the complexity of ABMs has constrained their adoption to all but the largest metropolitan areas, often with support from academic researchers. Smaller urban areas and rural states like Vermont could benefit substantially from adopting ABMs. The goal of this project is to continue current National Center for Sustainable Transportation (NCST)-funded work on implementing a statewide ABM in Vermont using the POLARIS modeling system developed by Argonne National Lab. The current project is focused on initial model setup and testing. This Phase 2 project will focus on calibration and validation. The expected outcome is a calibrated implementation of the POLARIS modeling system for the state of Vermont that can be used for the evaluation of statewide and regional transportation planning and policy decisions and to advance research on rural transportation challenges.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:58:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691726</guid>
    </item>
    <item>
      <title>Analysis of Changes in the Activity Prisms of Individuals to Predict a Shared Life Experience Metric Over Different Regions and Sociodemographic Groups</title>
      <link>https://rip.trb.org/View/2440050</link>
      <description><![CDATA[Technology has changed individuals’ travel behavior and time-use in many ways. As much as it offers variety of benefits to societies, it may add to social exclusion, since the need for travel is being replaced by a click of a button in cell-phone. People do not feel the need to leave their home to carry out their tasks. They work from home, they order their items online, and even if they want to attend a meeting, they no longer are obliged to travel. Technology, in fact, creates an invisible bubble around individuals, which the size and the thickness of the bubble may vary across different individuals and households. Wouldn’t this make people feel lonelier and more excluded? Research shows that equity in transportation and mobility is closely tied to happiness and well-being. Ensuring that transportation systems are accessible, affordable, and inclusive can lead to reduced stress level, improved quality of life, better health, and greater opportunities, all of which contribute to greater happiness in communities and societies. Public policies, urban planning, and social factors all play a role in shaping this complex relationship. In their earlier works, the researchers have discussed Shared-life Experience (SLE) metric, where they defined it as the likelihood that individuals would interact with others due to their travel patterns; and the researchers also highlighted the importance of travel and access to transportation in having a higher SLE. In this project, the researchers aim to expand the concept in three ways: (a) the researchers define a new SLE metric which is based on the activity prisms of individuals; (b) the researchers analyze the changes in the SLE metric in the individual level over multiple years, using City Wide mobility data that is collected annually; (c) the researchers run a probabilistic analysis to predict changes in the SLE metrics to identify how different regions and different sociodemographic groups will be impacted by. The results of the analysis will identify the most vulnerable areas and groups of people.]]></description>
      <pubDate>Thu, 10 Oct 2024 16:10:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440050</guid>
    </item>
    <item>
      <title>Investigating the Impacts of Smart Charging on Electric Vehicle
Charging Choices Within an Activity-based Framework
</title>
      <link>https://rip.trb.org/View/2420065</link>
      <description><![CDATA[The objective of this project is to forecast the impacts of spatio-temporal electricity pricing
on electric vehicle (EV) charging behavior, drawing on established EV charging models and
incorporating considerations such as smart charging acceptance and joint charging-activity
time planning decisions. The research team aims to elucidate how current and future electricity rates may
influence EV driver behavior, while accounting for heterogeneity in charging preferences and
value of travel time. Using the Los Angeles metropolitan area as a case study, the team develops future
year scenarios for EV adoption and charging infrastructure access to evaluate the effects of pricing
strategies on the demand for public infrastructure and EV-related trips. The analysis encompasses
a range of smart charging policies, including spatially and temporally-varying electricity prices linked
to factors such as charging speeds, utility control, location, and enrollment in bill assistance
programs. Results, segmented by travel characteristics such as household income, home charger
access, and EV range will quantify the impact of EV charging prices on both aggregate and
disaggregate network metrics (e.g., vehicle miles traveled and charging cost savings, respectively.)
The results provide insights into the broader implications of electricity pricing strategies for EV
integration within the transportation network. The findings of this project are expected to contribute
to a deeper understanding of EV charging behavior and access and inform the development of
effective demand management strategies within the evolving landscape of transportation
electrification.]]></description>
      <pubDate>Thu, 22 Aug 2024 16:10:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/2420065</guid>
    </item>
    <item>
      <title>Impacts of Remote/Hybrid Work and Remote Services on Activity and Transportation Patterns</title>
      <link>https://rip.trb.org/View/2313276</link>
      <description><![CDATA[This project will provide science‐based robust information on the behavioral impacts of information and communication technology (ICT) based remote activities on travel choices, including telework, hybrid work, and online shopping. The study will analyze data from the California Mobility Panel, which has been built with rounds of data collection in 2018, 2019, spring and fall 2020, 2021, and 2023, and will be complemented by a new round of data collection in fall 2024. With unique, rich panel data, the project will model complex relationships around remote activities in a single modeling framework, which examines cross‐domain and bidirectional causal effects. The project will employ robust analytical approaches to estimate the effects of remote activities on travel patterns under different land use configurations. The project will greatly improve the understanding of the impacts of remote/hybrid work and other remote services and inform State and planning agencies by shedding light on the complex ways remote activities affect short‐term daily routines (e.g., telecommuting vs. commuting trips, travel mode choice, and spatial/ temporal trip distributions) and long‐term choices (vehicle choice, residential location and real estate development), and will help understand the impacts on vehicle miles traveled (VMT).]]></description>
      <pubDate>Thu, 21 Dec 2023 12:53:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2313276</guid>
    </item>
    <item>
      <title>Activity Based Model Analysis Considerations</title>
      <link>https://rip.trb.org/View/2287566</link>
      <description><![CDATA[In the last decade, numerous peer regions and states have adopted a new approach to travel demand modeling by transitioning from traditional trip based methodologies to activity-based frameworks. While traditional trip-based models generate trips between zones based on aggregate productions and attractions, activity-based models simulate individual households and travelers seeking to access various destinations over the course of a day. Activity-based models capture peoples’ choices and constraints more realistically (Rasouli and Timmermans, 2014), and therefore can better represent pricing (e.g. managed lanes), provide more realistic representation of non-home- based trip making, and give forecasters the ability to consider project benefits and impacts at a finer resolution than is possible with a trip- based model (Bills et al., 2012). Additionally, activity-based models are better able to represent or support innovative transportation modes, complex public transit path choices, the effects of travel time reliability in trip making and destination choices, and dynamic network assignment procedures.
Many of Utah Department of Transportation's (UDOT’s) peer agencies have transitioned to activity-based models, including the Oregon and Idaho departments of transportation and the Denver, Portland, Seattle, and Phoenix metropolitan planning organizations. At the same time, the additional complication of methods and software implementations has reportedly created additional costs for agencies and their contractors in terms of staff training, computational resources, and model development and support contracts. This research seeks to illustrate the tradeoffs of these modeling approaches and help UDOT understand all relevant considerations to potentially supporting an activity-based modeling framework.]]></description>
      <pubDate>Wed, 08 Nov 2023 11:16:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2287566</guid>
    </item>
    <item>
      <title>Multi-Horizon Urban EV Charging Infrastructure Planning: Integrating Activity Patterns, Grid Dynamics, and Uncertainty
</title>
      <link>https://rip.trb.org/View/2283485</link>
      <description><![CDATA[While huge resources have been set aside for the deployment of Electric Vehicle (EV) charging infrastructure, optimally deploying EV charging stations to encourage the decarbonization of the transportation system is a non-trivial task. Indeed, EV charging interacts with several dimensions that operate on different temporal and spatial scales: activity patterns, electric grid operation, land use—to name a few. In this project, the research team aims to develop a multi-horizon planning model that can assist policymakers in determining the timing and location of EV charging stations in an urban environment. The model incorporates several dimensions of decision-making, operation, and planning relevant to EV charging. First, at the lower level, the team incorporates activity scheduling and its interaction with charger and parking location, availability, and price. Additionally, the team accounts for the uncertainty in EV adoption, as it directly impacts the usefulness and availability of charging. Second, the model incorporates the power grid and its operational constraints, paying especially attention to its stability and dynamics. Third, at the upper level, the team considers a multi-horizon planning problem whose aim is to optimally deploy EV charging stations both in space and time. The team pays special attention to the fact that the future is uncertain and, hence, deploying stations as fast as possible might not always be optimal. The model and insights will prove valuable to several stakeholders: policymakers; federal, state, and city transportation and planning agencies; and power grid operators and regulators.]]></description>
      <pubDate>Mon, 30 Oct 2023 22:53:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2283485</guid>
    </item>
    <item>
      <title>Understanding the Impacts of Extreme Heat on Human Activity-Mobility and Time Use Patterns</title>
      <link>https://rip.trb.org/View/2274314</link>
      <description><![CDATA[There is growing interest in understanding the interaction between weather and transportation and the ability of communities and the nation’s infrastructure to withstand extreme conditions and events. In recent years, extreme heat conditions are being felt across the globe with increasing frequency. This research project aims to provide detailed insights into how people adjust and change their activity-travel patterns and time use behavior in the face of extreme heat conditions. The American Time Use Survey (ATUS) data series is used to facilitate the analysis. Weather data is merged with time use records to enable a comparison of activity-mobility patterns between extreme heat days and non-extreme days. A series of models are estimated to understand the impact of extreme heat even after controlling for other variables. The findings reveal that heat has a significant impact on time use and activity-mobility patterns, with some groups exhibiting potentially greater vulnerability arising from the inability to adapt sufficiently to extreme heat. Designing dense, shaded urban environments, declaring heat days to allow people to stay home, and providing transportation vouchers for vulnerable populations can help mitigate the ill-effects of extreme heat. ]]></description>
      <pubDate>Tue, 24 Oct 2023 14:32:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2274314</guid>
    </item>
    <item>
      <title>An Analysis of Changes in Time Use and Activity Participation in Response to the COVID-2019 Pandemic in the United States: Implications for Well-being</title>
      <link>https://rip.trb.org/View/2087515</link>
      <description><![CDATA[This research project aims to investigate the well-being implications of changes in activity-travel and time use patterns brought about by the COVID-19 pandemic. The study uses American Time Use Survey (ATUS) data from 2019 and 2020 to assess changes in activity-travel and time use patterns. It applies two methods – a well-being scoring method and a time poverty analysis method – to evaluate the impacts of these changes on society. The results show that individuals experienced diminished well-being during the pandemic even when their time poverty statistics showed an improvement; this is because the pandemic did not allow individuals to pursue activities in a way that would enhance well-being. In general, well-being is positively associated with the pursuit of discretionary activities in the company of others in favored out-of-home locations. This explains why people have rapidly embraced traveling again in a post-pandemic era. At the same time, people desire more discretionary time (less time poverty); because the elimination of the commute contributes to this, workers are reluctant to return fully to the workplace. Planning processes need to account for a new normal in which activity-travel patterns will be increasingly shaped by the human desire to accumulate positive life experiences.]]></description>
      <pubDate>Tue, 14 Mar 2023 12:25:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2087515</guid>
    </item>
    <item>
      <title>Evaluating the Adoption and Impacts of Telecommuting for Incorporation into the Modeling Process</title>
      <link>https://rip.trb.org/View/1987581</link>
      <description><![CDATA[This project aims to (1) explore how employees’ and employers’ attitudes, perceptions and preferences toward telecommuting may have changed due to the pandemic, and (2) provide insights into predicting future behavior in terms of telecommuting adoption and the potential impacts on other daily activity-travel participation.]]></description>
      <pubDate>Thu, 30 Jun 2022 12:26:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/1987581</guid>
    </item>
    <item>
      <title>Exploration of the Relationships between Leisure Activity Participation Frequency and Social Capital</title>
      <link>https://rip.trb.org/View/1983978</link>
      <description><![CDATA[This study examines the effects of social capital on the frequency of leisure activity participation. Two retrospective surveys of activity behavior were completed in Fall 2019 and 2020. The surveys included questions to ascertain individuals’ instrumental and expressive social capital through position, resource, and generalized name generators. Respondents were asked about participation across a vast list of specific leisure activities over the previous three months. Respondents with greater expressive social capital participated in social leisure activities more frequently than those with less expressive social capital. This relationship was found to not hold for the activities: drinking and socializing, attending church, and dining out.]]></description>
      <pubDate>Tue, 21 Jun 2022 09:32:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/1983978</guid>
    </item>
    <item>
      <title>Emerging Econometric and Data Collection Methods for Capturing Attitudinal and Social Factors in Activity and Travel Behavior Modeling</title>
      <link>https://rip.trb.org/View/1983973</link>
      <description><![CDATA[This study presents a survey designed to test for correlations between social resources and leisure activity behavior. An outcome was tested, leisure activity participation variety, which showed positive correlations with instrumental and expressive social resource access. This work motivates the use of this survey instrument with a larger sample to increase the inferential strength of this social capital theory in explaining leisure activity behavior. It is important to note that although the Qualtrics Panel sample had the greatest issue with validity, this was partially due to greater mobile device usage. The greater diversity of that sample may outweigh this and careful consideration of survey design for mobile devices is warranted.]]></description>
      <pubDate>Tue, 21 Jun 2022 09:10:00 GMT</pubDate>
      <guid>https://rip.trb.org/View/1983973</guid>
    </item>
    <item>
      <title>Megaregional Traffic Impact of COVID-19 Pandemic: Analysis of Activity Restriction</title>
      <link>https://rip.trb.org/View/1878376</link>
      <description><![CDATA[The COVID-19 pandemic brought unprecedented levels of disruption to countries throughout the world. In the United States, governmental directives varied over time, beginning with voluntary stay-at-home requests and restrictions on large public gatherings, then, later, virtual statewide lock down quarantines. However, travel in various forms continued throughout the country. Most notable of these were activities deemed essential for the public good, such as for people to access food, medical care, and other basic life necessities for public health, welfare, and safety. While the ultimate intent of these restrictions, to slow the progression of the virus and limit fatalities, will take time to assess, other effects of travel and social interaction restriction can already be studied. Therefore, this study seeks to assess people interaction and social behavior using travel data at a megaregional level during COVID-19 pandemic which could be useful for operational and strategic planning of recovery efforts and for dealing with future pandemics.]]></description>
      <pubDate>Thu, 16 Sep 2021 16:56:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/1878376</guid>
    </item>
    <item>
      <title>Development of a Toolkit on Innovative Finance Strategies to Accelerate Bicycle and Pedestrian (Active Transportation) Project Delivery: "Diversifying DOT’s Build America Bureau (BAB) Portfolio to Include Underutilized Stakeholders."
</title>
      <link>https://rip.trb.org/View/1758946</link>
      <description><![CDATA[This research will provide state of the practice information on implementing innovative finance strategies to accelerate active transportation project delivery, convene peer exchange among noteworthy implementers, and develop an innovative finance toolkit. The innovative finance toolkit is intended to help transportation agencies and practitioners identify strategies for accelerating delivery of active transportation infrastructure projects (bicycle, pedestrian, network connections to transit, and other active transportation modes). ]]></description>
      <pubDate>Tue, 22 Dec 2020 15:54:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/1758946</guid>
    </item>
    <item>
      <title>Understanding Relationships Between the Built Environment, Physical Activity, Public Health, Urban Mobility, and Traffic Congestion: Graduate Curriculum Development (Project L2)</title>
      <link>https://rip.trb.org/View/1540504</link>
      <description><![CDATA[Evidence-based research from the public health profession has determined adverse factors associated with the built environment, transportation network, urban land use patterns, and travel mode choices are contributing to declining public health and rising healthcare costs in U.S. metropolitan areas. The objective of this project is to develop a multidisciplinary graduate-level course addressing the intersection between public health, transportation and the built environment. The methodology of this course will focus on establishing basis of need for and potential benefits from implementation of optimal solutions to the challenging dilemma of how the built environment impacts urban mobility, transportation infrastructure, network connectivity, sustainability, livability, and public health. Interconnections between the fields of physical activity, public health, public policy and engineering planning and design will be identified. The goal is for students with diverse backgrounds, in a variety of academic fields, to be able to evaluate urban, suburban communities, and neighborhoods to identify positive and adverse effects of the built environment on levels of physical activity and measures of public health, with an emphasis on adoption of polices and approaches for improving desirable outcomes supporting healthier communities. Currently, there is recognition of the need for physical activity, public health, and transportation professionals to work collaboratively. However, these three disparate fields have distinct methods and languages that often inhibit meaningful collaboration. To the best of our knowledge, this course will is the first of its kind. As such, it will bring together content from physical activity, public health, civil engineering, and transportation planning and community design. Anticipated result of this course will be education of professionals who will have requisite skills, knowledge, and abilities to facilitate collaborative efforts across multiple disciplines to improve physical activity, public health, built environment, and traffic congestion outcomes.]]></description>
      <pubDate>Thu, 06 Sep 2018 12:38:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/1540504</guid>
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