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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>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>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>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>
    </item>
    <item>
      <title>Proof of Concept Study on a Random Moments Approach for Travel and Activity Surveys by MCCOG</title>
      <link>https://rip.trb.org/View/1511784</link>
      <description><![CDATA[This project is a Proof of Concept Study on random moments approach for travel and activity surveys by the Madison County Council of Governments (MCCOG).]]></description>
      <pubDate>Tue, 08 May 2018 11:56:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/1511784</guid>
    </item>
    <item>
      <title>Transportation Access and Individuals with Disabilities’ Community Integration </title>
      <link>https://rip.trb.org/View/1483620</link>
      <description><![CDATA[This study examined the relationship between transit service patterns and the spatial organization of individuals with disabilities’ activities of daily living residing within Utah’s Wasatch Front region to provide recommendations to improve the design, planning, and management of the Utah Transit Authority’s public transportation system. The study objectives included an accessibility Index of Transit Provision to represent fixed-route bus and light-rail service capacity, and an Index of Transit Need representing the spatial organization of individuals with disabilities’ activities of daily living and
indicators of transportation disadvantage. The findings suggest that 58.7% of individuals with disabilities living within the Wasatch Front Region do so in areas with greater than average transit disparity. The results identify 26 areas with very high
transit disparity. Addressing those areas of higher transit disparity through prioritizing new transit investment or the reallocation of existing transit services will contribute to greater equity in individuals with disabilities’ access to activities of community living across the Wasatch Front Region.]]></description>
      <pubDate>Fri, 22 Sep 2017 14:14:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/1483620</guid>
    </item>
    <item>
      <title>Monitoring Daily Activities and Linking Physical Activity Levels Attributed to Transportation Mobility Choices and Built Environment </title>
      <link>https://rip.trb.org/View/1483422</link>
      <description><![CDATA[The relationship between transportation and health may play a significant role in improving the public’s wellbeing due to physical activities and health benefits of active transportation. Travel behavior researchers need to investigate the relationship between transportation mode choices and human health by observing traveler
behaviors and their effect on physical activity and public health. This research identifies and categorizes the health outcomes from daily physical activity and daily travel activities by employing wearable devices with sensing and GPS tracking technology. In this study, the research team develops an integrated data collection and processing platform named “PASTA” to monitor the participant’s daily travel and physical activities. This platform automates data collection and integrates the big data processing of daily travel GPS trajectories from a mobile application designed by the research team with physical activity data from the Fitbit Charge 2/3. The study collects data from a total of 120 participants from Kalamazoo, MI, and Arlington, TX for a 6-month period. The PASTA platform requires many different analyses to observe the transportation impact on health outcomes; these include user activity/trip recognition and transportation mode detection. The study also explores the association between physical activity and an individual’s socioeconomic-body composition profile and proposes fusion into an integrated transportation and health impact model (ITHIM). Arlington participants use more active transportation (bicycle and walking) than Kalamazoo participants who use public transit more frequently. For activity/trip recognition, the study observes the highest accuracy for the combined Geohash-GIS approach with a dwell time of 5 minutes. The Random Forest model outperforms the other machine learning models in terms of transportation mode detection. The participant’s BMI, age, gender, and baseline active travel time show a direct effect on the transportation-related physical activity level. This research provides a method to quantify the amount of physical activity and health benefits associated with transportation activity.]]></description>
      <pubDate>Thu, 21 Sep 2017 14:02:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/1483422</guid>
    </item>
    <item>
      <title>An Activity-based Toolbox for Planning Applications with Special Relevance to Transit</title>
      <link>https://rip.trb.org/View/1448971</link>
      <description><![CDATA[This research proposes to develop a comprehensive activity based travel demand forecast model that integrates different variations of discrete choice models, mathematical programming models of activity scheduling and travel choice, fuzzy concepts and machine learning techniques. The research is designed with a main goal of producing an activity-based travel demand toolbox that can be used in practical planning applications. As envisioned, the toolbox will enable users to predict activity patterns and trip chains at both disaggregate and aggregate levels for a study region, analyze public transportation market share, and evaluate the impacts of different policies on travel pattern of individuals. Core codes of the toolbox will be in Matlab and Python, and use Visual Basic for the user interface. The codes will be standalone executable files that have minimum software requirements for execution. As a demonstration of the toolbox, the project will apply the procedures to examine potential modifications to transit services provided by the Orange County Transportation Authority (OCTA). Estimation and validation of the forecast tool will be based on a set of 78 household samples in Orange County, drawn from the California Household Travel Survey data. Scenarios for analysis will be developed in consultation with OCTA.]]></description>
      <pubDate>Thu, 02 Feb 2017 11:09:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/1448971</guid>
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