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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>Development of Methods to Produce High-Quality Household-Based VMT Dataset</title>
      <link>https://rip.trb.org/View/2724823</link>
      <description><![CDATA[Household-based Vehicle Miles Traveled (VMT) is essential for Oregon Department of Transportation's (ODOT’s) strategic initiatives. Traditionally, ODOT has reported on-road VMT to the Federal Highway Administration (FHWA) by monitoring traffic on public roads, but new planning and modeling requirements now emphasize household-based, light vehicle VMT—tracking passenger vehicle travel by Oregon residents, regardless of location. A variety of policy actions may have an impact on resident-generated VMT.  Accurate household-based VMT data is critical as ODOT implements new local and metropolitan reporting requirements, tracks VMT per capita as a Key Performance Target in the 2022 Oregon Transportation Plan, and reports vehicle type VMT to the legislature. Without this research, the agency lacks an empirical method for measuring household-based VMT.

The research will establish a framework for developing a high-quality household-based VMT measurements by integrating available empirical data and evaluating each dataset’s strengths and weaknesses for monitoring household-based.  This research will leverage Oregon’s 2023-2024 household travel survey, which collected 1- to 7-day travel diaries from 22,000 households—a rare opportunity, as such surveys occur only about every 13 years. Additional VMT data sources include DMV and DEQ odometer readings, which require evaluation for completeness, privacy, and accuracy; ODOT’s OreGO program, which provides high-quality VMT data but has limited participation and self-selection bias; and third-party data vendors, which have been used by other state DOTs but remain untested for accuracy in Oregon.  This research will assess the strengths and limitations of these data sources and develop a methodology for integrating reliable household-based VMT data. If critical flaws are identified, it will provide recommendations to improve VMT measurement through future data collection and administrative processes.]]></description>
      <pubDate>Wed, 08 Jul 2026 14:46:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2724823</guid>
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    <item>
      <title>Low-Frequency High-Impact Travel for Data Analysis</title>
      <link>https://rip.trb.org/View/2709248</link>
      <description><![CDATA[Low-incidence travel behavior is difficult to capture in a traditional household travel study, where typically one to seven days of travel are collected from a representative sample of households. These behaviors may include travel modes used frequently by a small number of people (bicycling, carshare/vanpool), emerging modes not yet widely adopted (e-bikes, scooters, automated vehicles), complex household travel interactions, or infrequent behaviors such as rideshare use, long-distance travel, and trip replacement behavior such as home delivery of goods and services.

Because these behaviors occur infrequently, traditional survey methods often fail to collect enough observations for accurate estimation in travel demand models. A sufficient number of surveys—approximately 1,000 observations per market segment—is needed to support reliable analysis and forecasting. Despite their low incidence, many of these behaviors have significant impacts on transportation systems.

More than 40 state departments of transportation (DOTs) maintain statewide travel models that require accurate long-distance travel data to support costly intercity highway and rail investments. Emerging travel modes are also becoming critical policy issues in regional and statewide planning efforts.

The objective of this research is to identify and analyze methods for sampling people, households, and incidences of rare or emerging travel behaviors and determine how these methods can be incorporated into household travel survey data collection.]]></description>
      <pubDate>Tue, 02 Jun 2026 13:56:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709248</guid>
    </item>
    <item>
      <title>Agentic LLM-powered Synthetic Transportation Agent Response System (AL-STARS)</title>
      <link>https://rip.trb.org/View/2703689</link>
      <description><![CDATA[Household travel surveys help to gather data on travel trends and are a key part of transportation planning. Gathering this data has become challenging, however, as response rates have been decreasing. This project will develop a web-based travel survey tool (AL-STARS) that generates realistic, synthetic travel data. AL-STARS will use an advanced AI model to simulate travelers in Illinois, allowing planners and modelers to test transportation ideas and survey questions virtually before real-world use. The project will help IDOT make better decisions in infrastructure and policy by providing a more accurate, diverse and cost-effective way to understand how different communities — especially undersampled population groups — actually travel.]]></description>
      <pubDate>Fri, 15 May 2026 09:28:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703689</guid>
    </item>
    <item>
      <title>Analysis of the Potential Revenue and Equity Impacts of an E-commerce Household Delivery Fee in Oregon</title>
      <link>https://rip.trb.org/View/2593956</link>
      <description><![CDATA[Oregon Department of Transportation (ODOT) faces a combination of declining revenues from traditional sources like fuel taxes due to electric vehicle (EV) adoption, fuel-efficient vehicles, and potential travel patterns changes (e.g., telecommuting). In addition, maintenance and operation costs have seen large increases due to inflationary pressures. In this context, ODOT needs to innovate its revenue models to ensure the long-term sustainability of the transportation system.
A potential innovation is the introduction of an e-commerce deliveries fee. This type of fee could be a sustainable long-term source of revenue because:
(a) The last two decades saw a rapid growth of e-commerce sales, both in the US and Oregon. This trend gained further momentum during the global pandemic. According to e-commerce sales reports released by the US Department of Commerce, e-commerce sales accounted for approximately 7% of total retail sales in 2015 and 16% of total retail sales in 2024 (US Department of Commerce, 2024).
(b) Long-term growth is expected to remain strong due to demographic changes, new generations will be more used to online-shopping, and also because retailers are continuously expanding their online offerings and products.
Although an e-commerce deliveries fee may seem appealing there is no study or data available that can assess the financial impact for households and potential equity implications.
The lack of data and studies in this area prompts several questions, including: (1) What type of households across the state are likely to pay more e-commerce delivery fees? (2)  For households, how significant will the fees be in relation to the value of the products being delivered or other transportation related fees? (3) How would this fee impact households across the state, i.e. in rural vs urban areas? (4) What is the potential equity impact of this fee for lower income households?
This project is a necessary first step that will provide valuable insights to understand the impacts of an e-commerce delivery fee in terms of equity and potential revenue at the household level.]]></description>
      <pubDate>Thu, 28 Aug 2025 14:25:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593956</guid>
    </item>
    <item>
      <title>Understanding Drivers of Change in Vehicle Availability and Ownership in North Carolina</title>
      <link>https://rip.trb.org/View/2452920</link>
      <description><![CDATA[Access to a safe, reliable car is, for many North Carolina households, a prerequisite for accessing employment, education, and meaningful social interaction. Without regular access to one or more working cars, households may find themselves ‘transportation-disadvantaged’ if suitable alternative travel options are not available. However, the relationship between car access—regular, unlimited access to a reliable car through lease or ownership—and transportation disadvantage (TD) is not straightforward.

Whether a household has access to a car is based on a unique set of factors, including characteristics of the household and of the cultural norms, built environment, and transportation system in which the household is situated. For some households, a lack of access to a car may indicate disadvantage, while for others it may be a sign of relative advantage and a statement of preference. The relationship between car access (CA) and TD is further complicated by the growing availability of ride shares and micromobility options, increased overall costs of driving, and increased demand for location-efficient 
housing.

NCDOT’s Transportation Disadvantage Index maps various sociodemographic attributes to identify communities in which households’ ability to access critical opportunities may be constrained. The index includes CA as an indicator of TD, while recognizing the increasing challenges in interpreting CA with respect to TD. Understanding the role of CA—its causes and its impacts—is critically important to ensuring that NCDOT’s policies and investments meet the needs of the state’s constituents, today and in the future.
Thus, this research proposes to develop a knowledge base to support creation of updated metrics of household-level TD that capture the complex, dynamic relationships among household mobility pressures, CA decisions, and TD. The transdisciplinary, multi-institutional team will build this knowledge base through (1) a detailed review of the literature on and methods used to assess the relationship between CA and TD, (2) targeted community-based listening sessions with community stakeholders to collect exploratory data on the factors underlying CA and TD, (3) thematic content analysis and interpretation of exploratory data, (4) validation of findings with community stakeholders, and (5) dissemination of findings through a report and slide deck.

This work will uncover critical new information about the forces behind household CA and of how CA relates to mobility and accessibility options for people across North Carolina’s various geographic and socio-demographic contexts. This new knowledge—and the recommendations that will accompany it—can then be used to develop longitudinal survey instruments and geospatial indicators that can be used to enhance NCDOT’s Transportation Disadvantage Index, the Complete Streets Policy, and other policies and tools.]]></description>
      <pubDate>Fri, 15 Nov 2024 16:18:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2452920</guid>
    </item>
    <item>
      <title>Exploring the Changing Dynamics of Household Vehicle Ownership and Use in the U.S.</title>
      <link>https://rip.trb.org/View/2440045</link>
      <description><![CDATA[This project is driven by a pressing need to understand the rapidly evolving landscape of household vehicle dynamics amidst technological advancements and significant societal changes. It focuses on the growing urgency of climate change mitigation and adaptation, a push for equitable mobility for all, and the transition towards vehicle electrification. Aiming to fill the knowledge gap in how households are adapting to these transformative forces, the project will design and deploy a comprehensive nationwide survey, called Evolving Vehicle Ownership Preferences and Use Survey (EVOPUS). This survey seeks to collect data on vehicle ownership, use, and preferences in the context of societal and environmental changes as well as related changes in household energy use (e.g. the adoption of residential solar photovoltaics and battery storage). The major contributions of the project are the following: 1) a nationwide dataset including data on travel behavior, household characteristics, vehicle ownership/transactions and use, mobility patterns as well as attitudes, perceptions, preferences, and lifestyles, made available to other researchers; 2) enhanced understanding of key barriers and drivers of electric vehicle adoption in distinct population segments; 3) a basis for new policies and programs and improvements to existing policies and programs to enable an equitable transition to sustainable mobility across heterogeneous population segments throughout the country.]]></description>
      <pubDate>Thu, 10 Oct 2024 17:16:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440045</guid>
    </item>
    <item>
      <title>Investigating Travel Survey Representativeness: Who’s Missing and What Can We Do?</title>
      <link>https://rip.trb.org/View/2440263</link>
      <description><![CDATA[The core source of data for transportation planning and forecasting comes from household travel surveys. Travel surveys are used to obtain insight into the behavioral decisions of travelers; for example: (1) trip purposes such as work or shopping; (2) means/mode of transport such as car, walk, bus, etc.; (3) travel time; and (4) time of day/week. However, these surveys tend to underrepresent the views and needs of people of color and low-income travelers, precisely the groups that depend most on historically underfunded travel modes like public transit, biking, and walking. In addition to this underrepresentation, it is increasingly difficult to obtain high quality data from those who do respond (e.g., response biases, measurement errors for underrepresented groups), as well as to obtain the detailed contextual and psychological attribute information needed for accurate behavioral forecasting. The goal of this project is to investigate household travel survey biases to identify the causes and propose potential solutions.]]></description>
      <pubDate>Thu, 10 Oct 2024 16:16:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440263</guid>
    </item>
    <item>
      <title>Synthesis Study of Costs and Trip Rates of Recent Household Travel Surveys 
</title>
      <link>https://rip.trb.org/View/2233697</link>
      <description><![CDATA[Travel surveys are a staple of transportation planning.  They are conducted regularly by regional planning agencies and states, sometimes in conjunction with larger national surveys.  Their primary uses are in describing local travel patterns, updating travel models, evaluating major project proposals and monitoring travel trends.   Since the last time the Hartgen Group conducted a survey, many improvements in travel survey methods have been implemented, most notably the use of smartphones which measure trips across multiple days.  Additionally, lower response rates have been so prevalent that some agencies are now using convenience sampling to ensure adequate representation from all market segments. Both of these have added cost and the effects on trip rates and representation are somewhat confounded due to the different survey modes.

Ohio Department of Transportation (ODOT) is currently conducting Household Travel Surveys (HTS) and long distance (LD) surveys over a 10-year period.  Over the five years the survey has been fielded, the percentage of smartphone ownership and participation has increased.  However, the response rates from the address-based random sample have decreased to the point that the issue of moving to a convenience sample has arisen. To date, ODOT has not allowed convenience samples. However, if findings from an updated synthesis demonstrate that there is little difference in survey responses, then ODOT may allow convenience samples in order to obtain a more representative population. To assist ODOT in ensuring the utilization of the most effective and appropriate HTS and LD survey methods, research is needed.

The goal of this research is to enhance the fielding of ODOT's HTS and LD surveys and their sample design. 
The objectives of this research include the following: (1) update the 2009 synthesis report, Costs and Trip Rates of Recent Household Travel Surveys, conducted by the Hartgen Group; and (2) develop a Power-BI database and a supplemental MS Excel spreadsheet of all data utilized in the analysis including data from the 2009 synthesis report, missing data obtained to address any gaps in the 2009 report, and new data collected since the 2009 report. ]]></description>
      <pubDate>Fri, 25 Aug 2023 13:51:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2233697</guid>
    </item>
    <item>
      <title>Development of an Integrated Model System of Transport and Residential Energy Consumption</title>
      <link>https://rip.trb.org/View/2143615</link>
      <description><![CDATA[The energy footprint of households is inextricably tied to the amount of travel undertaken by households. The transportation energy consumption is dependent on the mix of vehicles that a household owns and uses, and the extent to which different vehicles in a household are driven. Integrated models of activity-travel demand and transport energy consumption often do not consider the mix of vehicle types owned and used by households, thus making it difficult to assess the energy implications of shifting vehicle/fuel type choices – particularly in a rapidly evolving marketplace. More importantly, integrated models of activity-travel demand and transport energy consumption do not consider the residential energy consumption implications of travel. If people travel more (and spend more time outside home), they may consume more travel energy, but consume less in-home residential energy. Thus, an integrated model system that tightly connects activity-travel demand, travel energy consumption (sensitive to vehicle fleet/fuel type), and residential energy consumption (sensitive to activity-travel choices) is needed to obtain a holistic picture of household energy footprints. This project describes the integrated model system that connects these three entities. The model is developed by fusing information between two survey data sets, namely, the National Household Travel Survey (NHTS) data set and the Residential Energy Consumption Survey (RECS) data set. The integrated model system is applied to a synthetic population for the Greater Phoenix area in Arizona to illustrate the efficacy of the model system. ]]></description>
      <pubDate>Sat, 25 Mar 2023 10:03:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2143615</guid>
    </item>
    <item>
      <title>Precarious Car Ownership Among Low-Income Households</title>
      <link>https://rip.trb.org/View/1892214</link>
      <description><![CDATA[This research examines two aspects of low-income households’ car ownership histories

First, how and where do households acquire cars? A car is likely a household’s most expensive durable asset. As such, understanding the processes of acquiring a car, the constraints that households face, and the compromises they make are crucially important. 

Second, when low-income households lose access to a car, how does this loss of mobility affect their employment, income, health, and other aspects of their lives? There is a wealth of transportation research examining the effects of acquiring a car, but losing a car is not well understood. This research will focus on the effects of losing a car on access to food, healthcare, recreational opportunities, childcare and child-serving travel, visits with friends and family, and the search for housing and employment.
]]></description>
      <pubDate>Wed, 17 Nov 2021 14:03:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/1892214</guid>
    </item>
    <item>
      <title>Investigating Attitudinal and Behavioral Changes in U.S. Households Before, During, and After the COVID-19 Pandemic</title>
      <link>https://rip.trb.org/View/1746077</link>
      <description><![CDATA[The COVID-19 pandemic has forced rapid, large changes in U.S. households’ social dynamics resulting in substantial changes in their behavior. Virtually overnight, a large fraction of U.S. households has transitioned from a reality of long commutes, in-person classes and business meetings, and in-store shopping to one of telecommuting, online classes and business meetings, and online shopping – even for groceries. Many of these changes were happening already, but COVID-19 has pressed the fast-forward button.
	In this proposal, the research team is interested to know, after the threat of contagion is gone, to what extent will American society “go back” to the pre-COVID-19 way of life? Which behavioral changes will be long-lasting, and for whom? How, if at all, are the attitudes that underpinned the American lifestyle shifting in this crisis, and will these shifts be long-term? Moreover, what are the largest impacts of confinement in terms of attitudes and behavior? Over the past month, thought leaders have published widely in the popular press on these topics (e.g., Fulton, 2020), but to the team's knowledge, no data yet exists that can substantiate or refute their predictions. This project will begin to answer the questions listed above by deploying a nationwide multi-wave survey focused on social dynamics, attitudes, and behavior of American households before, during, and after the COVID-19 pandemic. In the survey, the participants will be told upfront that they will be asked to fill follow up surveys at the 3-month, 6-month, and 9-month marks. Because the team expects participants to complete the (initial) survey at different times (in response to reminders), the data collection will, in effect, turn into a virtual continuous data collection protocol; thus, providing valuable longitudinal data that allows the near-continuous tracking of behaviors, attitudes, preferences, and perceptions.
]]></description>
      <pubDate>Tue, 20 Oct 2020 20:31:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/1746077</guid>
    </item>
    <item>
      <title>The role of transport in how we choose where to live: A qualitative investigation of residential location choice in the Phoenix, AZ region</title>
      <link>https://rip.trb.org/View/1746071</link>
      <description><![CDATA[There is an enormous literature on how people make daily travel choices – where to go and why, which transport mode to use, when to make their trips, and which route to take. One finding from this literature is that travel choices depend – at least in part – on where people live (Ewing & Cervero, 2010; Salon et al., 2012). 
In fact, travel options are critically constrained by where people live. If a person lives in a rural setting, the only useful option to access stores and services is likely to be a private vehicle of some kind – public transit is likely not available, and walking and biking are too slow. If a person lives in a city center, on the other hand, they can access most things without a private vehicle. Understanding how people make their choices about home location, therefore, is critical for understanding how they travel.
There is a large literature on the role of neighborhood “self selection” in models of transport choices (e.g. Ettema & Nieuwenhuis, 2017; Gehrke, Currans, & Clifton, 2018; Salon, 2009; Schwanen & Mokhtarian, 2005). Scholars here generally simplify the home choice to be only a choice of neighborhood – or even the choice of a type of neighborhood – and focus on the question of the extent to which people’s transport preferences play a role in their choice of where to live. Survey data-based quantitative models of neighborhood choice – often joint with transport choices – dominate this literature. A consensus of sorts has been reached which points to some degree of neighborhood “self-selection”, but which also suggests that a sizable fraction of households end up choosing to live in neighborhoods that are not “consonant” with their transport preferences.
The research team posit that understanding why this might be true requires taking a qualitative approach, delving into the complete home choice stories of recent homebuyers. The homebuyer’s choice is an especially complex one made in diverse ways by different households, and this diversity is difficult to capture in a quantitative modeling context. This literature includes relatively few studies of the home choice process that use in-depth interviews as evidence (two examples are Chatman, 2009; Senior et al., 2004). Thus, this project will contribute to the literature with a qualitative, interview-based study of how home buyers choose their homes, and the role of transportation factors in that choice.
The main objective of this project is to improve our understanding about how people choose where to live by asking households directly about how they made their choices. The team is interested in how the overall choice of home is made, but especially interested in the role that transportation preferences play in that choice. The objective is open-ended because there have been surprisingly few qualitative, interview-based studies on this topic.]]></description>
      <pubDate>Tue, 20 Oct 2020 18:25:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/1746071</guid>
    </item>
    <item>
      <title>Latent Vehicle Type Propensity Segments: Considering the Influence of Household Vehicle Fleet Structure</title>
      <link>https://rip.trb.org/View/1746067</link>
      <description><![CDATA[Understanding vehicle type propensities and choices is of interest to academics and practitioners in a wide array of fields. For example, market researchers may study vehicle type choices to predict consumer purchase behaviors and future market shares (Train and Winston, 2007), while energy researchers study individuals’ vehicle type preferences and corresponding driving habits to calculate energy consumption and emissions (Gao et al., 2019). Transportation scholars traditionally study vehicle type to understand and forecast individual and household travel behaviors (Bhat and Sen, 2006), while in recent times, there has been a proliferation of vehicle type studies intended to model the adoption of emerging transport technologies such as electric and automated vehicles (Higgins et al., 2017; Mocanu, 2018). In this study, the research team proposes to investigate vehicle type from a travel behavior perspective, identifying segments with the aim of understanding how personal and household mobility needs, along with a novel range of individual- and household-level characteristics, attitudes, and behaviors, influence vehicle type propensities. Based on the developed model, the team will further examine the relationships between vehicle type propensities, gender roles, attitudes, and current and future travel behavior choices/interests, focus areas that can have policy implications in transportation. The team will fuse multiple datasets, including those pertaining to two surveys (the National Household Travel Survey) completed by the same Georgia respondents, together with targeted marketing and land use data associated with those respondents.  Apply latent class cluster analysis to the data, to identify naturally occurring vehicle type segments based on the influence of both individual vehicle type choices and household vehicle fleet structures.  ]]></description>
      <pubDate>Tue, 20 Oct 2020 18:06:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1746067</guid>
    </item>
    <item>
      <title>Investigation of E-Commerce Enabled Freight Demand and Activities in Residential Areas</title>
      <link>https://rip.trb.org/View/1710226</link>
      <description><![CDATA[Given the above motivation, this research aims to incorporate household freight trip attractions into the freight demand analysis framework and addressing the impacts of e-commerce on travel demand and the highway network. The specific objectives include: (1) Capture household level consumption and attractions of goods and services. (2) Measure the relationships between household and land use attributes and freight trip generation. (3) Estimate the impacts of e-commerce on travel demand and the highway network. Residential deliveries enabled by e-commerce and on-demand delivery services have been increasing rapidly. This shift in how consumers receive goods and services have direct impacts on the state and local roadway network. Yet,  existing data and tools are not able to accurately reflect this trend. This research proposes an approach to addressing this missing component in the supply chain by incorporating household generated (attracted) freight trips into the demand analysis framework. This project will provide insights in quantifying last-mile demand to better reflect the actual freight demand and truck trips in the planning process. ]]></description>
      <pubDate>Thu, 04 Jun 2020 10:26:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1710226</guid>
    </item>
    <item>
      <title>SPR-4413: Assessing the Travel Demand and Mobility Impacts of Transformative Transportation Technologies in Indiana</title>
      <link>https://rip.trb.org/View/1649120</link>
      <description><![CDATA[The objective of this project is to develop a framework, KPIs, and models to quantify the potential travel demand and mobility impacts of disruptive transportation technologies in Indiana. An agent-based model will be developed to simulate the adoption, use, and travel behavior change at the household level for each case study city. The results of this study can help INDOT and MPOs in the study areas to anticipate the demand for these emerging transportation options and plan accordingly.]]></description>
      <pubDate>Tue, 03 Sep 2019 08:54:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1649120</guid>
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