<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.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>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>Transport poverty and social equity in South Africa: Evidence from the national household travel survey</title>
      <link>https://trid.trb.org/View/2689927</link>
      <description><![CDATA[Transport poverty, a dimension of social exclusion, occurs when high costs, long travel times, or limited service availability prevent reliable access to essential opportunities, such as employment, education, and healthcare. South Africa's historically segregated urban planning, urban sprawl, limited provision of public transport, and inadequate implementation of transport policies exacerbate transport poverty. It faces persistent and worsening transport affordability burdens, underscoring the need to measure transport poverty and its spatial distribution rigorously. This study employs a quantitative approach using a descriptive-comparative research design using the Household Budget Survey-based analytical framework. Utilising the 2020 National Household Travel Survey dataset, we test alternative transport poverty matrices, validate one specific to South Africa, and identify vulnerable households and the factors that drive transport poverty through multivariate discrete-choice and mixed logit modelling. The findings reveal that transport poverty is widespread, with approximately 15 million people affected. Household income and transport expenditure, and lack of access to motorised transport, are also significant indicators. There is an urgent need for policy interventions to improve transport accessibility and social equity through increased investment, incentivisation of private sector involvement in PT provision, and stringent measures to protect infrastructure.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689927</guid>
    </item>
    <item>
      <title>Investigating unfulfilled travel needs for people with travel-limiting conditions—Using the 2017 National Household Travel Survey (NHTS) data</title>
      <link>https://trid.trb.org/View/2678133</link>
      <description><![CDATA[Despite the increasing awareness of unmet travel needs of people with disabilities (PWD), there is a lack of research that explores the intersectionality of disability with other aspects of an individual’s identity, such as gender, race, ethnicity, employment status, income, and age. To bridge this research gap, this study investigated the disparities of unmet travel needs within specific subgroups of the disabled population using data from the 2017 National Household Travel Survey (NHTS). Studying the travel needs of PWD is crucial not only for understanding the unique challenges faced by PWD but also for uncovering potential gender and social inequities in transportation. A logit model with interaction effects was developed to identify the influential factors contributing to the decision to “reduce day-to-day travel”. By utilizing this modeling approach, the study identified key determinants that influence the occurrence of unmet travel needs among PWD and shed light on the underlying factors that contribute to the observed variations. The study uncovered the intricate relationships of gender, poverty status, race, and travel experiences among individuals with disabilities. The findings underscore the importance of exploring specific subgroups within these categories to gain deeper insights into the travel challenges and needs faced by disabled individuals. This study also reveals the potential suppression of travel desires and overlooked travel needs within the Hispanic disabled population, highlighting the necessity for targeted interventions and support to address these disparities. Policymakers are encouraged to develop strategies tailored to the specific difficulties and special travel needs of disadvantaged groups.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2678133</guid>
    </item>
    <item>
      <title>How Much Do Attitudinal Variables Improve Travel Demand Models? Evaluation Using an Overlap Sample from an Attitude-Rich Survey and the 2017 National Household Travel Survey</title>
      <link>https://trid.trb.org/View/2712619</link>
      <description><![CDATA[This study aims to evaluate the effectiveness of adding a handful of attitudinal marker statements to transportation surveys (instead of designing, deploying, and factor-analyzing a full set of attitudinal variables). We exploit the rare opportunity offered by the 2017 Georgia Department of Transportation (GDOT) Emerging Technologies (ET) survey and the 2017 Georgia add-on to the National Household Travel Survey (NHTS) having 1,245 respondents in common. The non-overlap GDOT ET survey dataset (N = 2,043) is selected as the donor sample, based on which elastic net regression (ENR) models are trained for imputation of attitudinal factor scores using marker variables (MVs). The overlap NHTS dataset (i.e., the recipient sample) (N = 1,245) is treated as if it has only MVs, with attitude scores needing to be imputed using the ENR models trained on the donor sample. The ENR models display high prediction performance in both the donor and recipient datasets, while MVs present excellent performance as well. Three travel behavior variables in the recipient dataset are modeled with no attitudes, predicted attitude scores, and MVs: household vehicle count, (personal yearly) vehicle miles driven, and hybrid/electric vehicle adoption. For each dependent variable, several attitudes show statistical significance, although their contributions to model fit vary. The results indicate that including attitudes leads to (a) better prediction of less-common alternatives (zero vehicles and hybrid/electric vehicle adoption), primarily by improving the prediction of the groups most likely to select such alternatives, and (b) discovery of additional non-attitude variables that would have been considered insignificant otherwise.]]></description>
      <pubDate>Tue, 16 Jun 2026 07:28:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712619</guid>
    </item>
    <item>
      <title>The Reverse Side of Online Shopping: Examining Sociodemographic and Built-Environment Determinants of Delivery Returns</title>
      <link>https://trid.trb.org/View/2712624</link>
      <description><![CDATA[The rapid growth of e-commerce has created new transportation challenges through increased product returns, yet the behavioral determinants of delivery return patterns remain understudied from a consumer-centric perspective. This research develops a comprehensive econometric framework to analyze online shopping frequency, delivery return rates, and return channel preferences using data from the 2022 National Household Travel Survey (NHTS). We employ a multivariate modeling approach integrating probit ordered-response and probit fractional response models to examine three interconnected outcomes: (1) frequency of online goods purchases, (2) proportion of online purchases returned, and (3) distribution of returns across four channels (home pickup, post office, Amazon drop-off, and physical store). The modeling framework accounts for causal relationships between outcomes while controlling for unobserved factors that lead to correlations across the three dimensions just listed. Results reveal significant sociodemographic heterogeneity in online purchasing and return behavior. Women, teleworkers, individuals with higher formal education, and those with higher incomes tend to exhibit increased e-commerce engagement. Older adults and zero-vehicle households, in contrast, have lower online purchase participation and return accessibility. Built environment factors significantly influence return behaviors, with rural residents showing reduced return rates and limited access to Amazon drop-off locations, while individuals residing in areas with high retail density exhibit increased use of Amazon drop-off and physical store returns. The analysis reveals causal relationships where higher online shopping frequency is associated with increased return rates, and both shopping frequency and return rates jointly influence return channel choices. These findings have important implications for transportation planning and urban logistics, highlighting the need for policies that ensure equitable return access and the importance of integrating e-commerce return trips into travel demand models.]]></description>
      <pubDate>Tue, 16 Jun 2026 07:28:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712624</guid>
    </item>
    <item>
      <title>The Changing Composition of U.S. Vehicle Miles Traveled: Evidence from VIUS, NHTS, and FHWA Highway Statistics</title>
      <link>https://trid.trb.org/View/2712613</link>
      <description><![CDATA[This project explores how the composition of vehicle miles traveled (VMT) shifted over the past two decades in the United States. The analysis integrates the Vehicle Inventory and Use Survey (VIUS) and the National Household Travel Survey (NHTS) to trace changes across household and non-household-related travel domains. Evidence shows that household VMT has declined while commercial activity has grown steadily over the last two decades. Household total VMT estimated from NHTS declined from 2.27 trillion miles in 2001 to 1.85 trillion miles in 2022. Over the same period, VIUS shows that between 2002 and 2021 vehicles with any reported business use increased by more than 6 million, pushing associated total VMT from 4.23 trillion to 5.38 trillion miles. These findings indicate a structural shift in the United States roadway demand. Household driving has contracted both in absolute terms and as a share of national travel, while commercial fleets have expanded and now account for a larger proportion of vehicle activity, though the increase in truck-related VMT has not fully offset the reduction in household mileage. Part of this shift likely reflects the lingering effects of the COVID-19 pandemic, which temporarily suppressed personal travel and may have accelerated freight and service activity, as the VIUS and NHTS data analyzed here are from 2021 and 2022. While upcoming data, such as the next NHTS, will help clarify these patterns, current evidence points to a lasting rebalancing between household and commercial travel.]]></description>
      <pubDate>Tue, 16 Jun 2026 07:28:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712613</guid>
    </item>
    <item>
      <title>Immobility or soft refusal? An empirical analysis of the association between respondents’ diligence and reported immobility in household travel surveys</title>
      <link>https://trid.trb.org/View/2639402</link>
      <description><![CDATA[In household travel surveys (HTS), some respondents may report immobility despite having actually traveled on the survey day to reduce survey burden, which is an instance of soft refusal. Since this can deteriorate the data quality of HTS, detecting possible soft refusals is important for HTS organizers and users. The respondents’ diligence can be used to detect possible soft refusals, but its examination is not sufficient. The objective of this study is to explore the association between their diligence and possible soft refusals in HTS. Data from the 2023 Kumamoto Metropolitan Area Household Travel Survey in Japan were used to examine this association. Firstly, we defined five types of less diligent respondents: item nonrespondents, nonrespondents to the open-ended questions (OEQ), proxy respondents, incentive seekers, and late submitters. Then, their immobility rates were compared with those of their more diligent counterparts. Binomial logit models were estimated to investigate the association comprehensively, and the model incorporating diligence variables was used to correct possible soft refusal bias. The results suggest that most less diligent respondents are more likely to report immobility, especially for the nonrespondents to OEQ and item nonrespondents. In contrast, incentive seekers are less likely to report immobility than non-incentive seekers, and late submitters show similar immobility rates to punctual ones. These findings suggest that handling less diligent respondents helps correct the overstated immobility rates. The results of this study contribute to the assessment and improvement of HTS data quality, which is important for transportation research and policymaking.]]></description>
      <pubDate>Thu, 12 Mar 2026 14:02:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639402</guid>
    </item>
    <item>
      <title>How Common is Pedestrian Travel To, From, and Within Shopping Districts?</title>
      <link>https://trid.trb.org/View/2635324</link>
      <description><![CDATA[Growing interest in sustainable transportation systems and livable communities has created a need for more complete measures of pedestrian travel. Yet, many performance measures do not account for short pedestrian movements, such as walking between stores in a shopping district, walking from a street parking space to a building entrance, or walking from a bus stop to home. This study uses a 2009 intercept survey and the 2009 National Household Travel Survey to quantify pedestrian travel to, from, and within 20 San Francisco Bay Area shopping districts. Overall, walking was the primary travel mode for 21% of intercept survey and 10% of NHTS tours with stops in these shopping districts. However, detailed analysis of pedestrian movements showed that walking was common on respondent tours (52% of intercept survey tours included some walking) and that walking was used on the majority of trips within these shopping districts (65% of intercept survey trips and 71% of NHTS trips within the shopping districts were made by walking). In general, Urban Core and Suburban Main Street shopping districts had higher levels of pedestrian activity than Suburban Thoroughfare and Suburban Shopping Center shopping districts. The detailed analysis in this paper provides a more complete picture of pedestrian activity than is commonly shown by national and regional household survey summaries.]]></description>
      <pubDate>Mon, 23 Feb 2026 16:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635324</guid>
    </item>
    <item>
      <title>An investigation of physical participation dissonance and virtual activity participation in the United States</title>
      <link>https://trid.trb.org/View/2610821</link>
      <description><![CDATA[Physical out-of-home (OH) activity accessibility has been studied extensively in the transportation sector, but the recent growth in virtual online activities highlights the need to consider the rich interplay between physical and virtual activity participation. In particular, telework and delivery services present opportunities for new modalities of activity access, potentially expanding activity opportunities for those with limited physical accessibility. In this paper, using data from the 2022 National Household Travel Survey in the United States, we investigate (a) the intensity (and heterogeneity across individuals in this intensity) of discord between how much individuals would like to partake in physical OH participation and how much they actually are able to (we refer to this discord as physical participation dissonance or PPD), (b) the subjective reasons for PPD (c) the intensity of, and heterogeneity across individuals in virtual participation (measured by the intensity of teleworking and home deliveries), and (d) whether or not virtual participation reduces or increases PPD, and by how much. Our results reveal that individuals from zero-worker households, households with fewer vehicles than drivers, low-income households, renting households, and households residing in rural areas all manifest a higher PPD, as do older individuals, racial minorities, non-drivers, and individuals with medical conditions. We find significant heterogeneity in the reasons for experiencing PPD and in virtual participation. Finally, virtual participation does seem to help reduce PPD for those in households with fewer vehicles than drivers, women, older adults, and individuals with medical conditions, but is not effective in reducing PPD for those in low-income, renting, and rural-residing households, as well as for racial minorities and non-drivers. These findings suggest a growing need to consider the relationship between physical and virtual participation, and provide insights for policymakers and transportation planners to improve overall activity accessibility (including expanding access to virtual opportunities) for disadvantaged populations.]]></description>
      <pubDate>Mon, 12 Jan 2026 09:26:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610821</guid>
    </item>
    <item>
      <title>Exploring Changes in Residents' Daily Activity Patterns through Sequence Visualization Analysis</title>
      <link>https://trid.trb.org/View/2448782</link>
      <description><![CDATA[Abstract The analysis of people's daily activities has played a crucial role in various applications, such as urban geography, activity prediction, and homogeneous population detection. However, limited studies have explored changes in the residents? activity patterns in a particular region across various periods. To explore the changes, a methodological framework of sequence visualization analysis based on machine learning that extracts the activity patterns across various periods using sequence analysis, visualizes the activity patterns by calculating the frequency of different activities at time points and categorizes them through graphical similarity, and then compares the activity patterns in terms of activity and demographic characteristics is proposed. Empirical testing on the New York Metropolitan data of the National Household Travel Survey (NHTS) is conducted for 2001, 2009, and 2017. The findings reveal significant intra-similarities, inter-differences, and distinct changes in activity patterns across three periods for different social populations in the New York Metropolitan. From the perspective of information analysis, this work is anticipated to enhance the understanding of travel needs for diverse social populations in a particular region, thereby facilitating targeted policy adjustments for the departments concerned.]]></description>
      <pubDate>Fri, 21 Nov 2025 08:44:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2448782</guid>
    </item>
    <item>
      <title>Behavioral and infrastructure influences on electric vehicle charging and grid impact</title>
      <link>https://trid.trb.org/View/2620892</link>
      <description><![CDATA[Electric vehicle (EV) adoption reshapes travel and electricity demand. We integrate a Cumulative Prospect Theory (CPT) model with a dynamic reference-point mechanism into daily travel routines derived from the 2022 U.S. National Household Travel Survey. Using empirical data from 283 EV users, the simulation generates charging behavior for 10,000 synthetic individuals. Four behavioral archetypes emerge: Home-Focused, Work-Focused, Anxious Opportunistic, and Long-Distance drivers, with unique charging preferences and infrastructure requirements. While behavioral factors significantly influence individual charging decisions, our sensitivity analysis demonstrates that varying CPT parameters across their observed ranges alters aggregate daily energy consumption by less than 1%. Conversely, infrastructure changes, such as increased workplace charging, significantly shift evening charging to morning (up to 50.9%) and reduce evening peak load (9.3% reduction). These results suggest that while behavioral modeling is important at the individual level, infrastructure design plays a more critical role in managing overall grid impacts.]]></description>
      <pubDate>Fri, 21 Nov 2025 08:44:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2620892</guid>
    </item>
    <item>
      <title>Hang-ups-Looking at Non-Response in Telephone Surveys</title>
      <link>https://trid.trb.org/View/2582566</link>
      <description><![CDATA[Since the mid-1980's, telephone surveys have become the standard practice for obtaining data on household travel in the U.S. (Stopher, 1996). But, for a variety of reasons including changes to the North American telephone numbering system, the availability of intercepting technologies, such as caller-ID, and the multiple contacts required to complete a two-stage survey, telephone-based travel surveys seem to be suffering from declining response rates. Recent regional telephone surveys of household activity or travel surveys in the U.S. have had household response rates ranging from 20 to 40 percent (Zimowski, Tourangeau et al, 1997). The information presented in this paper was obtained from the pretest of the National Household Travel Survey (formerly the NPTS/ATS). The 2000 pretest included a number of method and content tests, but for this research the test of nine contact attempts versus nineteen contact attempts and the embedded non-response follow-up survey were examined. For the pretest as a whole, the household recruitment rate (called the cooperation rate in this paper) was 44 percent, and the final response rate was 28 percent. This very low response rate prompted a hard look at where potential respondents were lost in the survey process. Non-response is made up of refusals and non-contacts. The call-disposition file (a tally of the results of each attempt to contact each household) and the non-response follow-up survey were used to try and understand more about non-response. From the call disposition file it was found that non-contact is a much larger area of loss than direct refusal. For example, almost 30 percent of call attempts reached an answering machine, and never contacted a person. Another 25 percent of call attempts rang with no answer. When response rates fall low enough, questions about the representativeness of the respondents are raised. This is probably the biggest challenge facing telephone-based and random-digit dialing (RDD) surveys. As contact rates and response rates fall, survey practitioners should consider a range of techniques to increase contact and participation.]]></description>
      <pubDate>Mon, 10 Nov 2025 11:04:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582566</guid>
    </item>
    <item>
      <title>Changing Attitudes and Transportation Choices: 2017 National Household Travel Survey</title>
      <link>https://trid.trb.org/View/2582122</link>
      <description><![CDATA[The National Household Travel Survey (NHTS) is designed to provide a snapshot of demographic and travel behavior across the United States. While an essential part of the survey is to obtain details of daily person travel, it also includes an attitudinal component where respondents are given the opportunity to provide their opinions on several topics. These topics change in each NHTS, with the 2017 attitudinal questions focusing on the cost of travel and why people do not walk more. Other questions ask about active travel, the use of ride-hailing services, and how one might travel if a vehicle were not available. Each of these topics is explored in this report.]]></description>
      <pubDate>Mon, 03 Nov 2025 18:08:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582122</guid>
    </item>
    <item>
      <title>Travel Trends for Teens and Seniors: 2017 National Household Travel Survey</title>
      <link>https://trid.trb.org/View/2582120</link>
      <description><![CDATA[Over the past 16 years, the proportion of trips reported by six age groups (i.e., 13–15, 16–17, 18–34, 35–64, 65–74, and 75+) has remained relatively stable. According to the 2001, 2009, and 2017 National Household Travel Survey (NHTS), travel by teens (i.e., ages 13–17) has steadily accounted for 7% of all trips reported in each survey period, while that of seniors (i.e., ages 65+) has increased slightly from 13% of all trips in 2001 and 2009 to 16% in 2017. While the overall proportion of trips has generally been the same for the youngest (i.e., ages 13–17) and oldest (i.e., ages 65+) travelers, the purpose of this report is to explore where and how the composition of those trips has changed over time with respect to trip length, trip duration, trip purpose, and travel mode. For teens, there is significant interest in trend-related differences in travel patterns when school is in session versus out, reports of delays in driving, and related implications for travel mode patterns. For seniors, there are questions of trends and the resulting implications of seniors working and driving longer and possible changes in their ability to access medical care. This report provides insights into these age-related trends, as summarized in the next two sections. The final section of this report summarizes the results.]]></description>
      <pubDate>Mon, 03 Nov 2025 18:08:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582120</guid>
    </item>
    <item>
      <title>Trends in Discretionary Travel: 2017 National Household Travel Survey</title>
      <link>https://trid.trb.org/View/2582121</link>
      <description><![CDATA[Over the past three decades, life in the United States has changed significantly. Technological advancements have revolutionized shopping and personal interactions and have led to expanded opportunities for remote working and online classes. Many cities have invested in new social and recreational opportunities, and developers continue to respond to changes in the economy, both of which impact land use patterns and levels of density. From a travel behavior perspective, these technological and land-use changes translate into new travel patterns that are reflected in the proportion of trips for different purposes reported in the National Household Travel Survey (NHTS) as well as the distance, duration, and time of day of those trips. This report presents an exploration of changes in travel behavior by focusing on trips for (1) shopping, (2) family/personal business, (3) visits with friends/family, (4) social/recreational, and (5) medical/dental purposes. These trip purposes are often referred to as "discretionary" travel, as compared to trips for work and school, which are characterized as taking place at a fixed location at scheduled times. times. The purpose of each trip recorded on the assigned travel day was provided directly by NHTS respondents. The NHTS data series includes both the responses provided for each trip as well as a summary trip purpose variable that allows for comparison of trip purpose over time. This report uses the summary trip purpose variable (i.e., WHYTRP90). A table lists each summary trip purpose category used in this report as well as the underlying individual trip purposes respondents selected to describe their trip. The remainder of this section provides an overview of the changes in these five discretionary trip purposes over time, by weekday/weekend, vehicle availability, and home location. Then, in the second section, each purpose is explored in more detail, particularly with respect to changes in trip duration, trip distance, and time of day of travel. The final section of this report summarizes key trends in discretionary travel.]]></description>
      <pubDate>Mon, 03 Nov 2025 18:08:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582121</guid>
    </item>
    <item>
      <title>Travel Behavior Trend Analysis of Workers and Non-Workers: 2017 National Household Travel Survey</title>
      <link>https://trid.trb.org/View/2582119</link>
      <description><![CDATA[Travel behavior trends are impacted by a variety of factors, including household characteristics, built environment, and transportation service, among others. Transportation planning has traditionally focused on work trips and related rush hour congestion, but in the past decade, more recognition has also been given to the role of non-work trips as well. As a result, employment status is one of the primary determinants of travel behavior at both the individual and household levels. Connecting people to opportunities is an important function of the U.S. transportation infrastructure system. Understanding how employment status impacts travel trends in the United States is a major benefit of the National Household Travel Survey (NHTS). This report provides an analysis of U.S. travel behavior trends by employment status using NHTS data from 2001, 2009, and 2017 with a focus on person-miles traveled (PMT) and person trip rates. Unless otherwise noted, all trips are person trips, and all miles traveled reflect PMT. This report compares travel behavior statistics for full-time, part-time, and non-workers over time and across different characteristics such as age, income, gender, presence of children in the household, vehicle ownership, and metropolitan statistical area (MSA) size. In order to provide a balanced comparison of travel for all three categories of workers, the focus of this report is on adults ages 23–65 (thus excluding adults 18–22 who are predominantly college students and those older than 65 who are predominantly retired).]]></description>
      <pubDate>Mon, 03 Nov 2025 18:08:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582119</guid>
    </item>
  </channel>
</rss>