<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>Investigating the Stability and Evolution of Transportation-related Attitudes: Evidence from Two Longitudinal Survey Samples</title>
      <link>https://trid.trb.org/View/2775062</link>
      <description><![CDATA[Despite extensive evidence linking attitudes to behavior in the academic literature, challenges in measuring and forecasting attitudes remain key barriers to incorporating travelers' attitudes into practice-oriented regional travel demand models. Complementing prior studies on measurement challenges, this project addresses forecasting challenges, by examining temporal changes in transportation-related attitudes. Two panel samples are employed: overlapping respondents from two surveys administered in Georgia in 2017 and 2022 (N=142) and the 2024 and 2025 waves of the Transportation Heartbeat of America Survey (N=701).

After identifying attitudinal factors using exploratory factor analysis, this project will compare attitudinal changes in subsamples with varying socio-economic and demographic characteristics, focusing on whether raw attitudinal variables or attitudinal factor scores are more stable, which attitude types (e.g., travel, residential preferences, technology) exhibit greater stability, and for whom. Additionally, disaggregate-level statistical models of attitudinal changes are developed to provide a more comprehensive understanding of the observed changes. Overall, this project will provide foundational insights that can help analyze travel demand under scenarios involving changes in key input variables — including attitudes — with plausible and empirically-grounded assumptions about how attitudes may evolve and how they relate to other factors, which ultimately will advance the incorporation of attitudes into demand modeling and planning practice.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:38:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775062</guid>
    </item>
    <item>
      <title>Who Stays and Who Leaves? Examining Sample-Source Effect on Attrition in a Longitudinal Panel Survey</title>
      <link>https://trid.trb.org/View/2775054</link>
      <description><![CDATA[Longitudinal panel surveys are a cornerstone of travel behavior research, enabling measurement of behavioral change over time and facilitating before-and-after studies of policy interventions. Despite their scientific value, panel surveys suffer from a well-documented challenge: attrition. When respondents exit the panel non-randomly between different waves of longitudinal surveys, the resulting stayer sample becomes biased, thereby undermining both sample size and representativeness. While prior literature has examined how individual socio-demographic characteristics influence panel retention, little attention has been paid to whether the initial recruitment channel through which respondents enter a panel survey shapes their propensity to remain across multiple waves. This gap limits the ability of transportation researchers and survey practitioners to design longitudinal studies that yield reliable, representative data.

This project aims to examine whether recruitment strategy significantly influences panel survey retention, even after controlling for socio-economic, demographic, and attitudinal factors, and to identify which recruitment approach yields the highest retention rates. The study will use data from the COVID Future Survey, a three-wave nationwide longitudinal panel survey conducted between April 2020 and November 2021. Wave 1 recruited 8,385 valid respondents through three distinct channels: convenience sampling, mass email outreach, and a commercial online survey panel. Of the original sample, 22.5% responded to all three waves, 11.1% responded to Waves 1 and 2 only, and 66.4% responded only to Wave 1.

The project will formulate and estimate a Generalized Heterogeneous Data Model (GHDM) that jointly treats recruitment strategy membership and panel survey retention as endogenous outcomes, accounting for correlated latent attitudinal constructs and socio-demographic characteristics. Latent constructs to be incorporated include Risk Perception, Work-from-Home Propensity, and Virtual Activity Perception, specified through exploratory and confirmatory factor analyses to capture shared unobserved heterogeneity that simultaneously influences recruitment channel membership and panel retention propensity.

The insights derived from this project are expected to provide actionable guidance for transportation survey practitioners on recruitment strategy selection, improve the reliability and representativeness of longitudinal travel behavior data, and strengthen the data foundations underlying travel demand models and transportation planning decisions.]]></description>
      <pubDate>Fri, 04 Sep 2026 14:56:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775054</guid>
    </item>
    <item>
      <title>Activity-Travel Behavior in New Normal: Insights from a Longitudinal Study</title>
      <link>https://trid.trb.org/View/2713850</link>
      <description><![CDATA[The growing relevance of work-from-home (WFH) in modern work culture brings benefits and challenges such as work-life balance, physical activity, stress management, and effective communication. This study aims to provide a descriptive analysis of WFH impacts on daily activities, including household care, shopping, childcare, exercise, and entertainment, to develop strategies that mitigate its negative effects. By examining WFH trends since the pandemic, the study provides insights into the evolution of remote work practices. It also analyzes WFH frequency among different socio-demographic groups to address equity issues. Additionally, the study compares expected and actual WFH frequencies post-pandemic, identifying reasons behind any discrepancies. Data from the COVID Future survey, conducted in four waves in the U.S. from 2020 to 2024, is utilized, focusing on Wave 4, conducted from May to July 2024. The survey covers socio-demographics, job characteristics, home environment features, WFH choice, frequency, attitudes, productivity, commute and non-commute trips, online shopping, telemedicine, long-distance travel, relocation, and lifestyle attitudes. This comprehensive dataset offers a robust foundation for analyzing WFH impacts and trends in the post-pandemic landscape. Key findings reveal dynamic shifts in WFH patterns: high WFH frequency during early pandemic stages, diversifying to hybrid models as restrictions eased. Social activities decreased on WFH days, favoring in-home interactions. Child/adult care engagement rose on WFH days due to greater flexibility, while personal business tasks also benefited from this. Household care and entertainment activities integrated better into daily routines on WFH days. Grocery shopping and eating-out activities declined, indicating a shift towards home-based dining.]]></description>
      <pubDate>Mon, 24 Aug 2026 08:44:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713850</guid>
    </item>
    <item>
      <title>Evolution of Mode Choice: Examining the Relationship Between Telecommuting and Transit Use</title>
      <link>https://trid.trb.org/View/2712617</link>
      <description><![CDATA[This project aims to quantify the impacts of telecommuting on transit use. Data for this analysis is derived from the 2019 and 2023 editions of the Puget Sound Regional Council (PSRC) household travel survey and a joint model of telecommuting and transit use frequency is estimated to understand the nature of the relationship in the pre- and post-pandemic periods. The findings reveal a U-shaped relationship between telecommuting and transit use. Lower transit frequency was observed at both the highest and lowest levels of telecommuting, while higher transit frequency was associated with medium or hybrid levels of telecommuting. This pattern became even more pronounced in 2023. Computations of average treatment effects show that transitioning from medium-level (hybrid) telecommuting to non-telecommuting resulted in a 21 percent decrease in transit use in 2019, and a steeper 35 percent decrease in 2023. Similarly, moving from hybrid to frequent telecommuting led to a six percent reduction in transit use in 2019, and a larger nine percent reduction in 2023. These findings suggest that the loss in transit ridership in the post-pandemic era is likely to persist and that compelling workers to return to the workplace full-time is unlikely to yield significant gains unless transit agencies find innovative ways to attract non-telecommuters (full commuters) back to transit. Instead, embracing a hybrid work modality while providing incentives to promote transit use may yield greater benefits.]]></description>
      <pubDate>Tue, 16 Jun 2026 11:38:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712617</guid>
    </item>
    <item>
      <title>The San José's Mobility Credit Pilot: A Delayed Randomized Control Trial Evaluation</title>
      <link>https://trid.trb.org/View/2691659</link>
      <description><![CDATA[The San Jose Mobility Credit pilot (MCP) tests a new approach that allows individuals the freedom to travel when, where, and how they want to go. The pilot provides MCs that enable individuals to maximize travel while minimizing costs. Interest in these programs is growing throughout the U.S. The research team has experience evaluating similar programs in the U.S. The project will include a delayed longitudinal randomized control trial (RCT) to evaluate the MCP. The design of the 18-month MCP in-person participant recruitment, training, and support by the City of San Jose will support high participation and survey response rates. The study will be the first to use a delayed RCT design with a difference-in-differences (DID) statistical analysis to evaluate an MCP. In general, RCTs are rarely used to test the effectiveness of transportation projects and policies. The proposed study will evaluate the effects of the MCP, not only on individuals’ overall travel freedom, but also on transportation security (e.g., travel speed, time, and reliability), community participation (e.g., church, family, school, and volunteer activities), employment, education, and overall health (which could lead savings in health care costs). Few studies have evaluated the significance of transportation access interventions on these measures. The longer duration of the MCP may allow for a better assessment of evaluation measures. The MCP evaluation will be one of few studies that examine the causal effects (randomized control trial with difference-in-differences analysis) of a transportation intervention on multiple evaluation measures.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:10:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691659</guid>
    </item>
    <item>
      <title>Exploring Cycling Behavior Shifts among Young Adults through a Longitudinal Cohort Survey</title>
      <link>https://trid.trb.org/View/2683116</link>
      <description><![CDATA[Young adults often use sustainable transportation options, such as public transportation, cycling, and walking for daily transportation. However, evidence on the retention of sustainable travel behaviors is unclear, and longitudinal analysis of travel behavior changes among young adults is scarce. The disruption in travel behavior caused by the COVID-19 pandemic, and significant attention toward the promotion of active transportation during and after the pandemic, offered an opportunity to explore this topic in a quasi-experimental setting. We utilized survey data from two waves (baseline and follow-up) of a longitudinal online cohort of 552 respondents in the Greater Toronto and Hamilton Area, Canada, who were post-secondary students in 2019. Using the data, we explored the association between changes in commute-related cycling frequency (unchanged, started cycling, and stopped cycling) and respondents’ socio-demographic characteristics, pre-pandemic travel satisfaction, and life events during the pandemic years. About 8% of respondents self-reported that they started cycling for commuting after the pandemic, and another 6% reported that they stopped cycling. Results from a discrete choice multinomial logit model indicate that younger age and pre-pandemic travel satisfaction with active transportation modes were associated with higher odds of starting to cycle after the pandemic. Furthermore, starting full-time work was associated with higher odds of stopping cycling for commuting purposes. Moving residence to more urban locations was associated with higher odds of starting to cycle, but this association was not statistically significant when other factors were taken into account.]]></description>
      <pubDate>Sun, 22 Mar 2026 17:18:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683116</guid>
    </item>
    <item>
      <title>Tensions in air and rail integration based on a European longitudinal case study with stakeholders</title>
      <link>https://trid.trb.org/View/2632935</link>
      <description><![CDATA[Integrating air&rail systems requires collaboration among transportation stakeholders. This study used Action Research to explore tensions during a 16-month real-life air&rail integration effort, structured around co-creation sessions. The single case study identified six system-level tensions: no control over airport slots, conflicting priorities in train stop allocation, misaligned scheduling, different business models, fragmented booking systems, and different passenger experiences. Additionally, three collaboration-level tensions emerged: limited mutual understanding, embedding systems thinking in organizational processes, and differences in organizational momentum. While these tensions primarily arose between air and rail operators, resolving them also requires infrastructure managers and government involvement. The identified tensions indicate that the actors tended to prioritize organizational interests over passenger needs. While co-creation fosters understanding, challenges extend beyond peer-level collaboration. Our findings suggest that involving an orchestrator and a European governing body could facilitate system-level decision-making. This may help overcome institutional and regulatory boundaries, for the benefit of air&rail integration.]]></description>
      <pubDate>Thu, 18 Dec 2025 15:38:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632935</guid>
    </item>
    <item>
      <title>Traffic-Calming Measures and Road Traffic Collisions and Injuries: A Spatiotemporal Analysis</title>
      <link>https://trid.trb.org/View/2381613</link>
      <description><![CDATA[The authors of this study assessed the impact of traffic-calming measures (TCMs) by means of a longitudinal design. There have been reports of TCMs, which are physical modifications of roadways intended to improve safety, which showed that TCMs do reduce crashes and injuries. The pre-/post- designs of those studies, however, led to criticisms. The authors evaluated 8 TCMs in Montreal, Canada, that were put in place from 2012 to 2019. Although arterial roads were the scene of most crashes in the study, local roads were where most of the TCMs were implemented. The authors recommend the implementation and identifications of TCMs on arterial roads that are counterparts to those on local roads, in order to enhance traffic safety.]]></description>
      <pubDate>Tue, 21 Oct 2025 10:30:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2381613</guid>
    </item>
    <item>
      <title>Monitoring and evaluation of travel behaviour change in mobility-as-a-service (MaaS) trials: Insights from a longitudinal study</title>
      <link>https://trid.trb.org/View/2583802</link>
      <description><![CDATA[Travel behaviour change towards more sustainable modes of transport is required to achieve environmental sustainability. Mobility as a Service (MaaS) trials have been implemented in various cities over the past decade to reduce private car usage, and promote sustainability. Monitoring and evaluation (M&E) is a key component in developing MaaS trials, as it enables a comprehensive understanding and analysis of the MaaS's impact on modal shift. Although the insights from various MaaS trials have been explored in the literature, studies on the travel behaviour change M&E of these trials with longitudinal approaches were lacking. This paper proposes a M&E approach employing the Differences-in-Differences (DiD) method and incorporating three Key Performance Indicators (KPIs), to monitor and evaluate the travel behaviour change after the launch of the MaaS app. It uses longitudinal (baseline and recall) data to analyse the impact of MaaS on travel behaviour change for its users compared to non-users. The application of the proposed DiD-based M&E framework has been tested and validated on a real-world MaaS project with 887 MaaS users and 757 non-MaaS users. The results suggest that the private car use is reduced while public transport use increased for MaaS users, while for the non-MaaS users, the private car use is increased and public transport is decreased. Moreover, the DiD analysis showed that the impact of MaaS on the decrease in the private car and increase in public transport use is statistically significant. These findings and insights guide policymakers and transport practitioners in appraising the effectiveness of new MaaS systems, especially in car-dependent regions in the world.]]></description>
      <pubDate>Tue, 30 Sep 2025 09:33:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2583802</guid>
    </item>
    <item>
      <title>Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life</title>
      <link>https://trid.trb.org/View/2559339</link>
      <description><![CDATA[Agent-based models (ABMs) in transportation modeling simulate activity and travel decisions at the disaggregate level of households and individuals. To do this, ABMs require detailed and realistic information on agents’ socioeconomic and demographic characteristics. Various synthetic population generators have been proposed to address this need. However, most of those currently in practice are cross-sectional in nature and do not account for the dynamics within households and individuals as they progress through life events over time. This is a major shortcoming, as literature has shown that transportation decisions are affected by the transition between and co-occurrence of life cycle events. While some demographic evolution simulators have been proposed to address this issue, they are developed using cross-sectional data and capture only a small set of life cycle events and their interdependence. Addressing these drawbacks, we propose a demographic microsimulator (DEMOS) that captures the “continuum of life” by considering a range of household- and individual-level life cycle events. DEMOS is developed using the Panel Survey of Income Dynamics, one of the world’s longest-running longitudinal surveys. The DEMOS submodels consider key life cycle events that are influenced by agents’ demographic variables. DEMOS is applied to evolve the population of the San Francisco Bay Area over a 9-year horizon. Results demonstrate how DEMOS generates life trajectories and how DEMOS outputs match the observed demographic trends. DEMOS is expected to enable longitudinal analysis in the context of ABMs and expand ABMs analyses relating to dynamic processes such as household-level vehicle transactions.]]></description>
      <pubDate>Sun, 01 Jun 2025 18:15:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2559339</guid>
    </item>
    <item>
      <title>From Cross-Sectional to Longitudinal: The Impact of Sampling Strategies on Measuring Mobility Choices</title>
      <link>https://trid.trb.org/View/2549396</link>
      <description><![CDATA[Respondent surveys continue to serve as a main source of data for a variety of applications in the transportation field. However, the reliability of these surveys, which is dependent on the assumption that they are representative of the population under study, is becoming increasingly questionable. This is due to the ever-growing heterogeneity of today's populations, where diverse lifestyles, norms, values, attitudes, and perceptions evolve within niche subgroups. Thus, ensuring that a respondent sample is reasonably representative has become a significant challenge. Traditional survey sampling practices, which often rely on socio-economic and demographic attributes as control variables to mirror the aggregate population distributions, may not capture the complexity of an increasingly diverse population. This limitation potentially reduces the generalizability of survey findings to the broader population and may introduce unforeseen biases into the data. This study aims to explore the potential systematic biases introduced by current survey data collection methods. It uses data from a large-scale nationwide panel survey collected over three waves during the COVID-19 pandemic. This survey is unique in that its respondents were recruited through three different channels, and it is longitudinal, allowing the research team to examine not only the representativeness and inherent biases of various sampling strategies but also to observe how these samples evolve over time in terms of mobility choices. With this dual focus, this research offers a deeper understanding of survey representativeness amidst an increasingly heterogeneous population and evaluates the impact of sampling strategies. The (expected) findings aim to enhance existing research and inform future approaches to data collection in travel behavior research.]]></description>
      <pubDate>Mon, 05 May 2025 16:10:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2549396</guid>
    </item>
    <item>
      <title>A Longitudinal Driving Experience Study with a Novel and Retrofit Intelligent Speed Assistant System</title>
      <link>https://trid.trb.org/View/2407591</link>
      <description><![CDATA[Speeding is the primary cause of traffic accidents. To improve road safety, the European Union started implementing a new regulation mandating that all new vehicles coming to the EU market must be equipped with an Intelligent Speed Assistance (ISA) system from 2022 onwards. However, the rule did not include existing vehicles on the roads. This research aims to fulfill this gap by investigating user experiences and acceptance with a retrofit system developed by V-tron, a Dutch company. Seven participants signed up for the study and the technicians installed the ISA system on their cars. They then used the car for more than one month and reported their experiences weekly. The authors also recruited a driving school and conducted a focus group with five instructors. Using interview and questionnaire methods to collect their first-person experiences, the authors saw that all participants acknowledged the vision and potential of ISA systems in reducing speeding and improving traffic safety. While the retrofit system is easy to use, the technology needs to be improved in accuracy and robustness. The overruling mechanism also needs to minimize the latency and consider secondary users unfamiliar with the speed control. Three design concepts were proposed to improve user experiences and eventually promote the adaptation of ISA systems.]]></description>
      <pubDate>Tue, 31 Dec 2024 09:04:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407591</guid>
    </item>
    <item>
      <title>LTPP Data Analysis: Feasibility of Using LTPP Data to Improve Use of FWD and Longitudinal Profile Measurements</title>
      <link>https://trid.trb.org/View/2458789</link>
      <description><![CDATA[Pavement deflections obtained from falling weight deflectometer (FWD) measurements and roughness obtained from longitudinal profile measurements are used together with other measurements to assess pavement condition.  These measurements are influenced by the temporal and diurnal changes; the consideration of this influence is necessary for accurate assessment of pavement condition. The Long-Term Pavement Performance Program (LTPP) Seasonal Monitoring Program (SMP) was initiated to obtain data on the influence of temporal changes on pavement deflection and roughness. However, the temporal and diurnal data contained in the LTPP database have not been applied to improve the practices of measuring deflection and roughness, and their use has not been demonstrated. In addition, there is a concern about the adequacy of available LTPP data to accomplish this task. There was a need to assess the feasibility of using the LTPP SMP and diurnal measurements for developing guidelines for improved use of FWD and longitudinal profile measurements. The findings of this assessment will help make a decision regarding the need for further research. OBJECTIVE: The objective of this research was to evaluate the feasibility of using data from the LTPP SMP and diurnal measurements for developing guidelines for improved use of FWD and longitudinal profile measurements data in evaluating pavement condition. The research shall address both asphalt and concrete pavements.

 ]]></description>
      <pubDate>Mon, 18 Nov 2024 19:44:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2458789</guid>
    </item>
    <item>
      <title>Retaining bus riders: A lifecycle longitudinal analysis of behavioral status transitions from entry to exit</title>
      <link>https://trid.trb.org/View/2399353</link>
      <description><![CDATA[Amidst a global decline in bus ridership, this study pioneers a longitudinal approach to understanding individual-level transitions and churning in urban bus systems. Utilizing a novel framework that leverages smart card data, the authors construct and analyze user behavior transition matrices over time, employing Markov processes and the Chapman-Kolmogorov Equation. The authors' analysis, derived from a 22-month dataset from Shenzhen, reveals a two-stage churning process: users first decrease travel frequency before transitioning to irregular travel patterns. Crucially, this study introduces targeted retention policies, including tiered usage incentives and personalized communication strategies, aimed at different stages of the user lifecycle. By offering free subsequent trips to irregular travelers and combining policy approaches for users at high risk of churning, the author provide actionable insights for transit operators to counter the trend of declining ridership.]]></description>
      <pubDate>Fri, 09 Aug 2024 15:31:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2399353</guid>
    </item>
    <item>
      <title>Yearly patterns of transit usage: a cumulative clustering approach using 7 years of smart card data</title>
      <link>https://trid.trb.org/View/2325442</link>
      <description><![CDATA[This paper presents results of the application of clustering methods to report on the yearly patterns of transit ridership, at metro stations and on bus lines, as observed using 7 years of smart card validations (2015-2021). Results from the k-means approach are presented in this paper while various methods were tested using the same vectors of data. The results confirm the ability of the proposed approach to understand the global changes in ridership patterns. The cumulative clustering approach used allowed to understand the evolution of both bus and metro across the seven years from a temporal and spatial perspective.]]></description>
      <pubDate>Tue, 06 Aug 2024 17:02:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2325442</guid>
    </item>
  </channel>
</rss>