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    <title>Transport Research International Documentation (TRID)</title>
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    <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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Identifying Systematic Origin–Destination-Level Mismatches Between Household Travel Surveys and Mobile Phone Data: A Case Study of Seoul</title>
      <link>https://trid.trb.org/View/2775065</link>
      <description><![CDATA[This study examines mismatches between Household Travel Survey (HTS) and mobile phone-based LTE/5G Cellular Signaling (LCS) data in representing origin–destination (OD) trip patterns in Seoul, South Korea. By comparing OD flows across subgroups defined by travel type, age group, gender, and arrival time, we quantify distributional mismatches using standardized root mean square error (SRMSE) and the relative zero-cell ratio. The results reveal larger mismatches for children, young adults, and noncommuting trips. A spatial regression analysis, using origin-level SRMSE as the dependent variable, shows that areas with larger numbers of households, higher average land prices, and larger tertiary sector business size exhibit lower mismatches, whereas areas with concentrated residential land use and high OD pattern diversity show higher mismatches. The findings suggest that observed mismatches between HTS and LCS are associated with subgroup characteristics and spatial context. These insights underscore the need for tailored data integration and sampling strategies that account for behavioral and spatial biases when combining survey and passively collected mobility data for travel behavior analysis.]]></description>
      <pubDate>Wed, 09 Sep 2026 08:49:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775065</guid>
    </item>
    <item>
      <title>Consumer Monitor 2025: EU Drivers’ View on Electric Cars: European Alternative Fuels Observatory Consumer Monitor 2025</title>
      <link>https://trid.trb.org/View/2686618</link>
      <description><![CDATA[The European Alternative Fuel Observatory (EAFO) Consumer Monitor 2025 provides an EU-27 perspective of how car drivers perceive battery electric vehicles (BEVs), how ready they are to adopt them, and what still blocks mass uptake. The analysis is based on an online panel survey conducted in October 2025 across all EU-27 Member States among more than 3,000 respondents holding a driving licence. The report connects attitudes to practical feasibility by analysing a funnel from general perception, personal compatibility and intention and timing of purchase.]]></description>
      <pubDate>Tue, 08 Sep 2026 10:49:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686618</guid>
    </item>
    <item>
      <title>ODOT SPR 877 – Developing Guidance on Leading Pedestrian Intervals and Curb Extensions to Improve Pedestrian Safety at Signalized Intersections [supporting dataset]</title>
      <link>https://trid.trb.org/View/2767317</link>
      <description><![CDATA[Leading Pedestrian Intervals (LPIs) and Curb Extensions (CEs) are operational and geometric countermeasures that are designed to reduce pedestrian injuries and fatalities. While Oregon Department of Transportation (Oregon DOT) implements both of these treatments, there is limited guidance on how to implement these options (separately or in combination with other treatments). The present research study aimed to fill gaps in existing knowledge to improve pedestrian safety at signalized intersections by developing enhanced guidance on when and where to implement LPIs either solely or in conjunction with complementary treatments such as CEs. The field work data includes field observed volumes and conflicts between pedestrians and motor vehicles. The driving simulator data includes turning speed, approach speed, total fixation duration across four areas of interest (traffic signal, pedestrian signal, No Turn on Red (NTOR) sign, pedestrian), and survey data (pre-survey and post-survey).]]></description>
      <pubDate>Tue, 08 Sep 2026 10:46:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767317</guid>
    </item>
    <item>
      <title>Data Collection Plan for Evaluating the Greenhouse Gas Impacts of High-Occupancy Toll Lane Projects</title>
      <link>https://trid.trb.org/View/2767319</link>
      <description><![CDATA[This report presents a data collection plan for evaluating the greenhouse gas (GHG) impacts of high-occupancy toll (HOT) lane projects implemented under California Department of Transportation's (Caltrans') Carbon Reduction Strategy. Because HOT lanes can affect route choice, departure time, vehicle occupancy, and destination choice beyond the managed lane corridor, the report recommends evaluating impacts across the regional travel shed. We recommend two approaches for reliable before-and-after estimates of vehicle miles traveled (VMT) by speed bin. The household-based survey approach uses OBD-II, global positioning system (GPS), or smartphone-based tools to observe a representative sample of households within the travel shed, yielding the most detailed evidence on regional VMT and how impacts vary across the population. The infrastructure-based approach combines continuous freeway sensor data with sampled counts and speeds on arterials, local roads, and ramps; it is less costly and easier to implement but offers less insight into the travelers behind observed changes. For each approach, the report describes what data to collect, where and when to collect it, and how to validate and integrate it with EMFAC. The report also includes order-of-magnitude cost estimates for the two alternatives.]]></description>
      <pubDate>Tue, 08 Sep 2026 10:46:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767319</guid>
    </item>
    <item>
      <title>Queen City Metro Media Habits Study</title>
      <link>https://trid.trb.org/View/2742514</link>
      <description><![CDATA[This report summarizes findings of Media Habits research conducted for Queen City Metro (QCM) during November 1978. The purpose of this research is to profile media habits--television viewership, radio listenership, and newspaper readership--among current riders. The objective of this study is to provide media direction in order to more efficiently and effectively utilize QCM communication dollars. Methodology, analytical framework, findings, and recommendations are presented in the report. Data tables and the survey questionnaire are included in the appendix.]]></description>
      <pubDate>Mon, 07 Sep 2026 10:57:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742514</guid>
    </item>
    <item>
      <title>Implementing Microtransit Within Broader Network Design</title>
      <link>https://trid.trb.org/View/2772583</link>
      <description><![CDATA[As part of TCRP Project A-47, “Transit Capacity and Quality of Service Manual, Fourth Edition,” the research team conducted a series of small research tasks to develop new content for the manual. This Transit Cooperative Research Program (TCRP) research results digest (RRD) is one of 12 presenting the results of these research tasks. Technology-enabled on-demand transit service, better known as microtransit, may be suitable in areas with residents likely to use transit service but with development densities too low to support fixed-route service. This RRD describes the state of the practice for planning and implementing microtransit service in the United States, based on literature review findings and case studies.]]></description>
      <pubDate>Sat, 05 Sep 2026 11:31:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2772583</guid>
    </item>
    <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>Measuring the Motivators of Home Delivery Decisions</title>
      <link>https://trid.trb.org/View/2775056</link>
      <description><![CDATA[Over the last decade, many shoppers in the U.S. and around the world have quickly become reliant on home-based deliveries of household and retail goods. While growth in e-commerce is easily demonstrated from retail sales records, relevant activity trade-offs and resulting impacts on passenger and freight travel activity remain less well-understood. Some home deliveries are made out of necessity, while others are discretionary. Some deliveries replace in-store shopping, while others are supplementary to in-person shopping activities.

This study leverages a novel national survey, the second wave of the Transportation Heartbeat of America Survey, that simultaneously captures: (1) motivations for specific types of online purchases and/or reasons for not purchasing goods online; (2) household delivery frequencies by type (carrier-delivered parcels, crowdsourced packages, prepared food, and groceries); (3) personal travel behaviors; and (4) individual and household characteristics. In this study, the research team will directly investigate the underlying motivators for home delivery, and the barriers to home delivery, that influence Americans' home delivery choices. First, leveraging location-specific survey responses, the team will conduct descriptive and spatial analysis of results to identify relevant trends. Next, the team will apply exploratory factor analysis (EFA) to identify latent motivation dimensions and latent class analysis (LCA) to identify distinct behavioral segments based on stated delivery motivations. Third, the team will estimate and compare delivery frequency models, including ordered probit models, zero-inflated negative binomial models, hybrid choice models, or integrated choice and latent variable models. By comparing model performance with and without new motivational variables, this study will test whether controlling for stated motivations improves model fitness and reduces inconsistencies observed in prior work.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:01:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775056</guid>
    </item>
    <item>
      <title>Analysis of National Travel Trends</title>
      <link>https://trid.trb.org/View/2775055</link>
      <description><![CDATA[Over the past few decades, the transportation landscape has undergone a significant transformation, driven by technological innovations, demographic shifts, and evolving socio-cultural norms. The proliferation of information and communication technologies has revolutionized how and where individuals undertake daily activities, increasingly substituting physical travel with virtual alternatives and reshaping work through telework. Changes in technology have also altered mode characteristics and performance and introduced new options such as micromobility, ride-hailing, autonomous vehicles, and robotic delivery.

Concurrently, demographic changes have altered travel needs and preferences. The population is aging, years spent in educational systems are increasing, and labor force participation rates have been declining. Migration trends from urban centers to suburban and exurban areas, as well as between metro areas, raise critical questions about service levels and infrastructure needs. Increasing diversity in lifestyles, attitudes, and values has added complexity to travel choices and patterns of activity, mobility, and time use.

This multi-stage project aims to shed light on trends in time, travel, transit, telework, and transportation spending over the past two decades and beyond. The effort will expand and extend ongoing analysis that the project team has conducted over many years, seeking to identify emerging trends such as behavior stabilization following the COVID-19 pandemic and the pace of influence from emerging travel options. Understanding these trends is crucial for addressing current and future challenges in transportation management, economic resilience, and societal well-being.

The project will utilize data from the American Time Use Survey (ATUS), the Consumer Expenditure Survey (CES), the American Community Survey (ACS), and the new National Household Travel Survey (NHTS 2025), supplemented by national metrics on e-commerce, telework participation, roadway vehicle miles traveled, transit ridership, and airline travel. An advanced data fusion approach will integrate these diverse datasets through compilation, cleansing, merging, and aggregation, applying rigorous statistical tools to ensure accuracy and reliability.]]></description>
      <pubDate>Fri, 04 Sep 2026 14:58:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2775055</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>Computational Intelligence and Automation for Resilient Bridge Infrastructure</title>
      <link>https://trid.trb.org/View/2705489</link>
      <description><![CDATA[Bridges are essential assets in transportation systems, and their failure or prolonged closure can disrupt emergency response, supply chains, and daily mobility. Aging bridge inventories face increasing demands from traffic growth, environmental exposure, and extreme events, making reliable in-service assessment and maintenance a structural engineering priority. This paper reviews how computational intelligence and automation are being used to support bridge condition evaluation and intervention decisions during the in-service phase by improving monitoring, inspection, damage assessment, and maintenance decision-making. A PRISMA-guided search was conducted in the Scopus database using title, abstract, and keyword terms related to bridges, computational intelligence, automation, and in-service operation. The initial search returned 109 records; after restricting the results to English-language journal articles published between 2000 and 2025 and screening for bridge-specific studies that applied computational intelligence and/or automation technologies to in-service monitoring, inspection, damage assessment, deterioration prediction, or maintenance decision-making, 37 studies were retained for combined scientometric and systematic analysis. The literature is organized into five application areas: structural health monitoring, damage detection and condition assessment, predictive maintenance and optimization, robotic and autonomous inspection, and post-event decision support. The review shows a clear rise in research activity since 2023, with deep learning leading advances in time-series monitoring and vision-based inspection, while unmanned and robotic platforms are making data collection safer and faster. However, most studies still rely on limited validation data, inconsistent performance reporting, and weak links between detected defects and decisions about service continuity, operability, and recovery. From a resilience perspective, these developments are most valuable when they reduce the time required to detect damage, classify safety and functionality, and prioritize interventions following disruptive events (e.g., floods, earthquakes, hurricanes, and climate-driven extremes). The review therefore interprets computational intelligence and automation not only as tools for routine condition assessment, but also as enablers of resilience-based management that supports continuity of transportation service, faster recovery, and risk-informed allocation of limited maintenance resources. Key future directions include uncertainty-aware models that translate detected defects into performance and functionality loss, validated workflows for rapid post-event screening, and integration with network-level decision-making for resilient cities and infrastructure systems.]]></description>
      <pubDate>Thu, 03 Sep 2026 09:37:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2705489</guid>
    </item>
    <item>
      <title>State-of-the-Art Review on Composite Material Fatigue/Damage Tolerance</title>
      <link>https://trid.trb.org/View/2742396</link>
      <description><![CDATA[A state-of-the-art review on composite material fatigue damage tolerance was conducted to investigate the literature for fatigue life prediction methodologies including stress-based methodologies, strength degradation models, and damage growth models. A critical review was made of each methodology and its commensurate basic equations of importance. Experimental data were reviewed and the behavior of specimens was correlated with that of civil aircraft components. The report also examined the six recognized methods for the nondestructive testing of fibrous composite materials and identified the most effective methods.]]></description>
      <pubDate>Tue, 01 Sep 2026 10:04:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742396</guid>
    </item>
    <item>
      <title>Examining the adoption potential of a new travel chain integrating electric vehicle sharing and rail transit</title>
      <link>https://trid.trb.org/View/2708310</link>
      <description><![CDATA[Electric Vehicle Sharing (EVS) can reduce transport-related emissions, yet its scalability faces operational and cost barriers. Integrating EVS with Rail Transit (EVS + RT) offers a sustainable mobility pathway by enhancing first-/last-mile connections. Prior studies often view EVS as a substitute for traditional modes, overlooking multimodal adoption willingness. This study investigates EVS + RT adoption via a web-based commuter survey, analyzing demographics, travel patterns, and latent preferences. A Mixed Logit model quantifies the roles of income, environmental awareness, commute distance, and station proximity. Findings show that access to transit hubs, charging convenience, and unified payment platforms raise adoption, while cost sensitivity and car dependence hinder it. Results highlight the need for user-centered planning, such as optimizing EVS station placement and dynamic pricing. By integrating practical and psychological factors, the study provides policy insights to scale EVS + RT and support low-carbon urban mobility.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2708310</guid>
    </item>
    <item>
      <title>Understanding aggressive driving: a dual-theory approach using the norm Activation model and the theory of planned behavior</title>
      <link>https://trid.trb.org/View/2709497</link>
      <description><![CDATA[This study integrates the Norm Activation Model (NAM) and the Theory of Planned Behavior (TPB), along with two additional constructs – Multitasking While Driving (MWD) and Ego-Driven Driving Perception (EDDP) – to examine the psychological mechanisms underlying aggressive driving behaviors (ADB). A cross-sectional online survey was conducted among urban drivers in Tehran, Iran, using a convenience sampling strategy. The survey was distributed via social media platforms, and participation was voluntary, yielding 510 valid responses. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that personal norms, influenced by awareness of consequences and ascription of responsibility, reduce ADB, while attitudes, perceived behavioral control, and subjective norms intensify it. Notably, EDDP, influenced by MWD, weakens ethical norms and increases aggression, highlighting the interplay between personal morality and self-perceived expertise in shaping driving behaviors, while offering useful insights for urban traffic safety policies and driver behavior interventions.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709497</guid>
    </item>
    <item>
      <title>Getting there and feeling better: mechanisms from perceived accessibility to subjective well-being</title>
      <link>https://trid.trb.org/View/2767594</link>
      <description><![CDATA[Accessibility matters for subjective well-being, yet its underlying mechanisms remain insufficiently understood. Using survey data from 5591 respondents in ten Chinese cities, this study examines how perceived accessibility relates to hedonic and eudaimonic well-being through domain-specific travel satisfaction and social exclusion. After accounting for the indirect pathways, the structural equation modeling results show that perceived accessibility remains positively associated with flourishing and negatively associated with negative affect, whereas its total effect on positive affect is not significant. The mediation effect varies across travel domains and well-being outcomes. Leisure travel satisfaction emerged as the most important mediator for flourishing, while shopping travel satisfaction played a mediation role across all outcomes. Commute travel satisfaction was particularly salient for affective well-being. Social exclusion, meanwhile, constituted a strong and consistent pathway. The findings highlight that accessibility contributes to well-being not only through reaching destinations, but also through satisfying travel experiences and social inclusion.]]></description>
      <pubDate>Mon, 31 Aug 2026 08:50:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767594</guid>
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