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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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      <title>Activity duration dependent utility in a dynamic scheduling model</title>
      <link>https://trid.trb.org/View/2643215</link>
      <description><![CDATA[We present the use of duration-dependent activity utility within the dynamic scheduling model Scaper, which simulates individuals' full-day activity and travel schedules. In Scaper, agents make sequential choices in time which maximize expected future utility and respect time-space constraints. Using Swedish travel survey data, we estimate a new version of the model including piecewise linear utility functions for marginal activity duration by activity purpose. Our model reveals a strong duration dependence for work, leisure, and visit activities with differing functional shapes for each purpose. In simulation, the duration-dependent model better reproduces observed distributions of activity duration and performs as well across other metrics as the model without duration dependence. We illustrate the potential policy applications of the model using a scenario of shortened work days. The duration-dependent model offers useful predictions for the effects of the scenario on commute timing, nonwork activities, time spent at home, and trip chaining.]]></description>
      <pubDate>Wed, 25 Mar 2026 15:50:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643215</guid>
    </item>
    <item>
      <title>Harnessing household travel survey with smart card data to generate spatiotemporally-diverse activity schedules for transit users</title>
      <link>https://trid.trb.org/View/2651540</link>
      <description><![CDATA[Current activity-based models (ABMs) rely on household travel survey (HTS) data to generate daily activity schedules for transit users. However, HTS suffers from limited sampling, resulting in low spatiotemporal diversity. Smart card (SC) data offer broader transit coverage but lack sociodemographic, non-transit trips, and trip-level details, making integration with HTS challenging. This study introduces a novel two-stage data fusion framework that combines detailed but sparse HTS data with high-coverage SC data to generate complete, diverse, and up-to-date activity schedules for transit users. In Stage 1, the framework learns a latent class structure to align the spatiotemporal characteristics of transit trips across datasets and estimates a fused joint distribution over all attributes except the spatiotemporal details of non-transit trips. Stage 2 imputes these missing spatiotemporal details to complete full trip chains. A key innovation is the construction of a latent space with optimal complexity that preserves key statistical properties while enhancing the diversity of synthesized activity patterns. The framework ensures scalability by decomposing the fusion task into analytically tractable sub-problems. The model properties are first validated in a controlled experiment. Further validation using data from 3.4 million SC users in Seoul, South Korea, shows that the fused population closely aligns with external cellular signaling data and significantly outperforms HTS alone – generating up to 2.92 million unique synthetic schedules (an 82.8 ×  increase over HTS). In sum, the proposed method lays the groundwork for integrating diverse data sources into ABMs, enhancing their ability to generate diverse synthetic mobility patterns, including underrepresented segments.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651540</guid>
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    <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>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>Co-training framework for enhancing survey accuracy while reducing respondent burden in travel data collection</title>
      <link>https://trid.trb.org/View/2627576</link>
      <description><![CDATA[A major bottleneck in travel behavior analysis is the need for a substantial amount of labeled data, which typically places a burden on survey respondents for collecting travel behavior data. Our study addresses this issue by leveraging semi-supervised learning, specifically utilizing the co-training algorithm, which effectively incorporates both labeled (active) and unlabeled (passive) data. We extend the semi-supervised learning concept to be a part of the survey scheme that involves both data collection process and enrichment process of travel attributes. Our experiments, focusing on travel mode identification using GPS data from Hiroshima, Japan, demonstrate that our proposed method outperforms existing conventional supervised learning methods such as neural networks, KNN, and SVM, particularly when incorporating an increased proportion of unlabeled data. This strategic use of unlabeled data achieves two apparently conflicting goals: (1) reduces the reliance on extensive manual labeling, thereby alleviating respondent burdens, and (2) increases the accuracy of the prediction. The results of our experiments also reveal that the delicate balance between labeled and unlabeled data proportions plays a pivotal role in co-training performance. Beyond serving as a mode identification tool, our findings underscore the transformative potential of co-training as a valuable data filtering method: By optimizing the interplay between labeled and unlabeled data, co-training efficiently filters noise and refines the dataset. This contributes to enhanced survey accuracy while minimizing labeling burdens. Our results provide useful information to design an adaptive scheme that dynamically tailors the information solicited from respondents to optimize the balance between data quality and respondent burden.]]></description>
      <pubDate>Thu, 26 Feb 2026 14:51:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627576</guid>
    </item>
    <item>
      <title>Understanding travel behavior change with megacity development: Insights from decade-long household travel surveys in Beijing</title>
      <link>https://trid.trb.org/View/2663994</link>
      <description><![CDATA[Developing sustainable cities has become a consensus around the world. As for the transport field, a framework for capturing the mechanism behind residents’ travel behaviors, considering the spatial–temporal heterogeneous impact of diverse factors (e.g., life events, built environments, and context factors) during urban development, is essential; however, this remains scarce. Inspired by this, we employ two household travel surveys involving a decade in a megacity, Beijing, to analyze the heterogeneous impact of various factors on travel behavior during urban development. In doing so, two Multiple Discrete–Continuous Extreme Value models are developed and estimated. The estimated results show that travel patterns are almost the same, but there are some important differences, such as base preferences for travel modes and cooperative and competitive relationships between travel modes. Building upon the estimations, we further build the bridge between these two models, revealing the significant changes in the importance of diverse factors, particularly the increased importance of the metro mode for suburban residents. Furthermore, although the overall travel patterns become more sustainable, the examination of equity shows the reduced but still-existing disparities in PT accessibility among different social groups in 2023 The proposed framework enables quantitative analysis of the heterogeneous impacts and evolving importance of factors influencing travel behavior during urban development, offering a replicable approach for studying mobility transformations in megacities like Beijing.]]></description>
      <pubDate>Wed, 25 Feb 2026 09:10:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663994</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>From biases to opportunities: leveraging Location-Based-Service (LBS) data for next-generation transportation planning</title>
      <link>https://trid.trb.org/View/2622401</link>
      <description><![CDATA[Location-Based-Service (LBS) data sourced from numerous mobile devices that now accompany people everywhere has the potential to revolutionize the practice of transportation planning in data collection, model development and policy designs. Its potential is however hampered by the lack of transparency on the part of researchers, transportation professionals, and LBS data vendors. There is also a dearth of understanding about how LBS data is generated and what the associated data quality attributes are. At the same time, transportation agencies now face overwhelming demand from LBS data vendors globally. The first aim of this paper is to provide an overview of the biases in LBS data. Specifically, we point out the key difference in data generation between LBS and household travel survey (HTS), and present data quality issues and their effects on the resulting mobility metrics that are commonly used in the planning process. We point out that passively-generated LBS data has been found to be unstable over time, sparse within a 24-hour time frame, and have representation biases. The second aim of the paper is to present our perspectives on how LBS data can aid HTS data and transform the field of transportation planning. We lay out four methodological advances (e.g., data pre-processing and HTS and LBS fusion in privacy-aware mobility digital twins) that the community shall pursue to realize the full promise of LBS data. Last and equally important, we discuss ways that the community can collaborate to establish benchmark datasets and standards for trip inference and reporting while adhering to privacy constraints.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2622401</guid>
    </item>
    <item>
      <title>Developing a Data Fusion Tool for Improved Traffic Crash Exposure
Analysis and Modeling</title>
      <link>https://trid.trb.org/View/2663603</link>
      <description><![CDATA[Accurate measurement of exposure is critical for understanding and preventing traffic crashes, as crash frequency is directly related to how much road users are exposed to risk. However, current exposure estimates rely on data sources with complementary but individually insufficient characteristics. Traditional traffic counts and Annual Average Daily Traffic (AADT) offer high accuracy but limited spatial and temporal coverage, while emerging Location-Based Services (LBS) data provide high-resolution mobility patterns but are often biased and less reliable. This fundamental mismatch between accuracy and coverage prevents agencies from developing the complete and reliable exposure estimates needed for effective safety analysis and planning.
This project develops a data fusion tool that integrates traffic counts and AADT, LBS data, and socio-demographically representative survey data from the National Household Travel Survey (NHTS) into a unified measure of exposure. Unlike previous efforts that focused on a single travel mode or low temporal resolution, the proposed framework generates exposure estimates for motor vehicles, pedestrians, bicyclists, and scooters at fine spatial scales (intersection and mid-block) and temporal scales (daily and monthly). The tool is evaluated in Washington, D.C., using three alternative fusion paradigms: Bayesian fusion through hierarchical or state-space modeling, Dempster–Shafer theory for explicit uncertainty representation and accommodation of LBS coverage gaps, and model-based fusion employing structured error modeling with NHTS socio-demographics to correct LBS data bias.
The fusion methods are compared through crash prediction models estimated with fused exposure measures against models using individual data sources, evaluated via pseudo-R², AIC, BIC, and out-of-sample prediction error, with a target improvement of at least 10% in predictive performance. Fused exposure patterns are further validated against Washington, D.C.’s High Injury Network and independent ground-truth count data where available. The final tool is delivered as an open-source Python package with documentation and secure coding practices. Agency outreach, including engagement with D.C. stakeholders managing the High Injury Network, informs tool refinement and supports preparation for future pilot deployment. This research supports USDOT’s Safety priority by generating more accurate and complete multimodal exposure measures that enable better identification of high-risk locations, improved crash prediction, and targeted safety interventions
]]></description>
      <pubDate>Tue, 03 Feb 2026 15:31:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2663603</guid>
    </item>
    <item>
      <title>Value of travel time savings for children: an analysis of questionnaire surveys in Japan</title>
      <link>https://trid.trb.org/View/2602566</link>
      <description><![CDATA[The value of travel time savings (VTTS) is a critical parameter for estimating the social benefits of transport projects. However, only a few studies have focused on VTTS for children. We conducted two questionnaire surveys in Japan: a nationwide web-based survey and a mail-based survey in the regional city of Hitachinaka, where a new railway station was built in front of a school. Respondents were asked to compare VTTS for children with that for adults. In the nationwide survey, many participants stated that VTTS for children was similar to that for adults. However, in Hitachinaka, most respondents perceived VTTS for elementary school students to be higher than that for adults. Even in the nationwide survey, responses indicating that VTTS for children is ‘higher than for adults’ outnumbered those indicating it is ‘lower’ or of ‘little value’. The key reasons cited included parental concerns for safety during travel and children's higher absorptive potential. These findings suggest that current evaluations may underestimate the benefits of reducing children's travel time, potentially distorting public transportation investment decisions.]]></description>
      <pubDate>Mon, 22 Dec 2025 16:07:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2602566</guid>
    </item>
    <item>
      <title>Where They're Coming From and How They're Getting Here: A Mobility Assessment on Little Tokyo’s Nonresident Population</title>
      <link>https://trid.trb.org/View/2613660</link>
      <description><![CDATA[Los Angeles Metro's Regional Connector is slated to open in 2020 with one of three new stations located near the heart of Little Tokyo at 1st Street and Central Avenue in Los Angeles. Coupled with the growth of Little Tokyo as a commercial destination, these transit investments have raised concerns that the changing landscape will jeopardize the preservation of culture within the historic Japanese-American neighborhood. To better understand the potential effect of these new transportation investments, this study examines the travel trends and behaviors of Little Tokyo's nonresident population. Using two public data sources and data collected through two travel surveys (an employee electronic travel survey and a visitor intercept travel survey), the research analyzes commute mode choice and distance, travel time, home destination, trip purpose, and demographic characteristics.]]></description>
      <pubDate>Mon, 15 Dec 2025 15:41:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613660</guid>
    </item>
    <item>
      <title>Addendum to the 2015-2016 Campus Travel Survey and the 2016-2017 Campus Travel Survey Reports</title>
      <link>https://trid.trb.org/View/2628277</link>
      <description><![CDATA[The University of California (UC) Davis Campus Travel Survey (CTS) is administered annually to a sample of students, faculty, and staff. Because the authors survey only a sample of the campus population and because some groups are more likely to respond to the survey than other groups, it is necessary to apply “expansion factors” and “weights” to the sample to achieve an accurate estimate of the responses for the entire campus population. In effect, the authors use the expansion factors and weights to make the sample of around 4,000 respondents look like the population of around 45,000. The calculation of the expansion factors and weights requires an estimate of the campus population by role group and gender, as explained in more detail below. The campus population is a difficult number to pin down, as it varies over the year and depends on whether and how different categories of people are counted. For the 2016-17 Campus Travel Survey, a new protocol was used to estimate the campus population, as explained in the posted report. In reviewing the report, campus officials noticed that the new population protocol produced an underestimate of students living on campus, which significantly changed the estimated mode split and other results. A third protocol was devised to correct the problem, and the authors re-analyzed results from the 2015-16 and 2016-17 surveys using population estimates based on this new protocol. This addendum explains the procedure for expansion factors and weights, describes the new population estimation protocol, and presents the revised results for selected tables from the CTS reports.]]></description>
      <pubDate>Mon, 15 Dec 2025 11:20:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628277</guid>
    </item>
    <item>
      <title>Sequence analysis in spatially defined clusters based on 2018 to 2022 travel surveys in the Metropolitan Area of Barcelona</title>
      <link>https://trid.trb.org/View/2572641</link>
      <description><![CDATA[Transport systems are an essential component in the path towards sustainable urbanization and the transition to more sustainable living. In recent decades, European cities have undergone significant changes, and suburbanization poses new challenges. Suburban areas are often more affordable in terms of housing, but they tend to be car-oriented neighborhoods. This leads to higher commuter costs, immobility, transport and time poverty, pollution, accident rates, and a lack of social interaction. To offer sustainable mobility options to citizens, it is essential to have a comprehensive understanding of their specific mobility practices, together with their personal characteristics, and the built environment information. This study is centered on the Barcelona Metropolitan Region, which has a public transport network that covers the entire area. The aim of this study is to examine the relationship between travel behavior, modal use, individual characteristics, and place of residence, by making use of detailed information sources. Herein, the authors make use of the annual travel survey conducted in the Barcelona region from 2018 to 2022 together with land-use and other socio-economic information. The research suggests that transport policies have encouraged sustainable mobility practices, particularly in the center of Barcelona. Despite the positive results, there are significant disparities between the inner city and outer city, where sustainable mobility practices notably decrease. The findings indicate that promoting sustainable mobility policies requires further changes in transport, city, land-use and city planning that consider equity, the socioeconomic profile of citizens, and a mixed urban planning that considers the needs of residents and promotes essential services in the proximity.]]></description>
      <pubDate>Fri, 05 Dec 2025 17:12:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2572641</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>The CanBikeCO full pilot: Long-term results and analysis from an E-bike program in Colorado, USA</title>
      <link>https://trid.trb.org/View/2593772</link>
      <description><![CDATA[Personal micromobility devices like bicycles, e-bikes, and scooters are low- or zero-energy alternatives to single-occupancy vehicles. However, a lack of data has led to a dearth of data-driven research on personally owned e-bike usage. We present longitudinal findings from the CanBikeCO program, focused on e-bike adoption and use across demographics, trip characteristics, and geographies in the state of Colorado. CanBikeCO recorded travel survey data from low-income individuals provided with personal e-bikes by the Colorado Energy Office in six communities across Colorado from July 2021 to December 2022. The data were collected using a custom instance of the National Renewable Energy Laboratory OpenPATH platform, which combines passive data collection with semantic information such as trip mode and purpose labels. To our knowledge, there are no prior travel survey data on personally owned e-bikes with this range and scope. Insights from this unique dataset include: (i) work trips were 17% more likely than average trips to be taken on an e-bike, (ii) e-bikes were most often reported to replace cars (34% of e-bike trips) and other personal micromobility devices (22%), and (iii) participants favored walking for trips less than 1 mile, e-bikes for trips of 1–3 miles, and e-bikes, cars, or shared rides for trips of 3–20 miles. The data used to generate these results have been made available in the Transportation Secure Data Center. We find e-bike use is appealing across age groups and may be related to characteristics of land use, urban form, occupation, income, and car ownership. We conclude for this population that the energy demand added by e-bike use (induced demand and replacing non-motorized modes) is outweighed by the reduction in energy demand from replacement of single-occupancy vehicle trips with e-bike trips. Our findings suggest considerable potential for energy savings from personal e-bike ownership.]]></description>
      <pubDate>Tue, 18 Nov 2025 11:04:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593772</guid>
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