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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>From coast to coast: Understanding electric vehicle adoption across Canadian regions</title>
      <link>https://trid.trb.org/View/2689433</link>
      <description><![CDATA[Battery electric vehicles (BEVs) have experienced rapid growth in market share both in Canada and globally over the past decade. However, adoption rates vary significantly across Canadian regions, raising questions about the sustainability of current growth trajectories. This study (1) identifies the drivers of regional disparities in BEV adoption within Canada, and (2) projects nationwide adoption levels. We exploit a proprietary, nationwide Canadian dataset of vehicle transactions, geocoded at the postal-code level, from 2016 through 2024 and combine it with several open datasets. Using this dataset, we calibrate and compare three technology‐diffusion models to capture historical uptake and forecast future penetration. Our analysis reveals that regional BEV adoption is predominantly shaped by a combination of socio-demographics, dwelling characteristics, ease of ownership and relative availability of the desired vehicles. Model comparisons indicate substantial uncertainty in long-term forecasts. These findings underscore the importance of targeted policy interventions and localized strategies.]]></description>
      <pubDate>Tue, 14 Apr 2026 10:09:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689433</guid>
    </item>
    <item>
      <title>Not All Travelers Think Alike: Segmenting Travel Behavior Under Air Pollution Exposure Using a Hybrid Latent Class and Discrete Choice Approach</title>
      <link>https://trid.trb.org/View/2691734</link>
      <description><![CDATA[Commuters face their highest exposure to air pollution while traveling; however, behavioral responses to this risk remain poorly understood in urban transport research. This study investigates how air quality awareness, sociodemographic factors, and travel context influence mode choice. Household survey data were collected from 723 commuters in Kolkata, India, during winter, when ambient pollution levels are elevated. A latent class cluster analysis was conducted to uncover unobserved behavioral segments based on attitudes toward air quality information (AQI) and travel behavior. This yielded three distinct commuter profiles: (1) low awareness and passive (24%); (2) aware but inactive (57%); and (3) proactive AQI responders (19%). These segments were further characterized using sociodemographic and travel attributes to understand profile-specific tendencies. Then, a multinomial logit model was estimated to examine how latent class membership, along with factors such as income, trip purpose, distance, and reported health symptoms, influenced travel mode decisions. The results show that access to real time AQI information, particularly through mobile apps and public displays, was associated with reduced use of high-exposure modes, such as two-wheelers and auto-rickshaws. However, many commuters who reported health symptoms, such as eye irritation, continued using these modes, indicating limited flexibility or a lack of alternatives. The metro was preferred by lower-income individuals, while higher-income, AQI-aware commuters tended to choose private cars, probably viewing them as more protective. These findings highlight the need for integrated urban transport and environmental strategies that pair risk communication with equitable access to clean, safe, and protective travel options.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:26:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691734</guid>
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    <item>
      <title>Climate-induced mobility disruption: Demographic disparities to cumulative heat effect in urban China</title>
      <link>https://trid.trb.org/View/2689436</link>
      <description><![CDATA[Climate-induced extreme heat events under urban climate pose significant disruptions to urban life, and human mobility offers a valuable approach to quantifying these mobility disturbances and associated resilience gaps. This study examines how heatwaves disrupt human mobility across demographic groups and urban spaces, using Shenzhen, China, during a specific 2022 heatwave as a case study, with high-resolution mobile phone data enriched with demographic attributes. We develop a Mobility Disruption Index and use Random Forest models to examine nonlinear effects of cumulative heat, built environment, and socioeconomic factors on human mobility. Results suggest pronounced mobility reductions among women, older adults, and discretionary travel. Spatially, mobility tends to decrease in cooled urban cores but shows relative increases in peripheral areas lacking climate-resilient infrastructure. Mobility exhibits a non-linear pattern, with an initial decline under increasing cumulative heat effect followed by partial recovery as heat persists. Our findings provide case-based evidence against purely linear assumptions, underscoring adaptive behaviors and calling for targeted cooling interventions and resilient urban planning strategies.]]></description>
      <pubDate>Mon, 13 Apr 2026 09:37:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689436</guid>
    </item>
    <item>
      <title>Behavioral Heterogeneity in Mobility as a Service Bundle Choice: A Latent Class Logit Model Approach in Beijing</title>
      <link>https://trid.trb.org/View/2686273</link>
      <description><![CDATA[Mobility as a Service (MaaS) is a key approach to advancing sustainable urban mobility, mainly accessible to users through bundled services via mobile platforms. However, empirical studies focusing on preference segmentation and willingness-to-pay (WTP) for bundled MaaS services remain limited, especially in rapidly urbanizing cities such as Beijing. To address this gap, this study developed a latent class logit model integrating latent psychological attitudes along with sociodemographic and travel attributes to identify latent user classes and determine key factors influencing bundle choice behavior, which subsequently provides a comprehensive perspective for understanding behavioral heterogeneity in MaaS bundle choices. Based on 485 stated preference questionnaires collected in Beijing, three distinct latent user classes were identified: potential adopters (Class 1, 29.0%); MaaS-indifferent individuals (Class 2, 58.8%); and avoiders (Class 3, 12.3%). These classes exhibit significant differences in characteristics, preferences, and WTP for the monthly bundle components. Class 1 shows a strong preference for metro ridership quotas with a WTP ¥1.01, a negative attitude and reluctant to pay ¥1.83 toward taxi mileage, and high WTP ¥84.47 for the shared function. Class 2, most representative of Beijing’s population, shows a positive preference for bus ridership with WTP ¥2.48 and is more strongly influenced by psychological attributes. Class 3 prefers “pay-as-you-go” but shows positive preferences for bus, metro and shared function in the bundle with respective WTP ¥1.90, ¥2.05, and ¥191.18. This study contributes empirical evidence of behavioral heterogeneity in MaaS adoption and offers practical implications for targeted MaaS product design and policy making.]]></description>
      <pubDate>Thu, 02 Apr 2026 08:54:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686273</guid>
    </item>
    <item>
      <title>Dynamic social disparities in the U.S. electric vehicle charging infrastructure system</title>
      <link>https://trid.trb.org/View/2679194</link>
      <description><![CDATA[Equitable access to public electric vehicle (EV) chargers is crucial for reducing barriers and promoting adoption. Although recent studies have examined social disparities of charging accessibility in the United States, they usually focus on a single year, with long-term trends remaining unclear. This study proposes a novel framework to examine long-term changes in social disparities in access to EV chargers by charger type, gender, race, and age, using two-part generalized additive models and nationwide data from 2014 to 2023. Our results indicate that charger access tends to be higher in counties with above-median household income, larger populations, and greater shares of multi-unit dwellings. Counties with higher proportions of males, Asian, Black Americans, and younger adults generally experience lower charging accessibility. After 2020, Asian Americans saw improved accessibility, while Black Americans still faced widening disparities. These findings underscore the need for equitable strategies to reduce long-term charging inequities.]]></description>
      <pubDate>Wed, 25 Mar 2026 11:44:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679194</guid>
    </item>
    <item>
      <title>Streamlining population synthesis by spatializing aggregated locations into real coordinates using geospatial distributions</title>
      <link>https://trid.trb.org/View/2676347</link>
      <description><![CDATA[Modeling and simulation are crucial for decision-making in transportation, often relying on synthetic populations when real data are unavailable due to privacy concerns or insufficient resolution. While considerable literature has focused on generating synthetic individuals and their sociodemographic attributes, the allocation of these individuals to real geographic locations remains underexplored. This paper addresses this gap by introducing SpatialzOSM, an open-source tool designed to spatialize synthetic populations by converting aggregated spatial data into real coordinates using three sampling techniques: across areas, along roads, and within building footprints. The authors apply SpatialzOSM to synthetic populations from Townsville and Frankfurt am Main, demonstrating its adaptability, scalability, and transferability to different urban contexts and zoning levels. The results highlight trade-offs between realism, computational efficiency, and accuracy. SpatialzOSM enhances the realism and accuracy of explicit-location-based models, offering a flexible solution for synthesizing geographically explicit locations in urban studies. The approach provides researchers and practitioners with a reproducible pathway to create foundational data that captures the crucial link between where people live, where they conduct activities, and how they travel.]]></description>
      <pubDate>Tue, 24 Mar 2026 16:18:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676347</guid>
    </item>
    <item>
      <title>Analysis of Car Sharing Membership Behavior in Japan Considering Access to Car Sharing Stations</title>
      <link>https://trid.trb.org/View/2674221</link>
      <description><![CDATA[Car sharing is gaining attention in Japan as a remedy for environmental impacts, congestion, and inefficient land use stemming from private car ownership. This study evaluates drivers of car-sharing membership by integrating individual data from the 2021 National Urban Travel Characteristics Survey with 2023 station locations. A logistic regression model incorporating sociodemographic traits and spatial accessibility is estimated, and a latent-class approach corrects for imprecise home geocoding. Membership is more likely among younger, male, high-income individuals and service-industry workers. Spatially, higher population density and a greater number of car-sharing stations within 1.5 km of home significantly raise the odds of joining. These findings underscore the combined influence of personal and locational factors on car-sharing adoption in Japan.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:21:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674221</guid>
    </item>
    <item>
      <title>Individual Behavioral Richness in Urban Activity Patterns: Insights from Greater Jakarta's Multi-Day Travel Diary Data</title>
      <link>https://trid.trb.org/View/2655495</link>
      <description><![CDATA[Individual activity patterns demonstrate complex combinations of repetition and variation, exhibiting varying degrees of spatial and temporal flexibility. This study investigates the behavioral richness of individual activity patterns in Greater Jakarta—defined as the spatial diversity of activity patterns— using smartphone-based multi-day travel diary data. We propose a modified Herfindahl-Hirschman Index (HHI) to quantify behavioral richness while incorporating distance effects, thus accounting for the spatial distribution of activities. Analysis shows distinct patterns, with Social and Eat Out activities exhibiting the highest spatial diversity, whereas medical activities were more spatially concentrated. Regression analysis was conducted to identify factors affecting behavioral richness, and confirm that gender, education years, and household head status are identified as significant factors. We also found that private vehicle and ride-hailing users tend to have greater behavioral richness. These findings offer a solid basis for enhancing the distribution of urban amenities, thereby more effectively addressing diverse activity needs.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655495</guid>
    </item>
    <item>
      <title>The Influence of the Fare of Municipal Community Transport Systems on Local Communities in Japan</title>
      <link>https://trid.trb.org/View/2655491</link>
      <description><![CDATA[This study examined how to determine the appropriate beneficiary burden to assess the effectiveness of introducing municipal community transport (a municipally operated mode of transport that supports residents’ daily lives) through an elasticity analysis of users of municipal community transport. This study aimed to identify the optimal level of service for local mobility and beneficiary burdens. Both multiple regression and stochastic frontier analyses were applied to compare productivity and efficiency. Consequently, factors related to the population, such as the number of people and population density along the bus route, influenced the increase in the number of users. In contrast, fares and the number of persons per household affected the decrease in users.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:20:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655491</guid>
    </item>
    <item>
      <title>Fatal distracted driving pedestrian-involved crashes in Ghana: Exploring cluster-specific factor associations using cluster correspondence analysis</title>
      <link>https://trid.trb.org/View/2657981</link>
      <description><![CDATA[Distracted driving presents a serious threat to pedestrian safety, especially in low- and middle-income countries (LMICs) where pedestrian infrastructure is limited. Although distraction-related vehicle-pedestrian crashes result in numerous fatalities each year, few studies have examined the factors contributing to these incidents, particularly in LMIC contexts. This study addresses this research gap by exploring the latent patterns associated with fatal pedestrian crashes caused by distracted driving in Ghana. A robust unsupervised machine learning method, Cluster Correspondence Analysis (CCA), was used to analyze 1638 fatal crash records from 2014 to 2018. This method simultaneously classifies crash observations into homogeneous clusters and identifies key factor associations within each cluster. The analysis revealed seven distinct clusters based on pedestrian manoeuvres, crash location, and vehicle type. Findings indicate that most fatal distraction-related pedestrian crashes occurred during crossing manoeuvres, often involving privately operated vehicles in daylight and clear weather. One cluster revealed that older heavy vehicle drivers (aged 45–64) were often involved in crashes with younger pedestrians (under 25) walking along the road. Another pattern showed that female drivers were mainly involved in fatal multi-vehicle crashes at night, typically during turning manoeuvres at intersections with medians. Additionally, crashes involving buses and minibuses were frequently associated with brake failure, involvement of multiple vehicles, older drivers (65+), and a higher likelihood of being hit-and-run incidents. This study contributes to pedestrian safety research by uncovering crash-specific patterns associated with distracted driving in an LMIC setting. Insights obtained from the study are discussed, and targeted education, engineering, encouragement, and enforcement countermeasures are developed based on the study's findings.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657981</guid>
    </item>
    <item>
      <title>Propagating synthetic populations with dynamic Bayesian networks: a framework for long-horizon demographic forecasting</title>
      <link>https://trid.trb.org/View/2647522</link>
      <description><![CDATA[This study presents a dynamic demographic microsimulator using dynamic Bayesian networks to forecast long–term changes in household and individual life events. Leveraging longitudinal Panel Study of Income Dynamics (PSID) data, two networks for individuals and households were modeled to simulate transitions in employment, income, education, marriage, childbirth, leaving the parental home, home ownership, mortality, and household formation or dissolution. Across 1,000 simulation runs spanning 24  years, household–level outcomes remain highly accurate and individual–level predictions reasonable. Although accuracy naturally declines with projection horizon, performance remains promising at both levels. This study addresses a key limitation of existing population synthesis models, which typically generate only a single static snapshot of the population. By introducing a framework that propagates cross-sectional outputs into the future, the microsimulator enables the tracking of demographic evolution over time, enhances realism in population-based simulations, and supplies credible inputs to agent-based travel demand models.]]></description>
      <pubDate>Fri, 20 Mar 2026 17:00:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647522</guid>
    </item>
    <item>
      <title>Spatial Concentration and Dispersion of Foreign Travelers by Nationality at Prefectural Level in Japan</title>
      <link>https://trid.trb.org/View/2646019</link>
      <description><![CDATA[The purpose of this paper is to assess the geographical concentration and dispersion of foreign travelers to Japan by nationality at the prefectural level, using the data in 2013 and 2023. After providing an overview of the foreign travelers to Japan and the tourism policies by the government, the paper investigates two types of diversities in the foreign travelers to Japan by the Herfindahl-Hirschman Index (HHI): a diversity of nationalities over prefectures and a diversity of prefectures over nationalities. The paper then examines the concentration of foreign travelers to Japan by nationality and prefecture by the location quotient (LQ). The paper finally demonstrates how the foreign travelers to Japan actually developed over the last decade. The paper sheds light on the HHI and the LQ to refine our understanding of the spatial concentration and dispersion of foreign travelers to Japan, which is one of the key contributions of this paper.]]></description>
      <pubDate>Thu, 12 Mar 2026 16:30:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646019</guid>
    </item>
    <item>
      <title>How Does Transportation Insecurity Compare and Relate to Other Indicators of Material Hardship in the U.S.?</title>
      <link>https://trid.trb.org/View/2672775</link>
      <description><![CDATA[Increasingly, researchers have used measures of material hardship (food, housing, utility, bills, medical) to better understand economic distress. Missing from this literature are hardships related to transportation. This descriptive paper uses recent nationally representative data and a newly validated measure of transportation insecurity to address gaps in our understanding of how transportation insecurity compares and relates to other indicators of material hardship. We find that transportation insecurity is a very common material hardship experienced by U.S. adults. The demographic groups most likely to experience transportation insecurity are also those disproportionately affected by other hardships, especially food insecurity. Additionally, we find that transportation and food insecurity are the hardships most likely to co-occur with other forms of hardship. Finally, we examine the association between these hardships and self-rated health and depressive symptoms, finding that transportation insecurity is similarly associated with these outcomes as food insecurity and unmet medical needs. Overall, these results suggest that transportation insecurity behaves similarly to other hardships, especially food insecurity, and underscores the importance of addressing transportation insecurity in efforts to reduce material hardship and improve overall wellbeing.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:54:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672775</guid>
    </item>
    <item>
      <title>The state of modelling for evaluating health equity impacts of freight emissions</title>
      <link>https://trid.trb.org/View/2673074</link>
      <description><![CDATA[Evaluating health equity impacts of freight emissions is crucial for developing a sustainable and just freight system. It is a complex process that requires interdisciplinary knowledge, including transportation, environment, and public health. Full-chain simulation is an important approach for forecasting freight planning outcomes. However, a systematic framework that integrates available models in full-chain and is specifically designed for the freight sector has not been developed. We review 36 empirical studies covering this interdisciplinary topic, and summarise the commonly used models. We find that EMission FACtor (EMFAC) and Motor Vehicle Emission Simulator (MOVES) models are commonly used to estimate freight vehicle emissions, with their outputs serving as inputs for air quality models, such as Community Multiscale Air Quality Model (CMAQ) or Intervention model for air pollution (InMAP). To estimate the health effects, concentration-response (C-R) functions, combined with static or dynamic demographic and socioeconomic data, are used to quantify the relationship between changes in pollutant concentrations and health outcomes. Then, disparity analysis relies on the assumption of age-specific C-R functions and examines statistical differences between demographic groups – including racial/ethnic groups, income levels, age groups, and other vulnerable communities. This study comprehensively outlines this state-of-the-art, integrated framework identified through the synthesis of this interdisciplinary literature. This framework can support future researchers in this field and policymakers.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:54:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673074</guid>
    </item>
    <item>
      <title>Transportation Insecurity in the Motor City</title>
      <link>https://trid.trb.org/View/2672776</link>
      <description><![CDATA[In this paper we examine what transportation insecurity looks like in Detroit. We do so by considering the following questions: (1) What is the prevalence of transportation insecurity in Detroit? (2) What symptoms of transportation insecurity do Detroiters experience? (3) Who experiences transportation insecurity in Detroit? (4) How is transportation insecurity related to transportation access and mode use? (5) How is transportation insecurity related to the costs associated with transportation? (6) How satisfied are those experiencing transportation insecurity with their ability to get around? The data for this study were collected as part of the Detroit Metro Area Communities Study (DMACS). DMACS is a panel survey of Detroit residents launched in 2016. Data on transportation insecurity was collected as part of the Winter 2023 survey, collected between November 2, 2023 and December 19, 2023. The Winter 2023 DMACS wave included the 6-item Transportation Security Index (TSI6), a validated scale designed to capture an individual’s experience with transportation insecurity. In Detroit in 2023, more than a third (36%) of residents experienced transportation insecurity. Notably, the estimated prevalence of transportation insecurity in Detroit is considerably higher than national estimates.]]></description>
      <pubDate>Tue, 10 Mar 2026 09:54:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672776</guid>
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