<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>The recycling use of MSWI bottom ash as road construction material for carbon emissions reduction based on life cycle assessment-A case study in China</title>
      <link>https://trid.trb.org/View/2611438</link>
      <description><![CDATA[This study evaluates the potential of municipal solid waste incineration-bottom ash (MSWI-BA) as a partial substitute for natural aggregates (NAs) in asphalt pavement, focusing on carbon emissions reduction. A comparative life cycle assessment (LCA) was conducted for a 5.142-km municipal road (Guangying Avenue, Sichuan, China) under two scenarios: a conventional pavement with NAs and a pavement incorporating MSWI-BA aggregates (MAs) at optimized (Alternative A) and maximized (Alternative B) substitution rates. The LCA covered raw-material production, transportation, and construction phases, using carbon emission factors (CEFs) and a Data Quality Indicator-Monte Carlo uncertainty analysis for robust quantification. Results indicate that high substitution of Mas yields significant carbon savings. Under Alternative B (70 % MA in the base layer and 100 % in the subbase/capping layers), the pavement saved approximately 2.78 x 104 tons of NAs and reduced life-cycle emissions by about 31 %. The greatest reductions occurred in lower layers, with base, subbase, and capping courses achieving 51 %-78 % CE reductions, compared to only 5 % in the surface layer. Transportation emissions partially offset benefits, as MAs were transported 156 km compared to 8 km for NAs. Local processing (Alternative B within 50 km) is recommended to maximize net gains. Despite a modest 7 % increase in construction-phase energy use for MA paving, the overall carbon benefit remains clear. These findings highlight the scalability of MSWI-BA for low-carbon road construction and provide practical insights for engineers and policymakers pursuing sustainable infrastructure solutions.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611438</guid>
    </item>
    <item>
      <title>A Large-Scale Image Repository for Automated Pavement Distress Analysis and Degradation Trend Prediction</title>
      <link>https://trid.trb.org/View/2604561</link>
      <description><![CDATA[In recent years, automated detection technologies for large-scale pavement distress have become a focal point of research in the transportation sector. With the rapid advancement of deep learning technologies, data-driven artificial intelligence recognition algorithms have gradually emerged as the industry mainstream. The effectiveness of such algorithms largely depends on the reliability and quantity of the samples. However, existing datasets exhibit significant shortcomings in terms of sample size, category diversity, and support for distress tracking. In this study, a large-scale image dataset was meticulously constructed. This dataset includes 51012 road images for pavement distress identification and 8928 images for long-term tracking of pavement distress. Using this dataset, six mature object detection algorithms were trained and evaluated, with the results demonstrating the performance of these algorithms. To the best of the authors’ knowledge, this is the first large-scale pavement distress dataset that includes long-term tracking of pavement distress, providing reliable data support for dynamic tracking and monitoring of pavement distress as well as for optimizing road maintenance strategies.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604561</guid>
    </item>
    <item>
      <title>Transportation Services in Society for Individuals Living with Dementia in Long-Term Care Facilities: A Scoping Review</title>
      <link>https://trid.trb.org/View/2604543</link>
      <description><![CDATA[Significant numbers of adults with dementia require long-term care services. For example, around 750,000 people who live in nursing homes have a diagnosis of dementia. Transportation insecurity for the long-term care facility population has not received sufficient attention. This scoping review aims to explore the literature on nonemergency medical transportation for long-term care facility residents with dementia and identify research gaps related to transportation challenges faced by this vulnerable population. A scoping review. This review included research studies published in peer-reviewed journals regarding transportation services for long-term care facility residents with dementia. Arksey and O'Malley's framework and PRISMA-ScR checklist were followed. The search was performed in 5 databases, including PubMed, CINAHL, PsycINFO, Scopus, and Embase. Titles, abstracts, and full-text articles were independently reviewed by 2 reviewers, and all discrepancies were resolved by a consensus discussion. Of the 1405 publications screened, 5 studies met the inclusion criteria for analysis and synthesis for this review. The need for adequate transportation services was expressed by caregivers, health care professionals, and individuals with dementia. Dementia-specific challenges and other transportation insecurity issues related to social determinants of health, such as finance and rural and urban contexts, were disclosed. Surprisingly, no studies explored transportation insecurity as a primary focus for individuals with dementia in long-term care facilities, representing a significant literature gap.  The findings of this review address critical literature gaps related to transportation challenges faced by long-term care facility residents with dementia and provide evidence to guide and develop transportation service systems as well as potential interventions for long-term care residents, especially those with dementia. Further explorations and experiments are needed to establish long-term care facilities with dementia-friendly features, which enhance accessibility and quality of life for these residents.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2604543</guid>
    </item>
    <item>
      <title>Examining Emerging Risks of Vehicle Electrification in Emergency Medical Transport</title>
      <link>https://trid.trb.org/View/2657950</link>
      <description><![CDATA[This article examines the potential risks of using electric ambulances (EAs) in place of fuel-powered ambulances (FAs) in emergency medical transport, most notably delays in emergency medical transport times.  The authors explored the effect of battery recovery time on the performance of the EMT system with EAs and outline the carbon-reduction benefits in deploying EAs compared to fuel-powered ones. The authors present a queuing model that characterizes the EAs with two battery-recovery strategies: plug-in charging and battery swapping.  Using this model, they found that, when the ambulance fleet is small and most of the ambulances are EAs, the throughput time for EMT increases significantly. However, with a larger ambulance fleet, incorporating EAs can deliver a level of transportation service comparable to that of the fuel-powered ambulances, especially when the battery-swapping strategy is employed.  The authors stress that, while the use of EAs raises initial costs, achieving a critical scale of EAs can enable the reduced energy cost and the social cost of carbon to quickly offset that initial investment. They conclude with a discussion of policy recommendations regarding the need for battery-recovery infrastructure and the deployment scale and timing of vehicles.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657950</guid>
    </item>
    <item>
      <title>Low-frequency Consensus knowledge Transfer in PEM Fuel Cells for Cross-Domain Online Voltage Degradation Prediction</title>
      <link>https://trid.trb.org/View/2685622</link>
      <description><![CDATA[Traditional time–frequency domain methods face critical limitations in predicting voltage degradation of proton exchange membrane fuel cells (PEMFCs). Time-domain models struggle to robustly separate long-term degradation-related low-frequency trends from contaminated voltage signals under highly dynamic and non-stationary conditions, while conventional frequency-domain analysis loses essential time-localized information during feature extraction. Both approaches exhibit significantly degraded prediction performance under limited data conditions. To overcome these challenges, this paper proposes a time–frequency fusion algorithm that integrates TimesNet with long short-term memory (LSTM), effectively combining 2D frequency-domain representations with 1D temporal memory to enhance voltage degradation prediction under dynamic conditions. Based on the capability of TimesNet-LSTM to extract low-frequency voltage features, a transfer learning technique grounded in low-frequency consensus knowledge (LCK-TL) is further developed. By selectively transferring low-frequency voltage features that robustly reflect aging patterns, LCK-TL considerably reduces distribution discrepancy between source and target domains, achieving joint optimization of predictive modeling and transfer mechanisms. Leveraging the inherently low computational cost of transfer learning, LCK-TL enables rapid multi-step predictions while maintaining accuracy, providing effective guidance for cross-device and cross-condition fuel cell health management.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685622</guid>
    </item>
    <item>
      <title>Topology-Oriented Multi-Objective Optimization for Interior Permanent Magnet Synchronous Motors Using AutoML-Based Surrogate Models</title>
      <link>https://trid.trb.org/View/2683127</link>
      <description><![CDATA[With the rapid expansion of the electric vehicle market, interior permanent magnet synchronous motors (IPMSMs) have become increasingly critical for high-performance drive systems. However, the diversity of rotor topologies and the computational intensity of traditional finite element analysis (FEA) present significant challenges for efficient motor design optimization. This paper proposes a multi-topology optimization framework based on automated machine learning (AutoML) that enables simultaneous optimization across diverse rotor configurations. The system integrates a unified representation method for multiple topologies with an ensemble-based surrogate modeling approach that automatically selects optimal algorithms and hyperparameters. Cross-topology validation across four representative rotor topologies demonstrates that the proposed framework achieves prediction accuracies exceeding 95% while reducing computational time to less than 1/8000 of traditional FEA methods. The optimization system generates Pareto-optimal solutions for torque maximization and ripple minimization within 19 minutes, providing multiple viable design alternatives that meet specific performance requirements across different topological configurations.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683127</guid>
    </item>
    <item>
      <title>Travel Time to Methadone Treatment Via Personal Vehicle Vs Public Transit</title>
      <link>https://trid.trb.org/View/2672660</link>
      <description><![CDATA[The requirement for in-person, often daily, attendance at opioid treatment programs (OTPs) makes travel times a barrier to methadone treatment. Research on methadone accessibility has primarily focused on travel via personal vehicle, and there is uncertainty about public transit travel time to methadone treatment. To estimate travel time via personal vehicle vs public transit to methadone treatment in the state of Connecticut. This cross-sectional study included geospatial analysis of median travel time to nearest OTP via personal vehicle and public transit from all census block groups (CBGs). This study took place in the state of Connecticut in 2023. Participants were all CBGs in Connecticut., Exposures: Participants were characterized by racial and ethnic demographics; household income; car ownership; urban, suburban, or rural designations; and per-capita opioid overdose deaths. The primary outcome was the median travel time to nearest OTP by via personal vehicle and public transit. Spatial error models using k-nearest neighbor spatial weight matrices were estimated to assess the associations between sociodemographic characteristics and travel times for each transportation mode (personal vehicle vs public transit) at the CBG level. From the centroids of the 2702 CBGs in Connecticut, the median (IQR) travel time to the closest OTP was 11.0 (7.5-16.3) minutes by personal vehicle and 41.7 (31.0-49.5) minutes via public transit, with 1431 CBGs (53%) lacking access to public transit or having high public transit times (>60 minutes or no trip available). Travel times via public transit increased along the urban-rural gradient and across CBGs with an increasing percentage of non-Hispanic White residents. Median (IQR) travel times to an OTP from the 489 CBGs with the highest per-capita overdose death rates were 8.2 (5.9-11.7) minutes by personal vehicle and 37.6 (27.8-48.5) minutes by public transit, with 166 (34%) lacking public transit access.  The findings of this cross-sectional study of barriers to access to methadone treatment suggest that areas with high overdose death rates, low car ownership, and high public transit travel times should be targets for interventions (e.g., mobile services or greater use of take-home doses for patients) to lower travel-based barriers to methadone. Current federal statutes and regulations governing methadone provision are the greatest barrier, as they directly require often daily transit to opioid treatment clinics. Reducing this barrier requires policy changes.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672660</guid>
    </item>
    <item>
      <title>Worldmove, a Global Open Data for Human Mobility</title>
      <link>https://trid.trb.org/View/2694335</link>
      <description><![CDATA[High-quality human mobility data is crucial for applications such as urban planning, transportation management, and public health, yet its collection is often hindered by privacy concerns and data scarcity, particularly in less-developed regions. To address this challenge, the authors introduce WorldMove, a large-scale synthetic mobility dataset covering over 1,600 cities across 179 countries and 6 continents. The method leverages publicly available multi-source data, including gridded population distribution, point-of-interest (POI) maps, and commuting origin-destination (OD) flows, to generate realistic city-scale mobility trajectories using a diffusion-based generative model. The generation process involves defining city boundaries, collecting multi-source input features, and simulating individual-level movements that reflect plausible daily mobility behavior. Comprehensive validation demonstrates that the generated data closely aligns with real-world observations, both in terms of fine-grained individual mobility behavior and city-scale population flows. Alongside the pre-generated datasets, the authors release the trained model and a complete open-source pipeline, enabling researchers and practitioners to generate custom synthetic mobility data for any city worldwide. WorldMove not only fills critical data gaps, but also lays a global foundation for scalable, privacy-preserving, and inclusive mobility research, empowering data-scarce regions and enabling universal access to human mobility insights.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694335</guid>
    </item>
    <item>
      <title>Topological Data Analysis of Departure Delay Co-occurrence in Japanese Domestic Aviation System</title>
      <link>https://trid.trb.org/View/2674201</link>
      <description><![CDATA[The authors introduce a topological data analysis (TDA) framework to characterize departure‐delay co-occurrence in Japan’s domestic airline networks. By constructing a delay‐based filtration on daily delayed flight networks for All Nippon Airways (ANA) and Japan Airlines (JAL), the authors track how airports form delay co-occurrence loops through persistent homology using Vietoris–Rips complex filtration technique. The authors find JAL’s network is more fragmented, while ANA shows wider, longer-lasting delay loops before COVID-19. In winter, airports take longer to join into connected groups, and delay loops last longer. During COVID-19, delays shrink for both airlines. As flights recover, multi-airport loops return, and some last longer than before. Tokyo Haneda Airport and its primary feeder airports emerge as central to the most delay loops, suggesting targeted buffering and schedule adjustments at these airports. The findings demonstrate TDA’s unique ability to uncover higher‐order delay dynamics and assist departure delay management strategies.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674201</guid>
    </item>
    <item>
      <title>Mining Hidden Ridesharing Patterns: A Data-Driven Gap Analysis of Chicago TNC Trips</title>
      <link>https://trid.trb.org/View/2673005</link>
      <description><![CDATA[This study paved the way for developing digital twins of smart and emerging urban mobility systems, using shared mobility services such as ridesharing as a key case study. As cities contend with challenges, such as traffic congestion, environmental sustainability, and transportation equity, shared mobility platforms (e.g., UberPOOL and Lyft Shared) have emerged as promising solutions. Leveraging Chicago’s Transportation Network Companies (TNCs) shared mobility data set, this research uncovers latent patterns in user behavior and trip-sharing dynamics through data mining and exploratory analysis. It distinguishes between trips, where users authorized ride-sharing and those that were actually pooled, revealing key spatial, temporal and behavioral difference. Economic factors also played an important role. For instance, the hourly gap between authorized and successfully pooled trips was narrower on weekends, suggesting more stable matching opportunities, while users who authorized but were not pooled tended to pay less per mile than the general trip population. Building on these insights, this study integrates both supervised and unsupervised machine learning methods to enhance the understanding of ridesharing dynamics. Density-based spatial clustering of applications with noise (DBSCAN) was employed to uncover latent trip groupings, which served as the foundation for developing predictive models that estimate the likelihood of successful ride matches. Multiple classifiers, including Logistic Regression, Random Forest, and XGBoost, were implemented and rigorously evaluated to identify the most effective predictive model. This integrated approach not only provides a comprehensive perspective on ridesharing behavior and trip shareability within current mobility platform, but also builds the foundation for early-stage digital twins that can simulate, optimize, and inform decision-making in future smart mobility systems, including autonomous vehicle fleet operations.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673005</guid>
    </item>
    <item>
      <title>From Microscopic Driving Behavior to Macroscopic Traffic Dynamics Using Drone Observations</title>
      <link>https://trid.trb.org/View/2683132</link>
      <description><![CDATA[In this paper, the existence of a statistically significant relation between microscopic traffic variables that reflect the individual driving behavior with macroscopic traffic dynamics at network level is investigated. Empirical evidence and machine learning techniques are used, applied on a publicly available dataset of vehicle trajectories recorded using Unmanned Aerial Units in the city center of Athens, from which acceleration, speed and outflow information is extracted. The exploratory analysis conducted reveals that extremely high or extremely low average acceleration and deceleration values, as well as high heterogeneity of driving behavior, are related to adverse traffic conditions, while intermediate values are associated with optimal outflow. Furthermore, the regression analysis based on a simple yet powerful Random Forests model showed that the macroscopic outflow rate in the region of interest can be accurately predicted by the microscopic behavioral variables, such as acceleration. The further analysis of the strength of the developed relationships using Shapley additive explanations analysis, provided important insights regarding the influence of driving behavior on the observed outflow, validating the finding that aggressive or extremely cautious driving is connected with reduced traffic conditions. The specific findings can have far reaching implications for traffic management and Connected Cooperative Automated Mobility (CCAM), such as optimizing traffic conditions through controlling behavior, as well as more scalable simulation and an alternative perspective of observing traffic conditions.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683132</guid>
    </item>
    <item>
      <title>Blame attribution mechanisms in AV accidents: A multi-dimensional exploration integrating social media and survey data</title>
      <link>https://trid.trb.org/View/2687063</link>
      <description><![CDATA[Before the full autonomous vehicle (AV) era, the present is an extraordinary era of shared control between humans and AV systems. In the current era, AV accidents are increasingly inevitable, and the occurrence raises complex ethical and legal challenges, resulting in the difficulty in determine the responsibility. Previous research primarily employed qualitative or quantitative analyses based on hypothetical scenarios to identify public blame targets in AV accidents and reached conflicting conclusions. Importantly, the research merely confirmed the existence of attribution bias, it has not sufficiently explained the underlying mechanisms. By integrating multiple analytical methods, this study developed a comprehensive attribution theory framework to explain the reasons for attribution bias in AV accidents. The research collected 90,426 valid comments from the Chinese social media platforms about AV accidents from April 1, 2021, to January 20, 2025. The three dimensions of concern topic, comment volume, and sentimental polarity identified the AV system as the main blame target. Based on the qualitative analysis results, Heider's naive attribution theory was adopted as the foundational theoretical framework to construct a comprehensive model of responsibility attribution in AV accidents. The authors found that trust and affective tagging are core factors in blame attribution. There is a gender tradeoff between knowledge and sentiment in AV accidents. Additionally, media exposure revealed a selective activation mechanism of media effects in the death scenario among males.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687063</guid>
    </item>
    <item>
      <title>Investigation of luminance influence on driver risk perception according to visual time-to-collision</title>
      <link>https://trid.trb.org/View/2687056</link>
      <description><![CDATA[Risk perception plays a crucial role for drivers in the observation, analysis, and prediction of traffic accidents. Research on risk perception continues to focus on optimal visual conditions, although drivers rely on limited visual input in real-world scenarios. This study addresses this gap by analyzing the influence of degradation in luminance on risk perception. A perceptual bias analysis method based on visual time-to-collision (TTC) is proposed to assess the luminance influence. Twenty-two participants performed a collision prediction task in which the approaching stimulus was occluded after a period of visible motion under varying luminance conditions. A multiple regression modeling approach was employed to examine the effect of luminance variations on the accuracy of estimated TTC. Statistical analysis indicates that decreasing luminance significantly induces the overestimation of TTC by an average of 79.64 ms compared with the constant luminance condition. This overestimation is particularly enhanced in higher velocity conditions. Longer occlusion duration statistically significantly increased the overestimation of TTC, with an average increase of 0.17 ms in estimated TTC for each millisecond of occlusion. However, the mixed-effects model revealed that the magnitude of this effect varied significantly across individuals, indicating substantial inter-individual differences in risk perception. These findings provide quantitative analysis that luminance degradation impairs the ability to estimate TTC. The results may be helpful to develop safety guidelines at tunnel entrances and exits that account for visual limitations and individual perceptual differences, improving driving safety under limited visibility conditions.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687056</guid>
    </item>
    <item>
      <title>Understanding the choice of road traffic as a means of suicide: Insights from multidisciplinary investigations of attempted suicides</title>
      <link>https://trid.trb.org/View/2686846</link>
      <description><![CDATA[There is limited understanding of why some individuals choose to end their lives in road traffic. This study aimed to explore the underlying reasons for such decisions by analyzing information on suicide attempts collected by Finnish multidisciplinary road crash investigation teams. The sample comprised 56 incidents over a ten-year period (2013−2022), nearly all involving deliberate motor vehicle crashes. The analysis drew on both computerized data and original investigation reports. Reasons for choosing road suicide were identified in 16 of the 56 cases (29%). The most commonly cited motivations included perceptions of the method as lethal, easy, simple, and quick, as well as associations with impulsivity. Given the inherent challenges in accurately identifying suicidal intent in traffic crashes, it appears that investigation teams may primarily focus on distinguishing suicides and suicide attempts from unintentional accidents. In the authors’ assessment, the teams generally provided sufficient evidence to support their conclusions regarding the intentional nature of the crashes. However, the authors argue that whenever a suicide attempt is suspected on the road network, efforts should also be made to understand the factors influencing the choice of this method. Such insights are essential from both traffic safety and suicide prevention perspectives.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686846</guid>
    </item>
    <item>
      <title>Logistics 4.0 adoption in emerging economies: An economic complexity perspective</title>
      <link>https://trid.trb.org/View/2697261</link>
      <description><![CDATA[A dynamic and diversified economy can facilitate the adoption of Logistics 4.0 (L4.0). However, limited research exists on how economic complexity influences L4.0, either by facilitating or hindering its development. Therefore, this study investigates the drivers and barriers to the adoption of L4.0 by Logistics Service Providers (LSP), using an economic complexity approach. The authors conducted semi-structured interviews with sixteen logistics experts operating within the context of an emerging economy (Brazil). Content analysis was applied to examine the data collected. The results reveal that the adoption of L4.0 is influenced by internal factors (i.e., cost reduction, productivity, quality, and increased competitiveness) and external pressures such as customer demands, regulations, competition, and technological advancements. Key barriers include resistance to change, limited technical skills, high technology costs, and inadequate infrastructure. The interdependence between technological transition and economic complexity underscores the need for context-sensitive strategies that align logistics practices with regional economic conditions. This study contributes to both theory and practice by offering guidance for managers and governments on the relationship between L4.0 and economic complexity, how to leverage its benefits, and how to overcome the challenges inherent in the process.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697261</guid>
    </item>
  </channel>
</rss>