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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>The human element in autonomous shipping: a study on skills and competency requirements</title>
      <link>https://trid.trb.org/View/2636070</link>
      <description><![CDATA[This study examines the evolving landscape of the shipping industry in the context of Maritime Autonomous Surface Ships (MASS), with a focus on the critical role of Maritime Education and Training (MET). As the sector undergoes rapid transformation, there is a pressing need for MET providers to adapt their curricula and training programs to meet emerging industry standards. Despite growing research interest in future skills and competencies for the MASS workforce, a comprehensive framework for assessing and ranking these skills remains lacking. To address this gap, we propose the application of multi-criteria decision-making (MCDM) techniques, specifically fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to evaluate and prioritise proficiency requirements for MASS. The analysis, based on the responses of 174 experts, yields consistent and robust results, identifying ‘Operational Skills’, ‘Digital Skills’, and ‘Maritime Competency’ as the most crucial skills and competencies for MASS operations. A number of insights and recommendations are provided to guide MET institutions in updating their educational offerings to meet the demands of the evolving maritime industry.]]></description>
      <pubDate>Tue, 12 May 2026 16:56:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636070</guid>
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    <item>
      <title>Advancements and Insights in Assessing Cognitive Load During Driving: A Comprehensive Narrative Review</title>
      <link>https://trid.trb.org/View/2672899</link>
      <description><![CDATA[In the context of rapid advancements in the automotive industry and intelligent transportation systems, assessing cognitive load during driving has become a key factor for driving safety and user experience. This paper presents a comprehensive narrative review of theories, methods, and technological advancements in assessing cognitive load during driving. Rather than following a systematic review protocol,the structure of the review is organized around key research questions and critical gaps identified in the current literature. We assess the applicability and performance of major evaluation methods, including physiological indicators, behavioral measures, subjective self-report scales, and data-driven approaches, across various driving scenarios. The review also discusses the integration of multi-source information and propose a conceptual framework for holistic and adaptive cognitive load assessment. Furthermore, it highlights challenges in current practices, such as technical constraints, environmental variability, and individual differences. Special emphasis is placed on the relevance of cognitive load assessment for industrial informatics, particularly in the context of advanced driver assistance systems (ADAS), autonomous driving technologies, and driver training programs. This review aims to provide a structured synthesis of current approaches, offer practical insights for application and system design, and guide future research toward developing more robust, generalizable, and context-aware assessment tools. By analyzing the state of the art, we contribute a timely reference to support both academic development and industrial implementation for next-generation intelligent vehicle systems.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672899</guid>
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    <item>
      <title>Beyond binary: an integrated approach to assessing BOSIET competencies</title>
      <link>https://trid.trb.org/View/2632895</link>
      <description><![CDATA[In the ever-evolving realm of offshore safety, the Basic Offshore Safety Induction and Emergency Training (BOSIET) program holds paramount importance in ensuring the proficiency of personnel in the challenging offshore setting. Traditionally, BOSIET assessments have utilised a binary evaluation, categorising individuals as either competent or incompetent. Nonetheless, this binary approach oversimplifies the multifaceted nature of offshore competencies. Our paper advocates for a paradigm shift towards an integrated assessment framework that transcends a simplistic pass/fail dichotomy. Drawing inspiration from diverse industries, our proposed approach encompasses a spectrum of competencies, recognising the varied skills requisite in offshore contexts. By embracing this integrated methodology, the BOSIET assessment endeavours to furnish a more nuanced comprehension of an individual's readiness for offshore duties. In conclusion, embracing this integrated approach ensures that offshore personnel are not solely proficient in technical aspects but also equipped with the diverse skills essential for navigating the intricacies of offshore operations.]]></description>
      <pubDate>Mon, 02 Mar 2026 08:55:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2632895</guid>
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    <item>
      <title>A Global Perspective on Road Condition Assessment and Maintenance: Trends in Research and Technology Integration</title>
      <link>https://trid.trb.org/View/2659661</link>
      <description><![CDATA[This study conducts an extensive bibliometric review to evaluate the global evolution of pavement condition assessment and maintenance research over the last three decades. Using a dataset of 634 Scopus-indexed publications, the study applies co-authorship, citation analysis, keyword co-occurrence, bibliographic coupling, and text-based mapping to explore scholarly productivity, thematic clusters, and technological routes. Findings highlight a growing concentration around core evaluation indices such as PCI and IRI, with newer studies embracing machine learning, remote sensing, and decision-support systems. Research leadership remains concentrated in countries with advanced road infrastructure, particularly the United States, China, and Canada although contributions from emerging economies are steadily growing. Despite technological progress, a gap still exists between academic innovation and practical application. Only a small portion of the literature has influenced real-world practice, mainly because of limited resources, fragmented data, and a lack of interdisciplinary collaboration. Case studies from the Washington State Department of Transportation (DOT) and Maine DOT illustrate effective implementation of data driven and AI-enabled pavement systems. This underscores the potential of AI to automate condition assessments, enhance predictive modeling, and optimize rehabilitation strategies. However, the study also reveals key limitations, including limited model generalizability, challenges in reproducibility, and varying levels of stakeholder readiness. Future research should focus on developing open-access datasets, collaborative validation frameworks, and practical toolkits that can be readily adopted by road agencies. Moreover, integrating AI into research workflows offers valuable opportunities to connect theory with practice, creating smoother pathways from academic innovation to real-world implementation and ensuring that research delivers tangible, lasting impact.]]></description>
      <pubDate>Wed, 25 Feb 2026 14:10:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659661</guid>
    </item>
    <item>
      <title>Measuring road segment resilience patterns under frequent disturbances: A comprehensive metric framework</title>
      <link>https://trid.trb.org/View/2656104</link>
      <description><![CDATA[Urban road network resilience assessment often provides limited guidance for traffic management, as existing indicators are either too complex to correspond to operational measures or suffer from unclear boundaries and instability, leading to inconsistent results. To address these limitations, this study proposes a novel comprehensive resilience assessment framework based on three clearly separated dimensions—rapidity, robustness, and recovery. The study further introduces quantitative methods for assessing boundary clarity and statistical stability, enabling explicit and objective evaluation of whether indicators respond exclusively and reliably to different management measures. Using these metrics, simulation-based scenario validation shows that the proposed indicators exhibit distinct, non-overlapping responsiveness to specific traffic management strategies and achieve 66.06 % lower variability than benchmark indicators, with stability scores consistently above 0.94 across all scenarios. The framework is further applied to the Guangzhou Airport Expressway, where the proposed indicators reveal segment-level differences in resilience and identify the underlying factors contributing to weak performance. These insights offer actionable guidance for future traffic management and targeted resilience enhancement in real-world networks.]]></description>
      <pubDate>Wed, 25 Feb 2026 09:10:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2656104</guid>
    </item>
    <item>
      <title>Advancing unpaved road assessment in Africa: Leveraging multimodal machine learning and large language-and-vision assistants across satellite imagery resolutions</title>
      <link>https://trid.trb.org/View/2652379</link>
      <description><![CDATA[Over 53 % of African road network is unpaved, yet systematic monitoring remains limited. This study introduces a cost-effective machine learning (ML) solution to help local authorities monitor and plan road maintenance. Building on earlier work using high-resolution satellite imagery in Tanzania, the analysis extends to Madagascar, incorporating medium- and low-resolution imagery to reduce costs. Two distinct methodologies were evaluated: traditional ML and multimodal ML. The multimodal ML model achieves 93.2 % accuracy with high-resolution imagery and maintains satisfactory performance with medium (84.0 %) and low-resolution (85.3 %) imagery, aided by transfer learning. The framework demonstrates robust cross-resolution performance across Tanzania and Madagascar contexts. Additionally, a pilot study explored a fine-tuned Large Language-and-Vision Assistant (LLaVA) model, which demonstrated potential for natural language-based condition reporting and maintenance recommendations, offering an interpretable alternative to quantitative classification outputs. Whilst LLaVA currently exhibits lower classification accuracy than the multimodal ML model, multi-turn conversational approaches show promise for enhancing performance whilst maintaining natural language interpretability. This study contributes to Sustainable Development Goal 9.1 by delivering a scalable, affordable strategy to support resilient infrastructure and economic development in low-income regions.]]></description>
      <pubDate>Mon, 23 Feb 2026 11:24:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652379</guid>
    </item>
    <item>
      <title>Use of ArcGIS and Fuzzy Analytical Hierarchy Process for Base Condition Assessment of NMDOT Culverts</title>
      <link>https://trid.trb.org/View/2562063</link>
      <description><![CDATA[This research investigates the baseline structural and non-structural conditions of a geo-located database subset of small culverts as part of the Culvert Asset Management Plan (CAMP) of the New Mexico Department of Transportation (NMDOT) to support long-term risk-based asset management. Fuzzy Analytical Hierarchy Process (FAHP) Pairwise Comparisons (PWC) based on expert opinion were implemented to develop weightings for selected culvert structural or non-structural defects. Data mining of the inspection inventory using pre-selected NMDOT Schema Codes coupled with derived FAHP weightings allowed for the development of a Base Condition Assessment (BCA) rating score for each culvert based on five inspection attributes. This metric provides an assessment of the criticality of culvert condition on a relative numeric scale from good (1) to bad (4.27) and allows for phased prioritization of statewide culverts for further maintenance or replacement action. Expert consensus between each PWC criterion was evaluated using a Double Entropy Index for inter-rater agreement. Additionally, Hot Spot Analysis, using Spatial Analysis in ArcGIS Pro, helped identify statistically significant spatial clusters of high values (hot spots) and low values (cold spots) of attribute ratings with respect to specific environmental conditions throughout the state. An area of focus was on hot spots within Corrugated Metal Culverts (CMCs), as CMCs represent 79% of culverts surveyed. A composite BCA hot spot map for CMCs identified a pronounced area within the northern portion of the state, suggesting a critical area for additional inspection and/or needed maintenance. Histograms of BCA ratings for each culvert type show a low percentage of culverts above a predetermined critical cutoff for additional assessment.]]></description>
      <pubDate>Tue, 27 Jan 2026 16:16:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2562063</guid>
    </item>
    <item>
      <title>Instructor Autonomy and Training Structures in Simulator-Based Education: A Study of Maritime and Aviation Training Approaches</title>
      <link>https://trid.trb.org/View/2624144</link>
      <description><![CDATA[Simulator-based training is an essential component of both maritime and aviation education, yet the regulatory frameworks and pedagogical approaches governing these fields differ significantly. Aviation training operates under highly standardized and prescriptive regulations, ensuring structured progression through predefined exercises, while maritime training is more flexible, guided by the International Maritime Organization’s (IMO) Standards of Training, Certification, and Watchkeeping for Seafarers (STCW) convention. This study explores how these differences impact simulator training design, instructor autonomy, and student learning experiences. Using a qualitative research approach, data was collected through instructor interviews and observations of simulator training sessions in both maritime and aviation institutions. Findings reveal that maritime instructors have significant freedom to design and adapt training exercises, leading to high levels of customization but also inconsistencies across institutions. In contrast, aviation instructors follow strict, externally approved training manuals, ensuring coherence but limiting adaptability. Another key difference is in assessment structures—aviation training includes mandatory level confirmation checks throughout the program, whereas maritime training relies on final exams, with simulator exercises seen as learning opportunities rather than evaluative assessments. This study highlights the advantages and challenges of both approaches. While the flexibility in maritime training fosters innovation and adaptability, it risks a lack of coherence between courses. Conversely, aviation’s structured training ensures standardization and regulatory compliance but may hinder responsiveness to technological advancements or evolving industry needs. The study suggests that a balanced approach—incorporating aviation’s structured assessments into maritime training while preserving instructor-driven adaptability—could optimize learning outcomes in the maritime sector, and that a balanced approach also could be considered for the aviation sector. This research contributes to the ongoing discourse on simulator-based education by identifying areas for cross-sector learning and improvement. Recommendations include enhancing coordination between maritime training programs, implementing structured assessment milestones, and exploring adaptive simulation techniques to enhance both standardization and flexibility in training methodologies.]]></description>
      <pubDate>Tue, 27 Jan 2026 09:21:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624144</guid>
    </item>
    <item>
      <title>LLM-based Maritime Training Feedback System: Implementing RAG-Enhanced Assessment Analysis with STCW Compliance</title>
      <link>https://trid.trb.org/View/2624142</link>
      <description><![CDATA[This paper presents the implementation and evaluation of a Retrieval-Augmented Generation (RAG) system designed to provide automatic STCW- compliant feedback on maritime assessment questions. Building on preliminary findings from ongoing research into technological proficiency [1] (ß=0.457) and institutional readiness [2] (ß=0.341), this implementation addresses a critical gap: the need for automated feedback systems that maintain regulatory alignment while reducing instructor workload. The system utilizes the Mistral-7B large language model optimized with QLoRA for efficient local deployment, combined with a RAG architecture to ensure contextually relevant feedback. Evaluation results demonstrate the system’s ability to generate accurate feedback with response times under 15 seconds and STCW concept coverage of 85%, addressing key implementation barriers identified in our previous studies. The paper discusses how this implementation addresses technological proficiency barriers (ß=0.457, p<0.001) and enhances perceived usefulness through automated, standards-compliant feedback that supports both individual competency development and institutional readiness.]]></description>
      <pubDate>Tue, 27 Jan 2026 09:21:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624142</guid>
    </item>
    <item>
      <title>Airline performance assessment using an improved neutral cross-efficiency method: Principal component analysis through Q-methodology</title>
      <link>https://trid.trb.org/View/2633824</link>
      <description><![CDATA[Assessing the performance of airlines is vital in the aviation industry, as it affects multiple stakeholders, including airlines, travelers, regulatory authorities, and investors. It is known as a key driver of growth and sustainability in the aviation sector. Hence, the main aim of the current study is to utilize the Principal Component Analysis (PCA) through Q-methodology (referred to as QNCEM) as an innovative technique to provide an assessment framework for airlines. QNCEM offers policymakers numerous advantages as it permits the elimination of irrelevant perspectives during the assessment process, enables the determination of each Decision-Maker’s (DM) contribution, and plays a crucial role in achieving consensus by leveraging factor analysis to extract perspectives that are representative of the group’s opinions. In this research, the efficiency of 17 Iranian airlines is assessed using QNCEM, considering both desirable and undesirable outputs, such as flight delays, demonstrating its practicality and effectiveness. The selection of a loading factor of 0.626 allowed QNCEM to encompass a comprehensive range of viewpoints from 17 DMs. This deliberate choice ensures the inclusion of a diverse set of perspectives, maximizing the richness of the analysis and explaining a cumulative variance of more than 96%.]]></description>
      <pubDate>Mon, 29 Dec 2025 09:35:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2633824</guid>
    </item>
    <item>
      <title>Assessing emergency medical services (EMS) accessibility before and after road pricing: A dynamic 2SFCA approach</title>
      <link>https://trid.trb.org/View/2628457</link>
      <description><![CDATA[Road pricing has been regarded as an effective tool for mitigating traffic congestion in cities. It could, however, exacerbate inequity through negative externalities. This study compares accessibility to emergency medical services (EMS) before and after the road pricing policy in Hong Kong. We develop a dynamic two-step floating catchment area (2SFCA) method to account for changes in speed. The spatio-temporal patterns of changes in EMS accessibility are captured. Results show that while the maximum EMS accessibility values increased after the policy, the average values decreased, particularly during the morning peak, which indicates a polarisation effect. Global Moran's I analysis informs that EMS accessibility changes are spatially clustered, and bivariate local Moran's I reveals that improvements in EMS accessibility are associated with areas with high elderly population density. Sensitivity analysis further supports our findings. Findings highlight the horizontal inequity and vertical equity in relation to the policy, bridging the gap between road pricing and public health literature. Policy implications are discussed.]]></description>
      <pubDate>Tue, 02 Dec 2025 09:53:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2628457</guid>
    </item>
    <item>
      <title>Game changer or costly innovation? A cost competitiveness assessment of battery-swappable electric vehicles</title>
      <link>https://trid.trb.org/View/2611311</link>
      <description><![CDATA[Battery-swappable electric vehicles (BSEVs) enhance convenience and driving range by enabling quick battery replacement. However, their total cost of ownership and competitiveness have not received research attention. Consequently, this study employs 5- and 10-year total life cycle cost methodology to evaluate the ownership cost and competitiveness of BSEVs with battery electric vehicles (BEVs) and internal combustion engine vehicles (ICEVs). Results revealed that in five years, though BEV-sedans are the most cost-competitive, BSEV-sedans reached near-parity with BEV-sedans and outperform ICEV-sedans. For SUVs, BSEVs only achieved parity with ICEVs. In 10 years, BSEV-sedans reached near-parity with BEV-sedans and ICEV-sedans. In SUVs, BEVs stayed cost-effective, but BSEVs matched ICEVs with a slight edge. Under battery replacement, BSEVs emerged as the most cost-competitive, reaching parity with BEVs and moving from a cost deficit to surplus in both vehicle categories. The study recommends potent strategies to ensure the economic attractiveness of BSEV for sustainable mobility.]]></description>
      <pubDate>Tue, 28 Oct 2025 13:42:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611311</guid>
    </item>
    <item>
      <title>Jet Engine Health Assessment Using EGT Time Series</title>
      <link>https://trid.trb.org/View/2407304</link>
      <description><![CDATA[In the presented work, the engine and its sets of parameters in two conditions were observed: before and after repair. All parameters show, that engine was deteriorated and engine performance was restored during repair, however, all parameters were within limits. Exhaust gas temperature (EGT) was selected as the main parameter for analysis due to its high impact on engine health. EGT time series for the engine before and after repair were compared and it was shown, that the fractal dimension is higher for the engine after repair. That means that deteriorated engine processes are less complex compared to processes in the repaired engine. Based on this information, two nonlinear models were developed for EGT prediction.]]></description>
      <pubDate>Fri, 24 Oct 2025 09:44:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407304</guid>
    </item>
    <item>
      <title>Assessment of Present Pavement Condition Using Machine Learning Techniques</title>
      <link>https://trid.trb.org/View/2407401</link>
      <description><![CDATA[Quantification of present pavement condition in terms of an index term i.e., Pavement Condition Index (PCI) is one of the most important and primary steps while taking decision related to Maintenance and Rehabilitation of Pavements. PCI as proposed by ASTM D6433 rates pavement in seven conditions viz. Good, Satisfactory, Fair, Poor, Very Poor, Serious and Failed. Determination of rating condition of pavement using distress severity and extent turns out to be tedious process. Hence, present study investigates application machine learning techniques for assessment of present pavement condition. Three different algorithms i.e., Logistic Regression, Naïve Bayes and K-Nearest Neighbor have been tested in the present study using Long Term Pavement Performance database consisting of over 10,000 datapoints. The dataset was divided into 7:3 ratio for training and testing phase. Employed algorithms were tested based on accuracy, precision, recall and f-measure. Logistic Regression Classifier was found to have highest accuracy of 0.92 among three classifiers used in the study.]]></description>
      <pubDate>Mon, 22 Sep 2025 17:13:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407401</guid>
    </item>
    <item>
      <title>Pavement Condition Assessment Using Lidar and Arcgis: An Experience from Malaysia</title>
      <link>https://trid.trb.org/View/2592126</link>
      <description><![CDATA[Pavements require regular maintenance due to the wear and tear caused by traffic loads and environmental conditions, which lead to various surface defects. This study explores the use of Light Detection and Ranging (LiDAR) technology and ArcGIS software to identify pavement defects in selected regions of Malaysia. The Jambatan Sultan Abdul Halim Muadzam Shah Expressway (JSAHMSE) and the Guthrie Corridor Expressway (GCE) were chosen as test sites for evaluating this approach. Initially, point cloud data were collected from both expressways using LiDAR, and related images were processed through ArcGIS software to identify defects on the road surfaces. The analysis revealed defects such as shoving, bleeding, longitudinal cracking, potholes, and patching on the GCE, while raveling, longitudinal cracking, bleeding, and edge cracking were observed on the JSAHMSE. Simultaneously, manual visual inspections were conducted, and defects were documented. A comparison of the results from both methods showed that LiDAR and ArcGIS effectively identified the types and sizes (length and surface area) of the defects. However, ArcGIS struggled to accurately measure the depth of certain defects, making it difficult to assess their severity in detail.]]></description>
      <pubDate>Tue, 02 Sep 2025 08:45:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2592126</guid>
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