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    <title>Transport Research International Documentation (TRID)</title>
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    <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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    <item>
      <title>Glossary for Transport Statistics 6th Edition: 2026</title>
      <link>https://trid.trb.org/View/2674271</link>
      <description><![CDATA[The Glossary for Transport Statistics, first published in 1994, serves as a crucial resource for standardising transport terminology globally. Since then, it has undergone six editions, each expanding and refining the content to address new trends and feedback from diverse stakeholders. The current sixth edition results from close collaboration among UNECE, ITF, Eurostat, and various experts from international sectoral bodies, agencies, and member countries, ensuring relevance amid dynamic global transport changes. The revision was influenced by the streamlined Eurostat/ITF/UNECE Common Questionnaire on Inland Transport Statistics, to be implemented from 2026. This edition comprises 884 definitions and remains vital for those engaged in transport statistics, offering thoroughly updated content across all ten chapters to ensure information is current and comprehensive]]></description>
      <pubDate>Tue, 09 Jun 2026 10:56:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674271</guid>
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    <item>
      <title>Legal and Linguistic Nuances of Key Terminology in the STCW Convention: The Case of Montenegro</title>
      <link>https://trid.trb.org/View/2703856</link>
      <description><![CDATA[The aim of this paper is to examine the use of specific terms in the International Convention on Standards of Training, Certification and Watchkeeping for Seafarers (STCW), one of the key conventions established by the International Maritime Organization. This interdisciplinary study highlights the importance of accurately understanding specialized terminology, which is essential for the effective implementation of the STCW Convention, particularly in the training and education of seafarers. Recognizing that legal texts reflect broader social, economic, legal, and political narratives (van Dijk, 2001, 2008; Fairclough, 2004), the paper uses Critical Discourse Analysis to explore how language reveals legal paradigms within the Montenegrin regulatory context. The findings show that the nuances of legal discourse extend beyond terminology; proper interpretation and implementation require a deeper understanding of social, cultural, and political factors. © 2026, Faculty of Maritime Studies. All rights reserved.]]></description>
      <pubDate>Thu, 21 May 2026 09:10:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703856</guid>
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    <item>
      <title>Definitions for Cycling Infrastructure</title>
      <link>https://trid.trb.org/View/2667222</link>
      <description><![CDATA[This paper presents a set of harmonized definitions for cycling infrastructure developed by the UNECE Group of Experts on the Cycling Infrastructure Module (2022–2024). Drawing on recent developments across UNECE member countries, the Group recognized significant changes in cycling practices, infrastructure types, signage, and regulations. In response, it formulated standardized definitions intended for broad international use. The definitions cover both linear infrastructure such as cycle tracks, greenways, cycle lanes, sharrows, mixed-traffic roads, cycle streets, contraflow cycling streets, bus‑and‑cycle lanes, and cycle route networks, and non‑linear infrastructure, including cycle crossings, grade‑separated crossings, advanced stop lines, two‑stage turn provisions, cycle parking, and cyclist traffic‑light exemptions. Each definition is accompanied by an explanatory note detailing its source, examples of practical application, and relevant signage based on the 1968 Convention on Road Signs and Signals or national regulations. To support clarity, the paper also includes illustrative images of signs and infrastructure.]]></description>
      <pubDate>Mon, 04 May 2026 11:19:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667222</guid>
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    <item>
      <title>AI-Powered Requirements Engineering &amp; Design Optimization of Electric Powertrains</title>
      <link>https://trid.trb.org/View/2692120</link>
      <description><![CDATA[The development of electric vehicle powertrains is driven by diverse and often conflicting requirements. In early development phases, these requirements are often vague, incomplete, continuously refined and subject to change as development progresses. Moreover, powertrain designs must be competitive regarding multiple key performance indicators (KPIs) such as performance, cost, energy efficiency, and package integration. This challenges engineers to concurrently develop the powertrain design alongside the requirements on which the design is based on. Managing this combination of uncertain requirements and multi-KPI design optimization represents a complex challenge in automotive engineering. The present work introduces a requirements engineering approach based on OPED (Optimization of Electric Drives). OPED digitalizes the transition from requirements to technical solutions by integrating parametric system models with an AI-based evolutionary optimization algorithm. This enables systematic exploration of trade-offs, robust handling of uncertainties, and the effective specification of requirements. The outcome is a Pareto front of optimal and feasible powertrain solutions, providing engineers and decision makers with a quantitative basis for requirement definition and product design in the development process. A case study demonstrates the approach by determining the optimal requirement regarding the maximum speed of an electric passenger car. OPED evaluates the influence of the maximum speed requirement on cost, energy efficiency, and ensures a suitable package integration. A Pareto front is generated that contains optimal powertrain solutions alongside the respective maximum speed requirement. Results show that OPED effectively combines requirements engineering and system design optimization, thereby supporting agile and robust powertrain development.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692120</guid>
    </item>
    <item>
      <title>AI-Driven Data Consistency and Relationship Inference System for Agile Component Library Management</title>
      <link>https://trid.trb.org/View/2691883</link>
      <description><![CDATA[Reliable component libraries are the foundation of the engineering process and the starting point for all intelligence within CAD tools. In practice, however, libraries created and maintained by librarians often contain incomplete, inconsistent, or outdated data. This paper introduces the component data consistency and relationship inference AI system, developed within Amoeba software, which addresses these challenges by improving component library quality. The system uses AI to infer component attributes such as component type, gender, color, material, etc. Moreover, it can identify relationships such as the family a connector is associated with based on its attributes and geometry. The system improves data consistency in areas such as resolving mismatched wire size constraints imposed by the connector and cavity components. It also utilizes computer vision to identify common connector footprints, cavity sizes, and 2D symbol geometries. Deployed within Amoeba software, the system has shown an ability to create parts ~30 times faster than manual methods with 98.81% accuracy. The novelty of this system is two-fold. First, it represents a unique integration of AI-based attribute inference and relationship reasoning for improving component library data quality. Second, the system enables a new paradigm of on-demand component creation within Amoeba software that allows engineering teams to obtain tailored components immediately rather than waiting for delivery from librarians. By enabling agile component library management and maintaining data integrity, the system brings benefits in the environment of Industry 4.0 and the increasing digitization of engineering processes.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691883</guid>
    </item>
    <item>
      <title>A human-centered taxonomy of driver responses to cyber-attacks in automated vehicles</title>
      <link>https://trid.trb.org/View/2684603</link>
      <description><![CDATA[Automated vehicles (AVs) are increasingly vulnerable to cybersecurity threats, yet the human-factors dimensions of such threats remain underexplored. This study develops a human-centered taxonomy of driver responses to AV cyber-attacks, grounded in semi-structured interviews with thirteen domain experts. Using an iterative coding process grounded in theory, the authors identified four sequential stages of driver responses: (1) Perception, (2) Comprehension, (3) Decision-Making, and (4) Action, along with cross-cutting Integrative Factors. Within each stage, findings are organized around (a) envisioned scenarios, (b) limitations & barriers, (c) interventions & design factors, and (d) implications & responsibilities. Results highlight challenges in detecting subtle anomalies, interpreting irregular cues, and making timely decisions under uncertainty, with trust dynamics, workload, and preparedness shaping outcomes across stages. The taxonomy advances theoretical understanding by extending established models of situation awareness and trust calibration into adversarial contexts. Practically, it offers a structured roadmap for designing alerts, training programs, and policy measures that support calibrated human interventions and strengthen resilience in automated driving.]]></description>
      <pubDate>Thu, 09 Apr 2026 10:07:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684603</guid>
    </item>
    <item>
      <title>“Stop” and Think about It: How the Different Interpretations of What Counts as a “Major Transit Stop” in California Make a Difference</title>
      <link>https://trid.trb.org/View/2683221</link>
      <description><![CDATA[“Major transit stop”: how these three words are defined determines what can be built where, throughout much of California. In order to address housing supply constraints, the state legislature has enacted a number of laws that streamline approval and remove zoning constraints in areas close to high-quality transit. But what, exactly, is a “major transit stop”? Planners, developers, and elected officials construe the sparse definition in state law in many ways — though genuine interpretive disagreement, due to modeling and data constraints, and/or in order to serve political goals of encouraging or stymying development. Differences in interpreting the definition of “major transit stop” collectively make a big difference in what areas are covered by state zoning incentives. A maximal approach to defining “major transit stop” grows the eligible area by over three times more than a minimal approach. The area within half a mile of a major transit stop has generally increased over time. But areas with low vehicle travel are doing more to drive affordable housing eligibility than areas with quality transit. Finally, tying transit service to land use regulations has created a perverse incentive to cut transit service in order to avoid state housing mandates.]]></description>
      <pubDate>Tue, 07 Apr 2026 17:08:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683221</guid>
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    <item>
      <title>Thermosetting waterborne epoxy asphalts: State of the art and challenges</title>
      <link>https://trid.trb.org/View/2655101</link>
      <description><![CDATA[Asphalt emulsion (AE), a key binder of cold mix asphalt (CMA), has been adopted in cold paving technology due to its environmental benefits, cost-effectiveness, and energy-efficiency. However, the limited mechanical properties of AE-based CMA restrict its applications primarily to secondary roads and hinder broader implementation in cold paving. While thermoplastic polymer latexes enhance the low-temperature performance of AEs, they still fail to deliver adequate high-temperature properties and adhesion. Consequently, thermosetting waterborne epoxy asphalt (WEA), a composite of waterborne epoxy resin (WER) and AE, has gained significant attention in cold paving applications over the past decade owing to its environmental sustainability and crosslinked molecular structure. This review provides a comprehensive examination of WEA technology. To ensure clarity and conciseness, given the lengthy terminology in the literature, standardized terminology is adopted for “waterborne epoxy asphalt” and “polymer-modified waterborne epoxy asphalt”, and common usage inaccuracies regarding “waterborne epoxy resin”, “curing agent”, and related terms are clarified. This article systematically covers key aspects, including formulation design, manufacturing processes, film formation, phase-separate morphology, glass transition temperature (Tg), and applications. It analyzes how the primary components (AE and WER) and preparation methods influence overall performance. Film formation mechanisms of WEA and its primary components are examined and compared. Despite its advantages, the inherent brittleness of WERs can compromise the ductility and low-temperature properties of WEAs. To overcome these limitations, various polymer modifiers are introduced; their modification mechanisms and synergistic effects are elucidated herein. The phase-separated morphology of WEAs is compared with that of conventional polymer-modified AEs, and differences in Tg between WEAs and solvent-free epoxy asphalts are examined. Finally, current challenges in WEA formulations and performance are discussed, outlining directions for future research.]]></description>
      <pubDate>Wed, 01 Apr 2026 11:46:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655101</guid>
    </item>
    <item>
      <title>Standardising bicycling infrastructure terminology: A Delphi study of Australian experts</title>
      <link>https://trid.trb.org/View/2640929</link>
      <description><![CDATA[In Australia and farther afield, there is an absence of ontology and great variation in terminology for bicycling infrastructure. Ambiguity and imprecision in bicycling infrastructure terminology inhibits communication, raises analytical challenges, restricts opportunities for comparisons across cities, and is a critical barrier to infrastructure provision as terminology ultimately determines what is built. This project presents steps toward the development of standardised bicycling infrastructure terminology in two major stages: 1) term identification from policy documents and, 2) term selection and weighting via a three round Delphi survey of expert input. Term identification found 113 terms that were used to describe 38 types of bicycling infrastructure. Through the Delphi survey an additional 13 types of infrastructure were added. Final Delphi survey results returned a single agreed upon term for 19 of 51 infrastructure types (37%). In addition, 25 of 51 infrastructure examples (49%) resulted in a highly rated term. This study provides governments, advocacy groups, academics, and private industry with a best practice guide to terminology for diverse types of cycling infrastructure.]]></description>
      <pubDate>Wed, 11 Mar 2026 16:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640929</guid>
    </item>
    <item>
      <title>Steering through uncertainty: a systematic review of liability communication in autonomous vehicles</title>
      <link>https://trid.trb.org/View/2673078</link>
      <description><![CDATA[Autonomous vehicles (AVs) represent a potential technological transformation of transportation systems, however, incidents involving them have highlighted the complex challenge of assigning liability. While a growing body of literature addresses legal and technical liability, the communication of liability – how legal, moral, or financial responsibility for adverse outcomes is conveyed among stakeholders such as manufacturers, users, insurers and policymakers – remains a critical gap. This multidisciplinary systematic literature review analyzes 90 academic articles published between 2015 and 2024 across a range of disciplines to map the current state of liability communication. Specifically, it examines how liability is communicated: who or what is held accountable for potential harms, under what conditions and through what mechanisms. We find that liability communication is often reactive, inconsistent and poorly aligned with public understanding. Despite the development of expert legal and technical frameworks, communication practices frequently fail to bridge the gap between expert discourse and end-user comprehension. The analysis is organised across five key themes: governance challenges; safety concerns; ownership models; cross-country comparisons; and future AV deployment. Across all five, communication failures are consistently linked to ambiguous terminology and the absence of proactive, standardised protocols. Together, these themes contribute to a more nuanced understanding of how liability is communicated within the evolving AV ecosystem. They also highlight an urgent need for updated policies and more effective, stakeholder-oriented communication strategies. In response, this study offers a necessary reframing of the problem – calling for the development of stakeholder-centric communication practices capable of functioning even amid legal uncertainty. Addressing these challenges is essential not only for effective AV integration but also for ensuring that this transformation unfolds safely and equitably.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:44:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673078</guid>
    </item>
    <item>
      <title>A Taxonomic Odyssey: Evolution, Criticisms, and Future Directions of Driving Automation Taxonomies – The Case of SAE J3016</title>
      <link>https://trid.trb.org/View/2659654</link>
      <description><![CDATA[While the Society of Automotive Engineers (SAE) International’s classification system (J3016) has provided a framework for categorizing sustained driving automation systems, concerns have arisen about its clarity and ability to incorporate emerging technologies . Therefore, this study explores how various stakeholders, including end users, vehicle manufacturers, and policymakers, use the driving automation taxonomy. The results show that driving automation taxonomy is communicated through media, incorporated into vehicle purchasing decisions for users, and utilized for external and internal communication by vehicle manufacturers and policymakers. The discussion highlights that utilizing specialized terminology in automation enhances communication efficiency. However, there is also a discrepancy between the SAE J3016, which is today’s prevalent taxonomy, and their audience in terms of both (1) clarity provided by the taxonomy vs. understanding of the stakeholders and (2) topics addressed by the taxonomy vs. needs of the stakeholders. The study also highlights that, while SAE J3016 is being criticized, proposing a clearly better taxonomy is far from straightforward. However, the authors underscore the importance of revising and updating the current taxonomy to align with stakeholder needs and technological advancements. By enhancing the clarity and relevance of the driving automation taxonomy, stakeholders can make more informed decisions, fostering innovation and improving communication across the industry.]]></description>
      <pubDate>Wed, 25 Feb 2026 13:58:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659654</guid>
    </item>
    <item>
      <title>AI-related papers in the transportation field: Statistics and evolutionary trends</title>
      <link>https://trid.trb.org/View/2644895</link>
      <description><![CDATA[It has become feasible to analyze research trends in the transportation field using NLP-based methods. However, existing studies mostly focused on the entire transportation discipline or on specific modes of transport, without specifically addressing the development of artificial intelligence (AI) technologies in transportation. To address this gap, this study collects AI-related publications in the transportation field and conducts a temporal evolution analysis. The selected journals include three categories: Transportation Research (TR) series, IEEE Transactions on Intelligent Transportation Systems (IEEE-ITS), and other representative Q1-Q2 transportation journals. Based on descriptive statistics, the authors apply LDA and STM method for topic modeling, and also use Vosviewer to analyze the network relationships among various elements. The results reveal several similarities among three categories: China ranks first in AI-related publication counts, followed by USA, with China surpassing USA in most cases between 2016 and 2019; Chinese institutions dominate the top positions in institutional statistics; the high-frequency terms in abstract texts are highly consistent across all journals; keyword clustering results consistently fall into three categories centered on machine learning, deep learning, and reinforcement learning. At the same time, notable differences are observed, including disparities in AI-related publication volumes, institutional affiliations, and topic modeling outputs. In addition, some unexpected patterns emerge: IEEE-ITS has seen a recent decline in publication volume, the keyword structure of IET-ITS has changed significantly, and some words once thought popular like “big data” and “data mining” have declined in prominence. Moreover, no new phenomena are identified when using STM, prompting further discussion.]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2644895</guid>
    </item>
    <item>
      <title>Proximity-centred accessibility – A conceptual debate involving planning practitioners worldwide</title>
      <link>https://trid.trb.org/View/2602226</link>
      <description><![CDATA[In recent years, the concept of proximity has garnered increasing attention in both transportation research and practice, albeit under various terms and interpretations. Among these, the concept of the 15-minute city has catalysed attention in planning practice, with recent evolution to the x-minute city and city of proximities. In research, proximity-centred accessibility has been offered as an umbrella term to express the ability to reach activities and destinations at short distances. Regardless of the terminology used, the essence of proximity lies in the ease with which one can access desired activities and destinations within reasonable travel times, independent of speed-enhancing transport modes most notably through walking. This research investigates the nuanced meanings ascribed to proximity-centred accessibility by planning practitioners globally, spanning diverse regional and local contexts. For this, we used an online survey, disseminated among over 9000 practitioners from 22 countries across 5 continents, which generated over 1300 responses. The survey explored the preferred terms for proximity-centred accessibility and their definitions, specifically emphasizing time and distance thresholds and the identification of relevant activities. By juxtaposing our findings with an earlier survey of accessibility researchers, this study also contributes to the groundwork for a conceptual framework for proximity-centred accessibility. Our findings affirm a relatively consistent interpretation of proximity among global planning practitioners, predominantly extending up to 1600 m, in accordance with earlier results for accessibility researchers. Despite some relevant dissimilarities among practitioners from megacities compared to their smaller city counterparts, or in specific countries (most notably the Netherlands), the distance that is considered proximate is the attribute that generates the most consistent results across different contexts. Also consistent was the relevance of short distances (up to 15 min walking) for activities such as primary and pre-primary schools, playgrounds, parks, food shopping, and pharmacies, reinforcing the importance of proximity to basic and caregiving activities. No term was found to be consistently meaningful across different contexts, although terms like local and neighbourhood accessibility and walking/pedestrian, or cycling accessibility, show higher preference in the global sample.]]></description>
      <pubDate>Thu, 20 Nov 2025 17:07:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2602226</guid>
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    <item>
      <title> Real Driving Database for Automotive Drive System Development</title>
      <link>https://trid.trb.org/View/2608415</link>
      <description><![CDATA[Knowledge of real-world driving behavior is fundamental to the development of drive systems. The derivation of representative requirements or driving cycles for use case-specific vehicle use allows a customer-centered drive system design. These datasets contain data such as distance, standstill times, average accelerations or a customer driving style estimation. In addition, the real-world data can be used for regulatory purposes such as the definition of utility factors or the definition of real driving emission cycles. In a research project funded by FVV e.V., we have developed a universal database software including data storage, user interface and general data plausibility functions for real driving data. The database contains detailed time series measurement data on component and vehicle level such as torque and speed of electric motors and internal combustion engines as well as general mobility data such as driving distance statistics. A key objective of the database development is generalization to ensure industry-wide applicability of the data for OEMs and suppliers as well as regulatory authorities and research institutions. In this paper we will present the database development, the database structure, its functionalities and application possibilities as well as the included publicly available data within the database. Based on the current data, we perform a statistical analysis of customer operations and discuss the applications of these data for drive system concept optimization and engine dynamometer testing.]]></description>
      <pubDate>Tue, 14 Oct 2025 10:35:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608415</guid>
    </item>
    <item>
      <title>Do Words Matter? Autonomous, Self-Driving, or Driverless Vehicles—The Impact of Word Choice on Individual Perceptions</title>
      <link>https://trid.trb.org/View/2582693</link>
      <description><![CDATA[This paper explores the perception and acceptance of autonomous vehicles (AVs) and the potential influence of different naming choices on public attitudes. Understanding how people perceive AVs becomes crucial in promoting their acceptance. This study examines three different naming options for AVs as “Autonomous,”“Self-Driving,” and “Driverless” vehicles, to investigate their impact on public perception. Key research questions include whether various names affect attitudes toward AVs, which demographic groups show more favorable or unfavorable responses to different naming choices, and individuals’ reactions concerning interest in riding in AVs, feeling safer with friends, sharing rides with strangers if information about them is provided, and perceived helpfulness of these vehicles for travel needs. This work employs binary probit models and an ordered probit model to tackle the research questions. In addition, a multivariate (quadrivariate) ordered probit model is formulated to address correlations between responses from the same respondents. Results show that the effect of wording indeed exists, mostly under the term “Self-Driving,” which was found to be the one preferred over “Driverless” and “Autonomous,” in that order. Age, gender, ethnicity, ridepooling experience, and walking or biking for work and non-work trips were found to be statistically significant as predictors for usage of AVs. Also, the results of the multivariate model reveal that certain estimates deviate from the individual models’ findings, thus corroborating the assumption that disregarding correlations between responses of the same individuals may lead to the risk of drawing inaccurate conclusions.]]></description>
      <pubDate>Fri, 08 Aug 2025 08:49:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2582693</guid>
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