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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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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    <item>
      <title>DOT Guide to CAB Sunset</title>
      <link>https://trid.trb.org/View/2730693</link>
      <description><![CDATA[The sunset of the Civil Aeronautics Board (CAB) on December 31, 1984, was a milestone event, not only for the airline industry but also for the entire transportation sector. On January 1, 1985, most of the residual functions of the CAB transferred, together with the associated CAB staff, to the Department of Transportation (DOT). The CAB's residual functions have been integrated into the existing DOT organizational structure, as described in detail in this manual.]]></description>
      <pubDate>Sun, 02 Aug 2026 17:23:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730693</guid>
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    <item>
      <title>The integration of neural networks in visual effects design for new energy vehicles</title>
      <link>https://trid.trb.org/View/2698268</link>
      <description><![CDATA[As the intelligent automotive industry thrives, artificial intelligence has deeply integrated into automotive design, driving the advancement of new energy vehicle (NEV) design. The focus lies in catering to users' design preferences to provide utmost interior comfort. This study utilises representative automotive exterior samples to train a backpropagation neural network model and to establish correlations between design element encodings and sensory evaluation metrics. The effectiveness is tested via samples. Additionally, users' sensory imagery words are collected for factor analysis. Subsequently, the exterior samples are refined; sensory word groups are identified; and the model is retrained through reassembly. This integration of AI and user sensory imagery analysis provides systematic methodologies for designers and aids in project design. Ultimately, based on factors such as colour, material and surface treatment, an ideal exterior sample for NEVs is predicted, offering a fresh perspective and approach to NEV design.]]></description>
      <pubDate>Wed, 29 Jul 2026 09:16:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2698268</guid>
    </item>
    <item>
      <title>Development of stress concentration factor curves for round sleeve terminal cavity with different r/d ratios in automotive application</title>
      <link>https://trid.trb.org/View/2680741</link>
      <description><![CDATA[Automotive connectors used in safety and infotainment systems employ round sleeve terminals for connectivity. In polarization insertion test, the plastic cavity is subjected to stress concentration at the sharp edges, avoided by introducing edge blends. The test per automotive specifications requires for round sleeve terminals to be restricted from entering the cavity during assembly below a specified force. This condition was not being met and certain mechanical failures were observed at the cavity edges. Why-Why analysis revealed the most likely root cause for this failure as stress concentration at the sharp corners. To overcome this, two re-design ideas were considered. The double edge blend and single edge blend features were thus taken up for the stress concentration study but the same was scarcely reported in literature and thus the development of Stress Concentration Factor curves was identified as the gap. The model was meshed with adaptive refinement near the edge blends in Ansys 2024 R1. The maximum stress was noted down and parametric simulation was run for r/d ratio ranging from 0.01 to 0.25 with steps of 0.01 for D/d of 1.3, 1.2 and 1.15 and chamfer angles of 30°, 45° and 60°. The data was used to consolidate the Stress Concentration factor values and the consolidated curves were developed subsequently. Second order polynomial regression equations and logarithmic best fit curve equations along with respective acceptable correlation coefficients were summarized. 3D surface plots for interaction of parameters were obtained. A comparison of maximum stress values derived from the best fit curves and finite element analysis for various combinations showed a good agreement with a maximum deviation of 6.6%, thereby indicating Stress Concentration Factor curves generated in the study can be faithfully used for similar configurations.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:46:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680741</guid>
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    <item>
      <title>National Transportation Safety Board: Use of Competitive and Noncompetitive Contract Awards to Acquire Products and Services</title>
      <link>https://trid.trb.org/View/2732351</link>
      <description><![CDATA[The National Transportation Safety Board (NTSB) is an independent establishment of the U.S. government that plays a vital role in advancing transportation safety. Specifically, NTSB investigates and determines the probable cause of transportation accidents, issues safety recommendations, and promotes safety improvements. NTSB is responsible for investigating every civil aviation accident in the United States and certain significant events in other modes of transportation—railroad, highway, marine, pipeline, and commercial space. To support its mission, NTSB procures a variety of products, such as laboratory equipment, and services, such as information technology services, to develop and maintain systems and applications used to investigate and establish the probable causes of accidents. According to NTSB’s reported data, in fiscal year 2024, NTSB obligated approximately $22.5 million on contract awards. Under federal law, agencies, including independent establishments like NTSB, are generally required to promote full and open competition in awarding contracts, with some exceptions. However, in some circumstances, NTSB may consider other options when procuring products and services in accordance with statute, the Federal Acquisition Regulation (FAR), and NTSB’s policies and procedures. Specifically, NTSB may award contracts for which all responsible sources may compete (competitively awarded contracts) or award contracts using other procedures (noncompetitively awarded contracts). The FAA Reauthorization Act of 2024 includes a provision for the Government Accountability Office (GAO) to report on NTSB’s procurement and contracting planning, policies, and practices, including those related to awarding noncompetitive contracts. (Pub. L. No. 118-63, § 1223, 138 Stat. 1025, 1433 (2024)). In this report, GAO describes the statutes, regulations, executive orders, and internal guidance that govern NTSB’s procurement policies and procedures. GAO also describes NTSB’s procedures for awarding noncompetitive contracts. In addition, GAO provides information on NTSB’s contract obligations for fiscal year 2020 through fiscal year 2024.]]></description>
      <pubDate>Mon, 27 Jul 2026 09:06:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2732351</guid>
    </item>
    <item>
      <title>Evolution of Automotive Electrical Equipment Industry. Electroprecizia Sǎcele-Braşov Case Study</title>
      <link>https://trid.trb.org/View/2579327</link>
      <description><![CDATA[The history of electrical equipment for road vehicles is directly linked to the evolution of the conception and development of vehicle manufacturing, following the trends of their modernization, as well as the requirements to increase automotive performance, given that restrictions on environmental pollution have become more and more severe. The cornerstone in the development of electrical equipment for vehicles was to establish appropriate methods of dimensioning electrical equipment that would guarantee their safe and economical operation and ensure the designed performance of vehicles. Thus, the development of the automotive electrical equipment industry followed that of the automotive industry. In this paper a brief overview of the history of automotive electric equipment development worldwide, especially following innovations in the field of automotive ignition system, is provided. A more detailed analysis is made in connection with the development of the electrical equipment industry in Romania. The case study conducted on the evolution of automotive electric equipment manufacturing at the enterprise Electroprecizia Sǎcele-Braşov in Romania shows that the socio-economic transformations undergone by the Romanian society after World War II influenced the conditions in which the technological boom took place, transitioning from products assimilated based on license, to those based on original designs.]]></description>
      <pubDate>Mon, 13 Jul 2026 10:46:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579327</guid>
    </item>
    <item>
      <title>Two Personalities Who Attest to the Bivalent Connection Between the Development of the Electrical Automotive Equipment Sector and Electrotechnical Higher Education in Brasov</title>
      <link>https://trid.trb.org/View/2581401</link>
      <description><![CDATA[With the implementation of Romania’s industrialization plan in the 1950s, production activities at Electroprecizia Săcele factory has been oriented towards manufacturing electrical equipment for tractors and trucks. New investments were made, so that in 1955 the production of automotive electrical equipment exceeded that of electric motors. It was during this period when the new electromechanical engineer Mircea Cristea (1926–1963) got a job at Electroprecizia Sǎcele, soon becoming one of the basic pillars of the enterprise in designing and launching electrical equipment into production. The activity of innovator and good organizer engineer Mircea Cristea continued within the Faculty of Mechanics of the Polytechnic Institute of Brasov, where, as a teacher, he contributed in 1962 to the establishment of the study program of Electromechanics and of the Department of Electrotechnics and Electrical Machines, as well as to the introduction of the discipline of Automotive Electrical Equipment. A supporter of electrotechnical education, with developments in the field of automotive electrical equipment, was Professor Ioan Valer Matlac (1939–2015) who formed research teams in the field and who introduced the discipline of Automotive Electrical Equipment into the specializations of Electromechanics, Electrotechnics, continuing the collaboration with the Department of Automotive within the Polytechnic Institute of Brasov. This article analyses and highlights the activity of these two personalities who contributed to the development and implementation of the production of electrical equipment for automotive and promoted the electrotechnical higher education in Brasov in this field.]]></description>
      <pubDate>Mon, 13 Jul 2026 10:46:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581401</guid>
    </item>
    <item>
      <title>Vision-language model-based scene understanding and decision-making for autonomous vehicles with a tailored augmented reality vehicle-in-the-loop testing platform</title>
      <link>https://trid.trb.org/View/2673124</link>
      <description><![CDATA[Vision-language model (VLM) demonstrates substantial potential for autonomous driving, yet adapting VLM to safety-critical scenes presents significant challenges, particularly in achieving robust spatio-temporal scene understanding and decision-making, which further constrains closed-loop operation under on-vehicle computational constraints. In this paper, we present a VLM-based framework that integrates scene understanding with decision-making, specifically designed for efficient deployment. Initially, a domain-aligned VLM is fine-tuned on a natural dataset, and a pre-decoder token selection module compresses multi-frame visual tokens before the language interface, substantially reducing inference latency while preserving task-relevant semantics. Subsequently, high-level intentions are translated into interpretable, safety-aware trajectories through a lightweight sampling-based motion planner. Furthermore, to evaluate the stack realistically yet safely, we develop a Vehicle-in-the-Loop platform featuring virtual-real image fusion, where simulated agents and infrastructure are composited into live camera streams, enabling controlled testing of complex scenarios on PC-class hardware. Ultimately, the fine-tuned VLM achieves robust scene understanding and 82.9% decision accuracy. A real-world roundabout and T-intersection case study further demonstrates reliable closed-loop driving performance, executing smooth merging maneuvers and evasive actions while maintaining appropriate safety margins. These results provide a practical foundation for deploying interpretable VLM-guided autonomous systems while bridging the sim-to-real gap, promoting intelligent transportation system design and testing.]]></description>
      <pubDate>Wed, 24 Jun 2026 13:22:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673124</guid>
    </item>
    <item>
      <title>Latest Reliable Power Electronics Technologies for Zero-Emission</title>
      <link>https://trid.trb.org/View/2581613</link>
      <description><![CDATA[This paper presents use cases to demonstrate the advances in simulation, prognostic health management and digital twins, and power electronics for electric vehicles and gives an insight into the potential for the end users in the future.]]></description>
      <pubDate>Thu, 18 Jun 2026 08:54:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2581613</guid>
    </item>
    <item>
      <title>Topic mining and evolution analysis of civil aircraft operational risk factors: A novel domain-adapted BERTopic framework</title>
      <link>https://trid.trb.org/View/2681999</link>
      <description><![CDATA[Topic mining and evolution analysis of civil aircraft operational risk factors are essential for advancing proactive risk awareness capabilities. Given the complex operational scenarios and specialized safety text data, existing studies face challenges of insufficient domain adaptability, hindering accurate safety hazard troubleshooting. To address these issues, this paper proposes a domain-adapted BERTopic (DA-BERTopic) framework for mining and analyzing risk topics. First, a domain-adaptive topic embedding model is developed to enhance the risk semantic representation capability of general pre-trained models. Second, an enhanced topic dimensionality reduction method incorporating downstream task objectives is proposed to improve the model's scenario adaptability and generalization. Subsequently, an adjustable topic extraction algorithm is designed to strengthen core risk topic representation. Finally, based on the mined topics, key safety hazard troubleshooting points are identified, and risk evolution analysis is conducted. Using the Aviation Safety Reporting System (ASRS) and National Transportation Safety Board (NTSB) datasets, the proposed approach is evaluated against state-of-the-art baselines. The results demonstrate that the proposed framework significantly outperforms existing methods, achieving topic coherence (Cₙₚₘᵢ) scores of 0.999 and 0.992 on the two datasets, respectively, alongside high topic diversity. This improvement demonstrates the method’s potential to provide more accurate and actionable insights for safety decision-making.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:13:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2681999</guid>
    </item>
    <item>
      <title>Vulnerability assessment of vehicle access system using software-defined radio (SDR): attacks, testing, comparative analysis and future countermeasures</title>
      <link>https://trid.trb.org/View/2677527</link>
      <description><![CDATA[Remote Keyless Entry systems have become an integral component of contemporary vehicles, enhancing user convenience while simultaneously posing potential security challenges. However, with this convenience comes a growing concern over potential security vulnerabilities, particularly those involving radio frequency (RF) attacks such as replay attacks, relay attacks, and signal jamming. This research paper investigates the vulnerabilities of RKE systems across multiple car models, evaluating their susceptibility to RF-based exploits. Our testing results reveal significant disparities in the security robustness of different car models, highlighting the need for improved encryption protocols and signal authentication mechanisms. This study provides a comparative analysis of RKE vulnerabilities, offering insights into the current state of automotive security and proposing recommendations for mitigating risks associated with RF attacks. By offering a comparative analysis of performance and security parameters, this research aims to inform automotive manufacturers, cybersecurity experts, and consumers about the inherent risks associated with current RKE implementations and to advocate for the development and adoption of more secure vehicular access technologies.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2677527</guid>
    </item>
    <item>
      <title>Circuit–Field Cosimulation for Predicting Internal Transient EMI and Optimizing EMC in Electric Locomotives During Neutral Section Passage</title>
      <link>https://trid.trb.org/View/2665514</link>
      <description><![CDATA[Transient electromagnetic interferences (EMIs) occurred within electric locomotives during the neutral sections passing process can cause abnormal functioning of internal electronics systems, posing significant risks to safe and stable operation of locomotives. This article establishes a circuit-field cosimulation model to predict and analyze the transient electromagnetic environment inside locomotives during neutral sections transitions, achieved by performing transient simulations of circuit topologies encompassing traction power supply system and electric locomotive high-voltage system and providing transient interference source excitation for full-wave electromagnetic environment simulation within locomotives through circuit simulation. The accuracy of the proposed model has been validated through practical measurements, enabling the quantification of transient EMI levels and their frequency-domain characteristics during this process. Furthermore, the influence factors on in-cabin transient EMI levels have been systematically investigated, revealing breaker’s switching phase angle and arc reignition characteristics are the key parameters determination the interference level, providing critical insights for optimizing electromagnetic compatibility (EMC) design in electric locomotives.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:13:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665514</guid>
    </item>
    <item>
      <title>Freight rail industry dynamics and STB's 2021 demurrage policy revisions: An empirical analysis</title>
      <link>https://trid.trb.org/View/2692405</link>
      <description><![CDATA[In 2021, the U.S. Surface Transportation Board STB adopted revised demurrage regulations aimed at improving transparency and uniformity to ensure that demurrage charges promote the efficient use and circulation of railcars. This study evaluates whether demurrage charges from 2018 to 2021 reflected underlying operational conditions prior to the regulatory revision. Using a unique quarterly dataset of Class I freight railroad demurrage charges, we estimate a conceptual structural demurrage equation with firm-year fixed effects to identify the primary determinants of demurrage. The analysis investigates the extent to which these charges diverged from actual operational dynamics, providing empirical context to STB's revision to demurrage regulations. The results show clear and statistical significance difference in how individual railroads' pricing strategies and operational decisions influence demurrage. In contrast, both core-measures of network performance; terminal dwell time variance and system average train speed and seasonal variables do not fully account for changes in demurrage charges. This means that demurrage practices were not aligned with the Class I freight railroad industry operational dynamics. Further, this suggests that demurrage was not achieving its statutory purpose of incentivizing the efficient use of railcars. Simulations results of demurrage per carload reinforce the observation that industry dynamics were consistent with STB's intervention.]]></description>
      <pubDate>Thu, 30 Apr 2026 16:39:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692405</guid>
    </item>
    <item>
      <title>Personalized Driving Data-Based Bump Prediction Using a Cloud-Based Continuous Learning for Preview Electronically Controlled Suspension</title>
      <link>https://trid.trb.org/View/2659130</link>
      <description><![CDATA[An electronically controlled suspension with road preview enhances ride comfort and driving stability. For detecting road irregularities such as speed bump, look-ahead sensors such as camera and Light Detection And Ranging (LiDAR) sensors have been used. However, these preview systems perform poorly in night and adverse weather conditions. To overcome these limitations, this paper proposes a novel machine learning-based speed bump prediction system that predicts remaining distance to and height of speed bumps using in-vehicle data, which is robust to external environment and represent driving pattern of driver. The proposed system consists of three components: machine learning-based bump predictor, automatic training dataset generator, and cloud-based continuous learning system. The effectiveness of the proposed system was confirmed through tests with varied datasets in different drivers and locations. The prediction of the distance to the speed bump demonstrated a Root Mean Square Error (RMSE) of 6.37 meters, while the height prediction showed an RMSE of 0.9 cm. Additionally, after applying cloud-based continuous learning for driver-specific personalization, the RMSE for the distance prediction improved to 5.96 meters. Furthermore, experiments conducted under challenging conditions showed that our system outperformed existing systems.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659130</guid>
    </item>
    <item>
      <title>A method for considering road curvature's impact on aggressive turning</title>
      <link>https://trid.trb.org/View/2646875</link>
      <description><![CDATA[Addressing the current issue of not considering the influence of road geometry in the recognition of aggressive turning, a dangerous driving behavior, this paper proposes a method to eliminate the impact of road curvature on the recognition of aggressive turning based on On-Board Diagnostics (OBD) trajectory data and high-precision electronic map data. The method is validated using Jinan City as the study area. The results show that the proposed method effectively removes the influence of road curvature on the recognition of aggressive turning (left or right turns), reduces misjudgments of dangerous driving behaviors of drivers, and enhances the accuracy of identifying drivers' aggressive turning hazardous behaviors.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646875</guid>
    </item>
    <item>
      <title>TRB Core Program Services for a Highway RD&amp;T Program – Federal Fiscal Year 2026/TRB (State DOTs) Fiscal Year 2027</title>
      <link>https://trid.trb.org/View/2692353</link>
      <description><![CDATA[The transportation research community consists of numerous partnerships to aid in the conduct of research and the implementation of technologies and innovations.  The Federal Highway Administration (FHWA), the State Departments of Transportation (SDOTs), and the National Academy of Sciences (NAS) are among these partners, who work closely in many facets of the national research program. The Transportation Research Board (TRB)’s mission is to promote innovation and progress in transportation by stimulating and conducting research, facilitating the dissemination of information, and encouraging the implementation of research results. TRB fulfills this mission through the work of its standing technical committees and task forces addressing all modes and aspects of transportation; publication and dissemination of reports and peer-reviewed technical papers on research findings; administration of contract research programs; conduct of special studies on transportation policy issues; maintenance of Transport Research International Documentation (TRID); and hosting an annual meeting that attracts approximately 14,000 transportation professionals from throughout the United States and abroad. This pooled fund provides a mechanism for States to transfer funds to FHWA to add to the TRB Core Program Services cooperative agreement.

OBJECTIVE; The objective of this program is to provide a mechanism for State transportation departments to support the TRB's core program and services.]]></description>
      <pubDate>Tue, 14 Apr 2026 20:24:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692353</guid>
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