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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>Optimizing Traffic Light Control in Developing Cities Using Adaptive Federated Reinforcement Learning in Vehicular Ad Hoc Networks</title>
      <link>https://trid.trb.org/View/2712583</link>
      <description><![CDATA[Efficient control of urban intersections remains a major challenge in developing countries, where detection infrastructures are scarce or unreliable. This paper introduces a novel distributed architecture for traffic light management that relies exclusively on vehicle to infrastructure and infrastructure to infrastructure communications, avoiding the use of physical sensors embedded in the roadway or cameras. The approach integrates three complementary components: a long short-term memory model for traffic flow prediction, a deep q-learning agent for local phase optimization, and an adaptive federated learning mechanism ensuring coherence and robustness among traffic lights. The proposed framework was validated through simulations on the real-world “Ancien Dalip” intersection in Douala, Cameroon, using SUMO coupled with OMNeT++ and Veins. Results demonstrate significant improvements over baseline methods (fixed-time and deep q-learning), including reduced congestion, lower CO₂, NOₓ, and CO emissions, enhanced fairness across competing flows, and over 50% reduction in emergency vehicle waiting times. These findings highlight the relevance of combining predictive, reinforcement, and federated learning to design sustainable and scalable traffic management solutions tailored to resource-constrained urban environments.]]></description>
      <pubDate>Thu, 01 Oct 2026 09:25:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712583</guid>
    </item>
    <item>
      <title>Revocable Data Sharing Scheme without Ciphertext Update for VANETs</title>
      <link>https://trid.trb.org/View/2780903</link>
      <description><![CDATA[Revocable ciphertext policy attribute-based encryption (CP-ABE) provides an effective technology to achieve secure and dynamic data sharing for vehicular ad hoc networks (VANETs). However, existing revocable CP-ABE schemes rely on updating the ciphertext to ensure forward and backward security, where the legality of the updated ciphertext cannot be guaranteed and the cost of updating is also high. Aiming at this, a revocable data sharing scheme without ciphertext updates for VANETs is proposed. This scheme revokes attributes by introducing a chameleon hash function, and generating the update keys for users and the cloud, achieving constant ciphertext while ensuring the security of revocation. Furthermore, verification keys are developed to validate ciphertexts, guaranteeing they are generated with valid attribute encryption keys, thereby further ensuring the validity of ciphertexts. Also, outsourcing decryption and data verification are supported to efficiently optimize operations and maintain system security. The security analysis and performance evaluation results confirm the security and efficiency of the proposed scheme.]]></description>
      <pubDate>Tue, 29 Sep 2026 16:27:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780903</guid>
    </item>
    <item>
      <title>Cybertwin-Enhanced Distributed Authentication with Lightweight Cryptography for Secure Internet of Vehicles</title>
      <link>https://trid.trb.org/View/2761373</link>
      <description><![CDATA[The rapid development of the Internet of Vehicles (IoV) has increased the demand for robust authentication protocols capable of handling vehicle-node dynamics and resisting attacks. Traditional centralized methods, which suffer from latency and single-point failure issues, cannot meet IoV's real-time requirements. Although distributed authentication improves scalability and responsiveness, existing approaches still confront challenges regarding stability and reliability in dense vehicular networks due to static trust mechanisms and inefficient cryptographic operations. To address these challenges, we propose a Cybertwin-Enabled Distributed Authentication (CEDA) scheme that leverages lightweight cryptography and Cybertwin technology to reconstruct the authentication framework featuring dynamic trust distribution and real-time behavioral analytics. Specifically, CEDA establishes virtual digital twins of vehicles to enable real-time behavior tracking and dynamic simulation, thus enabling data-driven selection of vehicle agents based on real-time behavioral similarity. This mechanism reduces dependence on centralized trusted authorities by distributing trust across a network of dynamically selected agents, thereby enhancing fault tolerance and resilience against attacks. Furthermore, CEDA integrates an Edwards-curve-based digital signature algorithm to streamline cryptographic computations, significantly reducing authentication latency while strengthening security. A dynamic agent selection mechanism based on real-time vehicle behavior similarity further ensures efficient authentication in high-density vehicular environments. Comprehensive security analysis confirms the robustness of CEDA against a wide range of potential attacks, thus safeguarding the integrity of the IoV ecosystem. Simulation results demonstrate significant improvements in authentication efficiency and system scalability, particularly in scenarios involving frequent inter-regional vehicle authentications.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761373</guid>
    </item>
    <item>
      <title>A Learning-Based Incentive Mechanism for Mobile AIGC Service in Vehicular Edge Computing</title>
      <link>https://trid.trb.org/View/2761359</link>
      <description><![CDATA[In the field of Internet of Vehicles (IoV), the emergence of artificial intelligence generated content (AIGC) has provided a new approach for synthesizing, manipulating, and modifying driving data. The physical remoteness of cloud servers makes it challenging to ensure timely and secure data transmission. Therefore, it is essential to fully utilize edge servers and vehicular terminals equipped with small-scale generative AI servers to form a vehicular network. However, the allocation and transmission of tasks to vehicles face many challenging issues, such as underutilization of idle vehicle resources. In this paper, we establish an incentive mechanism driven by rewards based on different resource costs and design a multi-stage Stackelberg game under conditions of information asymmetry to address the issue of resource wastage in vehicular terminals. Specifically, self-interested vehicles are not forced to share resources or reveal their private information, such as vehicle dwell time and resource quantity. We first prove the existence and uniqueness of a Stackelberg equilibrium under complete information sharing. To handle information asymmetry without requiring vehicles to reveal private parameters, we then propose a proximal policy optimization (PPO)-based Vehicular terminal Resource Pricing mechanism (PVRP) that learns pricing strategies from interaction histories; this approach both preserves vehicular privacy and enables efficient convergence toward the Stackelberg equilibrium in dynamic settings. Extensive simulation results demonstrate that the proposed mechanism greatly outperforms existing methods in both static and dynamic vehicular networks.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761359</guid>
    </item>
    <item>
      <title>Mobility-Aware Cross-Region Federated Mutual Knowledge Distillation in Internet of Vehicles</title>
      <link>https://trid.trb.org/View/2761323</link>
      <description><![CDATA[By collaboratively training a global model without accessing private data from connected and autonomous vehicles (CAVs), federated learning (FL) enables efficient and privacy-preserving sharing and utilization of data in the internet of vehicles. However, in the real scenarios, these data often presents significant non-independent and identically distributed characteristics, which will affect model performance and communication efficiency. To address these problems, in this paper, we propose a mobility-based cross-region dynamic federated mutual knowledge distillation (MC-MKD) method. The proposed MC-MKD method first introduces a mobility-based CAV selection method and gives priority to CAVs with longer residence time and higher data quality, which ensures the collected data better represent the global data distribution. Secondly, a data filtering strategy based on density ratio estimation is proposed to eliminate abnormal samples with large differences in distribution from the perception range of the roadside unit. Building on this, the MC-MKD method employs a federated distillation (FD) framework with privacy protection, incorporates an R-Dropout regularization mechanism to mitigate overfitting, and introduces dynamic bidirectional knowledge distillation to achieve complementary knowledge transfer between the teacher and the student models. Experimental results show that the proposed MC-MKD method is superior to the existing FL methods in terms of model accuracy, communication overhead control, and privacy protection.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761323</guid>
    </item>
    <item>
      <title>GenAI-5GS Fusion Approach: A New Path for Enhancing Computation Offloading Efficiency of Complex Tasks in Internet of Vehicles</title>
      <link>https://trid.trb.org/View/2761309</link>
      <description><![CDATA[With the advancement of real-time communication and data interaction technologies in the Internet of Vehicles (IoV), the pivotal role of Mobile Edge Computing (MEC) in optimising vehicle service efficiency has become increasingly prominent. However, existing task offloading strategies still present significant room for improvement in handling complex tasks, delay control, and resource allocation for Roadside Units (RSUs). To reduce task offloading delay and enhance resource utilization, this paper proposes an offloading method integrating Generative Artificial Intelligence (GAI) with 5G Slicing (GenAI-5GS). First, a Graph-GAN-based vehicle trajectory prediction model is constructed. Operating with CPU/GPU utilization below 50% on resource-constrained RSUs, it enables precise prediction of vehicle movements to support proactive RSU deployment. Secondly, it innovatively integrates 5G slicing technology to dynamically allocate Resource Blocks (RB) within the dedicated 5.9 GHz IoV band. By combining multiple Modulation and Coding Schemes (MCS) to accommodate tasks of varying priorities, which enables slicing-based management and flexible resource allocation for RSUs. Finally, research findings demonstrate that the proposed method enhances trajectory prediction accuracy and resource utilization while reducing offloading delay, all while safeguarding multi-dimensional Quality of Service (QoS) requirements. Furthermore, this approach significantly improves IoV complex task processing performance and system stability, delivering safer and more efficient service experiences for in-vehicle users, thereby possessing clear engineering deployment value.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761309</guid>
    </item>
    <item>
      <title>A MW/MMW Shared-Aperture Antenna with Enhanced Dual-Band Performance Based on Structural Reutilization for Vehicular Applications</title>
      <link>https://trid.trb.org/View/2761298</link>
      <description><![CDATA[This paper proposes a dual-band shared-aperture antenna, consisting of a two-element microwave (MW) multiple-input multiple-output antenna and a four-element millimeter-wave (MMW) beam-steerable antenna array. Unlike general shared-aperture antennas, the proposed one not only realizes integration of two types of antennas, but also achieves dual-band mutual enhancement by leveraging the specific electromagnetic characteristic and structures of its sub-antennas. In the MMW frequency band, a column of quasi-mushroom shaped structures builds a compact array, and obtains improved gain and well beam scanning performance by reusing the natural metallic walls of the adjacent MW elements. Meanwhile, in the MW frequency band, the central independent MMW array also skillfully serves as a decoupling electromagnetic band-gap (EBG), suppresses the strong coupling between two planar inverted-F shaped antenna (PIFA) elements. More importantly, the MW decoupling frequency and MMW array operating frequency can be controlled independently, giving the design a certain degree of freedom. Experimental results show that the introduction of MMW antenna decreases the mutual coupling level of two MW elements from -9 dB to -18.2 dB, within bandwidth of 4.79-5.1 GHz (6.2%), satisfy the application in n79 frequency band. Simultaneously, MMW antenna array itself operating range from 25.9 GHz to 29.8 GHz (14%), covers n257 band, with a peak gain of 12.9 dBi and supports ±40° beam scanning. Hence, the proposed approach may be a good choice for designing a 5G vehicle communication antenna.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2761298</guid>
    </item>
    <item>
      <title>Digital Twin-Assisted Task Offloading and Resource Allocation in ISAC-Enabled Internet of Vehicles</title>
      <link>https://trid.trb.org/View/2780995</link>
      <description><![CDATA[The convergence of the Internet of vehicles (IoV) and 6G networks is driving the evolution of next-generation intelligent transportation systems. However, IoV networks face persistent challenges, including low spectral efficiency in vehicular communications, difficulty in achieving dynamic and adaptive resource optimization, and the need for long-term stability under highly dynamic environments. In this paper, we study the problem of digital twin (DT)-assisted task offloading and resource allocation in integrated sensing and communication (ISAC)-enabled IoV networks. The objective is to minimize the long-term average system cost, defined as a weighted combination of delay and energy consumption, while ensuring queue stability over time. To address this, we employ an ISAC-enabled design and introduce two transmission modes (i.e., raw data transmission (DataT) and instruction transmission (InstrT)). The InstrT mode enables instruction-level transmission, thereby reducing data volume and improving spectral efficiency. We then employ Lyapunov optimization to decompose the long-term stochastic problem into per-slot deterministic problems, ensuring long-term queue stability. Building upon this, we propose a Lyapunov-driven DT-enhanced multi-agent proximal policy optimization (Ly-DTMPPO) algorithm, which leverages DT for global state awareness and intelligent decision-making within a centralized training and decentralized execution (CTDE) architecture. Extensive simulations verify that Ly-DTMPPO achieves superior performance compared with existing benchmarks.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780995</guid>
    </item>
    <item>
      <title>An Efficient Vehicle Group Key Agreement Scheme Based on the Chinese Remainder Theorem in VANETs</title>
      <link>https://trid.trb.org/View/2780947</link>
      <description><![CDATA[In recent years, with the rapid advancements in vehicular technologies, the need for efficient group communication with minimal resource consumption in Vehicular Ad-hoc Networks (VANETs) has grown significantly. However, since vehicles predominantly use wireless communication with Roadside Units (RSUs), they face greater security risks and challenges. Recently, numerous researchers have combined the Chinese Remainder Theorem (CRT) with vehicular group key agreement schemes, leading to the development of various schemes that have garnered significant attention. However, in most of the existing schemes, it is required to use the cumbersome extended Euclidean algorithm to obtain the inverse element before the subsequent computation of the group key proceeds. Secondly, their schemes cannot support batch entry and exit of vehicles from the group. Finally, most of their schemes do not consider the possibility that an adversary may launch a security attack on the message during the group key generation phase. Hence, we present an efficient CRT-based vehicle group key agreement scheme in VANETs, which addresses the issues of existing schemes and improves the computational and communication efficiency. The security of the proposed scheme is proven through a formal security analysis based on the Real-or-Random (ROR) model. The final performance analysis and comparison demonstrates that our proposed scheme is better than the comparison scheme in security, computation and communication cost comparison. It is more suitable for group communication in VANETs.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780947</guid>
    </item>
    <item>
      <title>SDDS: A Scalable and Distributed Data Sharing Scheme in Vehicular Clouds</title>
      <link>https://trid.trb.org/View/2780946</link>
      <description><![CDATA[Vehicular clouds (VC) enable secure resource sharing among vehicles through ciphertext-policy attribute-based encryption (CP-ABE). However, most CP-ABE solutions depend on a Trusted Authority (TA) for private key generation, leading to the key escrow problem. While some schemes eliminate the TA, they often necessitate a centralized curator to manage parameters. In addition, these architectures are structurally bound to a pre-allocated user capacity, rendering them incapable of scalability. To overcome these limitations, we propose SDDS, a scalable and distributed data sharing scheme based on Registration-Based Encryption (RBE). SDDS leverages Secure Multi-Party Computation (SMPC) to achieve a decentralized framework, thereby removing the need for any trusted authorities or centralized curators. Furthermore, it supports unbounded user participation, facilitating flexible scalability without predefined user bounds. Formal analysis confirms the security, correctness of SDDS, while experimental evaluations show a significant reduction in computational and communication costs compared to related works.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780946</guid>
    </item>
    <item>
      <title>Polarization Reconfigurable Magnetoelectric Dipole Antenna with Broadband Stable Gain for Vehicular Satellite Communication System</title>
      <link>https://trid.trb.org/View/2780938</link>
      <description><![CDATA[To provide stable satellite communication quality for vehicles moving at high speeds or in extreme environments, gain robustness antenna represents an innovative solution. A polarization reconfigurable magnetoelectric dipole (ME-dipole) antenna with wide impedance bandwidths, broad 3-dB axial ratio (AR) bandwidths, and stable gain across different polarization states is proposed. To ensure consistent axial directional gain over a wide bandwidth, a design method based on the negative correlation complementarity of directional patterns in frequency variation is proposed. The ME-dipole radiator pair is composed of four rotationally symmetric end shorted patches, where the axial directional gain of the patches and metal vias structures in the ME-dipole exhibits a negative correlation with increasing frequency. Polarization agility is achieved through a reconfigurable feeding network, which utilizes multiple resonances and is implemented using an orthogonal coupling slot loaded with a DC-blocking gap and PIN diodes. When switching between different circular polarization (CP) states, the measured 3-dB AR bandwidths are 34.2% and 37.4% for left-handed circularly polarization (LHCP) and right-handed circularly polarization (RHCP), respectively. In addition to achieving peak gains exceeding 6dBic for both CP senses, the proposed antenna maintains a stable gain fluctuation of no more than 1.3 dB across a broad 31.03% operational bandwidths (1.96 - 2.74 GHz), which is crucial for enhancing the performance of vehicular S-band satellite communication systems.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780938</guid>
    </item>
    <item>
      <title>Personalized Federated Learning Scheme Based on Joint Communication and Intelligence Optimization for Tackling Long-Tailed Heterogeneity within IoV</title>
      <link>https://trid.trb.org/View/2781051</link>
      <description><![CDATA[Federated learning (FL) within Internet of Vehicles (IoV) leverages local data from vehicles or roadside units to train a shared intelligent model without raw data, reducing the local costs of computing and communication while enhancing security. However, FL within IoV faces severe data heterogeneity issues, resulting in poor performances for tail categories. Meanwhile, the constrained channels in dynamic IoV limit the data transmission, exacerbating the impact of heterogeneity on the efficiency and reliability of FL. This paper proposes a personalized FL scheme based on joint communication and intelligence optimization, to enhance the performances of long-tailed distribution oriented FL in IoV. Firstly, considering the two different imbalanced class distribution scenarios with task risks, we formulate and decouple the original problem. Then, for the general and lower-risk problem of class absence, we apply federated distillation to transfer the knowledge of missing classes from the global model to personalize the local models by fine-tuning. While for the frequent but higher-risk problem of class imbalance, we introduces a scheduling assessment on the clients' transmission rates and data distributions to prioritize their participation of FL. Furthermore, a time-threshold based filtering algorithm is proposed to exclude the participants with excessive delays. Simulation results demonstrated that the proposed scheme significantly improves the tail-class recognition accuracies on the CIFAR-10 long-tailed dataset, exceeding the baselines by over 10%, while reduce the convergence rounds and interactive costs, providing an efficient and reliable FL solution for the limited and dynamic IoV with low latency demands.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2781051</guid>
    </item>
    <item>
      <title>A Lightweight Privacy-Preserving Cache Sharing Scheme with Label Protection for Vehicular Networks</title>
      <link>https://trid.trb.org/View/2781047</link>
      <description><![CDATA[The growing Internet of Vehicles (IoV) requires efficient edge data management, where collaborative cache sharing between Roadside Units (RSU) and vehicles is crucial for reducing latency in applications like real-time navigation and collision avoidance. However, direct sharing of sensitive data such as vehicle locations and requests introduces serious privacy risks. To address this, we propose a lightweight privacy-preserving cache sharing scheme with label protection for vehicular networks. We first design an Oblivious Homomorphic XOR-PRF (Ohx-PRF) that employs key pre-distribution and homomorphic aggregation to eliminate oblivious transfer interactions. Building on this, we construct a non-interactive labeled delegated multi-party private set intersection (LDMPSI) protocol, where each data subset carries a classification label. Finally, we introduce a cache sharing scheme based on LDMPSI that uses efficient multi-point OPRF with only symmetric cryptography. By delegating computations to servers and processing data and labels in ciphertext, RSU can identify and store frequently requested content with minimal overhead. Formal analysis proves the proposed protocol and scheme are secure under the semi-honest model. Experiments show our scheme achieves over 85% cache hit rate, while LDMPSI reduces total runtime by 79% and communication overhead by over 80% compared to existing protocols, enhancing resource utilization and response capabilities in vehicular networks without compromising privacy.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2781047</guid>
    </item>
    <item>
      <title>ICRT: Intent-Driven Cross-Domain Routing Design for Task-Oriented Heterogeneous FANETs</title>
      <link>https://trid.trb.org/View/2781039</link>
      <description><![CDATA[Task-oriented heterogeneous Flying Ad Hoc Networks (FANETs) are emerging as network paradigms to meet increasingly complex task requirements. However, conventional cross-domain routing protocols face challenges such as heavy address matching, high routing overhead, and a lack of task-adaptive capabilities. We propose an intent-driven cross-domain routing framework, termed ICRT. The ICRT framework explores and exploits typical capabilities of intent-driven networks and named data networks to decouple the task control plane from the network forwarding plane to achieve routing flexibility. Furthermore, we propose a novel ICRT protocol to assign appropriate cross-domain forwarding routes to different services, ensuring that the transmission links of the services are adapted to the business requirements. Finally, we construct an emulation platform to validate the feasibility and efficiency of the presented ICRT framework and protocol. Simulation results show that the ICRT protocol outperforms the conventional FANET routing protocol, achieving a 350 ms reduction in end-to-end delay, an 11.8% increase in packet delivery ratio, a 1.5 Mbps improvement in network throughput, and a 10% reduction in routing overhead. It also ensures that the differences between all service requirements and link metrics remain within the order of 10-4.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:37:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2781039</guid>
    </item>
    <item>
      <title>A Tutorial on Future Vehicular Communications: From Performance Metrics to Open Research Directions</title>
      <link>https://trid.trb.org/View/2780973</link>
      <description><![CDATA[The study of vehicular networks has evolved into a new era thanks to rapid advances in electric, transportation, communication, and automobile technologies. However, the realization of modern applications like autonomous driving, intelligent transportation systems, and the smart city remain a major challenge due to the high requirements for performance metrics like data rate, latency, reliability, connectivity, coverage, and security. Moreover, in vehicular network, the performance metrics are not independent and may have a complex coupling relationship, i.e., optimizing one may result in degrading others. In this paper, we begin with discussing the coupling effect among the aforementioned performance metrics, aiming to provide a basic concept of the challenges of future vehicular networks. We aim to provide a comprehensive review of recent developments in vehicular communication technologies, such as the fifth-generation new radio (5G NR), dedicated short-range communications (DSRC), millimeter wave/terahertz communications, mobile edge computing (MEC), integrated sensing and communication (ISAC), uncrewed aerial vehicle (UAV) assisted vehicular networks, and blockchain-assisted security. The paper also covers the role of AI and its impact on the performance of each of the aforementioned applications. We then summarize several promising areas that can influence the future research in vehicular networks. Lastly, the paper concludes by highlighting key research challenges and future directions in the domain of vehicular networks.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:36:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2780973</guid>
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