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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>National Police Fleet Standards: First Edition</title>
      <link>https://trid.trb.org/View/2667388</link>
      <description><![CDATA[This item is one of a set of four standards drawn up by the Transportation Research Laboratory (TRL) for the United Kingdom (UK) National Police Chiefs’ Council (NPCC). The standards combine the best practices from police and operators of commercial vehicle fleets, creating a total approach to the life cycles of police vehicles. The standards accomplish several aims: Assemblage of best practices from police forces across the UK; Creation of a consistent national standard for fleet management; National minimum requirements and the encouragement of excellence; Empowerment and support for fleet managers; and a safe and effective police vehicle fleet. The standards reflect the unique characteristics and challenges of police fleets in the UK and will support adoption of new vehicle technologies as they emerge. The four standards cover the full life cycle of police vehicle fleets in the UK: Acquisition, conversion, maintenance, and disposal.]]></description>
      <pubDate>Mon, 11 May 2026 08:50:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667388</guid>
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    <item>
      <title>Mobile Electric Vehicle DCFC Infrastructure Deployment Opportunities</title>
      <link>https://trid.trb.org/View/2696128</link>
      <description><![CDATA[California Department of Transportation (Caltrans) is transitioning its vehicle fleet to electric vehicles (EVs) but currently faces challenges due to inadequate charging infrastructure. These infrastructure gaps cause operational delays, reduced fleet efficiency, and increased operational risks. To address these issues, Caltrans requires reliable mobile and semi-permanent direct current fast charging (DCFC) EV charging solutions. This study evaluates two charging systems, the EVESCO EVES-6060-NA and FreeWire Boost Charger 200, to determine their suitability for addressing Caltrans' specific operational challenges, compatibility concerns, and performance expectations.]]></description>
      <pubDate>Tue, 05 May 2026 10:19:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2696128</guid>
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    <item>
      <title>Overview of NLR Automated Mobility District Implementation Research – Phases I, II, and III The Convergence of Automation, Electrification, and On-Demand Services: Enabling Resilient Automated Mobility Districts</title>
      <link>https://trid.trb.org/View/2694446</link>
      <description><![CDATA[A research program by the National Renewable Energy Laboratory has been investigating the implementation prospects for fully automated passenger transport systems that are deployed to operate within dense urban settings, referred to as Automated Mobility Districts (AMDs). An AMD emphasizes the deployment of automated vehicles (AV) passenger transport services within a dense urban setting and other major activity centers with intense passenger origin-destination demand patterns, such as those found in large business districts, airports, and university and medical campuses. Phase I and II surveyed 10 early deployment sites and subsequently collected and evaluated the lessons learned from these early deployment sites, with particular attention to fleet operations, impacts of service reliability, and vehicle technology evolution as the field of companies was being progressively winnowed by the challenges of full automation. Phase III research assesses the implications of the simultaneous transformations of automation, electrification, and on-demand services for AVs operating as a "multimodal" transportation system within a large AMD. Topics investigated include the comparisons and contrasts between traditional fixed-route transit services with those of on-demand transit (ODT) services. The investigations also address the trade-offs between battery electric vehicle range (specifically battery capacity and the ability to add additional charge during service activity), charging parameters, power availability, and operations predictability with respect to policy and infrastructure configurations. Phase III also examines the criticality of station operations, also referred to as curb management depending on the reference context. Lessons and insights from simulations of automated transit network studies inform principles of design for boarding and alighting areas (referred to as stations in traditional transit, but often called pickup and drop-off zones in the context of modern on-demand mobility) for robust AMD operation. The benefits of integration of electrification (both opportunity and deep charging) into stations and the use of infrastructure intelligence both at intersections as well as at boarding and alighting zones is also examined. Lastly, due to the complexity of the tradeoffs between multiple sub-systems within an AMD, a systems engineering methodology is proposed to adequately simulate all the interplay in order to maximize the resilience and performance of large-scale AMD implementations that are anticipated to occur over the next 5-10 years. This report provides an overview of the findings that span phases I, II and III AMD research, and outlines critical areas of continued AMD development that will be the focus of phase IV research activities.]]></description>
      <pubDate>Tue, 05 May 2026 10:18:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694446</guid>
    </item>
    <item>
      <title>Do fringe benefit cars make the car fleet greener?</title>
      <link>https://trid.trb.org/View/2657065</link>
      <description><![CDATA[It has proven difficult to reduce carbon emissions from the transport sector; in fact, emissions from this sector are still increasing worldwide. Reducing emissions by reducing road transport is challenging; therefore, a transition to a vehicle fleet with low or zero emissions seems essential. Many new cars in OECD countries are sold to firms as fringe benefit cars (sometimes called company cars in the literature). The generous taxation of such cars has been shown to have negative welfare effects because it increases the consumption of cars. However, it is sometimes justified since it speeds up the transition of the car fleet to lower-emission vehicles. The purpose of this paper is to analyze how fringe benefit cars impact carbon emissions, fuel type, weight, size, engine power, and market value of new cars. We apply micro register data including all adult Swedes and their cars, spanning the years 1999 to 2020. By using a matching model that combines Exact matching and Mahalanobis distance matching, the fuel consumption of the fringe benefit car is compared to the hypothetical new private car that the employee receiving the fringe benefit would have otherwise purchased. We find that new fringe benefit cars tend to be larger, heavier, and more powerful than the hypothetical new private cars that fringe benefit car recipients would have otherwise purchased, However, we also find that new fringe benefit cars sold in 2019–2020 consumed 1.2 L less fuel per 100 km compared to hypothetical new private cars, a decrease of 20 percent. The lower fuel consumption of the fringe benefit cars in these years results from a higher share of electric vehicles among them. We also find that the likelihood of the fringe benefit car being an alternative-fuelled vehicle is 6 percentage points higher than if it was bought as a private car.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2657065</guid>
    </item>
    <item>
      <title>Fleet sizing with price-sensitive customers in Attended Home Delivery</title>
      <link>https://trid.trb.org/View/2597145</link>
      <description><![CDATA[Retailers offering Attended Home Delivery (AHD) struggle with thin profit margins due to high delivery costs and constrained routing flexibility. AHD requires retailers and customers to agree on specific time windows, limiting operational efficiency and increasing fleet requirements, particularly when customer preferences tend to cluster around peak times. While retailers have some ability to influence customer choices through pricing and availability strategies, failing to account for fleet costs and delivery constraints can lead to inefficient operations and reduced profitability. This study introduces an integrated approach to fleet sizing and time-window pricing for price-sensitive customers. We propose a Mixed Integer Programming (MIP) model that maximizes profit by balancing revenue and delivery costs, leveraging a nonparametric rank-based choice model to capture customer behavior while explicitly considering routing constraints and fleet ownership expenses over multiple periods. Using computational experiments on small-sized instances inspired by real-world data, we evaluate the impact of explicitly modeling routing costs, compare different pricing strategies, examine the effects of multi-period fleet planning, and assess sensitivity to varying customer and cost conditions. Results show that explicitly modeling routing constraints reduces profit loss by 29% compared to traditional cost approximations but increases computational complexity. To address this, we develop a Fix & Optimize (F&O) matheuristic approximate solution method that enables the application of our model to larger instances. Our findings emphasize the need for retailers to integrate demand management and fleet planning to optimize operational profitability.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2597145</guid>
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    <item>
      <title>Spatiotemporal forecasting and clustering of vehicle ownership in India: Mapping the future of personal, shared, and freight mobility (2001–2050)</title>
      <link>https://trid.trb.org/View/2686314</link>
      <description><![CDATA[District-level, long-horizon projections of vehicle fleet composition are central to aligning infrastructure investment, emissions policy, and mobility planning in rapidly motorising economies - yet most existing frameworks operate at national or state scale and collapse the functional diversity of vehicle types. This study develops a nationwide, district-level framework to project and classify long-term trajectories of personal motorised mobility, shared passenger transport, and freight movement. Monthly registration records from 1324 Regional Transport Offices, aggregated to 709 districts in India (2001–2024), are modelled using a rule-based, multi-model approach – Prophet, Holt–Winter, XGBoost, compound annual growth rate, and flat growth - selected according to district–category data characteristics. Forecasts for 2025–2050 are combined with hierarchical density-based spatial clustering (HDBSCAN) and uniform manifold approximation and projection (UMAP) to derive functional typologies. Historically, private vehicles expanded at 4.9% annually, shared commercial fleets at 8.97%, government-operated transit fleets at 3.81%, and freight vehicles at 6.8%. Projections indicate a slowdown in private vehicle growth to 3.07% annually, moderate expansion in government fleets (3.31%), sustained commercial demand concentrated in specific regions, and freight growth clustering around key logistics corridors. Clustering reveals stable, commercial-dominated zones, freight-oriented districts, and emerging private mobility hubs tied to industry, and trade networks. The results point to starkly uneven spatial trajectories: freight-oriented clusters in port-linked districts, persistent commercial dominance in the northeast, and high-growth private mobility zones in southern and central regions. The research provides a multi-model integration framework that integrates forecasting with spatial clustering for multimodal planning, low-carbon mobility strategy, and freight corridor development, thereby linking with the policy gap in transport geography for large, diverse economies.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2686314</guid>
    </item>
    <item>
      <title>Intelligent Speed Assistance in Fleet Management</title>
      <link>https://trid.trb.org/View/2693729</link>
      <description><![CDATA[This report presents lessons derived from the experiences of 13 fleets and six stakeholders in deploying active intelligent speed assistance (ISA) in commercial fleets. Largely, fleets with active ISA found that ISA can meaningfully reduce speeding and associated safety risks and even promote cost savings. The two main motivations cited by fleets engaged in this study were safety, for drivers and other road users, and financial considerations, such as reducing speeding violations and improving safety scores that can influence insurance costs. Across fleets with active ISA, speeding and related violations declined, and most reported downstream safety benefits such as fewer hard-braking events or potentially longer following distances. Several fleets also noted improved scores from the Federal Motor Carrier Safety Administration’s Compliance, Safety, Accountability program as speed violations diminished and roadside inspections decreased. Indirect financial benefits were reported through reduced incident and maintenance costs.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:55:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2693729</guid>
    </item>
    <item>
      <title>Energy and Operational Efficiency of Shared Autonomous Fleet Powered with Battery Electric and Internal Combustion Engine Technologies</title>
      <link>https://trid.trb.org/View/2692248</link>
      <description><![CDATA[Electrifying shared autonomous fleets (Robotaxis) presents challenges in balancing decarbonization, service quality, and operational costs, given the limited driving range, long charging times, and suboptimal planning of charging infrastructure. This study develops an integrated energy management and fleet dispatching simulation framework to support cost-effective, low-carbon Robotaxi deployment. The proposed system models both battery electric vehicles (BEV) and internal combustion engine vehicles (ICEV) technologies, and is extensible to other powertrain types. The study also integrates a life cycle assessment module to evaluate well-to-wheel carbon emissions. A total of 1,440 scenarios are designed to test the performance of two service modes (ride-hailing vs. ride-pooling) in terms of energy consumption, emissions, service quality, and operational costs, across varying levels of trip demand and market penetration of different powertrain technologies. The testing aims to verify the system’s effectiveness in improving energy efficiency, clarify the cost of autonomous vehicles electrification, and identify the most cost-effective low-carbon fleet composition under different scenarios. The results demonstrate that ride-pooling system outperforms both ride-hailing and private vehicles. Ride-pooling achieves 15–25% lower carbon intensity and 18–25% energy savings compared to private vehicles. It is also found that EVs present, on average, an 8–12% higher trip rejection rate than ICE fleets, demonstrating that electrifying Robotaxis comes at the cost of reduced service levels or increased costs. The study ultimately finds that electrifying Robotaxis at a moderate level (40–60%) can achieve a good trade-off between environmental benefits, service quality, and cost.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692248</guid>
    </item>
    <item>
      <title>Operational Feasibility of Battery Swapping in U.S. Transit: A Case Study of King County Metro</title>
      <link>https://trid.trb.org/View/2692179</link>
      <description><![CDATA[Battery swapping technology has emerged as a promising alternative to conventional charging for electric bus fleets, offering rapid turnaround times and improved vehicle availability. This paper utilizes existing bus routing information to perform an initial site evaluation for battery swapping stations. A Seattle-based public transit agency—King County Metro, a partner on this project—is used as a case study. Using General Transit Feed Specification (GTFS) data from King County Metro, a MATLAB model was built to reconstruct blocks and layovers, extracts dwell-time opportunities, and performs block-distance and block-time analyses to understand operational rhythms. based bus model was developed that maps route mileage, efficiency, and layover availability for battery swap decisions, using a look-ahead rule that defers battery exchanges whenever the next feasible layover can still be reached while respecting a minimum state-of-charge. The workflow estimates how many swaps each block requires over a service day, the effective driving range a pack can deliver between swaps, and the spatial clustering of recurring layovers. This clustering, combined with assumed battery swapping time provides initial identification of suitable battery swapping station placement. Results indicate that swap windows naturally emerge from scheduled layovers, enabling a swapping system to be layered onto current service patterns, providing estimates for station sizing by corridor demand, and planned within existing operational constraints. The approach offers a practical template for transit agencies to assess technical and operational feasibility and to start planning right-sized battery swapping infrastructure.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692179</guid>
    </item>
    <item>
      <title>Cradle-To-Grave Assessment of Austrian Passenger Car Traffic based on Actual Vehicle Movements and Derivation of Future Forecasts for Minimising Greenhouse Gas Emissions</title>
      <link>https://trid.trb.org/View/2692176</link>
      <description><![CDATA[As part of the decarbonisation process for passenger car fleet in Austria, battery electric cars in particular have been subsidised in recent years, as these vehicles are considered to be largely emission free during use and are expected to reduce emissions in future. However, in order to sustainably reduce the global greenhouse gas emissions of Austrian passenger car traffic, taking into account all types of fuel systems, it is necessary to apply a cradle-to-grave approach, as is commonly done in comparable analyses in the literature, which evaluates the emissions of the entire vehicle life cycle. The most important phase in the life cycle assessment remains the well-to-wheel phase, which includes emissions from energy supply and vehicle use. Due to the large number of influencing factors, highly simplified models are usually used for this phase in the literature. As part of this work, a methodology was developed that, allows an in-depth analysis of entire vehicle fleets by linking real vehicle movements with emissions data and energy consumption. By using real vehicle movements, environmental conditions (ambient temperature, etc.) and traffic situations (traffic jams, etc.) can be integrated into the emissions assessment. To capture the influencing factors more realistically, the assessment is performed at hourly rather than annual time intervals, unlike most previous studies. This new approach provides therefore a more detailed and realistic cradle-to-grave analysis of the Austrian passenger car fleet, making it possible to test individual measures in future scenarios and to define a coordinated strategy for minimizing the fleet’s future global greenhouse gas emissions.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692176</guid>
    </item>
    <item>
      <title>Techno-Economic Evaluation of Electrified Vehicle Options in Drayage Fleets</title>
      <link>https://trid.trb.org/View/2692149</link>
      <description><![CDATA[The electrification of drayage fleets offers potential economic and operational benefits, but the financial viability of electrified vehicles remains sensitive to battery cost, energy price, and fleet usage patterns. While total cost of ownership (TCO) is a useful benchmark, fleet operators and investors are equally concerned with investment performance metrics such as payback period (PB) and Internal Rate of Return (IRR), which better reflect financial risks and investment return timelines. This study develops a unified techno-economic framework that jointly evaluates TCO, PB, and IRR to determine when electrified trucks become cost-effective alternatives to diesel trucks. Building on a previously developed cost modeling tool and using real-world telematics data from a Class 8 drayage fleet at the Port of Savannah, the analysis incorporates projected battery cost trajectories, electricity and diesel price trends, vehicle efficiency improvements, and multiple battery capacities. Parameter ranges reflect widely cited projections and observed drayage-duty-cycle variability. A surrogate-modeling method approximates economic performance across thousands of battery cost–electricity price combinations, enabling high-resolution identification of conditions that achieve TCO parity, acceptable PB thresholds, and target IRR levels. Additionally, the study estimates the evolving share of the fleet that can feasibly electrify over time under multiple economic metrics. This integrated framework offers a novel, data-driven approach to inform risk-aware decision-making for fleet electrification and supports investment planning under evolving cost and operational conditions.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692149</guid>
    </item>
    <item>
      <title>Agile Safety Case Assessments for Autonomous Vehicle Fleets</title>
      <link>https://trid.trb.org/View/2692116</link>
      <description><![CDATA[Rapidly upcoming deployment of autonomous vehicles (AVs), including robotaxis and trucks, has intensified the need for rigorous safety assessment of complex AI-driven systems. While considerable effort has been invested in constructing safety cases for AVs, systematic approaches for evaluating these safety cases remain underdeveloped. This paper presents a three-stage methodology for assessing AV safety cases. A process for assessing argumentation is presented that involves traceability to pre-reviewed and peer-reviewed safety cases such as the Open Autonomy Safety Case (OASC). Next, we present a structured process for evaluating the quality of evidence supporting these arguments. We applied this methodology to evaluate safety cases from multiple AV developers, enabling iterative refinement throughout the development lifecycle. Our agile approach supports efficient assessments by establishing clear traceability to industry standards and enabling early identification of potential gaps. This work provides regulators, operators, and developers with a practical framework for systematically evaluating AV safety cases and identifies lessons learned and areas for continued improvement.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692116</guid>
    </item>
    <item>
      <title>Optimal Vehicle Sampling for Fleet-Wide Emissions Monitoring using Submodular Maximization</title>
      <link>https://trid.trb.org/View/2692000</link>
      <description><![CDATA[This article addresses the problem of optimal vehicle sampling for fleet-wide in-use emissions monitoring, a necessity driven by the absence of direct emissions sensors in modern production vehicles and the variable impact of in-use changes and operational factors (mileage, time-in-service, workload) on emissions performance across a fleet. Recognizing that comprehensive fleet testing is impractical due to significant downtime and cost, we propose a novel approach to identify a small, yet optimally informative subset of vehicles for sampling. The proposed approach leverages submodular function maximization, a technique rooted in optimal experimental design, specifically D-optimal design, to maximize the determinant of the information matrix (e.g., of XTX, where X is the regressor/design matrix in the case of a linear in parameters model). This approach ensures that the collected data yields maximum information for refining and building accurate models for emissions changes. We compare the submodular maximization strategy with conventional uniform and extreme sampling methods. Our simulation results demonstrate the potential for the submodular approach to outperform both alternatives by achieving lower variance (as measured by standard deviation and coefficient of variation) in estimating parameters for the assumed linear, quadratic, and simplified quadratic models for emission changes. The application of submodular function maximization is thus shown to be beneficial in vehicle fleet management for data collection in resource-constrained environments and leading to more accurate in-use emissions prediction. The envisioned process, in which a limited number of vehicles selected by our methodology are tested and the data are utilized to improve emissions models, can support the implementation of model-based strategies for engine emissions management.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692000</guid>
    </item>
    <item>
      <title>Survivability, Lethality, and Mobility Requirements of Military Vehicles Conducting Future Wet-Gap Crossings</title>
      <link>https://trid.trb.org/View/2691983</link>
      <description><![CDATA[Wet-gap crossings, which involve moving military forces across rivers and other water obstacles, remain among the most difficult operations to plan and execute. These maneuvers are complicated by choke points, fast-flowing water, and the exposure of forces and equipment to enemy fire. Despite these challenges, wet-gap crossings are critical to maintaining operational momentum during large-scale combat operations. This study examines doctrinal approaches to wet-gap crossings and explores the relationship between these operations and observed vehicle losses in the Russia-Ukraine War. Using a mixed-method approach, the analysis integrates daily operational reports from the Institute for the Study of War with visually confirmed equipment loss data from Oryxspioenkop. A custom Wet-Gap Relevance Score (WGRS) was developed using Natural Language Processing techniques to quantify the degree to which each ISW report focused on crossing operations. Statistical analysis shows that pontoon losses cluster within two days of major crossing events, confirming long-standing engineering doctrine regarding the vulnerability of bridging assets. However, the overall correlation between WGRS scores and total daily vehicle losses is weak, suggesting that broader attrition patterns obscure the distinct impact of crossing operations. These findings provide new empirical insight into how doctrinal principles manifest in modern conflict and underscore the design implications for future military vehicles. Effective wet-gap crossings require a diverse fleet: amphibious vehicles to establish bridgeheads, light vehicles that can be rafted to sustain momentum, and heavier vehicles that depend on bridging to continue the assault.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691983</guid>
    </item>
    <item>
      <title>Integrated DBSCAN-Based Segmentation of Tractor Activity into Productive and Non-Productive States from GPS Data</title>
      <link>https://trid.trb.org/View/2691931</link>
      <description><![CDATA[Accurate identification of Productive and Non-Productive States or tractor duty cycles—comprising working, idle, and transport states—is critical for performance analysis, fuel optimization, and emissions modeling in agriculture machinery and fleet monitoring. This study explores the application of integrated unsupervised machine learning (ML) techniques to classify duty cycles using GPS-derived parameters such as speed, location variance, and temporal patterns. Unlike supervised approaches, the proposed method does not rely on several labeled engine and vehicle parameters, making it scalable and adaptable across diverse operational contexts. Clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) in integration with hybrid rule-based and a road feature is employed to segment GPS data into distinct behavioral states. Feature engineering focuses on extracting motion signatures and spatial-temporal features that correlate with operational modes. Validation against manually annotated datasets demonstrates high accuracy in distinguishing idle, working, and transport phases. Furthermore, the present study demonstrates that by accurately determining the operational status of the tractor, unnecessary idling can be prevented through an idle avoidance system. Additionally, after assessing transport and working conditions, a movement-based control system for tire pressure adjustment is proposed. Both strategies have the potential to reduce fuel consumption by approximately 5-7%; however, this lies outside the scope of the present work. The framework offers a robust, data-driven solution for duty cycle monitoring and can be integrated into telematics systems for predictive maintenance and operational efficiency of the tractors.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691931</guid>
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