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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>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Driverless multipurpose vehicles for sustainable urban road transportation : integrated vehicle fleet optimization and routing</title>
      <link>https://trid.trb.org/View/2752088</link>
      <description><![CDATA[This thesis assesses the energy and operational implications of driverless multipurpose vehicles (DMV) deployment in urban road transportation from a system-level, operational perspective. It addresses three research questions. First, how the energy consumption of DMV fleets can be estimated and compared against human-driven battery-electric vehicle (BEV) and combustion vehicle (CV) fleets under realistic urban operating conditions. Second, how key vehicle-level and transportation system-level factors can be integrated into fleet-level optimization on realistic urban road networks. Third, what energy and operational implications emerge from DMVs with an interior-reconfigurable architecture (IRA) type compared with human-driven BEV and CV fleets across multiple cities and operational scenarios. The thesis also proposes a practice-based taxonomy of eight architectural strategies of DMVs grounded in design for changeability theory. The methodological contribution is a two-stage optimization framework coupling fleet sizing, mix, and routing with a deterministic microscopic energy consumption model on real urban road networks. The first stage solves energy-minimal shortest path problems incorporating edge-specific driving profiles and key vehicle-level and transportation-system-level factors, producing a reduced graph. The second stage solves the novel Fleet Size and Mix Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery on this reduced graph. The energy consumption model is evaluated against measured energy data from 18 trips of a battery-electric truck operating on a fixed urban route in Östersund, Sweden: it overestimates absolute energy use but reproduces subroute energy rankings, supporting its use for comparative fleet assessment. The framework is applied across Stockholm, Paris, and Lisbon with 50 transportation operations per city and per fleet type, and four objectives. CV fleets consistently have the highest energy consumption, while BEV and DMV fleets show comparable energy use across all cities and objectives.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752088</guid>
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    <item>
      <title>Characterization of electric vehicle usage patterns to estimate the flexibilities and potentials for smart charging</title>
      <link>https://trid.trb.org/View/2752085</link>
      <description><![CDATA[Electrification of passenger vehicles is an important measure to decarbonize the transport sector. An efficient introduction of electric vehicles (EVs) requires an understanding of how the charging of EVs impacts the electricity system and if, and to what extent, smart charging strategies, including vehicle to grid (V2G) services, can support the electric grid in the future energy systems. The aim of this thesis is to characterize the flexibility of smart charging including V2G by analyzing the real-world driving, parking, and charging patterns obtained from logged EVs. The analysis is based on data collected from 394 privately owned EVs and survey responses from their owners. The results reveal substantial flexibility potential for smart charging from several perspectives. However, the findings also highlight important limitations that must be carefully considered when estimating flexibility or implementing flexible charging into energy system models. Using the lower state of charge (SOC) threshold for charging decisions and the SOC when charging ends, the flexible battery capacity range is calculated to be 59% on average. The aggregated SOC for all logged EVs is within 60%-80% throughout the entire logging period. The results show that charging is needed in fewer than half of the days in a week for more than 73% of weeks, regardless of the attributes of the EV owners, including commuter category and battery capacity. Furthermore, EVs are charged more frequently than the minimum number of charging events required per week. Thus, there is potential for charging in a way that is flexible in time depending on, for example, grid congestion or spot prices. This is particularly the case for non-commuters with large-battery EVs. The amount of time that EVs are plugged in for smart charging differs by more than a factor of two if one assumes that EVs are plugged in whenever they are parked at home and that EVs are plugged in only when they charge during the parking event.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752085</guid>
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    <item>
      <title>Potential impacts of wind farms on shipping in the Bay of Bothnia</title>
      <link>https://trid.trb.org/View/2752074</link>
      <description><![CDATA[Winter navigation in the Bay of Bothnia relies on a highly adaptive routing system coordinated by icebreaker services, where commercial vessels follow routes instructed by icebreaker officers based on prevailing ice concentration, thickness, ice drift, and operational constraints. Planned offshore wind farms (OWFs) introduce fixed structures into this dynamic environment, raising concerns that they may constrain routing flexibility, alter local ice conditions, and increase operational risks during winter navigation. At present, however, systematic knowledge on how OWFs spatially interact with winter shipping routes in the Bay of Bothnia is limited. The aim of this pre-study is to provide an initial, evidence-based assessment of the potential interactions between planned OWFs and winter navigation. Specifically, the study seeks to identify where OWF areas overlap with winter shipping routes, examine how these overlaps change under different winter severity conditions, and capture operational concerns and risk perceptions from experienced winter navigation stakeholders. The analysis combines AIS-based reconstruction of winter ship trajectories with ice condition data and stakeholder input. Cargo vessels and tankers are analysed using an intersection rate, defined as the percentage of unique vessels whose winter routes intersect each OWF area each winter month. The icebreaker activity is analysed separately. Ice conditions are characterised using Copernicus Marine Service data on ice concentration, thickness, and drift, together with a winter-severity indicator derived from multiple sampling points across key traffic corridors and OWF zones. In parallel, qualitative insights are collected via an online questionnaire and in-depth interviews with shipmasters with experience in winter navigation. The study covers four representative winters, spanning mild, normal, and severe ice conditions.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:41 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752074</guid>
    </item>
    <item>
      <title>Urban planning and transport walking : examining the effect of built environment, psychological factors and socio-demographics on walking as a transport mode in a Swedish context</title>
      <link>https://trid.trb.org/View/2752067</link>
      <description><![CDATA[This thesis addresses how built environment characteristics shape walking behavior by examining the relationship between GIS-based built environment measures and GPS-tracked transport walking, along with self-reported socio-demographic and psychological characteristics of individuals, in two medium-sized Swedish cities, Umeå and Linköping, across multiple datasets collected in 2019 and 2021. A methodological foundation for the empirical work is established by evaluating two emerging data sources for pedestrian study: a Wi-Fi-based flow measurement system (Bumbee Labs) and a GPS-enabled travel survey application (TravelVu), finding that the two methods are complementary, with the former capturing large-scale pedestrian flow patterns and the latter providing individual-level route, distance, and attitudinal data suited to behavioral analysis. Built environment exposure, in this thesis, is operationalized at two spatial scales: 1. potential exposure, measured within a 750 m radius of each participant's home location, and 2. realized exposure, measured within a 15 m buffer along GPS-traced actual routes and their shortest-path alternatives. This dual-scale design enables a direct empirical comparison of which environmental features predict how much people walk versus which features shape the paths they take when they do. Socio-demographic variables and psychological variables, derived from a Theory of Planned Behavior questionnaire integrated into the tracking application, are examined both for their direct associations with GPS-measured walking outcomes and as potential mediators and moderators of built environment effects. Analytical methods include bivariate correlation analysis, structural equation modelling, linear mixed models, and discrete choice analysis.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752067</guid>
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    <item>
      <title>Contributions to branch-and-price methods for electric vehicle routing problems</title>
      <link>https://trid.trb.org/View/2752064</link>
      <description><![CDATA[The Vehicle Routing Problem (VRP) is a fundamental combinatorial optimization problem concerned with determining cost-efficient routes for a fleet of vehicles serving a set of customers under operational constraints. In recent years, the electrification of transportation has led to the emergence of the Electric Vehicle Routing Problem (EVRP), where routing decisions must account for battery charging requirements and energy-related constraints. These additional considerations significantly increase the complexity of the problem. This thesis studies exact solution approaches for the EVRP based on branch-and-price algorithms, the state-of-the-art methodology for solving large-scale vehicle routing problems to optimality. Branch-and-price combines branch-and-bound with column generation, where the master problem is solved using linear programming relaxation and new columns (routes) are generated dynamically by solving a pricing problem. For the EVRP, the pricing problem takes the form of an elementary shortest path problem with resource constraints, which is typically solved using labeling algorithms.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752064</guid>
    </item>
    <item>
      <title>Stochastic and learning-based control strategies for electric autonomous mobility systems</title>
      <link>https://trid.trb.org/View/2752048</link>
      <description><![CDATA[Electric Autonomous Mobility-on-Demand (E-AMoD) systems offer a path toward sustainable urban transportation through the coordinated operation of shared, zero-emission autonomous vehicles. Yet their deployment poses difficult operational challenges: fleet rebalancing, vehicle routing, and charging must be managed jointly, under uncertainty, and within tight computational budgets. A central argument of this thesis is that no single decision-making paradigm suffices. Optimization-based methods are well suited for strategic fleet control where uncertainty guarantees and feedback are essential, while learning-based methods become necessary at finer operational scales where real-time optimization is computationally prohibitive.Three contributions are presented, each targeting a different operational level. The first introduces a chance-constrained model predictive control (MPC) framework for station-level fleet rebalancing, combining Gaussian Process Regression for probabilistic demand forecasting with a hierarchical architecture that separates strategic rebalancing from tactical matching. The second extends this framework to electric fleets operating under multiple interacting uncertainties, employing a tailored Nested Benders Decomposition to maintain metropolitan-scale tractability without sacrificing MPC's receding-horizon feedback. The third contribution shifts to node-level electric dial-a-ride routing, including pickup-delivery sequencing, time windows, and ride-time constraints, and proposes a deep reinforcement learning approach built on a Graph Edge Attention Network capable of handling hundreds of requests with second inference times. Taken together, the three contributions show that optimization and learning serve complementary roles in E-AMoD operations, with the appropriate paradigm determined by the granularity and real-time demands of the problem at hand.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752048</guid>
    </item>
    <item>
      <title>Optimal control methods in charge- and trip-planning for electric vehicles</title>
      <link>https://trid.trb.org/View/2752044</link>
      <description><![CDATA[The transport sector is a major contributor to global greenhouse gas emissions, prompting increasingly stringent regulations and accelerating the transition toward electric mobility. Although electric vehicles (EVs) offer significant potential for emission reduction, their large-scale adoption is still hindered by range anxiety, i.e. the fear of the battery running out before a charging station is reached. Intelligent charge- and trip-planning (ICTP) is a way to address range anxiety by optimizing the charging station selection, the vehicle's energy consumption, the battery thermal management, and the charging process. However, the resulting problems are typically large-scale, nonlinear, and mixed-integer, which makes them computationally challenging to solve. This thesis develops optimal control methods to solve the ICTP problem in a computationally efficient way, to allow real-time onboard implementation. First, the computational tractability of the ICTP problem is improved through tailored warm-start strategies and the relaxation of binary decision variables, enabling the use of faster continuous solvers and achieving substan tial reductions in computation time. Second, a semi-analytical optimal control solver based on Pontryagin's Maximum Principle is developed for EV charging optimization. The solver yields explicit control laws and its low computation time allows for real-time embedded implementation. Finally, a nonlinear optimal control framework for mission planning of long-range solar-powered EVs is proposed, enabling the joint optimization of trip time and energy management under spatio-temporal constraints. The method was tested on a solar-powered vehicle racing across the Australian Outback.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752044</guid>
    </item>
    <item>
      <title>Road vehicle energy demand predictions under uncertain operating conditions</title>
      <link>https://trid.trb.org/View/2752007</link>
      <description><![CDATA[While the literature on routing algorithms is extensive, the focus has merely been on defining the optimization problem and algorithm, often using simple energy consumption models. In contrast, research in range es timation relies on rather complicated energy consumption models, which are often derived from vehicle data. These models do, unfortunately, have poor transfer ability between different drivers, environmental conditions, and vehicles. A great effort has thus been undertaken to model these effects in isolation, for instance, the study of rolling resistance and air drag. Building on models like those, numerous complex complete vehicle simulation models have been developed with excellent accuracy in controlled environments, but at the cost of being too computationally expensive for in-vehicle use. Additionally, these models seldom quantify uncer tainty, a crucial parameter for preventing battery depletion. To this day, the uncertainty of a range estimate is most commonly inferred from data, sensitivity analyses, or empirical model parameters. Methods relying on data or sensitivity analyses generally impose a constant uncertainty, owing to the estimation methods adopted. In contrast, using a model-based approach, for instance, derived from empirical model parameters, has the advantage of cap turing dynamic characteristics that vary between transport missions. Notably, these parameters may not necessarily convey any physical meaning, but instead exist solely as internal elements of a black-box model. In contrast, by adopting a physical model-based approach, variations in energy demand can be derived from exogenous parameters like those obtained from weather, traffic, mission, and road information. This approach aligns precisely with that adopted in this thesis.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:35:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752007</guid>
    </item>
    <item>
      <title>Advancing pedestrian models : a comparative review and vision for the future</title>
      <link>https://trid.trb.org/View/2751939</link>
      <description><![CDATA[Pedestrian mobility is increasingly recognized as a cornerstone of sustainable, healthy, and equitable urban environments. Yet, despite the growing policy emphasis on promoting walkable cities, pedestrian modeling has historically received limited attention compared to vehicle-based modeling. This report critically evaluates the current landscape of pedestrian modeling frameworks, identifies methodological gaps, and outlines opportunities to enhance the utility and policy relevance of these tools. The study is grounded in the proceedings of the International Research Seminar on Modeling Urban Pedestrian Mobility, held at Massachusetts Institute of Technology (MIT) in October 2023, which convened global experts to assess the state of the art in pedestrian modeling. Drawing on insights from the seminar and a rigorous comparative analysis, this work first aims to systematically evaluate five prominent pedestrian models-Urban Network Analysis (UNA), Multi-Agent Transport Simulation (MATSim), Model of Pedestrian Demand (MoPeD), Spatial Design Network Analysis (sDNA), and Place Syntax-using the classic four-step transportation modeling framework (trip generation, trip distribution, mode choice, and route choice), and then to highlight how these models can evolve to better inform planning practice and public policy.]]></description>
      <pubDate>Fri, 07 Aug 2026 08:34:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2751939</guid>
    </item>
    <item>
      <title>Artificial intelligence for enhanced prehospital stroke care : focus on efficient mobile stroke unit allocation and travel time estimation</title>
      <link>https://trid.trb.org/View/2666503</link>
      <description><![CDATA[This thesis aims to use artificial intelligence's power to enhance prehospital stroke care. To accomplish this, we study challenges in prehospital stroke care by focusing on three interrelated research challenges: Mobile stroke unit (MSU) allocation, ambulance travel time estimation, and improving travel time calculations within emergency medical service (EMS) simulation. We develop and analyze different optimization and machine learning (ML) methods to achieve improved analysis and planning of prehospital stroke care. In particular, we propose methods to solve the MSU allocation problem, which aims to identify the optimal locations for a fixed number of MSUs at the existing ambulance station locations within a geographic region. Moreover, we develop a machine learning-based regression method for ambulance travel time estimation. Next, we apply our pre-trained ML-based regression method to improve ambulance travel time estimation within an EMS simulation framework.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:32:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666503</guid>
    </item>
    <item>
      <title>Planning and operation optimization of mobility-on-demand services in the multimodal mobility system</title>
      <link>https://trid.trb.org/View/2598649</link>
      <description><![CDATA[Multimodal mobility systems provide seamless service by integrating various travel modes like driving, cycling, Mobility-on-Demand (MoD) services, and Public Transit (PT) services. With the advancement in autonomous driving and electric vehicles, MoD services show their significant potential in coordinating with other travel modes, especially for PT services. To make the best use of its potential, it is essential to investigate the planning and operations of MoD and PT services in the multimodal mobility system. In the multimodal mobility system, service operations on the supply side should focus on intermodal coordination. On the demand side, customers decide on routes and modes according to service levels such as travel time and price. However, research gaps exist in the planning and operations of integrated MoD and PT services. First, existing literature lacks in optimizing service operations that conform to customer behavior for multimodal mobility systems. Second, existing methods are not applicable to solve such an optimization problem with consistent 'expected' (from service operations) and 'actual' customer behavior. Third, there is a lack of operational optimization models with temporal dynamics for electric MoD vehicles integrated with PT service. To address the above issues, the included papers propose (1. service operation planning in multimodal mobility systems, (2. a generic mathematical solution algorithm for the choice-based optimization problem, and (3. electric MoD operation in multimodal mobility systems.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:19:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598649</guid>
    </item>
    <item>
      <title>The effect of crowding and comfort in public transport on travel choice : empirical pilot study</title>
      <link>https://trid.trb.org/View/2598561</link>
      <description><![CDATA[It is well known that crowding and comfort are important aspects for public transport passengers. Crowding and comfort are closely linked: one effect of high crowding is that comfort is perceived as lower through, for example, less freedom of movement and less chance of getting a seat. On-board comfort also depends on other factors such as noise, vibration, jerky acceleration and braking, seat design, and so on. This feasibility study focuses on in-vehicle crowding and the part of the perceived comfort that is due to crowding. Crowding occurs when so many people want to travel the same route at the same time that the amount of people approaches or exceeds the capacity of the public transport system. At the same time, crowding has a deterrent effect that makes some travelers choose other travel options. In order to calculate realistic passenger flows on different route segments and travel times between different origins and destinations in model-based forecasts, the deterrent effect of crowding needs to be calibrated against people's actual behavior. The purpose of this project has been to investigate the possibility of calibrating the effect of crowding in public transport on travel choices of travelers using ticket validation data and complementary supply data from the Transport Administration in Region Stockholm. The choices referred to are primarily route choices. We hope that the research will provide a better understanding of how comfort effects of crowding affect travelers’ route choices and how these effects can ultimately be integrated into forecasting tools such as Sampers together with Emme through changed algorithms, variables or parameter values. Better modeling of crowding effects can provide more accurate forecasts of passenger flows in public transport, as well as more accurate valuations of benefits that arise when congestion levels are affected by investments or policy measures.]]></description>
      <pubDate>Fri, 12 Sep 2025 10:18:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598561</guid>
    </item>
    <item>
      <title>Voyage segmentation and propulsive power allocation : a data-driven approach for short sea shipping</title>
      <link>https://trid.trb.org/View/2534328</link>
      <description><![CDATA[Short-sea shipping, a sustainable alternative to land-based transport, faces strict environmental regulations and operational constraints to reduce fuel consumption, emissions, and costs. This thesis aims to minimise fuel consumption in short-sea shipping while adhering to sailing time constraints by developing a framework for optimising engine power allocation across predefined maritime routes. To address the limitations of existing power allocation methods, specifically their limited adaptability to metocean conditions, performance accuracy challenges, and long optimisation times, three approaches are examined: (1. Data-driven modelling, (2. Power allocation optimisation, and (3. Route segmentation. The first part of the research project analyses a double-ended ferry. Here, data mining techniques were used to uncover trends in fuel consumption linked to power allocation of the ferry, revealing potential savings of up to 35% compared to actual operational data. Building on these findings, a decision support system (DSS) was developed, combining XGBoost to model fuel consumption and sailing time with Bayesian optimisation to recommend optimal engine speed and engine load. Full-scale experiments validated the DSS, achieving an average 18% reduction in the vessel's fuel consumption through the proposed engine power allocation strategies. In the second half, the developed data-driven methods were combined with a novel voyage optimisation method performed in two steps. 1. Route segmentation: ship routes were segmented using the metocean score-based pruned exact linear time (MS-PELT) algorithm to identify optimal segments for engine power adjustments; 2. Engine power allocation, a scenario-based analysis grid was generated for each segment, and dynamic programming was used to determine the optimal power allocation for the voyage. The combined approach was tested on three years of data from a chemical tanker. Numerical simulations showed a 14% reduction in fuel consumption compared to measurement data, with sailing time deviations below 1%.]]></description>
      <pubDate>Fri, 04 Apr 2025 15:16:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534328</guid>
    </item>
    <item>
      <title>Voyage optimization algorithms for intelligent shipping : considering energy efficiency and collision avoidance</title>
      <link>https://trid.trb.org/View/2534305</link>
      <description><![CDATA[Environmental emissions from shipping pose significant challenges caused by the rapid increase in energy consumption. Voyage optimization system is an valuable tool to address this challenge by enhancing energy efficiency, with optimization algorithms serving as its core, enabling better decision-making. The main objectives of this thesis are to develop voyage optimization algorithms to improve energy efficiency and investigate the capability of voyage optimization algorithms for ship collision avoidance. By achieving these goals, it aims to support intelligent shipping, characterized by enhanced decision-making capabilities. Weather routing, i.e., voyage optimization with the aim to increase energy efficiency in ship operations, rely on ship performance models to estimate energy costs and optimization algorithms to find optimal voyages. However, ship performance models may contain large uncertainties in estimating a ship's energy consumption and emissions. In addition, optimization algorithms should also consider uncertain and dynamic factors, e.g., weather conditions and market fluctuations, to ensure optimal operations. To achieve the overall objectives, this thesis first conducts a systematical literature review to help researchers and practitioners clearly understand weather routing and identify opportunities in current research for the development of its optimization algorithms. Based on the review, this thesis proposes two innovative approaches to achieve energy-efficient weather routing, an Isochrone-based predictive optimization algorithm (IPO) and a learning-based multi-objective evolutionary algorithm (L-MOEA). Furthermore, to ensure reliable operations in practice, this thesis investigates the uncertainty of fuel consumption caused by Specific Fuel Oil Consumption (SFOC) in ship performance models, and the impact of this uncertainty on weather routing. Finally, this thesis extends the research outcome on Isochrone-based algorithms to assist shipping in confined waterways.]]></description>
      <pubDate>Fri, 04 Apr 2025 15:16:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534305</guid>
    </item>
    <item>
      <title>Effective spatial decision support for charging infrastructure planning</title>
      <link>https://trid.trb.org/View/2534255</link>
      <description><![CDATA[The transition to electrified road transportation is crucial for achieving sustainability goals and reducing greenhouse gas emissions. However, the rapid adoption of battery electric vehicles (BEVs) depends heavily on the availability of a robust charging infrastructure. Effective charging infrastructure planning faces numerous challenges stemming from deep uncertainties inherent in transport electrification. These uncertainties encompass aspects such as rapid technological advancements, the variability of technology adoption and behavioral changes, the shifting landscapes of regulations, policies and subsidies, the variability in availability, cost, development lead-time for grid transmission capacity, real-estate, and related services, and the evolving market dynamics arising from competition. This thesis examines the complexities of charging infrastructure planning, addressing two critical knowledge gaps identified in the literature: the inadequate utilization of transport route information in charging network placement optimization and the lack of planning methods and tools that can help manage the uncertainties.]]></description>
      <pubDate>Fri, 04 Apr 2025 15:15:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2534255</guid>
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