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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzc0IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>AI-Enabled Spatio-Temporal Risk Assessment and Decision Support for Pipeline Infrastructure Preservation</title>
      <link>https://rip.trb.org/View/2732468</link>
      <description><![CDATA[The safe and efficient operation of pipeline systems is essential to the reliability of the United States’ energy supply chain and its integration with maritime and multimodal transportation networks. Pipeline failures can lead to significant disruptions, economic losses, and safety risks, particularly under the influence of aging infrastructure, human factors, and extreme environmental conditions. Building upon prior research, this project proposes to develop an integrated, artificial intelligence (AI)-enabled framework to support the preservation and resilience of pipeline infrastructure within maritime and multimodal transportation systems. The proposed research will have model development, but focuses on validation, system integration, and deployment of decision-support tools. The project will enhance existing spatio-temporal models by incorporating machine learning and explainable artificial intelligence techniques to improve predictive accuracy and interpretability of pipeline system failure risk under varying environmental and operational conditions. Multi-source data will be integrated into a unified analytical platform, including pipeline incident records and environmental datasets. A key innovation of this research is the development of a multimodal infrastructure risk framework that links pipeline systems with maritime transportation components such as ports, inland waterways, and freight corridors. Multi-layer network modeling and scenario-based simulations will be used to evaluate the impacts of infrastructure disruptions on system performance, including energy distribution, freight movement, and resilience under hazardous events. Through the integration of advanced analytics and multimodal system modeling, this project will deliver scalable, interpretable analytical solutions to enhance the safety, reliability, and resilience of pipeline and maritime transportation infrastructure systems.]]></description>
      <pubDate>Tue, 21 Jul 2026 16:53:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732468</guid>
    </item>
    <item>
      <title>Risk Mitigation and Update of Highway Bridge Design Guidance for Vessel Collisions</title>
      <link>https://rip.trb.org/View/2712182</link>
      <description><![CDATA[In 1991, the American Association of State Highway and Transportation Officials (AASHTO) adopted the Guide Specification and Commentary for Vessel Collision Design of Highway Bridges (GSVCD) as a result of the 1980 collapse of the Sunshine Skyway Bridge and following a research project sponsored by 11 states and the Federal Highway Administration (FHWA). The GSVCD requires that bridge structures be designed to minimize the risk of collapse after being struck by a ship. The second edition of the GSVCD (2009/current) was developed to incorporate lessons learned from the use of the 1991 GSVCD, incorporate the Load and Resistance Factor Design (LRFD) methodology, clarify the risk procedure, and highlight evaluation of existing bridges using the revised GSVCD.

On March 18, 2025, the National Transportation Safety Board (NTSB) issued the report Safeguarding Bridges from Vessel Strikes: Need for Vulnerability Assessment and Risk Reduction Strategies in the wake of the Francis Scott Key Bridge collapse by ship collision. The report recommended evaluation of 68 bridges for risk of catastrophic collapse from vessel strikes and potential development of risk reduction plans.

A recent workshop on “Large Ship Impacts on Bridge Piers” was organized by the City College of New York and the University of Michigan and attended by more than 700 engineers and researchers from around the world. The workshop provided extensive feedback from leading experts and engineers on needs and gaps in this area.

Since the AASHTO GSVCD publication, a lot of research studies have been carried out nationally and internationally.

The objective of the research is to identify needs and gaps for risk mitigation of large vessel or ship collisions and update the AASHTO GSVCD and the AASHTO LRFD Bridge Design Specifications (LRFD BDS). The research will be based on the evolving state of practice, the growth of the shipping industry and data collection, the feedback from applying the existing Guide Specs and the LRFD BDS, and recent advancement of national and international research. The updated guidance will build on the existing design guidance and apply to new bridge design and existing bridge evaluation, risk assessment, bridge protection, and/or countermeasures and retrofit associated with risk of highway bridge vessel collision.]]></description>
      <pubDate>Tue, 09 Jun 2026 15:51:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2712182</guid>
    </item>
    <item>
      <title>Louisiana International Terminal and the Violet Community: A Development Study &amp; Project Implementation Support Framework</title>
      <link>https://rip.trb.org/View/2698371</link>
      <description><![CDATA[As the Port of New Orleans moves forward with $1.2 billion in investments for the development of the Louisiana International Terminal and associated road and rail improvements in St. Bernard Parish’s Violet community, this three-phase project engages transportation industry stakeholders and the Violet, Louisiana community to identify the need for education and workforce development, local infrastructure improvements and capital project opportunities to support future community and economic developments within the area. This research seeks to determine how to mitigate impacts and optimize community benefit whenever a new billion-dollar maritime project is constructed, using Violet as a case study.]]></description>
      <pubDate>Fri, 01 May 2026 20:01:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2698371</guid>
    </item>
    <item>
      <title>Federal-Industry Waterway Governance Mapping</title>
      <link>https://rip.trb.org/View/2698370</link>
      <description><![CDATA[This project will produce the first comprehensive governance map of the U.S. inland waterway system, documenting how federal agencies, waterway commissions, port authorities, operators, industry associations, and advisory bodies exercise authority, coordinate responsibilities, and influence decisions across planning, operations, maintenance, and emergency response. While the inland waterway system depends on a complex interplay of federal ownership, federally authorized navigation channels, industry-operated vessels, federally maintained locks and dams, state commissions, port authorities, cooperative working groups, and advisory committees, there is currently no resource that synthesizes this institutional architecture into a clear, accessible structure. The research will analyze agency documentation, statutory authorities, standing committee structures, and operational guidance, complemented by targeted interviews with practitioners, to clarify how decisions flow through the system and how organizations interact across routine and non-routine conditions. The final product will provide a governance map and a narrative analysis that identifies gaps, redundancies, and friction points in the institutional landscape. This work will support planners, policymakers, and operators and offer a foundational understanding of how governance arrangements shape reliability, resilience, and system performance.]]></description>
      <pubDate>Fri, 01 May 2026 19:58:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2698370</guid>
    </item>
    <item>
      <title>Revolutionizing Coastal Infrastructure Durability with Pervious Concrete: A Cost-Effective, High-Performance Seawall</title>
      <link>https://rip.trb.org/View/2696019</link>
      <description><![CDATA[This project develops and validates a pervious concrete seawall system to reduce wave loads and mitigate scour-related degradation at lower cost and maintenance demand. The work integrates (i) high-fidelity finite element analysis for preliminary design, (ii) fabrication of pervious concrete with tuned porosity (15–35%) using durability-enhancing binders and engineered biochar, (iii) controlled wave flume experiments with instrumented specimens and backfill monitoring, and (iv) seawall design optimization accelerated by surrogate model and genetic algorithm.
To achieve the above mentioned integration, the research will proceed through a series of coordinated actions. First, the research team will build a high-fidelity finite element model, analyze the wave load in seawall, and achieve a preliminary design. Next, pervious concrete specimens with controlled porosity will be fabricated using the preliminary design and tested in a wave flume, which simulates real coastal conditions by generating programmable waves and measuring forces, displacements, and backfill scour behind the seawall. Finally, the team will apply a HyperNetwork, a neural architecture that dynamically generates predictive models, to estimate performance metrics such as energy dissipation and structural stability across different design configurations. The research team has rich experience in developing surrogate models for engineering applications and will complete building this HyperNetwork-based surrogate model in six months. This HyperNetwork will be used together with a genetic algorithm to search for Pareto-optimal designs that balance durability, hydraulic efficiency, and cost. This integrated approach ties together physical testing and advanced modeling to deliver practical, field-ready guidance with the objective of reducing wave-driven degradation and improving structural resilience in simple, cost-effective terms.
]]></description>
      <pubDate>Thu, 23 Apr 2026 16:44:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696019</guid>
    </item>
    <item>
      <title>Container-on-Barge Market Demand </title>
      <link>https://rip.trb.org/View/2673252</link>
      <description><![CDATA[This project will assess the market demand and policy levers that could expand container-on-barge (COB) services along the Missouri and Mississippi Rivers. The study will identify key shippers, high-potential commodities, infrastructure needs, and incentive mechanisms to make COB competitive with trucking and rail. This work directly supports the Missouri Department of Transportation's (MoDOT’s) freight, sustainability, and economic development goals, and aligns with the Missouri State Freight Plan and the U.S. Maritime Administration's (MARAD’s) America’s Marine Highway Program.]]></description>
      <pubDate>Tue, 24 Feb 2026 15:27:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2673252</guid>
    </item>
    <item>
      <title>Reinforcement Mechanism of Articulating Concrete Mats (ACMs) and Geosynthetic Fabric for the Design of Highway Embankment in Coastal Louisiana </title>
      <link>https://rip.trb.org/View/2646938</link>
      <description><![CDATA[Coastal highway embankments differ significantly from conventional highway embankments or levees due to their exposure to hurricanes and tropical storms. These events generate substantial hydrodynamic wave pressures that must be considered in design. Reinforcing soil fills at different elevations with geosynthetics is a common approach, but doing so effectively requires research that enhances existing design methods and clarifies their underlying rationale. Design elements such as tensile forces, reinforcement length, and vertical spacing depend on understanding the mechanical behavior of these materials under extreme loading.  

Because coastal embankments are subjected to wave pressures from storms with defined return periods, engineers must account for the maximum hydrodynamic loads these storms generate. In particular, the unique reinforcement roles of geosynthetics and articulating concrete mats (ACMs) must be thoroughly understood to optimize the design. Key factors include ACM layer thickness, the number and arrangement of non-woven geotextile separator layers, and failure modes such as tensile rupture and pull-out resistance in geogrids and woven geotextiles.  

Building on the results from Southern Plains Transportation Center (SPTC)-funded Cycles 1 and 2, this project will use experimental and numerical methods to evaluate the behavior of geosynthetic reinforcements placed at various elevations within embankment fills. Emphasis will be placed on understanding how these materials fail under load and how their performance changes with elevation and storm intensity. In addition to continuing the work from earlier phases, this project will also assess the seepage-reduction capabilities of non-woven geotextiles and the surface stabilization benefits of ACMs applied to embankment slopes.  

Large-scale direct shear testing will be conducted to analyze both tensile rupture and pull-out failure mechanisms in conditions representative of coastal environments. Seepage and slope stability analyses will complement this testing to evaluate the combined performance of ACMs and geotextile separators under storm loading.  

The findings from this research will help validate and refine current design guidelines for coastal highway embankments that incorporate geosynthetics and ACM armor. The study will also contribute to a deeper understanding of conventional geosynthetic failure mechanisms in coastal applications. Ultimately, the research will yield practical, implementable steps for assessing both internal and external stability in coastal embankment design.  ]]></description>
      <pubDate>Mon, 05 Jan 2026 22:35:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646938</guid>
    </item>
    <item>
      <title>Operationalizing Smartwatch Technology to Measure and Mitigate Heat Stress Among Maritime Transportation Workers</title>
      <link>https://rip.trb.org/View/2620601</link>
      <description><![CDATA[Workers in the maritime transportation industry are being exposed to high-heat and high-humidity conditions, exacerbating their risk of developing heat-related illnesses. They include port and harbor operations, marine cargo handling, navigational services to shipping, and other support activities for water transportation. Heat-related illnesses range in severity from muscle cramps and spasms; to heat exhaustion, which if left untreated, can progress to a more serious condition; to heat stroke, a life-threatening emergency that requires immediate medical attention. Smartwatches have the potential to function as a means for detecting when a heat-related illness is imminent and/or progressing, leading to an opportunity for the risk to be mitigated through intervention. In a current MarTREC project that is nearing completion, the potential for smartwatch heat stress management has been explored by addressing the following research questions: (1) What are key indicators that can be used to quantify heat stress? (2) Can a smartwatch be used to measure heat stress in outdoor working environments? (3) Is it possible to communicate the onset of heat-related illnesses by incorporating a complete closed feedback loop? This project has made considerable progress in examining these questions, which began with conduct of a comprehensive literature review to identify the state-of-the-practice and where research gaps currently exist. These results then informed the development of a conceptual design with the goal of field-testing the application in a small-scale pilot study. The pilot study demonstrated promising results for the developed technology and its application to be feasibly utilized in an operational environment. The project aims to take this next step, namely to operationalize the technology and application in a maritime setting and to evaluate its effectiveness. More specifically, the goal of the research project is to implement the application for maritime use such that the technology can alert workers and their supervisors in real time about the onset of heat stress, provide immediate intervention, and contribute to the development of company-wide heat mitigation policy. Ultimately, the proposed project will determine the practicality of deploying smartwatch-based heat stress monitoring systems in this challenging environment.]]></description>
      <pubDate>Mon, 10 Nov 2025 09:36:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2620601</guid>
    </item>
    <item>
      <title>A Reinforcement Learning Framework for Dynamic Inland Waterway Maintenance Under Stochastic Shoaling and Annual Budget Allocation</title>
      <link>https://rip.trb.org/View/2620600</link>
      <description><![CDATA[This research proposes a dynamic, data-driven framework for long-term inland waterway maintenance planning that integrates reinforcement learning (RL), and stochastic modeling. Unlike traditional models that assume deterministic sedimentation, known multi-year budgets, and static decision horizons, the research team models shoaling as a stochastic process, budgets as annually realized random variables, and infrastructure deterioration as a gradual, condition-dependent process. The core of the methodology is an infinite-horizon sequential decision model that makes year-by-year dredging and lock maintenance decisions using RL. Dredging is modeled as a continuous decision variable, and policy learning is guided by a custom-designed simulation environment that reflects realistic physical and institutional constraints. The team trains RL agents using Proximal Policy Optimization (PPO). This work addresses the curse of dimensionality that limits conventional optimization techniques by learning generalizable policies rather than enumerating all possible scenarios. By finding the solution across various uncertainty regimes, the team provides both methodological insights and practical guidance for agencies such as the U.S. Army Corps of Engineers. The resulting framework offers a robust and adaptive tool for managing long-term infrastructure investment under uncertainty]]></description>
      <pubDate>Mon, 10 Nov 2025 09:33:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2620600</guid>
    </item>
    <item>
      <title>Automatic Boundary Detection and Change Analysis Using Static and Dynamic Imagery</title>
      <link>https://rip.trb.org/View/2616823</link>
      <description><![CDATA[This project aims to develop an automated segmentation and boundary detection system capable of identifying geometric features and changes in river boundaries using advanced image processing techniques on satellite imagery, digital photographs, and videos. Current aerial remote sensing and data collection techniques use LiDAR, photogrammetry, or other methods that require significant time and computational power to assess and identify key features of interest. Therefore, rapid or real-time monitoring of dynamic conditions such as flooding is difficult or impossible. By combining a fast segmentation algorithm with novel edge detection and artificial intelligence (AI)-based classification methods to analyze boundary changes, the proposed system will allow for temporal monitoring of river conditions and adjacent infrastructure, and aid in the detection of any deviations from established boundary norms. While this system has numerous potential use cases, the main focus of this research will be the creation and training of a system that can identify and quantify a number of key features used for asset management, flood monitoring, and disaster response associated with levees and adjacent transportation infrastructure.]]></description>
      <pubDate>Thu, 30 Oct 2025 14:39:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2616823</guid>
    </item>
    <item>
      <title>Resilient Multimodal Transport Systems via Container-on-Barge</title>
      <link>https://rip.trb.org/View/2600576</link>
      <description><![CDATA[Recent events like the crack in the I-40 bridge, California wildfires,
and the Dolton, Illinois landslide have revealed vulnerabilities in transportation systems, causing closures, delays, freight rerouting, and congestion. Traditional resilience measures such as redundancy and infrastructure strengthening help mitigate some disruptions but often fall short against natural disasters and lack long-term impacts. Container on barge (CoB) transport, although currently underutilized in the nation and less explored in academic research, can alleviate the pressure on the existing system during disruptions and offer a more flexible, cost effective and sustainable solution. Therefore, in this project, the research team proposes to conduct a holistic study examining the integration of CoB into intermodal freight transportation systems, focusing on system resilience. Specifically, the team will first develop a context-aware approach for uncertain disruption quantification, and then design  reliable CoBbased strategies to further mitigate disruptions. Additionally, the team will perform numerical experiments to validate the effectiveness of the proposed approaches.]]></description>
      <pubDate>Thu, 30 Oct 2025 14:37:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2600576</guid>
    </item>
    <item>
      <title>Measuring the Resilience of Texas and Louisiana Ports Within the Context of the National Marine Transportation System</title>
      <link>https://rip.trb.org/View/2509306</link>
      <description><![CDATA[This project will develop methods to assess the connectivity and resilience of Gulf Coast ports, focusing on port-to-port cargo movements. Understanding port criticality is essential for prioritizing infrastructure maintenance and improvements, especially in the context of limited funding. Building on previous research by the U.S. Army Corps of Engineers’ Engineering Research and Development Center (ERDC), this study will utilize Automated Identification System (AIS) data and the PageRank algorithm, which ranks port importance based on network connectivity rather than conventional metrics like tonnage.
In addition to the PageRank method, the study will explore other analytical tools, such as flow-based minimum cut and connectivity algorithms, to provide a more comprehensive assessment of the Marine Transportation System (MTS). Simplifying assumptions from ERDC’s work—such as using ship volume as a proxy for tonnage and accounting for international ports lacking AIS data—will enhance the accuracy and efficiency of the analysis.
The study will characterize the MTS using the most recent AIS data and perform an in-depth analysis of port criticality, with a specific focus on the seaports of Texas and Louisiana, as they fall within the Southern Plains Transportation Center’s scope. The results will guide decisions on port maintenance, resilience, and major infrastructure improvements, contributing to a more robust and disaster-resilient port network. The following tasks will be executed: Task 1 involves developing the required network dataset. In Task 2 the research team will apply various algorithms to the various MTS network representation developed in Task 1. Additional insights gained from these algorithms versus using a tonnage-only evaluation will be discussed in Task 3. This task will also recommend potential use of the outputs from this study. Task 4 involves disseminating the study findings. The research team will provide a copy of the report to the ERDC study team that produced the previous report.
]]></description>
      <pubDate>Thu, 13 Feb 2025 15:02:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2509306</guid>
    </item>
    <item>
      <title>A Simulation and Decision-Support Tool for Vulnerability Reduction in Hazardous Material Transportation via the U.S. IWTS</title>
      <link>https://rip.trb.org/View/2499032</link>
      <description><![CDATA[Stakeholders are faced with challenges in tracking and managing freight movement, evaluating human-infrastructure interactions (e.g., navigation, lockage), and suggesting alternative solutions in response to contingencies. On the other hand, vessel operators need to have good situational awareness to quickly and effectively avoid hazards and accidents. Among various types of products, hazard materials constitute a great portion of shipments. Such materials can be found in many forms on the U.S. inland waterway transportation system (IWTS), including petroleum products (e.g., diesel fuel, asphalt), chemicals (e.g., fertilizers, pesticides), and household and consumer products (e.g., paints, adhesives). Indeed, petroleum products make up over 75% of waterborne shipments. Compared to highways, rails and other modes of transportation for hazard materials, the waterways have the heaviest shipments. To minimize economic losses while ensuring safety and security during hazardous material transportation, it is invaluable to develop a computerized tool for sharing information and evaluating the impacts of a sequence of decisions, such as voyage planning and rerouting, on freight movement, costs and risks. With previous support from the National Science Foundation (NSF) and the Maritime Transportation Research and Education Center (MarTREC), this research team has developed an advanced NetLogo-based simulation tool that enables visualizing, evaluating and maintaining multimodal transportation infrastructure. This research project seeks to advance the simulation- and machine learning-based tool to help involved personnel understand how the IWTS currently performs, assess potential risks, and respond to various accidents and disruptions, especially those involving hazard material shipments. The goal is to provide an open-source software tool and machine learning-based decision-making approaches that assist the relevant stakeholders and operators in tracking hazardous material movement, making timely decisions, and enhancing the safety of the U.S. IWTS and beyond. The research findings to be achieved will be broadly disseminated to researchers and practitioners through research publications and presentations. The team will promote real-world applications of the tool by working with MarTREC partners and collaborators.]]></description>
      <pubDate>Wed, 29 Jan 2025 17:11:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2499032</guid>
    </item>
    <item>
      <title>An Elementary School STEMusical: Exciting the Future of STEM</title>
      <link>https://rip.trb.org/View/2499037</link>
      <description><![CDATA[This educational outreach project will build upon past efforts to educate and excite future science, technology, engineering, and math (STEM) leaders at the elementary school level (K-4). Partnering with educators at Root Elementary School in Fayetteville Arkansas, the project will create an updated STEMusical theatrical program centered around maritime engineering challenges (lock passage, barge impact, dredging operations, etc.), exciting elementary grade students about engineering through an entertaining, informative, and memorable experience. The project will develop and execute in-class experiential learning exercises that educate, while the created STEMusical lyrics and music will assist in knowledge retention. A total of two public student performances of the developed STEMusical will be held by the conclusion of the project and commercialization efforts will be undertaken to expand the developed STEMusical program to schools nationwide.  ]]></description>
      <pubDate>Wed, 29 Jan 2025 16:57:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2499037</guid>
    </item>
    <item>
      <title>Synthesis and Analysis of Maritime Supply Chain and Freight Indicators</title>
      <link>https://rip.trb.org/View/2499068</link>
      <description><![CDATA[In August 2024, United States container imports increased almost 13% year-over-year and remain greater than the 2.4 million Twenty-foot Equivalent Unit (TEU) mark which historically stresses maritime logistics infrastructure. After July 2024 saw a 26-month high in U.S. container imports, this second month of relatively great volume contributed to increased port transit time delays at 7 of the top 10 U.S. ports. Based on these freight trends and their effects on the supply chain there should be motivation to focus on the maritime side of port congestion (inside the gate) and its economic impact related to freight movement, transportation labor and capacity tightness. These supply chain and freight indicators are published by the Bureau of Transportation Statistics but require synthesis and analysis to greater benefit decision makers and the public.]]></description>
      <pubDate>Wed, 29 Jan 2025 16:49:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2499068</guid>
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