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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzg2IiAvPjxwYXJhbSBuYW1lPSJzdWJqZWN0bG9naWMiIHZhbHVlPSJvciIgLz48cGFyYW0gbmFtZT0idGVybXNsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjE2IiAvPjwvcGFyYW1zPjxmaWx0ZXJzIC8+PHJhbmdlcyAvPjxzb3J0cz48c29ydCBmaWVsZD0icHVibGlzaGVkIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM+PC9zZWFyY2g+" 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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Routing Autonomous Trucks on Dedicated Lanes</title>
      <link>https://rip.trb.org/View/2676007</link>
      <description><![CDATA[Trucks are known to have a significant impact on congestion during traffic peak hours due to their size and slower dynamics. Human operated trucks for freight transport are faced with two constraints: those imposed by the service demand and those imposed by the human driver. For long haul operations, for example, truck drivers must meet the constraints of hours of service. For short haul they have to meet family and personal constraints which often do not allow them to operate during odd hours. With automation the human constraints are removed which opens the way to view truck routing and scheduling under different and more flexible constraints. The major problem faced by automated trucks operating with the rest of traffic, however, is safety as due to the different sizes involved the sensing problem is more challenging and potential accidents can be catastrophic.


Under this project the research team plans to analyze and evaluate the use of automated trucks that will operate on the surface network at times that the traffic demand is very low, so that lanes can be switched dynamically to dedicated automated truck lanes without affecting traffic. By doing so we can keep the automated trucks separated from manually driven vehicles which may be using the network, thereby addressing the issue of safety. This project will address the potential benefits of automated trucks on dedicated lanes operating at low volume traffic hours. In addition, it will extend the approach to automated truck platoons where automation will also lead to significant fuel savings (up to 20%) due to reduction in aerodynamic drag, bringing the potential to lower costs. Moving trucks from times of high congestion to times of no congestion will bring considerable benefits to trucking companies as well as to all other users of the road network, as fewer trucks will be operating during peak traffic hours. In addition, trucking companies that are short of truck drivers will be able to operate without disruptions and without human imposed constraints, saving on labor costs. The team plans to use as an example a network that includes Interstate 710 (I-710) and the Ports of Los Angeles/Long Beach, a route that generates considerable truck traffic. The team will identify the lanes that can be dynamically dedicated to automated trucks at certain hours and estimate the impact on congestion and fuel savings. The team will use real truck and traffic data to validate their traffic simulators which they will then use to run different scenarios.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:31:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676007</guid>
    </item>
    <item>
      <title>Digital twin for managing the curb and reducing congestion</title>
      <link>https://rip.trb.org/View/2676005</link>
      <description><![CDATA[Curb space in dense urban cores is under intense pressure from e-commerce deliveries, service vehicles, ride-hailing, and passenger pick-up/drop-off. Without a data-driven view of curb regulations and heterogeneous delivery demand, cities face double-parking, spillback congestion, and safety conflicts. This project addresses the gap by creating an open-data-based digital twin that links curb regulations, observed curb activity proxies (differentiating between commercial and residential delivery behaviors), and network performance to support actionable curb management decisions.

This project is part of a larger project. The larger project is developing a proof-of-concept strategic curb digital twin to analyze curb demands and test curb management solutions. The digital twin will act as a virtual replica of a portion of downtown Los Angeles (DTLA), built using publicly available data to ensure the model is transparent, replicable, and directly useful to public agencies. This approach is centered on a transparent, agent-based simulation model. The research team will construct a high-fidelity virtual environment by integrating multiple open datasets and agency records, including geographic information system (GIS) road networks from the LA GeoHub, land use and business listings from DataLA, LADOT signal timing charts for realistic traffic control, and network details from OpenStreetMap. This allows the team to simulate crucial behaviors, such as a delivery driver’s search for parking calibrated using parking citation data, or a private car's decision process calibrated using open-source global positioning system (GPS) Exchange Format (GPX) data. This creates a reliable virtual testbed to evaluate various management strategies. The team can introduce and assess policies such as dynamic pricing for loading zones and passenger car parking, or time-of-day restrictions, and observe their combined effect on delivery efficiency and overall traffic congestion. The prototype will serve as proof-of-concept for this multi-agent simulation, establishing a foundational tool for holistic curb management.

This Phase 1 project focuses on freight deliveries and expands the larger project by introducing heterogeneity into freight delivery demands. Retail establishments may receive relatively large shipments and restaurants may receive daily shipments from multiple suppliers. Residents receive small package deliveries. Different types of deliveries imply differences in delivery vehicle dwell time, demand for a nearby parking space, and delivery route configurations. The team will use land use, employment, and demographic data to generate freight delivery demands. The team then classify these demands based on stop dwell times and commercial vs residential, because of the temporal differences in these demands. The different demands are operationalized as different agents within the model.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:23:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676005</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>Vulnerability assessment and durability of coastal freight networks (UPRM)</title>
      <link>https://rip.trb.org/View/2663230</link>
      <description><![CDATA[Project Description: Freight networks, including ports, coastal highways, bridges, and distribution hubs, are critical lifelines that sustain regional economies, enable everyday commerce, and support emergency response after catastrophic events. The coastal location of this essential transportation infrastructure makes these assets uniquely vulnerable to extreme natural events such as flooding, storm surge, coastal erosion, and compound hazards. The Puerto Rico’s 2050 Long Range Transportation Plan explicitly calls for reducing transportation vulnerabilities to extreme weather effects and improving connectivity. Puerto Rico could serve as a critical logistics hub for U.S. freight operations in the Caribbean, offering strategic access to regional markets and maritime routes. But recent storms Hurricane María (2017) and Hurricane Fiona (2021) have highlighted the freight network’s fragility and the urgent need for targeted resilience measures. 
The assessment of Puerto Rico’s freight network, one that relies solely on the performance of the highway system, can be a case study to evaluate the system vulnerabilities derived from natural flood hazards, aging infrastructure, urbanization in coastal areas, and congestion in strategic corridors. A rigorous vulnerability assessment combines data from hydrologic and coastal flood modeling with traffic flows, asset condition inventories, and safety records to identify critical and single-point-of-failure links. This integrated analysis can provide a method to reveal which corridors and nodes are most likely to fail under different flood scenarios, how congestion and limited redundancy amplify delays, and which assets require immediate reinforcement or operational changes. It can also uncover system-level interdependencies among ports, road networks, and distribution hubs that are not visible from isolated asset inspections. This project can assist local transportation agencies, freight operators, and decision-makers in identifying risks to the freight network, improving the assessment of infrastructure assets by including the interdependence between ports, road networks, and distribution hubs, and prioritize improvements in strategic planning and project development. This project is envisioned as a two-year program. Year 1 will define Puerto Rico’s primary freight network anchored at the ports of San Juan and Ponce, map major distribution points, and develop an interactive dashboard showing asset condition, corridor flows, crash hotspots, and flood-vulnerable links and nodes. Four analytical dimensions will be assessed: infrastructure condition, traffic flows, safety, and durability, using official data, operational reports, and geospatial analysis to identify hotspots and critical vulnerabilities. Year 2 will focus on network optimization and investment prioritization, applying stochastic and optimization models to produce a prioritized, implementable resilience strategy. A Texas State University team will collaborate in the review of stochastic and optimization approaches, the evaluation of data requirements and computational complexity, and provide recommendations about the best model(s) for optimizing freight flows and prioritizing investments from ports to distributors.

]]></description>
      <pubDate>Sat, 31 Jan 2026 11:32:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663230</guid>
    </item>
    <item>
      <title>Identifying and evaluating the most effective actions to prepare Puerto Rico’s primary ports and freight road transportation infrastructure for flooding disruptions using stochastic models</title>
      <link>https://rip.trb.org/View/2662990</link>
      <description><![CDATA[One of the seven issues listed in the freight assessment section of the 2050 Long Range Multimodal Transportation Plan (LRMTP, approved in 2023) encompasses the need for Puerto Rico’s ports and road freight transportation network (RFTN) to be less vulnerable to extreme weather events that affects the durability of the infrastructure and disrupts the movement of goods and services. Puerto Rico has an excellent geographic location for the transshipment of goods to other places in the Americas. Strategies to mitigate infrastructure damage to ports and roads resulting from overuse and to keep the system operating effectively will help Puerto Rico maintain its position as a global logistics hub. The development of an adaptable highway transport system is crucial, as railroads are not well-developed to undertake the freight transport needs, and the use of the marine-based freight M2 route connecting main and secondary ports is only emerging. 
The objective of this research project is to quantify and classify the impact of certain operational decisions made before and after flood-related weather events on four performance or optimization criteria: ports and RFTN infrastructure, traffic flows, safety, and flexibility to avoid delays and disruptions. The operational decisions to include are: increasing ports’ operating hours, locating regional hub-and-spoke points where freight coming from the ports is transferred from large trucks to smaller vehicles and routed to the distribution points, determining existing or to be developed alternative roads that reduce congestion at hotspots, and routing loads between ports. To accomplish the objective, TXST will develop a preliminary stochastic programming model to optimize a prototype of Puerto Rico’s RFTN, considering multiple flooding scenarios, forecasts of freight demand over 5 and 10 years, and the above-mentioned operational decisions and optimization criteria. A variant of the developed model, which represents the current operations of ports and roads without incorporating any of the proposed operational decisions, will be used for comparison purposes. The main freight distribution points and associated demands to input into the models will be identified in cooperation with the listed project partner faculty at UPRM.  Puerto Rico’s industry, government agencies, and consultants for these agencies will be sources to get the models’ input data, as well as information available online. If needed, the distribution points will be clustered.  In this preliminary model, the unavailable data will be identified and estimated. The model will demonstrate to the Puerto Rico Department of Transportation and Public Works, the Puerto Rico Highway and Transportation Authority, and other relevant agencies a process they can apply for making informed decisions to enhance the durability and resilience of port and RFTN infrastructure under uncertainty caused by flooding and the relevance of collecting any highly relevant and missing data.]]></description>
      <pubDate>Thu, 29 Jan 2026 16:19:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2662990</guid>
    </item>
    <item>
      <title>Improving Traveler Experience Via Alternatives to Roadway/Railway Grade Crossings  </title>
      <link>https://rip.trb.org/View/2646961</link>
      <description><![CDATA[There are more than 240,000 at-grade crossings between railroads and roadways in the U.S. and as the number of freight trains increases, the times of interface and blocked crossings also increases. USDOT reports numerous driver complaints about delays and frequent disruptions, and in some cases, there are delays to emergency vehicles due to excessive numbers of blocked trains. Work is underway to continue documentation and to consider strategies and address the frequent and repeated delays caused by long trains. The most requested remedy is grade separation. Grade separations are extremely expensive, and planning and construction lead times are long, so there is a need to identify other more short-term strategies that will offer travelers and emergency responders options to waiting on the long trains.  

The focus of this research will be Fort Bend County and Harris County, Texas, which include major freight corridors from Port Houston, the 3rd largest container port in the country. Between the two counties, there are at least 11,000 at grade crossings. Specifically, this work will assemble delay time data showing frequency and duration for the identified railroad crossings. The team will conduct literature review and on-line and in-person conversations to determine options and strategies underway by entities (e.g., railroad operators), municipalities, and others to address better traveler information and options to reduce and avoid delay time. Potential options include cameras noting delays and following with notifications to emergency services proximate to locations with frequent delays. The study team will examine whether this information distribution could be expanded to additional users. An additional option to be examined is message signs alerting travelers to blocked crossings in time to adjust their travel route. The expected research outcome is to provide an option to grade separations that will reduce delay time for travelers caused by blocked train crossings. ]]></description>
      <pubDate>Tue, 06 Jan 2026 17:10:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646961</guid>
    </item>
    <item>
      <title>Enhancing Rural Freight Resilience in the Southeastern U.S.: Data-Driven Modeling and Decision Support for Supply Chain Efficiency.

</title>
      <link>https://rip.trb.org/View/2643108</link>
      <description><![CDATA[This research aims to address the issue of limited alternative routes in rural freight systems by modeling rural freight networks to identify critical vulnerabilities and evaluate potential recovery strategies. The study also proposes new methods for addressing truck parking shortages using models such as reservation and automated allocation for predicting demand and optimizing supply. The project leverages network science, emerging data sources, and simulation tools to develop methodologies for assessing the resilience of rural freight networks. Additionally, the study will explore the potential of connected and autonomous vehicles (CAVs) for improving operational efficiency and reducing parking demand, particularly for middle-mile delivery and short-range freight operations. This research directly addresses these issues by (1) Developing network-based modeling techniques to analyze rural freight resilience, (2) Identifying critical corridors and evaluating alternative routing strategies, and (3) Proposing innovative truck parking solutions to improve operational efficiency. This includes broader operational strategies such as parking reservations, staging areas near hubs or ports, route reservations, and quicker incident resolution for truckers.  ]]></description>
      <pubDate>Sat, 20 Dec 2025 17:04:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643108</guid>
    </item>
    <item>
      <title>Decision Support for Dynamic Risks: Predicting Transportation Costs</title>
      <link>https://rip.trb.org/View/2625852</link>
      <description><![CDATA[The COVID-19 pandemic resulted in significant supply chain disruptions across many industries, with disruptions caused by both increases in demand, reductions in available supply, and changes in transportation availability. These supply disruptions hurt the US economy and disproportionately negatively impacted vulnerable populations. Initial research results on the project Decision Support for Dynamic Risks to Improve Supply Chain Resilience has underscored the importance of forecasting sources of risk in order to improve the management of transportation and supply chain systems. However, current research on demand forecasting relies on models that assume a stationary stochastic process. Such an assumption is not consistent with the rapid changes observed during a risk event such as the COVID-19 pandemic. This research seeks to continue prior work by partnering with industry to inform risk prediction models with real-world data. In particular, this research seeks to partner with companies in the transportation sector to develop methods to forecast transportation availability and transportation costs. The results of this research are anticipated to serve as inputs to a decision support tool to improve the management of transportation and supply chain networks in the event of systemic risk events.
]]></description>
      <pubDate>Tue, 18 Nov 2025 14:00:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625852</guid>
    </item>
    <item>
      <title>Overcoming the truck driver shortage in the United States: An Empirical Study
</title>
      <link>https://rip.trb.org/View/2625872</link>
      <description><![CDATA[In 2023, the trucking industry contributed approximately $389.3 billion to the United States' gross domestic product (GDP), accounting for most of the nation's highway freight movement. However, the current shortage of truck drivers is disrupting U.S. supply chain activities. Failure to address this challenge can have long-term implications, such as higher consumer prices, longer delivery times, and even product shortages in the marketplace. Therefore, the present study aims to identify the factors contributing to the truck driver shortage, model their contextual interrelationships, and uncover the root causes. The overarching goal is to help overcome the current truck driver shortage in the U.S., thereby enabling the freight transport sector to remain competitive and maintain its socioeconomic impact on job creation, productivity, the nation's revenue, and the wider supply chain costs. A three-stage methodology will be implemented. This will comprise an extensive literature review, semi-structured interviews with selected industry experts to conduct interpretive structural modeling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC). We will adopt a purposive (non-probability) sampling to engage with key stakeholders in the Missouri trucking industry, such as, but not limited to, regulators (e.g., DOT), truckers (private fleets, owner-operators, trucking companies/3PL), shippers, freight brokers, and consignees. The planned deliverables include: (1.) a literature synthesis analyzing truck driver shortage causes and solutions/remedies currently available; (2.) the development of a data-driven hierarchical interaction framework, and (3.) a final report to synthesize the findings, implications, and recommendations for industry professionals, policymakers, and academics.
]]></description>
      <pubDate>Tue, 18 Nov 2025 13:52:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625872</guid>
    </item>
    <item>
      <title>Optimal Design of Inland Waterway System to Increase Supply Chain Resilience</title>
      <link>https://rip.trb.org/View/2625874</link>
      <description><![CDATA[This project seeks to conduct research to develop mathematical models of supply chain network resilience that leverage the US inland waterway system. Extending the research conducted during year 1, which laid the foundation for developing advanced optimization and simulation methodologies to increase the resiliency of the inland waterway freight transportation system. Given the increasing threats from accidents, weather-related hazards, and terrorist attacks that have heightened risks for both freight and passenger transport systems, this project recognizes the pivotal role of inland waterways in mitigating these vulnerabilities. The resilience of intermodal systems, which are often significantly impacted by such events, leading to considerable economic losses, can be substantially improved by integrating inland waterways. This integration will be examined through the lens of network topology, investigating how different configurations and connectivity within the waterway system can influence key resilience metrics such as recovery time, system throughput, and adaptability in the face of disruptions. The expected deliverables include a characterization of resilient network topologies. A final synthesis report will present the research findings, including methodology, results, and recommendations for policymakers, stakeholders, and industry players. Reports will be shared with relevant stakeholders and research conferences, fostering public awareness of the benefits of inland waterway freight transport to increase supply chain resilience. The research endeavors seek to pave the way for a more resilient, interconnected, and environmentally friendly freight transportation network within the United States.
]]></description>
      <pubDate>Tue, 18 Nov 2025 13:46:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625874</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>Develop Multi-Modal Maritime-Rail-Roadway Transportation Model for the Texas Inland and Intercoastal Waterways</title>
      <link>https://rip.trb.org/View/2614515</link>
      <description><![CDATA[Texas plays a key role in freight transport nationally and internationally. Freight systems are highly multi-modal (maritime, rail, roadway) and complex, with infrastructure serving different cargo types (bulk goods, containers, hazardous material, etc.). The research team will develop a simulator and decision-support tool for routing and scheduling freight in this system. This work builds on and enhances simulation models and tools the research team has designed, developed, and deployed to successfully represent multi-modal freight operations in the Port of Houston, Houston Ship Channel, and Texas road and rail networks for past projects. This prior experience has shown that the greatest challenges involve data availability, computational speed for statewide modeling, and reflecting the complexities of real-world logistics in routing and scheduling algorithms. The work plan is specifically designed to address these challenges: in terms of data, the research team will use both publicly-available datasets (including the ones used to calibrate its past models) and its existing relationships with port and rail operators and other stakeholders; in terms of computation, the research team will explore a hybrid discrete-event simulator architecture; and in terms of realism, the research team will incorporate uncertainty and reliability in the decision-support tool.]]></description>
      <pubDate>Tue, 28 Oct 2025 11:09:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2614515</guid>
    </item>
    <item>
      <title>Guide on Truck Rest and Service Areas for Critical Supply Chain Delivery



</title>
      <link>https://rip.trb.org/View/2614489</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Mon, 27 Oct 2025 17:32:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2614489</guid>
    </item>
    <item>
      <title>E-Commerce Impacts on Oregon Household Level Deliveries, Trips, and VMT</title>
      <link>https://rip.trb.org/View/2593956</link>
      <description><![CDATA[Oregon Department of Transportation (ODOT) faces a combination of declining revenues from traditional sources like fuel taxes due to electric vehicle (EV) adoption, fuel-efficient vehicles, and potential travel patterns changes (e.g., telecommuting). In addition, maintenance and operation costs have seen large increases due to inflationary pressures. In this context, ODOT needs to innovate its revenue models to ensure the long-term sustainability of the transportation system.
A potential innovation is the introduction of an e-commerce deliveries fee. This type of fee could be a sustainable long-term source of revenue because:
(a) The last two decades saw a rapid growth of e-commerce sales, both in the US and Oregon. This trend gained further momentum during the global pandemic. According to e-commerce sales reports released by the US Department of Commerce, e-commerce sales accounted for approximately 7% of total retail sales in 2015 and 16% of total retail sales in 2024 (US Department of Commerce, 2024).
(b) Long-term growth is expected to remain strong due to demographic changes, new generations will be more used to online-shopping, and also because retailers are continuously expanding their online offerings and products.
Although an e-commerce deliveries fee may seem appealing there is no study or data available that can assess the financial impact for households and potential equity implications.
The lack of data and studies in this area prompts several questions, including: (1) What type of households across the state are likely to pay more e-commerce delivery fees? (2)  For households, how significant will the fees be in relation to the value of the products being delivered or other transportation related fees? (3) How would this fee impact households across the state, i.e. in rural vs urban areas? (4) What is the potential equity impact of this fee for lower income households?
This project is a necessary first step that will provide valuable insights to understand the impacts of an e-commerce delivery fee in terms of equity and potential revenue at the household level.]]></description>
      <pubDate>Thu, 28 Aug 2025 14:25:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593956</guid>
    </item>
    <item>
      <title>Evaluating Texas Ports Readiness and Opportunities for Alternative Fuels</title>
      <link>https://rip.trb.org/View/2593188</link>
      <description><![CDATA[The research team will conduct a comprehensive assessment of Texas ports' readiness to integrate alternative fuels, identifying infrastructure gaps, fleet transition opportunities, and economic growth potential. Key project outcomes will include a Texas Ports Readiness Index, a spreadsheet-based tool for evaluating port readiness, fleet transition feasibility assessment, and clear, actionable recommendations for advancing Texas ports readiness for alternative fuels. These outcomes will equip the Texas Department of Transportation (TxDOT) with the necessary data, tools, and strategies to position Texas as a leader in alternative fuels at ports, enhancing port resilience and economic competitiveness in the evolving global energy landscape.]]></description>
      <pubDate>Tue, 26 Aug 2025 12:37:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593188</guid>
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