<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
    </image>
    <item>
      <title>Smart Drop-Shipping and Stocking Decision Support System</title>
      <link>https://rip.trb.org/View/2703794</link>
      <description><![CDATA[Drop-shipping is an increasingly important order fulfillment strategy in modern supply chains, allowing firms to reduce inventory holding costs by shipping products directly from suppliers to customers. However, because inventory is not directly controlled by the firm, drop-shipping can introduce uncertainty in product availability, delivery lead times, and service reliability. To compensate, firms often rely on expedited transportation, which increases costs and may negatively affect safety and efficiency in freight operations. These trade-offs create a challenging decision problem: determining which products should be stocked internally, fulfilled through drop-shipping, or managed under a mixed fulfillment strategy.
Industry interviews with a major U.S. wholesaler indicate that firms tend to rely on drop-shipping for slow-moving products due to limited warehouse space and capital constraints, yet lack systematic, data-driven methods to guide these decisions Existing research largely focuses on single-product settings or coordination issues between retailers and suppliers and does not address multi-product decisions under warehouse capacity constraints.
This project aims to fill this gap by developing an optimization-based decision support framework for drop-shipping and inventory planning across multiple stock-keeping units (SKUs). The proposed approach integrates mixed-integer programming with meta-heuristic methods to support large-scale, real-world applications. The model incorporates demand patterns, inventory holding costs, transportation costs, service level requirements, and cash flow constraints. A complementary simulation framework will be developed to evaluate system performance under uncertainty in demand, supplier inventory availability, and delivery times.
The project supports Mid-America Transportation Center (MATC) themes of Safety and Transportation Systems of the Future by enabling more predictable and efficient freight movements, reducing reliance on expedited shipping, and promoting data-driven planning in distributed fulfillment networks. Expected outcomes include an implementable decision support tool, analytical insights for industry stakeholders, and dissemination through publications and conference presentations.]]></description>
      <pubDate>Sat, 16 May 2026 11:49:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703794</guid>
    </item>
    <item>
      <title>Decision Support for Dynamic Risks: Determinants of Model Adoption</title>
      <link>https://rip.trb.org/View/2703696</link>
      <description><![CDATA[Since the COVID-19 pandemic, significant supply chain disruptions continue to impact the U.S. economy and have negative impact on transportation networks. Sudden changes in demand or freight availability contribute to increased volatility in freight prices. In turn, volatile freight rates impact the management of transportation networks and increase the difficulty of decision making. This research addresses this problem through the development of decision support tools to proactively respond to initial indicators that predict changes in driver availability and freight cost with the goal of supporting enhanced, early actions to mitigate the risk of disruptions and promote safer transportation network operations.
Work on related prior projects has underscored the importance of forecasting sources of risk to improve the management of transportation systems and the need to understand the key decision components to maximize the value of information to the decision maker. The proposed research will rely on this prior work and make advancements towards the design of an implementable system by examining the end-user perception of decision support recommendations for transportation contracting decisions. 
The research will interview transportation professionals to identify factors that influence their current decision-making and factors that would affect their adoption of a decision support tool. The results of these interviews, in conjunction with prior findings in related research, will inform the design of features for a decision support tool. Design features will be identified for an initial prototype that is suitable for conducting future usability testing of the interactive features. This research continues progress towards the development of a dynamic decision support tool that can ultimately improve the quality of transportation management decisions and continue the legacy of leadership in America’s transportation networks. ]]></description>
      <pubDate>Fri, 15 May 2026 14:13:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703696</guid>
    </item>
    <item>
      <title>AAM-Enabled Intermodal Freight Strategies for Supply Chain Resilience and Efficiency</title>
      <link>https://rip.trb.org/View/2691666</link>
      <description><![CDATA[Ports and freight corridors are critical to the nation’s economy, yet recent disruptions have shown how vulnerable supply chains can be to congestion, weather events, and other unexpected shocks. While trucks and rail remain the backbone of freight movement, there is growing interest in whether emerging Advanced Air Mobility (AAM) and air-based technologies could help improve reliability and resilience for specific, time-sensitive freight needs. This project explores how new air mobility services could complement rather than replace existing port and landside freight systems. The research will examine how air-based freight services can be integrated into intermodal freight networks to support more resilient, efficient supply chains, particularly during disruptions. The study will focus on identifying freight use cases where air mobility may provide added value, such as time-critical deliveries, emergency response, or port operations affected by congestion or weather. The project will evaluate infrastructure needs, operational considerations, and decision-making factors relevant to transportation agencies and port authorities. The research will also examine planning and policy considerations to ensure that potential applications support safe and cost-effective transportation outcomes. Expected results include a practical framework for identifying when and where air mobility solutions may enhance freight system performance, guidance for integrating these services into existing transportation systems, and policy-relevant insights for public agencies. The findings will support transportation decision-makers in planning for resilient, efficient freight systems that meet current needs while remaining adaptable for the future.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:32:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691666</guid>
    </item>
    <item>
      <title>Spatial Modeling to support Supply Chain Policymaking across Metropolitan Areas in
Tennessee</title>
      <link>https://rip.trb.org/View/2684214</link>
      <description><![CDATA[Efficient supply chains are critical for economic growth of metropolitan areas. Despite the strong logistics and manufacturing sectors in Tennessee, a comprehensive understanding of supply chain dynamics across its key Metropolitan Statistical Areas (MSAs) remains limited. This research aims to bridge this gap by applying supply chain metrics and advanced modeling techniques to measure centrality, spread, and dispersion of economic activities. The project will systematically map the logistical landscape in key MSAs of Tennessee, providing a data-driven foundation for identifying economic clustering patterns. This research will serve as a decision-support framework to assist policymakers, transportation agencies, and industry stakeholders in designing supply chain strategies. This research will enhance freight efficiency and resiliency, reduce congestion, and improve economic competitiveness.

A deeper understanding of the spatial organization of economic activity is essential to support data-driven planning and policy development (Holguin-Veras et al., 2021). Effectively mapping the spatial dynamics to inform practical decision-making remains a significant challenge. A comprehensive view of supply chain structure across Tennessee’s MSAs would enhance the state’s economic competitiveness and support better coordination of land use and freight infrastructure. This research addresses that need by estimating spatial metrics to identify economic poles and quantify supply chain dispersion. It integrates spatial analysis and supply chain modeling to examine the distribution of economic activity and supply chain echelons, using network-based distances, industry-specific demand functions, and freight-relevant dispersion metrics. Project insights will help planners to assess freight systems and improve urban logistics efficiency.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684214</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>Quantum Computing and Quantum-Inspired Algorithms for Transportation Network Design</title>
      <link>https://rip.trb.org/View/2625857</link>
      <description><![CDATA[Quantum computing has the potential to transform the classical computing paradigm with improved efficiency for solving NP-hard combinatorial optimization problems modeled as a quadratic unconstrained binary optimization (QUBO) model, including those in transportation and supply chains. Although the quantum annealing (QA) algorithm is theoretically attractive, there is a lack of computational experience showing its superior performance over the traditional algorithms. The purpose of this project is to explore quantum computing and quantum-inspired algorithms on a class of transportation network design problems. We will develop and implement custom-designed algorithms to solve large-scale QUBO models for transportation network design, and evaluate their performance compared to that of QA. The model and algorithms are expected to provide optimal large-scale network design solutions efficiently. This project aligns with the DOT’s strategic goal of economic strength and global competitiveness, and supports MATC-TSE’s theme on transportation systems of the future.
]]></description>
      <pubDate>Tue, 18 Nov 2025 13:58:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625857</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>A Guide for Developing Airport Cargo Handling and Warehouse Infrastructure Through Public-Private Partnerships</title>
      <link>https://rip.trb.org/View/2588322</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Tue, 12 Aug 2025 10:06:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2588322</guid>
    </item>
    <item>
      <title>Supply Chain Study</title>
      <link>https://rip.trb.org/View/2563025</link>
      <description><![CDATA[The main objective of this project is to gather data on construction supply chains during crisis periods and provide actionable guidelines for enhancing resilience. The study will address how contractors, suppliers, manufacturers, and department personnel can better prepare and respond to crises, ensuring continuity and efficiency.]]></description>
      <pubDate>Tue, 10 Jun 2025 07:33:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2563025</guid>
    </item>
    <item>
      <title>Blockchain Application on Smart Transportation Systems</title>
      <link>https://rip.trb.org/View/2553154</link>
      <description><![CDATA[Blockchain technology, predominantly utilized within cryptocurrency, is being increasingly adapted across diverse sectors, and transportation systems is not an exception. Despite presenting several challenges, blockchain technology also offers various advantages. Understanding Blockchain’s potential applications and benefits in addressing future urban challenges is an emerging field of research which has not been fully investigated. In fact, what makes blockchain attractive for smart cities is the design scheme and underlying protocols. The decentralized computational aspect of blockchain enhances the reliability of data transmission across network nodes. Each data transaction between nodes is recorded with a unique identifier and it is validated by consensus among agents within the system. Transaction transparency mitigates the risk of passing inaccurate information throughout the network. 

The proposed project aims at comprehensively studying the application of blockchain technology within various domains of transportation systems. Emphasis will be placed on growth areas, anticipated hurdles, research gaps, and potential integration solutions of blockchain in transportation system design. The comprehensive literature review will cover a broad spectrum ranging from information exchange in connected vehicles to supply chain logistics and smart transit payment systems. The analysis will extend further by constructing a simulation-based platform to investigate the implementation of a blockchain-based fare payment system in transit. Within this simulation analysis, security, risk mitigation, failure prevention, and privacy preservation will be delved into. ]]></description>
      <pubDate>Tue, 13 May 2025 19:01:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553154</guid>
    </item>
    <item>
      <title>Supply Chain Outreach and Rail Economic Impact Study</title>
      <link>https://rip.trb.org/View/2505753</link>
      <description><![CDATA[North Carolina's supply chain is vital to the state's economy, driving job creation, supporting key industries, and facilitating efficient trade and commerce, which collectively enhance the state's competitiveness and economic resilience. Intricately linked to the productivity of North Carolina’s supply chain is North Carolina’s rail system, which serves as a critical network for shipping many raw materials, manufactured goods, and over-sized cargo around the state and across the country. Freight rail provides an important means to export North Carolina products to market and supplies vital manufacturing and agricultural inputs that support the North Carolina economy through business revenue and jobs. The economic benefits of rail are dispersed throughout the state, with recognized benefits occurring corridors with direct access to Class I and short line railroads as well as intermodal and transload facilities.


The purpose of this research project is to shine a spotlight on the economic importance of North Carolina’s supply chain and rail system through three primary aims. First, this
research aims to bring widespread awareness of the economic importance of North
Carolina’s supply chain by disseminating key findings of the Economic Contribution of
North Carolina's Supply Chain (RP2023-09) via meetings, webinars, and conferences to a variety of audiences, including transportation professionals, city and regional planners,
economic development agencies, and manufacturing organizations, among other key
stakeholders. Second, this research project aims to develop an online, interactive Storymap of the results from RP2023-09. Using online visualization as a value add to findings in a report enhances the accessibility and comprehension of complex data, allowing stakeholders to interact with and explore information dynamically, identify trends and patterns more easily, and make more informed decisions based on real-time insights. Third, this research aims to demonstrate the economic importance of passenger rail in Western NC based on the methods and techniques developed in the Economic Contribution of Rail in North Carolina (RP2022-19) and a study titled “Western North Carolina Passenger Rail Feasibility Study” completed by WGI, Inc.]]></description>
      <pubDate>Tue, 04 Feb 2025 16:40:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2505753</guid>
    </item>
    <item>
      <title>Comprehensive assessment of alternative fueling system supply chains in the heavy duty trucking sector</title>
      <link>https://rip.trb.org/View/2495007</link>
      <description><![CDATA[This project examines production supply chains for fueling systems of heavy duty vehicles.  The project uses life cycle analysis (LCA) and extends the method to consider impacts beyond energy consumption and associated emissions, including wider societal impacts, such as air emissions generated in the production or operations process, or labor conditions for those engaged in raw materials extraction or component production.  The project builds on current research that is developing prototype supply chains and identifying “hot spots” for particular impacts.  The purpose of the research is to examine strategies for relocating resource extraction, production, and manufacturing activity to reduce overall impacts.  The case of electric batteries for trucks is used to estimate the effects of taking advantage of locations with cleaner energy mix or more robust labor standards, as for example onshoring manufacturing to the US.]]></description>
      <pubDate>Fri, 31 Jan 2025 18:42:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495007</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>
    </item>
  </channel>
</rss>