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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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzg2IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8+PC9wYXJhbXM+PGZpbHRlcnMgLz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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    <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>
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    <item>
      <title>An Efficient Algorithm for Solving Collaborative Truck-Drone Parcel Delivery System Considering En-Route Launching and Recovery Points</title>
      <link>https://rip.trb.org/View/2692315</link>
      <description><![CDATA[The logistics industry faces significant challenges in keeping up with evolving demand and supply conditions, especially in urban areas. Traffic congestion during peak hours makes on-time delivery hard. Moreover, time-sensitive products, such as emergency blood and medicine, must be delivered to the customer at the desired time. Drones are a viable solution to urban logistics problems, as they offer several benefits for package delivery. Drones are resilient to traffic delays since they function independently of road infrastructure, unlike conventional vehicles. However, drones have capacity and other constraints; therefore, collaborating with a drone and a truck can make the delivery system more efficient. Although there has been significant research interest in developing truck-drone routing algorithms, a gap remains in developing models that allow for en-route drone launching points and recovery points. The prior research on truck-drone routing assumes that the truck can only reconnect with a drone at a customer location. This project will expand on the prior work to develop optimization models and algorithms to allow with en-route meet points. This added dimension has the potential to reduce truck vehicle miles and subsequently congestion. The solution framework will employ a dynamic programming-based algorithm for the initial solution and a synchronized drone dispatch algorithm to determine the launching and recovery points along the truck route. The proposed algorithm will be able to provide solutions for real-world large instances.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:15:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692315</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>Optimizing Last-Mile Delivery Using Micromobility and Autonomous Technologies: A
Scalable Framework for Future Logistics Solutions</title>
      <link>https://rip.trb.org/View/2684210</link>
      <description><![CDATA[Freight delivery is essential to urban mobility and the economy but contributes to congestion, emissions, and infrastructure wear. With e-commerce growth, it is vital
to improve last-mile delivery (LMD), which can comprise up to 51% of logistics costs. This proposal introduces a framework combining micromobility and autonomous technologies to optimize LMD. These solutions offer flexible and labor-efficient alternatives for dense, high-traffic environments. The goal is to ease congestion and enhance delivery efficiency. Real-world case studies in urban and semi-urban settings will assess the framework’s feasibility, scalability, and overall impact.
OBJECTIVES/GOALS:
• Develop a scalable framework for LMD that integrates various micromobility and
autonomous technologies to optimize routes and identify the best delivery options for
policymakers.
• Design algorithms for efficient route planning that enhance operational efficiency, reduce fuel/electricity costs, and improve delivery speed.
• Evaluate the feasibility of different delivery methods, taking into account constraints and
practical considerations to ensure real-world applicability.
• Improve system resilience by enabling real-time route adjustments to address real-world obstacles, such as road repairs and traffic disruptions.
• Validate the proposed framework through agent-based simulation using Amazon’s last-mile data  to demonstrate its effectiveness.
• Leverage AI-powered technology to analyze historical data and predict demand to enable dynamic adaptation of delivery methods, such as deploying more drones on weekdays and fewer on weekends in specific areas, to optimize operational performance.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:38:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684210</guid>
    </item>
    <item>
      <title>Comprehensive Modeling and Analysis of Energy Options for the US Trucking Freight
Transportation: Stakeholder Behavior, Infrastructure Planning, and Local Impacts (Phase 2)</title>
      <link>https://rip.trb.org/View/2684211</link>
      <description><![CDATA[Truck transportation is a vital component of the nation's economy, ensuring the efficient movement of goods across vast distances. Current energy policies emphasize unleashing domestic energy resources and streamlining regulatory frameworks to bolster economic growth and strengthen national security. Exploring all energy options for the trucking industry aligns with these objectives by potentially reducing logistics costs, enhancing national energy dominance, and supporting job creation within the transportation and energy sectors. To this end, a mixed-method approach will be employed to characterize and understand different energy options for the United States trucking freight sector. More specifically, this project investigates 1) stakeholder behavior in the adoption of different energy options in the US trucking sector; 2) national-level infrastructure planning and economic analysis for trucking energy production and distribution, and system evolution dynamics; and 3) local impacts of the adoption of different energy options by the US trucking sector. This project dovetails with the Center for Freight Transportation for Efficient and Resilient Supply Chain (FERSC) goal of maintaining the US economic competitiveness and security.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:33:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684211</guid>
    </item>
    <item>
      <title>A Robust Optimization-Based Approach for an Integrated Truck-Drone Emergency Resource Distribution System</title>
      <link>https://rip.trb.org/View/2684212</link>
      <description><![CDATA[The primary objective of this project is to develop and validate an integrated truck-drone coordination system that enhances emergency resource distribution through advanced optimization modeling and simulation. This system aims to improve delivery speed, service coverage, and operational efficiency during crisis situations. This project seeks to
address the challenges of disrupted transportation networks, uncertainty in demand locations, and inefficiencies in last-mile delivery during natural disasters. The primary stakeholders in this study include disaster relief agencies, emergency response teams, local government bodies, and logistics companies involved in post-disaster supply distribution. Efficient and adaptive delivery systems are crucial for these stakeholders, as traditional transportation methods often become inoperable due to damaged infrastructure limiting accessibility.

This proposal is about formulating multi-objective optimization models to coordinate multiple trucks and drones for emergence resource allocation. In such a coordination system, trucks can be used as depots, and drones can be used as delivery tools. To use drones beyond the last mile delivery, coordination points will be added between truck and customer locations. At such coordination points, drones may charge or exchange packages with other drones for longer delivery trips. Therefore, the research involves planning coordination points; coordinating delivery schedules; managing hand-offs between trucks and drones and between drones; and coordinating routes, altitudes, and timing for all active drones. The proposed model will improve emergency response efficiency and resilience during adverse conditions. The research team involves faculty members and students working in collaboration with North Carolina Department of Transportation (NCDOT) stakeholders.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:23:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684212</guid>
    </item>
    <item>
      <title>A Data-driven Approach in Improving Truck Parking Efficiency</title>
      <link>https://rip.trb.org/View/2684213</link>
      <description><![CDATA[Freight transportation systems are a critical component of the United States' economy, underscoring the importance of adequate truck parking to ensure safe and efficient operations. However, a significant disparity between truck parking demand and supply has resulted in numerous challenges, including increased road safety risks, regulatory non-compliance, and operational inefficiencies. This study aims to address this knowledge gap by conducting a comprehensive review of current truck parking management approaches, with a focus on data-driven prediction models, and truck parking pattern analysis. In collaboration with the North Carolina Department of Transportation (NCDOT), the study will analyze truck parking patterns along key freight corridors and develop data-driven solutions to enhance parking efficiency and address these pressing challenges.

This project aims to address this gap by conducting a comprehensive review of existing literature and offering a nuanced exploration of potential truck parking solutions. Using NC as a case study, the project will provide data-driven recommendations to improve the efficiency and utilization of existing parking facilities along key freight corridors. By enhancing the safety and efficiency of truck parking, this study will directly benefit truck operators, supply chain stakeholders, regulatory agencies, and local communities. The findings will serve as a foundation for informed policymaking and infrastructure planning, ensuring that North Carolina’s freight transportation network remains resilient, sustainable, and operationally efficient in the face of growing demands.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:16:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684213</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 Freight Safety and Efficiency for California’s Logging Industry: A Data-Driven Approach</title>
      <link>https://rip.trb.org/View/2684215</link>
      <description><![CDATA[The logging industry plays a vital role in the U.S. economy, particularly in California’s northern regions, where timber production supports local supply chains. However, the safe and efficient movement of logging trucks is increasingly challenged by road curvature, steep grades, aging infrastructure, and seasonal fluctuations in freight demand. These factors create high-risk conditions, exacerbated by overlapping tourist activity and inadequate roadway data. This research aims to develop a comprehensive, data-driven framework to identify and mitigate freight safety risks for logging trucks. By leveraging open-source tools, data collection efforts, 3D road profiling, and advanced statistical and machine-learning models, this study will identify and predict high-risk freight routes for California’s logging industry.

Problem: The terrain, road curvature, seasonal harvest demands, and aging infrastructure pose significant challenges to both roadway safety and freight efficiency. Certain high-risk locations - such as roads with sharp curves, steep grades, or deteriorating bridges - may be especially hazardous for large vehicles like logging trucks. Furthermore, the seasonal nature of logging, combined with heightened tourism activity, creates fluctuating traffic patterns and additional stress on key corridors.

Objectives/Goals: This proposal seeks to develop a comprehensive, data-driven framework to identify, analyze, and recommend improvements for critical freight corridors used by logging trucks.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:03:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684215</guid>
    </item>
    <item>
      <title>Emergency Truck Parking Location Modeling</title>
      <link>https://rip.trb.org/View/2684216</link>
      <description><![CDATA[This research project will develop and apply optimization methods for the modeling of the emergency truck parking problem. This research is directly aligned with the Center for Freight Transportation for Efficient and Resilient Supply Chain (FERSC) goal of advancing research and practice for resilient and safe freight transportation. The results of this research can be used to inform policy and identify needed investments in truck parking facilities. The end goal is to inform the establishment of safe parking facilities to minimize risks for truck drivers and the public that are associated with commercial vehicles stopping at inadequate (sometimes illegal) locations due to the lack of appropriate short- and long-term parking in emergency situations.

A top concern for truck drivers is finding adequate parking. Truck drivers need a safe place to stop for compliance with hours-of-service (HOS) regulations and for other reasons related and unrelated to their jobs. Finding adequate truck parking is even more critical in emergency situations when regular truck parking facilities might not be accessible. This research project will apply optimization methods for the modeling of the emergency truck parking problem. A mathematical programming approach will be used to identify appropriate locations for emergency truck parking under different scenarios of disruptive emergency events. The mathematical model will be tested with an instance developed for Oregon. The results of this research have the potential to inform policy and identify needed investments in truck parking facilities.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:59:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684216</guid>
    </item>
    <item>
      <title>Advancing Urban Co-Modality with Drone Delivery Services</title>
      <link>https://rip.trb.org/View/2684217</link>
      <description><![CDATA[Co-modality in transportation refers to the shared use of infrastructure between freight and passenger systems. While common in air and rail, its use in urban logistics and public transit is limited. This project proposes integrating drone-based last-mile delivery with public transit using fixed-route buses as mobile hubs. Drones would launch from and land on moving buses to enhance delivery flexibility and generate extra revenue for transit operators without raising operational costs. The research is divided into three thrusts: Thrust I assesses the business feasibility of urban co-modality by analyzing trade-offs between transit operators and last-mile logistics carriers. Thrust II examines the cost-effectiveness and reliability of drone delivery using performance modeling and simulations. Thrust III explores the integration between drone delivery and transit service based on the demand settings and drone configuration results from previous thrusts. The project will produce system design models and simulation tools for drone performance that apply to real-world scenarios.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:53:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684217</guid>
    </item>
    <item>
      <title>Strategic Investment Choice to Reduce Disruptions and Increase Resiliency of Roadway
Freight Network</title>
      <link>https://rip.trb.org/View/2684218</link>
      <description><![CDATA[The proposed research will develop models and algorithms to identify systematic investment strategies by reducing link disruption failure probabilities and enhancing overall roadway resilience for freight flows. A new stochastic programming modeling framework will be developed in which disruption probabilities depend on resource allocation decision variables and new algorithms will be developed to deal with the computational challenges caused by both the large number of scenarios and the nonlinearity in both first-stage and second-stage sub-problems. The framework, including data integration, models, and solution methods, will be programmed and tested with a case based on the freight network in the State of Tennessee.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:46:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684218</guid>
    </item>
    <item>
      <title>Fusing Shipper Behavior Models between Markets and Approaches</title>
      <link>https://rip.trb.org/View/2684219</link>
      <description><![CDATA[Understanding shipper behavior is critical for informed freight transportation planning and policy development. Despite the availability of various modeling approaches—including traditional analytical methods and emerging artificial intelligence (AI) techniques—significant variability persists across commodity types, shipment distances, and market scales. This project addresses the need for a unified and systematic framework to compare, integrate, and enhance shipper behavior models. Building on the complementary expertise of the Principal Investigator (PI) and Co-Principal Investigator (Co-PI), the study will conduct comparative analyses of existing models, focusing on the integration of AI-based and analytical approaches such as multinomial logit (MNL) models. The research will examine model performance across diverse market conditions and geographies, using the Commodity Flow Survey (CFS) data as a foundational resource. Emphasis will be placed on developing fusion techniques to bridge methodological gaps and improve predictive accuracy, particularly in the face of imbalanced datasets common in freight data. By unifying modeling strategies and addressing data limitations, this work aims to deliver a robust framework with enhanced generalizability and practical utility. The expected outcomes include improved forecasting tools, better policy support, and more effective use of publicly available data for national and regional freight planning efforts.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:39:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684219</guid>
    </item>
    <item>
      <title>Generating reliable freight disruption measures with freight telematics data</title>
      <link>https://rip.trb.org/View/2684220</link>
      <description><![CDATA[Freight network resilience is critical for economic stability, especially during disasters and infrastructure failures. This study refines disruption measures using Robinsight, COMPASS IOT, and Robinsight telematics data, alongside WAZE crowdsourced data and infrastructure-based instrumentation (TN RDS). Building on prior research, we analyzed freight mobility impacts from events like the Oregon Durkee Fire (2024), Hurricane Helene, and major bridge closures (I-40, I-55, I-84).

Year 3 focuses on validating key disruption indicators, enhancing predictive models, and integrating emerging data sources to assess infrastructure failures and safety risks from freight detours. Aligned with US Department of Transportation priorities, this research provides transportation agencies with actionable insights to improve freight mobility, inform infrastructure investments, and strengthen supply chain resilience. The findings will support data-driven decision-making, ensuring a more adaptive and robust freight transportation system.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:27:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684220</guid>
    </item>
    <item>
      <title>Mid-America Freight Coalition (MAFC) Phase 5</title>
      <link>https://rip.trb.org/View/2683017</link>
      <description><![CDATA[The Mid-America Freight Coalition (MAFC) pooled fund began in 2006 to support collaboration, innovation, and development in freight planning, freight policy, and operations across the 10-state Mid America Association of State Transportation Officials (MAASTO) region (Illinois, Indiana, Iowa, Kansas, Kentucky, Michigan, Minnesota, Missouri, Ohio, and Wisconsin). The Coalition’s operations are founded and guided by the Memorandum of Understanding (MOU) signed by the Board of Directors of MAASTO and the University of Wisconsin (UW)-Madison.

The MAFC’s major emphasis areas support advances in multimodal freight planning practices, freight operations and technology, and freight policy, all in a collaborative framework. Importantly, the emphasis areas are determined by the participating state professionals. The work is completed in service to both the states and the region, as well as towards advancing national freight planning priorities throughout the MAASTO region. The projects and activities of the MAFC support critical linkages between freight movement and services, as well as economic and community development. The freight coordination of the MAASTO region can provide guidance and identify best practices at a national level relating to multistate coordination of freight activities and in support of goals within the Infrastructure Investment and Jobs Act (IIJA).

This solicitation is for the fifth iteration of the MAFC pooled fund. Previous iterations were TPF-5 (156), TPF-5 (293), TPF-5 (396), and TPF-5 (509).]]></description>
      <pubDate>Thu, 19 Mar 2026 09:48:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683017</guid>
    </item>
    <item>
      <title>Leading Permitting Practices that Harmonize Enforcement of Divisible Load Permits across Jurisdictions</title>
      <link>https://rip.trb.org/View/2681239</link>
      <description><![CDATA[A February 2023 workshop with industry and state representatives identified challenges related to inconsistent interpretation and enforcement of divisible load requirements. Although 23 CFR 658.5 provides a definition of “divisible load,” both industry and state agencies report variation in how the definition is applied across states and, in some cases, within the same state.

Some states provide written guidance for operators and enforcement personnel, while others offer limited or no formal documentation. These differences can create operational challenges, including route adjustments, additional travel time, increased fuel use, parking constraints, and scheduling complications. Operators may also receive citations in one jurisdiction for loads that are permitted in another.

This scan will examine how divisible load requirements are interpreted and enforced across states, counties, metropolitan areas, municipalities, and other transportation agencies. It will document differences in practice, explore factors contributing to those differences, and incorporate input from industry partners regarding cross-jurisdictional challenges and potential solutions.]]></description>
      <pubDate>Tue, 17 Mar 2026 14:58:59 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681239</guid>
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