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    <title>Research in Progress (RIP)</title>
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    <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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    <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 Dedicate Lanes- Phase 2</title>
      <link>https://rip.trb.org/View/2733038</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 must 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. 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.

During the first phase of the project, the research team developed microscopic traffic simulation model which the team validated using real data from I-710. The network considered was part of I-710 and the team assumed as a first step single origin-destination (OD) flows. The team considered the scenario where trucks sharing the same road network as passenger cars become automated and operate on dedicated truck lanes 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, thereby addressing the issue of safety.

The ongoing phase 1 study shows that by removing a number of trucks which are about 0.4% of all vehicles during a high peak traffic and have them automated and operating on dynamically dedicated lanes during off peak traffic the travel time for trucks is reduced by 4.5% while the travel time of passenger vehicles during the high peak traffic decreases by about 3%. These preliminary findings suggest that temporal rescheduling of freight demand, combined with dynamic lane management, could improve both freight and overall network performance. In phase 1 the team simply used the traffic simulator to test their ad hoc approach of moving trucks from high peak to low peak traffic without any form of optimization.

In phase 2 the team plans to extend the approach as follows: (1) The team will expand the road network to include some of the most popular truck routes covering short medium and long-haul scenarios. The issue of parking and refueling in the absence of driver will also be addressed. (2) The team will extend the results of phase 1 to multiple interacting OD pairs, allowing the framework to capture more realistic freight demand patterns and network-level coordination effects. (3) The team considers the case of truck platoons which will include fully automated truck platoons but also the more realistic case where the first truck in the platoon has a human driver. In other words, the lead truck will be driven by a human driving and following trucks will be electronically connected and fully automated. Truck platooning is an attractive concept as it has shown to have the potential of reducing aerodynamic drag and contribute to significant fuel savings. (4) The team plans to optimize their decisions of temporal rescheduling of freight demand, combined with dynamic lane management to achieve the best possible outcome. The team views the problem as assigning loads in 2 dimensions temporal and spatial in a way that reduces travel time and lowers fuel cost for both trucks and passenger vehicles.]]></description>
      <pubDate>Wed, 22 Jul 2026 17:28:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2733038</guid>
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    <item>
      <title>Digital twin for managing the curb and reducing congestion (Phase 2)</title>
      <link>https://rip.trb.org/View/2732948</link>
      <description><![CDATA[Curb space in dense urban cores is under intense pressure from freight deliveries, service vehicles, and passenger car parking activities. Without a data-driven view of curb regulations and 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, and network performance to support actionable curb management decisions.

The research team is developing a strategic curb digital twin for a portion of downtown Los Angeles (DTLA), built using publicly available data to ensure transparency and replicability. In Phase 1, the team integrated multiple open datasets (GIS networks from the LA GeoHub, land use data from DataLA, OpenStreetMap) and developed heterogeneous freight demand models distinguishing commercial and residential delivery behaviors. Phase 2 adds an analytical curb allocation optimization layer with targeted microsimulation validation. While the broader research agenda includes multimodal curb demand modeling (pursued in parallel work), Phase 2 addresses the policy question: given competing freight delivery and passenger parking demands, how should curb space be optimally allocated across blockfaces and time-of-day periods?]]></description>
      <pubDate>Wed, 22 Jul 2026 17:17:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732948</guid>
    </item>
    <item>
      <title>Flood-resilient Transport System Through Integrated Modeling, ML &amp; Immersive AR/VR </title>
      <link>https://rip.trb.org/View/2732359</link>
      <description><![CDATA[Extreme rainfall events increasingly disrupt urban transportation systems by overwhelming drainage infrastructure and causing localized road flooding that impedes last-mile freight delivery, delays emergency response, and disrupts the broader multimodal supply chain. These disruptions limit access to essential services, delay emergency response, and threaten public safety. Building on the research team's previously developed framework (F25-26), this project advances a data-driven approach for high-resolution prediction of urban road flooding in Jackson, Mississippi, integrating geospatial databases, process-based H-H modeling (aligning with rigorous U.S. Army ERDC methodologies), and machine learning (ML) and artificial intelligence (AI) techniques to identify flood-prone road and railway segments. The ML-based surrogate models will maintain computational efficiency, enable timely identification of vulnerable transportation networks, and support emergency response by feeding into JSU Water Lab’s broader web-based-visualization interfaces. This project introduces an interactive K–12 STEM module, age-appropriate hands-on activities along with STEM curriculum module designed for upper-level Civil Engineering undergraduate and graduate students at JSU. This initiative transforms research outcomes into the classroom to modernize workforce training using integrated Augmented Reality (AR) and Virtual Reality (VR) and will be tested with summer student exchange programs. Students can explore and 3D print several transportation infrastructure components, such as culverts, bridges, and urban drainage systems, and evaluate their performance under simulated flood conditions, and experience AR/VR based immersive simulators. Using AR/VR tools, including the Meta Quest platform, available in the PI lab, future transportation engineers will visualize flood scenarios in immersive 3D environments built from existing topographical assets in Unity or Unreal Engine. By combining advanced predictive modeling with experiential learning, the project promotes advanced STEM engagement, and high-tech workforce development, aligning with broader goals of improving multimodal transportation system resilience. While the primary focus is on urban road and rail flooding, these transportation corridors serve as critical connectors to Mississippi's inland waterway freight network, including facilities linked to the Pearl River system and regional multimodal freight movements. Roadway disruptions during extreme rainfall events can delay freight access to ports, intermodal terminals, water-dependent industrial facilities, and affect supply-chain resilience. By identifying flood-vulnerable roadway and railway segments, the proposed framework will support more reliable connectivity between surface transportation infrastructure and maritime freight operations]]></description>
      <pubDate>Tue, 21 Jul 2026 16:34:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732359</guid>
    </item>
    <item>
      <title>Impact Study on Increasing Truck Permit Weight Limits: Infrastructure &amp; Economic Considerations</title>
      <link>https://rip.trb.org/View/2724819</link>
      <description><![CDATA[Oregon’s current weight restrictions (105,500 lbs.) for divisible loads are less than neighboring states like Idaho and Nevada, which permit up to 129,900 lbs. In response to industry requests for alignment with these states, Oregon Department of Transportation
(ODOT) needs a comprehensive impact assessment of what raising the weight limits will mean in terms of sustaining the current operational infrastructure its charged with maintaining. This study will evaluate infrastructure effects, highway safety, and maintenance costs, along with the implications for adaptation and community impacts. With neighboring states already designating heavier freight routes, increasing Oregon’s truck permit weight limits may support freight fleet electrification and promote regional integration of the shipping network while assessing the costs to maintain and manage this increased infrastructural burden that’s on ODOT’s horizon. This feasibility and impact study will assess selective extended weight designations in Oregon and survey existing programs nationwide while evaluating potential risks to structural integrity (pavement and bridges), traffic safety impacts, as well as community and environmental considerations. The findings will provide ODOT with data-driven insights to guide policy decisions. This study will also examine vehicle configurations and length factors necessary to maintain legal axle weights in Weight Tables 1 and 2 at a gross weight of 129,900 and determine if those lengths are consistent with the lengths allowed by the LCV (Longer Combination Vehicle) freeze in federal law.

(1) A comprehensive report containing recommendations to support an informed evaluation of increasing weight limits for divisible loads, including an infrastructure impact assessment detailing the effects of heavier loads on bridges, pavements, and highway safety, with a focus on high-frequency freight routes in Oregon. (2) An economic impact assessment will quantify the contributions of oversized freight to Oregon’s economy, balancing potential economic gains from increased freight capacity with the costs of infrastructure maintenance and safety considerations. (3) A strategic implementation plan will outline a phased approach to applying these findings, allowing ODOT to prioritize investments to engage communities and the public effectively.]]></description>
      <pubDate>Wed, 08 Jul 2026 13:35:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2724819</guid>
    </item>
    <item>
      <title>Developing a Standardized Framework for Real-Time Freight-Specific Traveler Information and Route Restrictions for Commercial Motor Vehicle Operators; Truck Parking Data Exchange Standards</title>
      <link>https://rip.trb.org/View/2709247</link>
      <description><![CDATA[Commercial motor vehicle (CMV) operations increasingly rely on maps and navigation systems that were not designed to address the unique needs of freight operations. This mismatch contributes to increased safety risks, including unplanned diversions, bridge strikes, congestion in freight corridors, lane geometry constraints, and other routing errors.

Today, the lack of a standard, consistent data structure or framework for sharing real-time freight-specific information remains a foundational challenge for public agencies and for the economy that depends heavily on the national roadway network. Public agencies currently lack a widely accepted standard or shared framework for communicating restrictions, alerts, and disruptions to CMV operators. Existing standards such as the Traffic Management Data Dictionary (TMDD) and SAE J2354 (Advanced Traveler Information Systems) support general traveler messaging but do not include freight-specific data elements.

In addition, the growing need for timely and reliable truck parking information, coupled with the rapid expansion of truck parking information systems, demonstrates the need for standardized methods to collect and disseminate truck parking data. As technologies used in these systems become increasingly ubiquitous, and as industry expectations and preferences continue to evolve, standardization of both information and dissemination tools becomes a critical next step.

OBJECTIVES: The objectives of this research are: (1) to develop a unified data framework for delivering time-sensitive, relevant, and actionable freight-specific traveler information messaging to CMV operators; and (2) to develop proposed data standards for real-time, public and private truck parking availability and attributes (including the number of spaces, size, hours of availability, and available amenities).

]]></description>
      <pubDate>Tue, 02 Jun 2026 14:33:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709247</guid>
    </item>
    <item>
      <title>Reducing Transportation Fire Risk Through Carbon Dot Addition for Diesel Fuels</title>
      <link>https://rip.trb.org/View/2706036</link>
      <description><![CDATA[Diesel fuel is essential to freight transportation across the United States and is transported in large volumes by pipeline, tanker truck, and rail. During transportation accidents such as highway collisions, tanker rollovers, and rail derailments, released diesel fuel can ignite and produce high-consequence fires that threaten motorists, infrastructure, first responders, and nearby communities. Current safety strategies focus primarily on vehicle and containment design rather than reducing the intrinsic flammability of the transported fuel.
This project evaluates a fuel-level fire mitigation strategy through the use of carbon dot nanoparticles as fire-limiting additives in diesel fuel. The research will experimentally quantify ignition delay, flame persistence, burning behavior, and extinction characteristics using droplet-scale combustion testing representative of accidental spill and spray conditions. In parallel, the study will assess suspension stability and compatibility of carbon dot–diesel mixtures to ensure practical storage, handling, and transportation performance. The objective is to identify additive concentrations that measurably reduce fire risk without degrading fuel performance.
]]></description>
      <pubDate>Sat, 23 May 2026 18:02:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706036</guid>
    </item>
    <item>
      <title>A Performance- and Cost-Based Framework to Evaluate the Value of Multimodal Logistics Infrastructure</title>
      <link>https://rip.trb.org/View/2703796</link>
      <description><![CDATA[This project develops a practical, data-driven framework to evaluate the value of logistics infrastructure in a multimodal freight region. Focusing on the St. Louis metropolitan area, the framework integrates freight performance measurement with generalized logistics cost modeling to translate travel time, reliability, and terminal access improvements into economic outcomes. Methods include assembling a regional freight network representation, computing corridor-level travel time and variability metrics, and applying scenario-based valuation to estimate marginal benefits of targeted investments. The project also includes a private-sector truck–rail–barge use case to quantify multimodal tradeoffs and assess the competitiveness of inland waterway transportation under alternative infrastructure scenarios. The resulting workflow provides agencies and regional partners with transparent, repeatable methods to support freight investment prioritization and decision-making.]]></description>
      <pubDate>Sat, 16 May 2026 11:52:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703796</guid>
    </item>
    <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>Empirical assessment of land use and other policy impacts on freight facility location choices in California</title>
      <link>https://rip.trb.org/View/2702676</link>
      <description><![CDATA[The rapid expansion of warehousing and logistics activities in California has reshaped land-use patterns and placed substantial pressure on transportation systems and nearby communities. Growth in e-commerce, supply chain restructuring, and regional economic development incentives have contributed to an uneven and largely uncoordinated proliferation of freight facilities. Although these facilities support regional economies, their concentration heightens concerns about congestion, safety, air quality, and the availability of quality job opportunities. Local and regional governments struggle to anticipate these impacts because they lack empirical tools to link policy actions to freight facility siting decisions. 

This project develops an integrated framework to evaluate how land-use (LU), transportation, and economic development policies influence the location of freight facilities in California, and how these patterns relate to economic and social outcomes. The research compares three regional case studies spanning 20 years, integrating semi-quantitative policy analysis, satellite imagery-based LU classification, and spatial econometric modeling. Expected results include a geospatial database linking freight facility development with LU and policy environments, empirical evidence of policy-driven LU and logistics trends, and indicators describing the social and economic impacts of freight facility proximity. The findings will support state, regional, and local agencies in designing policies that improve goods movement efficiency while minimizing local impacts, thereby contributing to California's economy.]]></description>
      <pubDate>Thu, 14 May 2026 16:42:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2702676</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>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>
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