<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/common/TRIS Suite/feeds/RSS.aspx?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzg2IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8%2BPHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8%2BPHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMTYiIC8%2BPC9wYXJhbXM%2BPGZpbHRlcnMgLz48cmFuZ2VzIC8%2BPHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM%2BPHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8%2BPC9wZXJzaXN0cz48L3NlYXJjaD4%3D" 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>Characterizing Operational Disruptions in Urban Freight Networks: Patterns, Propagation, and Recovery Behaviors in Atlanta</title>
      <link>https://rip.trb.org/View/2780199</link>
      <description><![CDATA[Urban freight systems are subject to recurring disruptions that can degrade travel time reliability and operational efficiency. Despite their cumulative effects on freight movement and supply chain performance, these disruptions remain relatively understudied at the network level, particularly when only publicly funded and openly accessible data are used. This project develops a data-driven analytical framework to detect, characterize, attribute, and model recurring freight-relevant disruptions and assess their impacts on urban network performance.

The research combines truck travel times, vehicle counts and classifications, congestion imagery, crash records, and street-network data to create an integrated representation of freight system conditions. Machine learning methods are used to detect and classify disruption events, while graph-based analysis and survival modeling examine their propagation and recovery dynamics. The resulting analyses distinguish disruptions that may be amenable to public-sector intervention from those that are largely exogenous and translate these patterns into freight-relevant performance measures. A proof-of-concept analytical prototype will demonstrate how the methods can support freight system assessment and performance-based planning. The Atlanta metropolitan area serves as the testbed, and the methods are designed to transfer to other regions using comparable public data.]]></description>
      <pubDate>Fri, 18 Sep 2026 10:12:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2780199</guid>
    </item>
    <item>
      <title>Integrating Microsimulation in a Multi-scale Local Freight Activity Model</title>
      <link>https://rip.trb.org/View/2775057</link>
      <description><![CDATA[This project builds on results from prior TBD Center projects to advance the development of a multi-scale, synthetic data-based simulation framework for evaluating urban freight policy and infrastructure interventions. While prior phases enabled representation of total activity within a traffic analysis zone (TAZ), they do not yet represent location- or segment-specific activity within the zone. However, many urban freight interventions of interest to local cities — such as street design changes, lane or signal prioritization, parking and loading regulations, and segment-specific access controls — require this more detailed granularity. This Phase 3 study aims to address this gap.

The aim of this project is to define a simulation framework that could integrate TAZ-scale vehicle activity inputs, be implemented within a TAZ at a population scale to simulate segment-level activities, and be adaptable to simulate multiple local decision types. This project phase has three objectives: (1) to identify best-available microsimulation approaches for evaluating micro-scale freight interventions; (2) to assess the utility of machine learning approaches — in particular reinforcement learning — to improve upon static decision-assignment processes within these simulations; and (3) to develop a scalable and adaptable framework for microsimulation of freight interventions that integrates TAZ-scale truck paths with high-resolution segment-level activity. Key project tasks include: a comprehensive synthesis of existing freight microsimulation studies, feasibility analysis of reinforcement learning for freight operator route choice and behavioral modeling, development of a proposed graph structure for simulating varied freight-related interventions within a traffic microsimulation model, and evaluation of common simulation tools (e.g., VISSIM, SUMO) to assess their native capability to represent the proposed graph structure.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:04:06 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775057</guid>
    </item>
    <item>
      <title>Measuring the Motivators of Home Delivery Decisions</title>
      <link>https://rip.trb.org/View/2775056</link>
      <description><![CDATA[Over the last decade, many shoppers in the U.S. and around the world have quickly become reliant on home-based deliveries of household and retail goods. While growth in e-commerce is easily demonstrated from retail sales records, relevant activity trade-offs and resulting impacts on passenger and freight travel activity remain less well-understood. Some home deliveries are made out of necessity, while others are discretionary. Some deliveries replace in-store shopping, while others are supplementary to in-person shopping activities.

This study leverages a novel national survey, the second wave of the Transportation Heartbeat of America Survey, that simultaneously captures: (1) motivations for specific types of online purchases and/or reasons for not purchasing goods online; (2) household delivery frequencies by type (carrier-delivered parcels, crowdsourced packages, prepared food, and groceries); (3) personal travel behaviors; and (4) individual and household characteristics. In this study, the research team will directly investigate the underlying motivators for home delivery, and the barriers to home delivery, that influence Americans' home delivery choices. First, leveraging location-specific survey responses, the team will conduct descriptive and spatial analysis of results to identify relevant trends. Next, the team will apply exploratory factor analysis (EFA) to identify latent motivation dimensions and latent class analysis (LCA) to identify distinct behavioral segments based on stated delivery motivations. Third, the team will estimate and compare delivery frequency models, including ordered probit models, zero-inflated negative binomial models, hybrid choice models, or integrated choice and latent variable models. By comparing model performance with and without new motivational variables, this study will test whether controlling for stated motivations improves model fitness and reduces inconsistencies observed in prior work.]]></description>
      <pubDate>Fri, 04 Sep 2026 15:01:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775056</guid>
    </item>
    <item>
      <title>Utilizing ITS at International Ports to Reduce PM2.5 and NO2 Emissions</title>
      <link>https://rip.trb.org/View/2772963</link>
      <description><![CDATA[The research team will develop an integrated Intelligent Transportation System (ITS) at four ports of entry, supplemented by low-cost PM2.5 and NO2 sensors, to support real-time information and monitor idling-related emissions from commercial vehicles. This project would supplement the existing ITS network managed by the City of El Paso by installing low-cost PM2.5 and NO2 sensors at ports of entry and across the network to monitor air quality and idling-related emissions. Mitigating PM2.5 and NO2 emissions from commercial vehicles is an essential component to reducing poor air quality, which has a major health impact on El Paso residents. The objective of the project is to monitor and visually-represent commercial vehicle activity and emissions in real-time simultaneously and identify congestion points and high-emission zones. This objective is achieved by combining dedicated PM2.5 and NO2 sensors with traditional ITS (fiber optic communications, lane management systems, dynamic message signs, closed-circuit television cameras, ground vehicle sensors, signal phasing and timing).  As part of Outreach Tasks, this model is intended to provide additional datasets that will be presented to the public, to logistics companies operating commercial vehicles at international ports, to decision makers that can make policy changes, and funding agencies like USDOT for funding construction improvements. Focusing on mitigating congestion and reducing PM2.5 and NO2 emissions at international ports will result in positive transportation-related health disparities and improve air quality for disadvantaged communities. This model can be replicated across all international ports along the U.S.-Mexico and U.S. Canada border and in international borders with similar commercial activity.]]></description>
      <pubDate>Thu, 03 Sep 2026 10:09:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772963</guid>
    </item>
    <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>
    </item>
    <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>
  </channel>
</rss>