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    <title>Research in Progress (RIP)</title>
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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>Developing a Nebraska-Specific Evaluation Framework for Superheavy Load Movements on Pavements</title>
      <link>https://rip.trb.org/View/2689391</link>
      <description><![CDATA[Nebraska continues to receive superload permit requests whose non-standard axle/tire layouts, slow-roll operations, and edge-proximity produce subsurface stress patterns that are not consistently captured by current screening practices. Although the federal framework and SuperPACK provide a sound mechanistic basis, they have not yet been exercised in a Nebraska-focused parametric fashion to reveal which combinations of pavement structure, material properties, axle weights, and spacings govern ultimate capacity and service-limit responses on state routes. Without that evidence, route approvals remain slower and less consistent, and operating conditions or mitigations are difficult to specify with confidence. This study will provide the Nebraska Department of Transportation (NDOT) with a focused, evidence-based screen for superload movements on Nebraska highways. By exercising SuperPACK in a structured parametric study and benchmarking marginal cases with an advanced finite element (FE) tool, the work will yield preliminary acceptance bounds that can be applied directly to permit reviews. The project will also clarify when a simple mechanics-based screen is sufficient and when an advanced check is warranted, improving the efficiency of reviewer time and aligning engineering findings with transparent cost-recovery logic. Because the analysis uses Nebraska-representative sections, inputs, and axle/tire nuclei taken from real permits, the results will be credible to internal reviewers and external stakeholders and will establish a practical pathway to extend the approach to rigid and composite pavements in subsequent phases.]]></description>
      <pubDate>Tue, 02 Jun 2026 12:24:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689391</guid>
    </item>
    <item>
      <title>Routing Autonomous Trucks on Dedicated Lanes</title>
      <link>https://rip.trb.org/View/2676007</link>
      <description><![CDATA[Trucks are known to have a significant impact on congestion during traffic peak hours due to their size and slower dynamics. Human operated trucks for freight transport are faced with two constraints: those imposed by the service demand and those imposed by the human driver. For long haul operations, for example, truck drivers must meet the constraints of hours of service. For short haul they have to meet family and personal constraints which often do not allow them to operate during odd hours. With automation the human constraints are removed which opens the way to view truck routing and scheduling under different and more flexible constraints. The major problem faced by automated trucks operating with the rest of traffic, however, is safety as due to the different sizes involved the sensing problem is more challenging and potential accidents can be catastrophic.


Under this project the research team plans to analyze and evaluate the use of automated trucks that will operate on the surface network at times that the traffic demand is very low, so that lanes can be switched dynamically to dedicated automated truck lanes without affecting traffic. By doing so we can keep the automated trucks separated from manually driven vehicles which may be using the network, thereby addressing the issue of safety. This project will address the potential benefits of automated trucks on dedicated lanes operating at low volume traffic hours. In addition, it will extend the approach to automated truck platoons where automation will also lead to significant fuel savings (up to 20%) due to reduction in aerodynamic drag, bringing the potential to lower costs. Moving trucks from times of high congestion to times of no congestion will bring considerable benefits to trucking companies as well as to all other users of the road network, as fewer trucks will be operating during peak traffic hours. In addition, trucking companies that are short of truck drivers will be able to operate without disruptions and without human imposed constraints, saving on labor costs. The team plans to use as an example a network that includes Interstate 710 (I-710) and the Ports of Los Angeles/Long Beach, a route that generates considerable truck traffic. The team will identify the lanes that can be dynamically dedicated to automated trucks at certain hours and estimate the impact on congestion and fuel savings. The team will use real truck and traffic data to validate their traffic simulators which they will then use to run different scenarios.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:31:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676007</guid>
    </item>
    <item>
      <title>Large Multimodal Models-based Undesignated Truck Parking Monitoring System at Rest Areas</title>
      <link>https://rip.trb.org/View/2669662</link>
      <description><![CDATA[Undesignated truck parking issues are prevalent in areas where truck parking facilities are scarce or overcrowded. When trucks park outside of dedicated spaces, they can obstruct emergency access routes, leading to public health and safety concerns, disrupt traffic flow, and increase the risk of theft. These problems are exacerbated in regions with a high demand for truck parking, such as District 8 in California, where nearly one-third of all parking incidents involve undesignated truck parking. Currently, the detection of undesignated parking relies heavily on manual enforcement, primarily through citations issued by patrol officers, which is costly and inefficient due to the significant resources required for patrols. Existing sensor-based truck parking detection systems also have less focus on undesignated parking due to lack of coverage. This project will develop an artificial intelligence (AI)-driven Large Multimodal Models (LMMs) based truck parking monitoring system that covers both designated truck parking and undesignated truck parking. It will build on existing work in the area of truck parking research, with a focus on incorporating new and innovative approaches.  Compared with traditional vision-based systems which can detect vehicles but lack the ability to interpret complex situations for undesignated truck parking, LMMs integrate both visual recognition and language interpretation to comprehend contextual information such as road signs, lane markers or surrounding environments. The research team will explore the integration of Set-of-Mark prompting with lightweight domain adaptation for LMMs, and the fine-tuned inference pipeline that takes advantage of site-specific labeled data to enable accurate, scalable truck parking monitoring for both designated and undesignated conditions. Based on data collected from the I-10 truck parking availability system through the research team’s recent project funded by Caltrans, the team will evaluate the proposed method’s effectiveness across multiple real-world parking lots under diverse visual conditions.  ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:44:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669662</guid>
    </item>
    <item>
      <title>Evaluate Thick Lift High Performance Grade (HPG) Mixes for Intersections, Border Checkpoints, and Other Locations with Slow Moving Heavy Traffic</title>
      <link>https://rip.trb.org/View/2604525</link>
      <description><![CDATA[Several Districts of the Texas Department of Transportation (TxDOT) recently reported rutting problems greater than 2 inches at intersections, border checkpoints, and other locations where heavy truck traffic either moves very slowly or is stationary while waiting in queues, even though the best mixes (e.g., stone matrix asphalt) were used. There is an urgent need to address such premature rutting problem in those places in a timely manner. The research team will identify and evaluate the suitable mixes and single lift construction technique to avoid premature rutting failures at those places, resulting in major cost savings and reducing wet weather accidents. The research team will review literature to identify the critical factors for improving mix rutting resistance. Based on the findings from the literature, the research team will develop and execute an experimental design to identify the suitable mixes for the slow-moving heavy traffic and then construct field trials with those mixes in a single thick lift and follow up their field performance. In the end, the research team will recommend specification changes and develop guidelines for constructing single thick lift with suitable mixes at intersections, border checking points, or other places.]]></description>
      <pubDate>Mon, 29 Sep 2025 16:16:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2604525</guid>
    </item>
    <item>
      <title>Integrating Weight-in-Motion (WIM) with Vehicle and Land-Use Data Sources to Characterize Freight Truck Patterns and Optimize WIM Site Placement</title>
      <link>https://rip.trb.org/View/2589064</link>
      <description><![CDATA[The objective of this research is to: support Georgia Department of Transportation (GDOT) in enhancing its freight monitoring capabilities by evaluating the effectiveness of its existing weigh-in-motion (WIM) network and assessing the potential of integrating multiple data sources to inform future WIM site placement.

]]></description>
      <pubDate>Thu, 14 Aug 2025 14:09:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2589064</guid>
    </item>
    <item>
      <title>Data for Forecasting Truck Parking and Land Use

</title>
      <link>https://rip.trb.org/View/2558396</link>
      <description><![CDATA[The United States faces a growing shortage of truck parking, a problem that affects safety, efficiency, and community livability. Despite efforts by the U.S. Department of Transportation (DOT) and state DOTs to address this issue, demand for safe and adequate truck parking exceeds supply.

Truck parking is inherently a multijurisdictional challenge that requires coordination among federal, state, regional, and local agencies. While NCHRP Project 08-141, A Guidebook for Local Truck Parking Regulations, is developing model ordinances to assist local jurisdictions, there is no consistent method to determine how much truck parking should be required for truck-generating development projects.

A variety of public and private datasets exist to estimate truck parking demand, but each has limitations. Developing a national framework is further complicated by diverse land-use requirements, regulations, and economic conditions across states.

Research is needed to develop an analytically sound, data-driven framework that will enable transportation agencies to estimate current and future truck parking demand and integrate this information into land-use and freight-planning decisions.

The objective of this research is to develop and validate a framework for transportation agencies to estimate truck parking demand for current and future conditions.]]></description>
      <pubDate>Wed, 28 May 2025 10:14:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558396</guid>
    </item>
    <item>
      <title>The Reverse Side of Online Shopping: Examining Sociodemographic and Built-Environment Determinants of Delivery Returns</title>
      <link>https://rip.trb.org/View/2553166</link>
      <description><![CDATA[The rise of online shopping has led to a significant increase in the return rate for items purchased online (or "delivery returns"). The process of returning items, once a rare occurrence in the traditional retail setting, has become a commonplace aspect of the e-commerce experience. Online purchase return rates (30%) significantly exceed those of physical stores (8.89%). Overall, these high return rates, have substantial financial, logistical, and transportation-related repercussions. From a transportation perspective, the large volume of returns necessitates additional truck trips, leading to increased freight vehicle miles traveled. This trend also results in more truck traffic at residential locations or return points. Despite the acknowledged impacts, this topic remains under-researched, with existing studies focusing on product characteristics, or retailer policies while overlooking consumer-level perspectives. This study aims to bridge this gap by examining how sociodemographic and built-environment factors influence the frequency and channel choice (physical store, mail carrier, Amazon drop-off, home pickup) for returning online purchases. Utilizing the National Household Travel Survey (NHTS) 2022 dataset, the research team analyzes responses on delivery return frequency across four channels. The team employs a multivariate probit ordered-response model to jointly analyze the full product returning behavior spectrum. This approach recognizes that behaviors are multifaceted, involving both the decision to return an item and the choice of a channel, and accounts for the interconnectedness of decision-making processes. The findings provide a foundation for developing targeted strategies to reduce return rates, streamline reverse logistics, manage travel demand, enhance customer satisfaction, and contribute to a more sustainable e-commerce future. ]]></description>
      <pubDate>Thu, 15 May 2025 14:53:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553166</guid>
    </item>
    <item>
      <title>Freight Route Management Application for the Port of Anchorage</title>
      <link>https://rip.trb.org/View/2512620</link>
      <description><![CDATA[The objective of this research is to develop and evaluate an intelligent transportation management application for improving the efficiency, safety, reliability, and cost-effectiveness of freight and fuel truck movement to/from the Port of Alaska located in Anchorage, Alaska. This is a partnership project between the City of Anchorage’s Port of Alaska and Alaska Department of Transportation and Public Facilities (DOT&PF).  Truck transportation network located at the port will be able to better route and stage cargo transport within the Port of Alaska footprint. The application could be used outside the port by truck drivers, Alaska 511, and traffic operations centers.]]></description>
      <pubDate>Fri, 21 Feb 2025 21:42:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2512620</guid>
    </item>
    <item>
      <title>Best Practices for Local Reviews of Industrial Developments</title>
      <link>https://rip.trb.org/View/2475835</link>
      <description><![CDATA[With respect to freight, demand continues to grow alongside population and economic growth and trucking plays a critical role in the supply chain and economy whereby trucks carry over 19 billion tons of freight valued at more than $18 trillion annually in the United States.  A Virginia Department of Transportation (VDOT) goal is to improve freight velocity and freight resiliency, and Virginia’s Transportation Plan includes elements of freight planning such as mid-and long-term planning, and strategic actions for freight investments as well as freight performance measures and critical freight corridor designations.  With respect to elements critical for the efficient movement of freight four topic areas will be explored as part of this research effort:  (1) industrial land-use, (2) truck parking, (3) freight resiliency, and (4) mobility innovations.  

Effective and efficient movement of goods is driven by how localities plan for land developments that will generate or attract significant truck trips. Virginia legislation has encouraged jurisdictions to identify freight corridors in their comprehensive plans, however there is limited knowledge of proactive policies for industrial developments at the local level.  The purpose of the research is to examine local planning practices and reviews of proposed industrial sites and to develop guidance with respect to industrial land-use, truck parking, freight resilience, and mobility. The value of this research is fourfold:  it would set out the scale of the problem, especially its impact on highway maintenance costs; provide a catalog of useful local practices; provide the state of the practice in land development reviews; and provide information that can help the Department better engage with localities. 
]]></description>
      <pubDate>Thu, 12 Dec 2024 11:14:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2475835</guid>
    </item>
    <item>
      <title>Impact of State Highway Pavement Traffic Overloading Following Natural Disasters</title>
      <link>https://rip.trb.org/View/2431181</link>
      <description><![CDATA[The 2021 Marshall Fire in Colorado was one of the most destructive wildfires in the United States with 1,084 homes destroyed and over $2B in losses in the town of Louisville, town of Superior, and in unincorporated Boulder County. Highway transportation infrastructure is vital during wildfire events to facilitate evacuation, rescue operations, and goods transportation. In the post-fire recovery, highways play a critical role by facilitating debris removal to landfills and transportation of reconstruction materials and services. Initial pavement damage during the fire is generally limited to localized excessive heat from burning cars and vegetation on the road causing surface scarring and raveling. More indirect damage to the highway pavement is caused by heavy truck operations during post-fire debris removal and reconstruction, but there are limited studies investigating this problem. The Federal Emergency Management Agency (FEMA) generally reimburses communities and agencies for direct damages to pavement from major wildfires, but not indirect damage to pavement. This study assesses a knowledge gap by collecting accurate data on the truckloads and number of trips involved in debris removal, construction vehicles, and other reconstruction related activities after a wildfire. This data would be used to predict pavement degradation and costs to repair pavement damaged by these activities.]]></description>
      <pubDate>Mon, 16 Sep 2024 09:57:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431181</guid>
    </item>
    <item>
      <title>Framework to Minimize Society’s Exposure to Primary Road Emissions</title>
      <link>https://rip.trb.org/View/2425220</link>
      <description><![CDATA[Traffic-related air pollution is caused by increased concentrations of pollutants from motor vehicle use, with greenhouse gas (GHG) emissions from connected freight trucks being particularly concerning. Despite representing only 5% of traffic, these trucks are estimated to contribute 25% of GHG emissions, affecting socio-economic conditions and public health. These impacts can be evaluated using Social Life Cycle Assessment throughout the roadway's life cycle, augmenting existing methods like Life Cycle Assessment and Life Cycle Cost Analysis. The main objective of this study is to develop a framework to assess and quantify the exposure to primary road emissions by: (1) conducting geospatial analysis to identify disadvantaged or environmental justice communities residing in close proximity to roadways; (2) correlating emissions and exposure with distance or proximity to roads; (3) proposing an exposure metric or index considering the Human–Technical–Environmental system framework; and iv) developing a case study to explore the impacts of both conventional trucking and connected platoon operations.]]></description>
      <pubDate>Thu, 05 Sep 2024 11:00:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/2425220</guid>
    </item>
    <item>
      <title>Assessing the Impacts and Challenges of Truck Platooning on Highway Infrastructure in Montana</title>
      <link>https://rip.trb.org/View/2413944</link>
      <description><![CDATA[The Montana State Legislature is anticipated to introduce legislation regarding the use and regulation of truck platooning in Montana during the next legislative session in 2025. Montana Department of Transportation (MDT) Planning Division expects to be requested by the State Legislature to provide expert guidance on how the emerging technology of truck platooning will impact transportation infrastructure and systems in Montana. The rapid evolution of transportation technologies, including the emergence of truck platoons, across infrastructure, vehicles, and systems, indicates a future characterized by intelligent infrastructure, interconnected vehicles, and autonomous driving. Projections indicate a gradual yet significant adoption of automated driving systems, with forecasts suggesting that by 2050, autonomous vehicles and advanced transportation technologies could represent 50% of the US vehicle fleet. While implementing truck platooning provides several potential advantages benefiting surface transportation, the trucking industry, and overall economic growth, it also introduces a host of new challenges for MDT to navigate. These challenges primarily revolve around the uncertainty regarding how truck platooning will impact existing highway infrastructure and the traveling public. Consequently, this research project will proactively prepare MDT for this emerging technology by identifying the needs for efficient testing and deployment of truck platoons, and evaluating the anticipated challenges associated with its implementation.
This research project will help prepare MDT for the legislative session by identifying the requirements and limitations associated with operating truck platoons along with a thorough examination of the multifaceted impacts. The objectives of this research project include reviewing the current state-of-the-practice regarding national, state, and local regulatory frameworks and legislation pertaining to truck platooning. This analysis will serve to pinpoint infrastructure, traffic management policy, roadway design, and standardization needs essential for facilitating the deployment of truck platooning. The research objectives will identify how truck platoons could impact the operation and safety of the highway system. The goal of this research is to provide practical guidance for MDT decision-makers to respond to the State Legislature regarding inquiries on how truck platooning could impact Montana highways and the traveling public.]]></description>
      <pubDate>Tue, 06 Aug 2024 11:05:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2413944</guid>
    </item>
    <item>
      <title>Improving Compatibility of Truck Platooning with Existing Infrastructure via Development of Dynamic Operational Rules on Highway Networks</title>
      <link>https://rip.trb.org/View/2398088</link>
      <description><![CDATA[Truck platooning presents a promising solution for reducing fuel consumption and emissions while enhancing freight transport capacity. However, the adaptivity of this technology to diverse roadway infrastructure remains a critical research need. In Wyoming, where freight transport is vital to the economy, determining how to manage truck platooning operations on existing highway networks is essential for infrastructure owner operators. This project aims to address the needs by designing and prototyping a dynamic truck platooning regulatory system. Two key algorithms will be developed: one for selecting compatible highway segments for platooning based on various roadway criteria, and another for dynamically determining operational rules for truck platoons considering real-time data such as weather and traffic conditions. These algorithms will form the basis of a comprehensive regulatory system aimed at ensuring safe and efficient truck platooning operations. Using Wyoming's roadway, traffic, and weather data, the system will be simulated and evaluated to assess its effectiveness in achieving safety, operational, and environmental objectives. By providing insights into the compatibility of truck platooning with existing highway networks and offering a dynamic regulatory framework, this project aims to pave the way for the widespread deployment of truck platooning technology while maximizing its benefits.]]></description>
      <pubDate>Fri, 28 Jun 2024 09:31:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2398088</guid>
    </item>
    <item>
      <title>Analyzing the Economic Development Impacts of Truck Parking in Louisiana </title>
      <link>https://rip.trb.org/View/2348487</link>
      <description><![CDATA[This research analyzes the economic development impacts of establishing truck parking within the two major metropolitan statistical areas (MSAs) of Louisiana. The two MSAs are major freight corridors that host the five major ports of the state which comprise the largest port complex in the world in terms of tonnage. Louisiana also ranks tenth in truck traffic nationally, with the Port of New Orleans ranked as the sixth largest container port in the U.S. This calls for improved efficiency in the intermodal system to improve productivity and safety. While there is high demand for the development of truck parking nationally, they are capital intensive and are associated with intense pushback from communities who view it as a nuisance within their neighborhoods. This study will assess how much economic development impact the establishment of truck parking may generate in the New Orleans- Metairie and Baton Rouge MSAs using the input-output method in IMPLAN. It is our aim that the findings could be utilized for securing funding for the creation of additional parking, as well as community support from the proposed neighborhoods in which the projects would be built. ]]></description>
      <pubDate>Tue, 05 Mar 2024 19:13:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2348487</guid>
    </item>
    <item>
      <title>Prediction of Pavement Damage under Truck Platoons Utilizing a Combined Finite Element and Artificial Intelligence Models</title>
      <link>https://rip.trb.org/View/2329135</link>
      <description><![CDATA[The characterization of platoon configuration encompasses three fundamental parameters: the lateral positioning of trucks, the spacing between them, and the total number of trucks within the platoon. The quantification of pavement damage stands as a paramount concern for roadway agencies, which is pivotal for the formulation of effective maintenance and rehabilitation strategies, ensuring the prolonged serviceability of roadways. Consequently, the development of a comprehensive framework capable of calculating pavement distresses as a direct function of these parameters becomes imperative. Therefore, the objective of this study is to introduce an innovative framework tailored to the investigation of pavement damage induced by truck platooning: (1) developing a new framework to simulate repetitive loading and predict accumulating pavement responses, including rutting prediction via a mechanistic model, and (2) proposing a physics-guided artificial intelligence (AI) model to predict pavement responses using the extensive 3D pavement finite element (FE) response database.
]]></description>
      <pubDate>Sun, 28 Jan 2024 12:35:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329135</guid>
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