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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzc1IiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>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>OpenRoad Link: A Public-Private Data Exchange for Safer, Smarter Trucking </title>
      <link>https://rip.trb.org/View/2646948</link>
      <description><![CDATA[Work zones, lane closures, and traffic incidents significantly impact roadway safety and efficiency. When lanes are blocked due to construction, crashes, or other disruptions, roadways no longer function as designed—leading unexpected congestion, increased crash risk, and reduced operational reliability. Many work zones are established to perform critical maintenance on aging infrastructure—essential to improving durability and extending the service life of roadways—but they also introduce temporary risks and delays that must be better managed.  Effects of lane blockages are particularly severe for commercial motor vehicles (CMVs), which require more time and space to slow or reroute and are subject to strict hours-of-service regulations that make delays especially costly. 

This project proposes to develop and evaluate a data exchange framework—OpenRoad Link—to integrate and share real-time lane closure, work zone, and incident data from the Oklahoma Department of Transportation (ODOT), the Oklahoma City and Tulsa Traffic Operations Centers (TOCs), and other key transportation and traffic enforcement partners. To build this framework, the project will first identify and assess the roadway data already collected and shared by these agencies, as well as the types of information currently accessible to the CMV industry through private telematics platforms. Building on national standards such as the Work Zone Data Exchange and SAE J2735 (the standard message set for vehicle-to-everything communications), the project will extend the data scope to include lane-blocking crashes, maintenance activities, and other short-term or unplanned restrictions not currently emphasized in existing feeds. Through collaboration with ODOT, city TOCs, and trucking industry partners—including a pilot with a major trucking company such as ABF—the project will demonstrate the delivery of curated, high-value information directly to in-cab devices or fleet management systems.  

Key tasks will include identifying and cataloging roadway and incident data currently collected by the Oklahoma Department of Transportation (ODOT) and the Traffic Operations Centers (TOCs) of Oklahoma City and Tulsa, as well as evaluating what information is already being shared with the commercial vehicle industry through private telematics platforms. The project will establish partnerships with ODOT, city transportation and public safety agencies, and private industry stakeholders to design and implement a unified, standards-compliant data exchange framework. Following the design phase, the team will develop and deploy the OpenRoad Link data feed, ensuring compliance with existing national standards and verifying data accuracy and reliability. A pilot deployment will be conducted in collaboration with a trucking company using a selected in-cab device to deliver actionable, real-time information directly to CMV drivers.  

Anticipated outcomes include improved safety for CMV drivers, a reduction in secondary crashes, enhanced freight reliability, and a validated proof-of-concept for scalable public-private data exchange. By producing a replicable model for collaboration between state DOTs and private-sector technology providers, the project aims to accelerate national adoption of interoperable safety data systems and promote safer, more efficient freight transportation. ]]></description>
      <pubDate>Tue, 06 Jan 2026 08:59:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646948</guid>
    </item>
    <item>
      <title>Enhancing Rural Freight Resilience in the Southeastern U.S.: Data-Driven Modeling and Decision Support for Supply Chain Efficiency.

</title>
      <link>https://rip.trb.org/View/2643108</link>
      <description><![CDATA[This research aims to address the issue of limited alternative routes in rural freight systems by modeling rural freight networks to identify critical vulnerabilities and evaluate potential recovery strategies. The study also proposes new methods for addressing truck parking shortages using models such as reservation and automated allocation for predicting demand and optimizing supply. The project leverages network science, emerging data sources, and simulation tools to develop methodologies for assessing the resilience of rural freight networks. Additionally, the study will explore the potential of connected and autonomous vehicles (CAVs) for improving operational efficiency and reducing parking demand, particularly for middle-mile delivery and short-range freight operations. This research directly addresses these issues by (1) Developing network-based modeling techniques to analyze rural freight resilience, (2) Identifying critical corridors and evaluating alternative routing strategies, and (3) Proposing innovative truck parking solutions to improve operational efficiency. This includes broader operational strategies such as parking reservations, staging areas near hubs or ports, route reservations, and quicker incident resolution for truckers.  ]]></description>
      <pubDate>Sat, 20 Dec 2025 17:04:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2643108</guid>
    </item>
    <item>
      <title>Guide on Truck Rest and Service Areas for Critical Supply Chain Delivery



</title>
      <link>https://rip.trb.org/View/2614489</link>
      <description><![CDATA[No abstract provided.]]></description>
      <pubDate>Mon, 27 Oct 2025 17:32:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2614489</guid>
    </item>
    <item>
      <title>Development of In-Pavement LFBG Sensors for Vehicle WIM System Measurement and Monitoring in Rural Low-Volume Road Conditions Phase One: Theoretical Research</title>
      <link>https://rip.trb.org/View/2596477</link>
      <description><![CDATA[The research aims to address the growing challenge of accurately monitoring overweight truck loads on low-volume roads, which present unique issues for both infrastructure durability and road safety. Low-volume roads, defined as those carrying fewer than 2000 vehicle per day (and often fewer than 400 vehicles per day in rural areas), account for over 80% of the roads in North Dakota. Given the state’s reliance on agriculture and natural resources transport, overload trucks frequently travel these roads, which are not designed to withstand the repeated stress of excessively heavy loads. While special permits are issued for trucks carrying heavy loads under specific conditions, enforcing weight limits on numerous low-volume roads remains a significant challenge. This issue compromises the longevity of the road infrastructure and poses safety risks for all road users. Therefore, accurate monitoring and enforcement of weight limits on low-volume roads is crucial for maintaining infrastructure and enhancing road safety.]]></description>
      <pubDate>Mon, 08 Sep 2025 16:01:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2596477</guid>
    </item>
    <item>
      <title>Streamlining the Registration Process for Motor Carriers</title>
      <link>https://rip.trb.org/View/2593941</link>
      <description><![CDATA[KRS 186.040 now authorizes interstate motor carriers to register commercial motor vehicles (CMVs) at or above 44,001 lb. directly with the Division of Motor Carriers (DMC). Despite this change, the county clerk in the county where a vehicle is registered still receives $30 of the registration fee. This change effectively consolidates the license plate and International Registration Plan processes. While DMC administrators and county clerks are working with software vendors and the KAVIS team to integrate these processes, doing so has introduced logistical challenges. As these processes continue to evolve, DMC administrators want to analyze how other states process CMV registrations, especially for apportioned vehicles.]]></description>
      <pubDate>Thu, 28 Aug 2025 11:32:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2593941</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>Integrating Large Commercial Motor Vehicle Safety into State Freight and Safety Planning




</title>
      <link>https://rip.trb.org/View/2558373</link>
      <description><![CDATA[According to the Federal Motor Carrier Safety Administration (FMCSA), Large Truck and Bus Crash Facts 2022, crash rates in the United States involving large trucks increased 25 percent from 2009 to 2021. Given their size and weight, large-truck crashes can result in closure of one or more lanes of a highway, particularly for rollovers or cargo spills. Large-truck crashes also have the potential to damage pavements, bridges, and other infrastructures. 

Large commercial motor vehicles include heavy-duty tractor-trailers and heavy equipment such as dump trucks. Data collection and reporting related to large commercial truck crashes and safety are the responsibility of federal and state agencies, diffusing the “ownership” of commercial truck safety among largely unrelated agencies. However, state department of transportation (DOT) officials often do not reach out to agencies with these responsibilities, such as the FMCSA or the state’s highway patrol agency, in their freight and highway safety planning processes. Plans developed from these planning processes are not informed by the data collected and managed by these agencies. The lack of agency coordination means that the infrastructure needed to support large commercial trucks are not fully considered in state highway and freight planning processes. Thus, infrastructure such as truck parking and emergency escape ramps may not be prioritized in highway safety and freight plans and funding programs. 

Research is needed to identify integrated approaches that consider large commercial motor vehicle safety in highway freight and safety planning processes and plans. 

The objective of this research is to develop a guide for the integration of commercial motor vehicle safety into state freight and safety planning processes. ]]></description>
      <pubDate>Thu, 29 May 2025 12:59:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2558373</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>Comprehensive assessment of alternative fueling system supply chains in the heavy duty trucking sector</title>
      <link>https://rip.trb.org/View/2495007</link>
      <description><![CDATA[This project examines production supply chains for fueling systems of heavy duty vehicles.  The project uses life cycle analysis (LCA) and extends the method to consider impacts beyond energy consumption and associated emissions, including wider societal impacts, such as air emissions generated in the production or operations process, or labor conditions for those engaged in raw materials extraction or component production.  The project builds on current research that is developing prototype supply chains and identifying “hot spots” for particular impacts.  The purpose of the research is to examine strategies for relocating resource extraction, production, and manufacturing activity to reduce overall impacts.  The case of electric batteries for trucks is used to estimate the effects of taking advantage of locations with cleaner energy mix or more robust labor standards, as for example onshoring manufacturing to the US.]]></description>
      <pubDate>Fri, 31 Jan 2025 18:42:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2495007</guid>
    </item>
    <item>
      <title>Truck Permits: Managing Increasing Loads and Mitigating Infrastructure Damage to Balance Freight Mobility</title>
      <link>https://rip.trb.org/View/2472700</link>
      <description><![CDATA[Non-reducible truck permits, essential for freight mobility, pose significant challenges to infrastructure integrity, contributing to accelerated fatigue, increased maintenance costs, and safety hazards. This study quantifies the scope and distribution of permit loads across Massachusetts, evaluates their impact on bridges and highways, and verifies their alignment with current regulations and industry standards. The research will integrate data on truck permits, freight volumes, and infrastructure conditions to develop data-informed recommendations for mitigating adverse effects. Outcomes include optimized permit management strategies, improved infrastructure durability, and expanded access to reliable transportation, aligning with US DOT priorities in safety and system performance.
]]></description>
      <pubDate>Mon, 09 Dec 2024 10:27:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2472700</guid>
    </item>
    <item>
      <title>Preventing Rear and Side Crashes of Heavy-Duty Tractor Trailer Combinations with Smart Sensors and Vision Systems</title>
      <link>https://rip.trb.org/View/2440025</link>
      <description><![CDATA[The proposed project aims to prevent fatal rear and side crashes related to heavy-duty tractor-trailer combinations. Specifically, the research team proposes to develop and test smart trailer sensors/vision systems that infer "dynamic safety zones” and use lighting signals (or other communication modes) to alarm following and overtaking vehicles, pedestrians, or other non-occupant situations. The proposed trailer sensors/vision systems automatically analyze videos, vehicle size, and loading and brake data to infer collision risks between tractor-trailer combinations and approaching vehicles and people. From 2019 to 2021, fatal rear crashes with large trucks with trailers, where passenger vehicles travel under the rear of the truck, increased from 16.8% to 18.0%. In 2021, other vehicles in the large truck lane (26.5%) and others encroaching into the large truck lane (36.0%) were the two critical pre-crash events that caused such crashes. Drivers usually underestimate the required distance when the safe distance suddenly increases because of the large weights and sizes of the vehicles, unexpected pavement conditions, and terrains that require extra separations between vehicles. Inter-vehicle dynamic safety zones change and differ by situations and changes over time, so manually estimating the safe following and overtaking distances could be unreliable. Sometimes, illusions, slipperiness caused by weather, and poor lighting conditions can bias human estimates and make the reaction too late to stop. The recent integration of computer vision and motion sensors has shown the potential to improve passenger vehicles. However, heavy-duty vehicles, especially trailers, need special consideration of vehicle size, motion planning, road conditions, and occlusions to ensure a reliable assessment of side and rear collision risks in different positions of the tractors and trailers.
The proposed project will integrate the expertise of the project team and two industry partners in developing and testing an intelligent tractor-trailer sensor and vision system and provide benchmark datasets. In construction and airport safety, the project team has integrated computer vision, robotic motion simulation, and spatiotemporal analyses to implement dynamic safety zone estimation solutions for aircraft and construction equipment. The project team has also developed the technique to find safe actions when there is uncertainty in the dynamic system models or environments. The proposed project will adapt these intelligent dynamic safety zone estimation solutions to implement the proposed smart sensors and vision systems on tractor-trailer combinations. An industry collaborator, Clarience Technologies, will work with the project team to use their tractor and trailer fleet to collect video, vehicle, and telematics data to support the development and testing of the proposed smart safety system. Clarience will also leverage its automotive and vehicular engineering background to support the 4D simulation and motion analysis of heavy-duty vehicles in given road and terrain conditions. Another industry partner, Safety Emissions Solutions, has collaborated with the team in integrating inspection reports, crash, and telematic data into ‘vehicle deterioration models’ that predict the crash risks of heavy-duty vehicles. Integrating this expertise, software, data, and hardware from the researchers and industry will ensure the timely delivery of the proposed dynamic safety zone estimation solution and the benchmark data sets. ]]></description>
      <pubDate>Sun, 13 Oct 2024 09:43:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2440025</guid>
    </item>
    <item>
      <title>Aftermarket Electronic Device Security for Heavy Vehicles</title>
      <link>https://rip.trb.org/View/2431172</link>
      <description><![CDATA[The security posture of aftermarket electronic devices connected to heavy vehicles is important to know, but hard to assess. Risk based approaches that consider assets, attacks, impacts, and feasibility can assist departments of transportation (DOTs) in understanding the risk profile of a cybersecurity attack against their vehicles and transportation systems, like the Automated Truck Mounted Attenuator (ATMA) and in-cab messaging. The project first endeavors to determine a representative inventory of devices connected to DOT operated vehicles. A subset of these devices will undergo penetration testing to determine the ease any discovered security vulnerabilities can be exploited. The final phase is to communicate these results in the form of a threat analysis and risk assessment to the stakeholders. ]]></description>
      <pubDate>Mon, 16 Sep 2024 09:25:04 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431172</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>
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