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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>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>Comprehensive Modeling and Analysis of Energy Options for the US Trucking Freight
Transportation: Stakeholder Behavior, Infrastructure Planning, and Local Impacts (Phase 2)</title>
      <link>https://rip.trb.org/View/2684211</link>
      <description><![CDATA[Truck transportation is a vital component of the nation's economy, ensuring the efficient movement of goods across vast distances. Current energy policies emphasize unleashing domestic energy resources and streamlining regulatory frameworks to bolster economic growth and strengthen national security. Exploring all energy options for the trucking industry aligns with these objectives by potentially reducing logistics costs, enhancing national energy dominance, and supporting job creation within the transportation and energy sectors. To this end, a mixed-method approach will be employed to characterize and understand different energy options for the United States trucking freight sector. More specifically, this project investigates 1) stakeholder behavior in the adoption of different energy options in the US trucking sector; 2) national-level infrastructure planning and economic analysis for trucking energy production and distribution, and system evolution dynamics; and 3) local impacts of the adoption of different energy options by the US trucking sector. This project dovetails with the Center for Freight Transportation for Efficient and Resilient Supply Chain (FERSC) goal of maintaining the US economic competitiveness and security.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:33:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684211</guid>
    </item>
    <item>
      <title>Strategic Investment Choice to Reduce Disruptions and Increase Resiliency of Roadway
Freight Network</title>
      <link>https://rip.trb.org/View/2684218</link>
      <description><![CDATA[The proposed research will develop models and algorithms to identify systematic investment strategies by reducing link disruption failure probabilities and enhancing overall roadway resilience for freight flows. A new stochastic programming modeling framework will be developed in which disruption probabilities depend on resource allocation decision variables and new algorithms will be developed to deal with the computational challenges caused by both the large number of scenarios and the nonlinearity in both first-stage and second-stage sub-problems. The framework, including data integration, models, and solution methods, will be programmed and tested with a case based on the freight network in the State of Tennessee.]]></description>
      <pubDate>Wed, 25 Mar 2026 16:46:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2684218</guid>
    </item>
    <item>
      <title>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>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>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. 

OBJECTIVE: 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>Express Lanes Benefitting Freight Mobility: Can Express Lane Systems and its Benefits to General-purpose Lanes Improve Freight Mobility?</title>
      <link>https://rip.trb.org/View/2553996</link>
      <description><![CDATA[The objectives of the research project include the following: (1) assess the impact of Express Lanes on freight movement efficiency; (2) evaluate changed in travel time and improve safety; (3) analyze the impact on freight delivery reliability and efficiency, examine the economic impacts of Express Lanes associated with freight movements; (4) quantify cost changes for truck drivers and truck companies; (5) assess potential economic benefits to the region, evaluate the environmental impacts of Express Lanes associated with freight movements; (6) estimate reductions in greenhouse gas emissions; and (7) estimate savings in fuel consumption.]]></description>
      <pubDate>Fri, 16 May 2025 07:20:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553996</guid>
    </item>
    <item>
      <title>Modeling Wolf Creek Pass Combination of Layers of Barriers</title>
      <link>https://rip.trb.org/View/2431164</link>
      <description><![CDATA[In the mountainous regions with rugged terrains like in Colorado and adjacent Rocky Mountain states, having sharp turns of roadways and passes to go around the terrain are common features with inherent extra safety concerns beyond ordinary consideration.  A prominent example is one of spots along Wolf Creek Pass where there were multiple severe or fatal vehicular accidents.   That spot features not only a small radius of turn but also a downward gradient which often leads to speeds higher than the posted speed limit and warning to the incoming vehicles and trucks.   
Despite the fact that numerous safety measures have been installed to warn drivers, in the past couple of years, Wolf Creek Pass has experienced a slight increase in vehicle accidents that have resulted in loss of lives and property damages in this area.  Additional safety measures are therefore required to mitigate the severity of future incidents.
The purpose of this research study is to examine one possible solution that may prevent future loss of lives in this area.  The expected outcomes of this study shall include a development of effective layers of barriers design to reduce the danger of roadway bend conditions such as those at Wolf Creek Pass where accidents can be fatal for heavy vehicles and trucks with a high center of gravity running into it at high speed.  These outcomes shall be achieved by using the advanced 3D nonlinear dynamic computer modeling and analysis of a combination of layers of barriers to absorb, redirect the kinetic energy and stop the momentum of heavy trucks as they approach at high speeds down the slopes toward the bend as well as prevent heavy freight vehicles from flipping over. ]]></description>
      <pubDate>Mon, 16 Sep 2024 08:56:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2431164</guid>
    </item>
    <item>
      <title>Embedded Hybrid Truck-Drone Delivery Routing Design for Rural Areas</title>
      <link>https://rip.trb.org/View/2422983</link>
      <description><![CDATA[According to the United States Department of Agriculture, around 14% of the U.S. population lives in rural areas, with a higher percentage of seniors and increased poverty rates compared to urban areas. Rural regions face unique challenges such as sparse population density, limited infrastructure, and geographic isolation, leading to economic and social disparities. To address these issues, logistics companies like DHL and UPS have introduced hybrid truck-drone delivery systems for better service coverage. This hybrid system combines the long-range capabilities of trucks with the flexibility of drones for last-mile delivery, offering potential solutions for rural logistic challenges. However, the success of these systems depends on their efficiency, particularly in solving the truck- drone routing problem to optimize delivery routes.]]></description>
      <pubDate>Thu, 29 Aug 2024 16:41:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2422983</guid>
    </item>
    <item>
      <title>Analyzing Truck Size and Weight Impacts on Vehicle Miles Traveled</title>
      <link>https://rip.trb.org/View/2398002</link>
      <description><![CDATA[Minnesota Department of Transportation (MnDOT) has a goal of reducing vehicle miles traveled (VMT) by 20% per capita by 2050. Additionally, Minnesota’s Next Generation Energy Act sets a greenhouse gas (GHG) emission reduction goal of 80% below 2005 levels by 2050. Trucking makes up a large proportion of VMT on the intercity highway network, and medium- and heavy-duty (classes 3-8) trucks account for 37% of the GHG emissions in the transportation sector. Currently, there are no tools or guidance for the freight community in Minnesota to move toward the VMT reduction goal. Changing the regulations on truck size and weight limits could affect VMT and the resulting GHG emissions. Currently, the allowed maximum truck weight for vehicles operating on Minnesota highways without an exemption or special permit is 80,000 lbs, while some provinces in Canada allow up to 137,788 lbs., depending on the vehicle configuration. There are federal studies that analyzed the potential impacts of changing truck size or weight limits, but no studies have analyzed the impacts on Minnesota highway network (including interstate freeways). This project will use vehicle telematics and freight movement data to develop a network analysis tool to project potential VMT changes under different regulatory scenarios. Moreover, the impacts of VMT changes on lifecycle cost of transportation infrastructure, such as bridges and pavements will be analyzed. Study results could inform policies and regulations and help MnDOT move toward its VMT reduction goal.]]></description>
      <pubDate>Wed, 26 Jun 2024 09:46:42 GMT</pubDate>
      <guid>https://rip.trb.org/View/2398002</guid>
    </item>
    <item>
      <title>Analyzing and Predicting Truck Travel Time Reliability</title>
      <link>https://rip.trb.org/View/2394459</link>
      <description><![CDATA[Truck travel time reliability (TTTR) is one of the most important performance measures for assessing freight movement on the interstates. The Virginia Department of Transportation (VDOT) and the Office of Intermodal Planning and Investment (OIPI) have been reporting TTTR as required by the FHWA and using TTTR in various project planning and performance measurement processes. They are interested in data-driven approaches to identify the locations and causes of unreliable truck travel times and to predict TTTR metrics. This study will address the research needs by developing a systematic method to identify the location, time period, and causes of unreliable truck travel times, and develop models to predict TTTR.   ]]></description>
      <pubDate>Tue, 18 Jun 2024 09:51:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2394459</guid>
    </item>
    <item>
      <title>Enhancing Rural Roadway Safety through Geospatial Analysis and
2SFCA-Driven Rest Area Serviceability Optimization for Trucks</title>
      <link>https://rip.trb.org/View/2265842</link>
      <description><![CDATA[Truck-involved crashes within the rural network pose a distinctive and multifaceted challenge that necessitates a specialized approach for comprehensive analysis and effective mitigation. Data from the Insurance Institute for Highway Safety (IIHS) indicates that fatal truck crashes frequently occur between 12:00 p.m. and 3:00 p.m., deviating from patterns observed in other vehicle crashes. This peculiarity arises from the distinct routines of truck drivers, often commencing journeys early, which amplifies the risk of driver fatigue and contributes to crashes during the early hours. These observations underscore the imperative to address driver fatigue and enhance rest area serviceability and functionality in rural contexts. Recognizing the unique characteristics of rural areas necessitates acknowledging that truck-involved crashes within these regions can have varied effects on groups such as older adults. These disparities emphasize the crucial need for an all-encompassing approach to enhance safety. Leveraging the context of Florida, this project endeavors to formulate a robust methodology that incorporates geospatial, optimization, and machine learning techniques. By incorporating these multifaceted techniques, the methodology aims to holistically evaluate the resilience of these communities, thereby contributing to a comprehensive comprehension of the repercussions of truck-involved accidents in rural areas.
The objective of this proposal is to improve rural roadway safety by enhancing the accessibility and facilities of rest areas for trucks along rural highways in Florida. By applying the specialized Two-Step Floating Catchment Area (2SFCA) method, this proposal aims to bridge the gap between rest area provisions, truck driver behavior, and rural truck-involved crashes. The data-driven insights generated through this project have the potential to significantly elevate rural roadway safety in Florida. This initiative intends to establish a clear correlation between the availability of rest areas, particularly truck parking lots, and the frequency of truck-involved crashes on rural roadways. The objective is to offer data-driven insights that guide strategic rest area development, thereby contributing to the reduction of truck-related accidents and fostering safer rural highways across Florida.
To meet this research demand, Signal4Analytics (S4A) and the Fatality Analysis Reporting System (FARS) serve as optimal platforms for collecting truck-involved crash data. Furthermore, the 2SFCA analysis acknowledges the distinctive features of trucking operations. It encompasses the delineation of catchment areas surrounding each rest area, adapting to rural travel conditions and operational constraints. Calculating accessibility scores for each rural catchment area factors in elements such as the availability of truck parking lots and the population density within each region. These scores provide insights into the potential utilization of rest areas by truck drivers in rural environments. The integration and analysis of truck-involved accident data from Florida’s rural roadways hold pivotal importance. This process uncovers patterns and hotspot locations, linking these incidents with computed accessibility scores to unveil potential associations between rest area accessibility and rates of truck-involved accidents in rural settings.
]]></description>
      <pubDate>Thu, 19 Oct 2023 16:40:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2265842</guid>
    </item>
    <item>
      <title>Completing the Picture of Crashes: Understanding Data Needs and Opportunities for Road Safety</title>
      <link>https://rip.trb.org/View/2067969</link>
      <description><![CDATA[Since 2009, fatal crashes involving large trucks have steadily increased to 4,237 fatal crashes in 2017, a 46.5 percent increase when compared to 2009. Over that same time period, non-fatal crashes involving large trucks have increased by 57.6 percent to an estimated 446,000 such crashes. This study will leverage existing data sources external to the agency, to gain more insight into crashes involving large trucks and buses, including, but not limited to: Federal Highway Administration (FHWA) roadway inventory data; National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS), CRSS and EDT; economic data (e.g. truck sales, employment trends, etc.). Once identified, the various data sets will be integrated and analyzed. The analysis will allow 
the Federal Motor Carrier Safety Administration (FMCSA) to identify areas of concern and develop countermeasures to drive new initiatives to reduce large truck and bus crashes on our nations roadways.]]></description>
      <pubDate>Mon, 21 Nov 2022 16:26:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2067969</guid>
    </item>
    <item>
      <title>ADAS Crash Safety Analyses via OBMS Data</title>
      <link>https://rip.trb.org/View/2067967</link>
      <description><![CDATA[The National Transportation Safety Board (NTSB), the Trucking Alliance, and other organizations have recommended the adoption of ADAS by motor carriers, touting the associated safety benefits. In September 2017, the American Automobile Association Traffic Safety Foundation (AAATSF) produced a series of reports titled “Leveraging Large-Truck Technology and Engineering to Realize Safety Gains.” These reports corroborate the benefits of installing automatic emergency braking, air disc brakes, lane departure warning systems, and video-based onboard safety monitoring systems on large trucks.]]></description>
      <pubDate>Mon, 21 Nov 2022 16:26:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2067967</guid>
    </item>
    <item>
      <title>ACE Program: Testing and Demonstration Activities</title>
      <link>https://rip.trb.org/View/2062465</link>
      <description><![CDATA[The object of this research is to explore and identify processes, communication methods, and inspection technologies to facilitate electronic safety inspections of ADS-equipped Commercial Motor Vehicle (CMV) operations on the roadway, at borders, and in other enforcement settings.]]></description>
      <pubDate>Tue, 15 Nov 2022 16:18:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2062465</guid>
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