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    <title>Research in Progress (RIP)</title>
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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxODA0IiAvPjxwYXJhbSBuYW1lPSJzdWJqZWN0bG9naWMiIHZhbHVlPSJvciIgLz48cGFyYW0gbmFtZT0idGVybXNsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjE2IiAvPjwvcGFyYW1zPjxmaWx0ZXJzIC8+PHJhbmdlcyAvPjxzb3J0cz48c29ydCBmaWVsZD0icHVibGlzaGVkIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM+PC9zZWFyY2g+" rel="self" type="application/rss+xml" />
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
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    <item>
      <title>Advanced Sustainable Transportation Workforce Development Initiative in California’s Inland Empire</title>
      <link>https://rip.trb.org/View/2692313</link>
      <description><![CDATA[Spurred by significant government investments and regulatory landscape, advanced sustainable transportation (connected, automated, energy-efficient, and shared vehicles) and its supporting infrastructure is well underway in Inland Southern California. Not only are advanced vehicles becoming common among California’s Inland Empire residents, but the region is at the heart of medium- and heavy-duty vehicle programs associated with goods movement. As a result, many advanced transportation and infrastructure manufacturers are now locating to the Inland Empire due to its favorable economic landscape. What’s lacking is an advanced sustainable transportation workforce in the region that is needed for: (1) manufacturing, maintaining, repairing advanced vehicles; (2) setting up, deploying, and maintaining advanced vehicle infrastructure; and (3) responding to incidents associated with advanced vehicles and their supporting infrastructure. The project team will launch a comprehensive Advanced Sustainable Transportation Workforce Development Initiative for California’s Inland Empire, pulling together a variety of existing educational programs, developing these programs further into a cohesive vehicle/infrastructure training program, and creating a coalition of local manufacturers in this advanced vehicle space. This initiative seeks to position the Inland Empire as a national leader in advanced vehicle manufacturing and adoption. This bold vision positions the region as a model for sustainable growth, advancing the region’s goals while uplifting communities. The key goals of the initiative include: (1) Integrating workforce development, industry needs, and policy goals into a cohesive, impactful strategy. This project will deliver comprehensive training programs in advanced vehicle technology, associated infrastructure, and managing vehicle incidents across a wide range of technologies (light-, medium-, and heavy-duty vehicles, buses, trucks, rail, aircraft). (2) Creating high-quality jobs in the region. The team’s plan is to fill the expected thousands of advanced transportation jobs with locally sourced talent, emphasizing pathways that promote societal advancement.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:12:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692313</guid>
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    <item>
      <title>Rural Vehicle Markets and Consumer Affordability</title>
      <link>https://rip.trb.org/View/2691725</link>
      <description><![CDATA[There is a need to better understand rural vehicle consumer choice and transportation affordability to inform efforts to support economic vitality in rural communities. Access to adequate vehicle choices at affordable price points may be limited in rural contexts due to the spatial location of vehicle purchase options. At the same time, access to affordable vehicle options has important implications for transportation affordability, mobility, and economic opportunity in rural areas. Prior research suggests that people living in rural areas are more vehicle dependent, and that vehicle affordability and access is related to mobility and economic opportunity. Recent research indicates that rural vehicle consumers face more limited options and higher prices for a small subset of vehicle options, however, little is known about the implications for consumer choice and vehicle affordability for the overall vehicle market. This project uses detailed vehicle data and vehicle dealership listings in Colorado, Maine, and Vermont to evaluate the relationship between vehicle options, distances people travel to purchase a vehicle, and the price paid for the vehicle in both urban and rural contexts. Findings from this research can inform policies that seek to expand access to affordable transportation options in rural communities.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:55:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691725</guid>
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    <item>
      <title>Optimizing External Human-Machine Interfaces (eHMIs) Designs in Autonomous Vehicles to Improve Communication with Drivers and Bicyclists</title>
      <link>https://rip.trb.org/View/2691668</link>
      <description><![CDATA[Autonomous Vehicles (AVs) will transform road safety and efficiency in the years to come, but achieving this requires large-scale deployment, trust, and understanding from all human road users, including drivers and bicyclists. External Human-Machine Interfaces (eHMIs) are becoming a crucial part of the process, enabling intuitive communication between AVs and other road users. This project aims to develop, assess, and optimize the concept of eHMIs to foster positive perceptions, build trust, and ensure safe interactions in mixed traffic scenarios. This study will involve a test of about 40 participants who will interact with AVs fitted with various eHMI prototypes under controlled conditions using driving and bicycle simulators. Behavioral metrics like the perception-reaction time (PRT), the perceived level of comfort, and the perceived level of trust, as well as transportation metrics like travel time, intersection clearance time, and near-miss incidents, will be assessed for different designs for the eHMI, including visual-based (LED Displays, Symbolic Messages, Color-coded Signals, Animated Indicators, etc.) and multimodal designs. Longitudinal experiments will measure the impact of acclimatization and determine the best eHMI setups, followed by field tests under realistic conditions for verification. User-focused optimization tools will also be designed to adapt enhanced eHMI setups to various demands and scenarios. Expected outcomes will include best-in-class eHMI designs for increased road safety, operational efficiency, and user confidence, providing valuable guidance for city planners, policymakers, and AV manufacturers.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:39:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691668</guid>
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    <item>
      <title>Machine Vision Toolkit for Automated Fleet Composition Assessment and Reporting</title>
      <link>https://rip.trb.org/View/2691665</link>
      <description><![CDATA[State Departments of Transportation (DOTs) and Metropolitan Planning Organizations (MPOs) employ fleet composition data (e.g., passenger vehicles, single-unit trucks, and combination trucks) in a variety of planning, economic, roadway performance, and safety applications. Accurate fleet composition data is essential for pavement management, safety analysis, and fuel consumption modeling. However, traditional methods are labor-intensive, costly, and often lack the temporal or spatial resolution required to capture variations between freeways, arterials, and managed lanes vs. general-purpose lanes. Using machine vision tools to quickly, efficiently, and accurately capture on-road percentages of light-duty vehicle, light-duty truck, medium-duty truck, and a variety of heavy-duty truck classifications will enhance analytical and modeling accuracy and reduce state DOT data management costs. Building upon prior National Center for Sustainable Transportation (NCST) research that developed machine vision algorithms for vehicle identification, this project will package those research findings into a deployable, open-source Automated Fleet Classification Toolkit for practitioners and researchers. The research team will develop and release comprehensive Standard Operating Procedures (SOPs) and software tools allowing agencies to convert standard roadside or overpass video feeds into high-resolution fleet composition data. The toolkit will utilize advanced object detection (e.g., YOLO architectures) to automate the identification of vehicle classes (aligning with FHWA 13-category schemes where possible) and propulsion types based on visual vehicle features. The system is designed to distinguish traffic conditions on complex roadway geometries, allowing users to generate separate classification profiles for managed lanes vs. general-purpose lanes, and separating freeway mainlines from adjacent arterial service roads. The project focuses on technology transfer: providing the "how-to" manuals, open-source code, and data processing protocols so that State DOTs, consultants, university partners and research institutes can replicate the data collection and extraction without relying on proprietary "black box" services.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:29:28 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691665</guid>
    </item>
    <item>
      <title>Personal Vehicle Ownership and Operating Cost Calculator (Version 2.0) for Quantifying On-road Vehicle Operating Costs</title>
      <link>https://rip.trb.org/View/2691663</link>
      <description><![CDATA[In 2018, the Georgia Tech National Center for Sustainable Transportation (NCST) research team developed the Vehicle Ownership and Operating Cost Calculator (VCC) Version 1.0, allowing users to calculate and understand total vehicle ownership costs over the lifespan of the vehicle. Traditional resources typically found on automotive websites offer five-year cost projections, but often overlook or simplify long-term expenses such as financing, maintenance, energy use, and depreciation, which vary widely based on region, vehicle type, and individual driving habits. By allowing users to input personalized data, the calculator provides a tailored, detailed analysis of ownership costs, helping users make more informed decisions about vehicle purchases. The VCC is designed to serve as an educational resource (highlighting the cost categories associated with vehicle ownership) and as an instructional aid in courses that examine transportation planning and economic assessments. The VCC allows users to input data specific to their circumstances, including vehicle purchase price, loan details, annual mileage, insurance, energy costs, maintenance, and other costs like parking and tolls. Using data from sources such as the Georgia Department of Revenue’s vehicle pricing database and the U.S. Department of Energy’s Fuel Economy Database, the calculator provides customized cost estimates. The tool provides users (students and the public) with a thorough understanding of the full costs associated with lifetime vehicle ownership, by offering a comprehensive breakdown of ownership costs, including hidden expenses often overlooked in purchase decisions. The original model became dated, because the tool did not have the ability to automatically ingest and update vehicle ownership cost data. This project will update the tool with new data, develop data ingestion procedures, and modify output formats to support economic assessments of roadway design alternatives. To make the VCC accessible and support technology transfer, this project will update the calculator to accommodate the latest vehicle technologies (2018-2025) and to generate an online model presence. The research team will update fuel prices, maintenance, insurance costs, and depreciation rates to capture recent market changes. The team will also assess and implement enhanced reporting features to provide users with more detailed breakdowns and visualizations of ownership costs. Finally, the team will modify the structure of the model so that the tool can compile operating costs per vehicle-mile for observed and modeled on-road fleet compositions and operating conditions. The deliverables will include an updated version of the calculator accessible as both an Excel tool and a web interface.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:22:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691663</guid>
    </item>
    <item>
      <title>Reducing empty miles of shared mobility on highway corridors</title>
      <link>https://rip.trb.org/View/2691661</link>
      <description><![CDATA[Smartphone-app-based technology has provided business opportunities to various demand-responsive urban transportation services, including e-hailing taxis, ride-pooling, and microtransit. These shared mobility services exhibit great potential for enhancing transportation services in rural communities. A common side effect, however, is a substantial portion of empty vehicle miles traveled (VMT) on highway corridors, which induces further congestion to highway traffic in peak hours. A quantitative analysis tool is necessary for planning agencies and policymakers to assess the impact of shared mobility on highway traffic. The researcher's recent work investigating ride-pooling systems serving uniformly distributed demands in a single community shows that their efficiency is highly sensitive to online matching schemes. This impact is expected to be even more significant in spatially imbalanced demand patterns, such as those between suburban/rural communities. This project will develop a traffic assignment model to allocate vehicular trips to corridor networks linking suburban and rural communities, which will assist policymakers in (1) understanding the relations between the spatial distribution of inter-community travel demands and excessive VMT; (2) identifying the most vulnerable corridors affected by shared mobility services; and (3) evaluating the potentials of various regulatory policies and public surcharges in reducing empty vehicle mileage. Ultimately, the analysis tool will enable planning agencies to explore practical measures to improve the accessibility of suburban and rural communities with shared mobility services.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:16:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691661</guid>
    </item>
    <item>
      <title>Evaluating User Acceptance and Effectiveness of Cognitive Measurements and Intervention for Shared Autonomy</title>
      <link>https://rip.trb.org/View/2690985</link>
      <description><![CDATA[Vehicles equipped with automated driving systems (ADS) have become more widespread in the trucking industry. On the one hand, ADS are known to be susceptible to occasional errors in environment perception, but on the other, ADS can demonstrate safer and more efficient behavior in situations where the driver is cognitively impaired. Shared autonomy systems thus have the potential to combine the best of both paradigms. Some early instantiations of such shared autonomy ADS use measurements of the human cognitive state to perform interventions, either in the form of sensory feedback, and/or by actively taking over the driving task. The main objective of this project is to address the gap in research on the effectiveness and acceptance of cognition-aware shared-autonomy methods with respect to the overall system safety. Qualitative data will be collected through semi-structured interviews with truck drivers and systematically encoded into operational design requirements and hypothesis-driven performance metrics that directly inform the design of cognition-aware shared autonomy systems. The research team will perform a driving simulator study that enables a controlled evaluation of adaptive cognition-aware intervention policies, including rule-based and data-driven triggering mechanisms that dynamically adjust system behavior based on real-time cognitive interventions. Researchers will study how specific design choices in cognition-aware intervention policies (e.g., trigger thresholds, modality selection, and intervention persistence) influence system acceptance, misuse, and compliance, enabling actionable design guidance beyond descriptive acceptance analysis. The datasets collected inform policy on the use of ADS in both drayage and long-haul trucking. This project will develop a methodology for designing and evaluating cognition-aware behavioral interventions that couple driver monitoring outputs with explicit control and feedback policies, enabling reproducible comparison across intervention strategies and deployment contexts.]]></description>
      <pubDate>Thu, 09 Apr 2026 14:23:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2690985</guid>
    </item>
    <item>
      <title>Tribal &amp; Rural Autonomous Vehicles for Efficiency, Livability and Safety (TRAVELS) </title>
      <link>https://rip.trb.org/View/2687129</link>
      <description><![CDATA[Tribal & Rural Autonomous Vehicles for Efficiency, Livability and Safety (TRAVELS) is the passenger transportation component of the U.S. Department of Transportation’s Rural Autonomous Vehicle (RAV) Research Program, led by the University of Wisconsin–Madison. The project addresses persistent mobility challenges in rural and Tribal communities, where residents often face limited transportation options, long-distance travel needs, restricted access to healthcare and essential services, and fewer after-hours or on-demand services. These challenges are further complicated by roadway, infrastructure, weather, and economic constraints that can limit the feasibility of conventional and emerging mobility solutions.


This research examines how automated vehicle technologies, supportive infrastructure, communications systems, and service design strategies can be integrated to improve safety, accessibility, and operational efficiency in rural settings. The project includes demonstration and deployment activities in Wisconsin, Georgia, and Oklahoma, focusing on use cases such as healthcare access, economic transportation, and tourist & event shuttle services. Through testing, evaluation, and before-and-after analyses, the project seeks to identify practical pathways to accelerate automated mobility deployment in rural and tribal areas.
]]></description>
      <pubDate>Fri, 03 Apr 2026 16:46:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2687129</guid>
    </item>
    <item>
      <title>Naïve Subject Testing – Suite Emergency Passage Features</title>
      <link>https://rip.trb.org/View/2686617</link>
      <description><![CDATA[Applicants for type design approval are working to support their airline customers by installing passenger suites that include doors between the passenger and exit.  To install these doors, an exemption to 25.813(e) is required in which one of the conditions of the exemption is that the applicant must show the emergency passage feature (EPF) is simple and obvious to open.  Applicants achieve this showing by completing a naïve subject test.  The test method currently being used combines test parameters from the naïve subject test for evacuation specified in Part 25 Appendix J, the naïve subject test for life vest donning specified in TSO-C13, and the naïve subject test for floor proximity markings outlined in AC 25.812-1 and AC 25.812-2a.  The test method has several variables involved that are debated amongst regulators and applicants on how they should be controlled.  As a result, the test is run inconsistently, and variations in how the test is performed has led to an unlevel playing field amongst applicants, delays in certification testing by seat suppliers, and conflicting design approvals.   ]]></description>
      <pubDate>Wed, 01 Apr 2026 10:17:47 GMT</pubDate>
      <guid>https://rip.trb.org/View/2686617</guid>
    </item>
    <item>
      <title>Successful Strategies in Providing Training Programs for State and Local Equipment Technicians</title>
      <link>https://rip.trb.org/View/2681233</link>
      <description><![CDATA[Fleet managers across surface transportation agencies face ongoing challenges in identifying training needs for fleet maintenance technicians and delivering effective programs. Prior to 2020, many states relied heavily on vendor-led training to support technician development on new and existing equipment. Following the COVID-19 pandemic, a number of vendors reduced or discontinued these offerings, requiring agencies to pursue alternative approaches.

As fleet equipment continues to incorporate more advanced technologies, the need for consistent, high-quality, and up-to-date technical training has become increasingly critical to maintaining safe, reliable, and cost-effective operations.

OBJECTIVE: This scan will examine organizations that have successfully identified and implemented sustainable training programs for fleet maintenance technicians. The team will document how agencies structure and manage their programs, measure effectiveness, and ensure appropriate leadership support.]]></description>
      <pubDate>Tue, 17 Mar 2026 15:03:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681233</guid>
    </item>
    <item>
      <title>Innovative Approaches in Acquiring New or Replacement Fleet Equipment</title>
      <link>https://rip.trb.org/View/2681240</link>
      <description><![CDATA[Fleet managers often face challenges securing sufficient funding to meet their agencies’ equipment needs. While some have developed alternative approaches to acquire fleet equipment, many agencies are not fully aware of these options or their potential advantages and limitations. Rising equipment costs and constrained budgets increase the need to examine flexible and cost-effective acquisition strategies. A review of these approaches and their associated considerations could provide practical value to state fleet managers.

This scan aims to identify practical insights that may support cost management, improve access to reliable equipment, and reduce long-term maintenance expenses. The findings will provide agencies with documented lessons learned and serve as a reference when considering strategies to extend fleet replacement budgets.]]></description>
      <pubDate>Tue, 17 Mar 2026 14:49:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/2681240</guid>
    </item>
    <item>
      <title>Advanced Mobility Innovation Lab (AMIL) and Beyond</title>
      <link>https://rip.trb.org/View/2666833</link>
      <description><![CDATA[As the United States navigates the Fourth Industrial Revolution - defined by the convergence of physical, digital, and biological technologies - STEM (Science, Technology, Engineering, and Mathematics) education has become increasingly vital to preparing students for the future workforce. Transportation, a sector undergoing rapid technological transformation, is deeply intertwined with STEM and plays a critical role in shaping economic development, public safety, and access to essential services. Yet, many K-12 students, particularly in rural areas, lack exposure to high-quality STEM learning opportunities.  The Advanced Mobility Innovation Lab (AMIL) was established through funding from the CR2C2 REE program to address this gap by providing portable, hands-on STEM experiences that fuse transportation technology demonstrations with project-based learning. This project will expand AMIL’s effort in projects R-EWD-1 and R-EWD-2, and increases the reach and impact by partnering with the University of Alabama’s robust K–12 STEM Education Outreach program, creating a collaborative model for multi-university engagement and STEM education. Together, these programs will deliver enriched STEM experiences that emphasize emerging transportation technologies, autonomous systems, and the STEM principles behind their development and operation. The initiative will culminate in two regional events - one in Alabama and one in North Carolina - featuring autonomous vehicle demonstrations and showcasing student learning outcomes.]]></description>
      <pubDate>Wed, 11 Mar 2026 15:46:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2666833</guid>
    </item>
    <item>
      <title>Advancing Rural Mobility through Innovative Charging Solutions</title>
      <link>https://rip.trb.org/View/2677557</link>
      <description><![CDATA[Electric vehicle (EV) ownership in the United States is growing. Since EVs have emerged as an additional mobility option available in the market, their proliferation requires charging infrastructure to support the growing number on EVs on the national highway system. While many EV owners predominantly charge their vehicles at home, EV fast chargers are needed to provide support for efficient long distance and interstate travel. The advent of Advanced Air Mobility (AAM) in rural areas has the potential to address long-standing challenges related to accessibility, connectivity, and service delivery using electric vertical take-off and landing [eVTOL] aircraft and drones. These mobility solutions also require charging infrastructure to enable their deployment.      

Development of new fast charging stations has been delayed by the need to connect the charging stations to the electric grid. Microgrids, which are small, local power grids that use locally sourced energy to supply electricity within that microgrid, provide an opportunity to provide needed energy without the need to connect to the electric grid. These solutions are preferable in areas where interconnection with the electric grid would be not feasible, not timely, or cost prohibitive, including rural areas. This project would investigate and evaluate microgrids and storage for EV charging solutions that allow for long-distance EV mobility, laying the groundwork for further research  and implementation to enable efficient mobility of electric vehicles across the US.  The project will also address non-traditional charging applications (beyond on-road vehicle charging) to address potential charging solutions for AAM operations in rural areas.     ]]></description>
      <pubDate>Wed, 04 Mar 2026 13:49:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2677557</guid>
    </item>
    <item>
      <title>Unmanned Aerial System Automation Using Artificial Intelligence Tools</title>
      <link>https://rip.trb.org/View/2676010</link>
      <description><![CDATA[This project will conduct an exploratory analysis of artificial intelligence (AI) tools to aid with the automation of Unmanned Aerial Systems (UAS) use case activities across transportation with a focus on potential applications for transportation system benefits. This is an area of great potential for innovation through the use of advanced technologies in a synergistic manner. The project will focus on representative use cases where AI can enable advanced data processing and decision-making, such as: infrastructure inspection (e.g., rail track condition monitoring, construction progress tracking); operations and safety (e.g., traffic monitoring for incidents and special events); and UAS operating conditions monitoring (e.g., wildlife detection, vegetation health assessment). 

These use cases represent areas where AI-driven computer vision, predictive analytics, and anomaly detection can significantly improve efficiency, safety, and sustainability. Additionally, the project will explore how AI-enabled UAS operations can contribute to energy benefits and cost savings by optimizing inspection schedules, reducing fuel-intensive manual operations, and supporting compliance with regulatory standards.  

In the context of the above-described use cases, the research team will conduct the following activities: (1) identify commercial AI-enabled tools currently available for purchase or license and assess their capabilities for UAS data integration; (2) evaluate how these tools can be modified or expanded to meet the specific needs of  transportation related monitoring applications; and (3) develop prototype      workflows demonstrating AI-enabled automation for UAS operations.  
]]></description>
      <pubDate>Tue, 03 Mar 2026 16:49:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2676010</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>
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