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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
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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>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Corridor Speed Management Strategies Toolbox</title>
      <link>https://rip.trb.org/View/2693730</link>
      <description><![CDATA[This project will develop a repository of speed management countermeasures that are applicable for use in Virginia based on existing evidence-based research of traffic calming and speed management practices in the United States. The scope will be limited to applications for state and regional highways. The toolbox will include countermeasure definitions and a description of the appropriate context for application. The countermeasure details may include contexts with evidence for speed management effectiveness, contexts where countermeasures may be appropriate, and contexts where further research is needed to justify their use. This contextual guidance will provide useful information for practitioners in Virginia.     ]]></description>
      <pubDate>Thu, 16 Apr 2026 10:46:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2693730</guid>
    </item>
    <item>
      <title>TRB Core Program Services for a Highway RD&amp;T Program – Federal Fiscal Year 2026/TRB (State DOTs) Fiscal Year 2027</title>
      <link>https://rip.trb.org/View/2692353</link>
      <description><![CDATA[The transportation research community consists of numerous partnerships to aid in the conduct of research and the implementation of technologies and innovations.  The Federal Highway Administration (FHWA), the State Departments of Transportation (SDOTs), and the National Academy of Sciences (NAS) are among these partners, who work closely in many facets of the national research program. The Transportation Research Board's (TRB’s) mission is to promote innovation and progress in transportation by stimulating and conducting research, facilitating the dissemination of information, and encouraging the implementation of research results. TRB fulfills this mission through the work of its standing technical committees and task forces addressing all modes and aspects of transportation; publication and dissemination of reports and peer-reviewed technical papers on research findings; administration of contract research programs; conduct of special studies on transportation policy issues; maintenance of Transport Research International Documentation (TRID); and hosting an annual meeting that attracts approximately 14,000 transportation professionals from throughout the United States and abroad. This pooled fund provides a mechanism for States to transfer funds to FHWA to add to the TRB Core Program Services cooperative agreement.
]]></description>
      <pubDate>Tue, 14 Apr 2026 20:24:09 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692353</guid>
    </item>
    <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>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>
    </item>
    <item>
      <title>Implementing an Advanced Open-Source Activity Based Travel Demand Model to Support Rural Transportation Planning and Policy Decisions</title>
      <link>https://rip.trb.org/View/2692312</link>
      <description><![CDATA[Travel demand models (TDMs) are used to support state and regional transportation planning and policy decisions. TDMs were originally developed to forecast passenger traffic volumes with the primary objective of identifying investments to reduce traffic congestion. Today, TDMs are used to support a much broader range of purposes, including multimodal and freight transportation planning, demand management strategies, forecasting accessibility outcomes, evaluating network resiliency to disasters, and modeling air quality and public health impacts. However, the aggregate, trip based TDMs used by most regional and state transportation agencies lack the fidelity and sensitivity to evaluate contemporary planning and policy decisions. Activity based travel demand models (ABMs) offer substantial improvements and their agent-based simulation platforms allow for integration with a wide range of other agent-based modeling including land use simulation, vehicle adoption, population growth simulation models among others. Despite their advantages, the complexity of ABMs has constrained their adoption to all but the largest metropolitan areas, often with support from academic researchers. Smaller urban areas and rural states like Vermont could benefit substantially from adopting ABMs. The goal of this project is to implement an open source and/or free for public use ABM in Vermont. Several ABMs meeting these criteria have been developed by US Department of Energy labs. This project will implement a modeling platform that University of Vermont can use in partnership with regional and state stakeholders to advance rural transportation planning and policy research; perform a case study to demonstrate the unique capabilities of ABMs to inform current transportation policy debates in Vermont; identify implementation barriers; and identify future research directions to address implementation barriers to enable wider ABM adoption outside of large urban areas.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:09:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692312</guid>
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    <item>
      <title>Transportation Corridor Fuel Consumption Calculator (TCFCC) Version 5.0</title>
      <link>https://rip.trb.org/View/2692310</link>
      <description><![CDATA[The Transportation Corridor Fuel Consumption Calculator (TCFCC) updates and enhances Georgia Tech’s 2018 spreadsheet-based modeling tool (http://fec.ce.gatech.edu/) that allows users to assess on-road fuel consumption under real-world traffic conditions. The team will incorporate the latest fuel use rates from the MOVES 5.0 model (2025) and extend the capabilities of the previous FEC to allow users to specify any one of more than 60 standard laboratory driving cycles that best represent corridor traffic congestion, and to incorporate any monitored or modeled second-by-second driving trace. Users specify fleet and model year composition, and the tool models corridor-level fuel consumption as a function of congestion. Hence, the tool allows users to assess the consumer fuel savings and cost savings of proposed congestion mitigation strategies that provide smooth traffic flow. The tool is directly applicable to the assessment of traffic signal coordination, ramp metering, express lane operations, etc. The research team will update the model to incorporate MOVES 5.0 model outputs, extend calendar year coverage to 2060, and introduce 40+ new driving cycles that are representative of urban, suburban, and freeway corridors. The project will deliver separate calculator spreadsheets for light-duty passenger cars, heavy-duty trucks, and express buses, each calibrated for mode-specific load factors and driving patterns. A new second-by-second fuel-use worksheet will allow users to input their own driving cycles for detailed vehicle-specific analysis. By focusing on fuel consumption, the project provides a technically neutral and performance-based approach for evaluating corridor operations and fleet technologies. The center will release the TCFCC as open source, encouraging further development and integration with travel demand and simulation models.]]></description>
      <pubDate>Tue, 14 Apr 2026 12:07:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692310</guid>
    </item>
    <item>
      <title>Administration of Highway and Transportation Agencies. Support for Development of AASHTO'S 2027-2032 Strategic Plan</title>
      <link>https://rip.trb.org/View/2692291</link>
      <description><![CDATA[The American Association of State Highway Transportation Officials (AASHTO) and its state department of transportation (DOT) members look to the AASHTO Strategic Plan to guide the organization over a multiyear period to achieve its highest priorities. AASHTO's 2020–2026 Strategic Plan has successfully articulated AASHTO’s vision, mission, values, goals, and objectives, and consistently guided the work of each AASHTO council and committee through their annual action plans. The next Strategic Plan presents an opportunity to build on this success while strengthening its resonance with AASHTO’s full range of constituents, ultimately enhancing service to its members and the public.

The objective of this research is to provide planning and analytical support for the development of the AASHTO 2027-2032 Strategic Plan, including reviewing current vision, mission, values, goals, and objectives to determine what remains relevant and what should be updated or revised. 

This work will engage AASHTO members, staff, and external partners to gather input on potential updates and changes to the Strategic Plan. The project will help establish a clear and shared strategic direction for the work of AASHTO, including alignment with AASHTO council and committee work plans, while supporting staff in advancing AASHTO’s priorities. The research will consider opportunities to strengthen and sustain AASHTO’s member-volunteer model, which remains foundational to advancing state DOT priorities. In addition, the project will develop communication and performance-tracking tools to support implementation of the Strategic Plan.

The selected subcontractor will be expected to work closely with the AASHTO deputy director–chief policy officer, deputy director–chief of staff, and Strategic Plan Advisory Committee. The Advisory Committee will be composed of AASHTO’s elected officials and other state DOT chief executive officers (CEOs), non‑CEO state DOT leaders from AASHTO councils and committees, and AASHTO staff representatives.]]></description>
      <pubDate>Mon, 13 Apr 2026 16:41:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/2692291</guid>
    </item>
    <item>
      <title>Implementing an Advanced Open-Source Activity Based Travel Demand Model to Support Rural Transportation Planning and Policy Decisions: Phase 2 – Calibration</title>
      <link>https://rip.trb.org/View/2691726</link>
      <description><![CDATA[Travel demand models (TDMs) are used to support state and regional transportation planning and policy decisions. TDMs were originally developed to forecast passenger traffic volumes with the primary objective of identifying investments to reduce traffic congestion. Today, TDMs are used to support a much broader range of purposes, including multimodal and freight transportation planning, demand management strategies, forecasting transportation access outcomes, evaluating network resiliency to disasters, and modeling air quality and public health impacts. However, the aggregate, trip based TDMs used by most regional and state transportation agencies lack the fidelity and sensitivity to evaluate contemporary planning and policy decisions. Activity based travel demand models (ABMs) offer substantial improvements and their agent-based simulation platforms allow for integration with agent-based population growth and land use simulation tools, among others. Despite their advantages, the complexity of ABMs has constrained their adoption to all but the largest metropolitan areas, often with support from academic researchers. Smaller urban areas and rural states like Vermont could benefit substantially from adopting ABMs. The goal of this project is to continue current National Center for Sustainable Transportation (NCST)-funded work on implementing a statewide ABM in Vermont using the POLARIS modeling system developed by Argonne National Lab. The current project is focused on initial model setup and testing. This Phase 2 project will focus on calibration and validation. The expected outcome is a calibrated implementation of the POLARIS modeling system for the state of Vermont that can be used for the evaluation of statewide and regional transportation planning and policy decisions and to advance research on rural transportation challenges.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:58:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691726</guid>
    </item>
    <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>
    </item>
    <item>
      <title>Anchorage Design and Detailing for Fabric-Reinforced Cementitious Matrix Retrofits of Transportation Concrete Structures</title>
      <link>https://rip.trb.org/View/2691724</link>
      <description><![CDATA[The repair and rehabilitation of transportation structures is urgently needed to restore structural capacity, slow deterioration caused by aging, overloading, and environmental stressors, and minimize disruptions associated with large-scale replacement projects. State DOTs and the Federal Highway Administration (FHWA) have implemented several advanced rehabilitation techniques, including fiber-reinforced polymer (FRP) composites, ultra-high-performance concrete, and fiber-reinforced cementitious matrix (FRCM) systems. FRCM consists of an open-grid textile made of FRP or steel strands embedded within an inorganic cementitious matrix. The system offers multiple advantages over traditional FRP, including mechanical compatibility with concrete and masonry substrates, improved fire and elevated-temperature performance, vapor permeability, durability in moist or cold environments, and ease of application in field conditions.

As an externally bonded strengthening system, the performance of FRCM is governed by the ability of the FRCM–substrate interface to maintain composite action and to transfer forces effectively. Premature interfacial slip, end debonding, or localized interface damage are commonly reported for unanchored FRCM systems. These brittle failure modes often occur at loads far below the tensile capacity of the textile, limiting the effectiveness of the strengthening system to 30–60% of its potential and undermining both safety and return on investment. Introducing anchorage mechanisms into FRCM systems provides an engineered means to restrain interfacial slip, delay debonding, promote more favorable failure modes, and enable the textile to mobilize higher tensile strains. However, the existing literature on FRCM anchorage is sparse, fragmented, and lacking in unified, design-oriented guidance. Quantitative provisions addressing anchor geometry, capacity, and interaction with the primary FRCM reinforcement remain absent from current codes and standards.

The primary objective of this research is to advance the understanding, design, and implementation of anchorage systems for FRCM-strengthened concrete members, with the goal of mitigating premature debonding and achieving ductile, and efficient strengthening outcomes. Specifically, the project aims to: (a) synthesize and critically evaluate the current state of knowledge on FRCM anchorage; (b) develop and experimentally validate practical anchorage systems including transverse wraps, mechanical anchors, and spike anchors; and (c) produce a design-oriented framework for selecting, proportioning, and detailing anchorage systems.

Two coordinated experimental programs are proposed: (1) bond-level tests to characterize the effects of anchorage presence and type on joint force transfer, slip response, and failure mechanisms; and (2) flexural tests on reinforced concrete beams strengthened with anchored and unanchored FRCM reinforcement, to evaluate the translation of bond-level behavior to member-level performance and to verify design expressions under combined shear and normal stresses. The proposed research will equip state DOTs with validated anchorage solutions, support cost-effective preservation strategies, and accelerate the adoption of durable composite materials for extending the service life of transportation infrastructure.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:52:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691724</guid>
    </item>
    <item>
      <title>New Travel Insights from Cell Phone GPS Data</title>
      <link>https://rip.trb.org/View/2691672</link>
      <description><![CDATA[Building and operating an effective and efficient transportation system requires deep insights into how people move from place to place. These insights include long term behaviors to understand trips that happen infrequently, determining which routes people choose, and understanding how different demographics use their transit options. This project proposes to build an open-source software application programming interface (API) that will produce new travel insights from cell phone global positioning system (GPS) data. Currently available transportation data analytics packages omit important travel insights that are important for transportation research, such as long-term patterns of life, route selection, and demographically stratified travel behavior. These deeper analyses are important for planning more efficient transit. Abundant cell phone GPS data serves to replace costly travel surveys with better coverage and currency. However, GPS data is challenging due to noise, sporadic sampling, and privacy. Building on the research team lab’s extensive experience with GPS data, the researchers will create an open-source API to robustly deliver the new insights. Unlike commercially available packages, the API will be transparent and extensible. The API can serve as a platform for new algorithms and expanded insights. The project team will demonstrate the API on three National Center for Sustainable Transportation (NCST)-relevant mobility insights that are not possible with commercial packages: (1) Determining long term patterns of individual travel behavior, (2) detailed route selection for individuals, and (3) variations in travel behavior among different demographic groups.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:48:02 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691672</guid>
    </item>
    <item>
      <title>Continuous approximation models for rural transit network design</title>
      <link>https://rip.trb.org/View/2691671</link>
      <description><![CDATA[The purpose of this project is to discover new continuous approximation models for public transit network design, with a specific focus on rural areas where access to transit and coverage present significant challenges. Rural transit systems face unique constraints in connecting dispersed population centers while maintaining economic viability, which necessitates a modelling approach that addresses multiple competing objectives simultaneously. The continuous approximation paradigm is a quantitative method for solving logistics problems using a small set of parameters to model a complex system, which results in simple algebraic expressions that are easier to manage than (for example) large‐scale optimization models. As a further benefit, one often obtains insights from these simpler formulations that determine what affects the outcome most significantly. Although continuous approximation models have been used for over 60 years in logistics systems analysis, there has been very little research conducted on their application to problems in rural transit networks, likely due to their distinctive spatial characteristics and coverage requirements. Recent research demonstrates that limited flexibility yields disproportionate benefits in logistics systems. This project will combine tools from geospatial optimization, computational geometry, and geometric probability theory to formulate new models that will solve these problems. Furthermore, these models will identify which complementary infrastructure investments would most effectively increase transit availability and ridership in rural counties. The research outcomes include both theoretical advances in continuous approximation methodology and practical planning tools for rural transit agencies with limited computational resources.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:45:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691671</guid>
    </item>
    <item>
      <title>Enabling Next-Generation Safe, Efficient and Reliable Traffic Signal Management via Advanced Sensing and Foundation Models</title>
      <link>https://rip.trb.org/View/2691670</link>
      <description><![CDATA[Urban traffic signal management systems often rely on outdated techniques and strategies that fail to adapt to dynamic roadway conditions, leading to safety concerns, congestion, and access issues for road users. In addition, current signal optimization approaches rarely consider energy efficiency as the main objective. This research proposes a next-generation safe, efficient and reliable traffic signal control framework powered by advanced roadside sensing and foundation models, specifically Visual Language Models (VLMs) and Multi-Modal Large Language Models (MMLLMs). By integrating high-definition cameras, LiDAR, and real-time data analytics, the system will accurately detect multimodal traffic flows, predict future traffic conditions, and optimize signal phase and timings to enhance mobility while minimizing energy consumption. The framework will be validated through a case study at the Riverside Smart Intersection testbed, leveraging real-world data and co-simulation environments.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:42:56 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691670</guid>
    </item>
    <item>
      <title>Sensor-informed Generative Digital Twin: High-fidelity Simulation for Sustainable Transportation and Policy Validation</title>
      <link>https://rip.trb.org/View/2691669</link>
      <description><![CDATA[Understanding the behaviors of vehicles and other traffic participants at busy urban intersections is critical for urban planning, infrastructure development, and policymaking. Unfortunately, such understanding often comes after a huge investment for implementation and deployment. Many complex interactions occur infrequently and are difficult to capture through after-deployment monitoring. This project will develop a sensor-informed generative digital twin that integrates real-world data from the Riverside Innovation Corridor’s sensor network. By continuously integrating real-time sensory inputs, the platform can be used to create high-fidelity scenarios and simulate rare and challenging transportation dynamics. The digital twin will serve as a decision-support tool for policy evaluation, traffic efficiency strategies, and urban mobility planning. Its predictive capabilities will assist in designing infrastructure for autonomous vehicles, optimizing multi-modal travel demand, and enhancing energy efficiency. Through engagement with policymakers and stakeholders, the project will pave the foundation for the digital twin’s application in real-world decision-making. The proposed research will serve as a bridge, connecting data-driven insights with policy implementation towards sustainable transportation systems.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:41:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691669</guid>
    </item>
    <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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