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    <atom:link href="https://rip.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJzdWJqZWN0aWQiIHZhbHVlPSIxNzcyIiAvPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSI3MzAiIC8+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>
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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>
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      <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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      <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>
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    <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>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>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>
    </item>
    <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>Influence of Traffic Noise and Light on Wildlife Movement Near Highways</title>
      <link>https://rip.trb.org/View/2691660</link>
      <description><![CDATA[Traffic results in noise and light propagated from roadways into surrounding landscapes, including human and natural communities. A previous National Center for Sustainable Transportation (NCST) project has supported the development of multi-scale (local to state) models of traffic illumination and noise extending from highways. These traffic-effect areas may be habitat for a wide range of species, including wildlife attempting to cross highways via existing culverts and bridges, or purpose-built wildlife crossings. With previous NCST research using camera traps, researchers found that coyote and mule deer have varying behavioral responses to traffic noise at wildlife crossings. This project extends both the noise and light modeling and the previous investigations of wildlife crossings to include more species (mountain lion, mule deer, and Peninsular bighorn sheep) and many more highways and regions. The research team will statistically model the effect of traffic noise and light from traffic on occurrence of these 3 species and movements of global positioning system (GPS)-collared individuals as they approach highways. This information is critical in informing and making more effective the massive investments that local, state, and federal governments are making in wildlife crossings and fencing to improve driver and wildlife safety. In other words, knowing the traffic effects on wildlife as they approach highways will allow mitigation of those effects using design and construction of the crossings, such as barriers and berms. Because wildlife crossings are the primary investments transportation agencies make to reduce wildlife impacts and increase driver safety, understanding wildlife ability to get to these crossings and use them is critical to the effectiveness of the investment and the role the investment plats in environmental sustainability.]]></description>
      <pubDate>Sun, 12 Apr 2026 23:12:49 GMT</pubDate>
      <guid>https://rip.trb.org/View/2691660</guid>
    </item>
    <item>
      <title>What Makes Complete Streets Projects Work?</title>
      <link>https://rip.trb.org/View/2690983</link>
      <description><![CDATA[This project will assess community reactions to complete streets projects that repurpose vehicular travel lanes or parking spaces for bicycle lanes, sidewalks or other pedestrian amenities, and/or transit-only or transit-priority lanes. The primary research goal is to better understand the decision-making processes – how and why cities have developed complete streets project proposals and engaged with stakeholders who may have competing interests and perspectives, such as local business owners, homeowner and community groups, bicycle and active transport groups, and public transportation agencies including Caltrans and relevant transit agencies. The project will explore whether and how stakeholder concerns overlap and align and where they do not, and how conflicts are addressed and resolved, when possible. The project will also explore whether stakeholder perspectives change over time (including after project completion). The primary research method will be to conduct case study research in a sample of communities which will vary by regional location and community type. The case studies will involve interviews of key stakeholders, a survey of local business owners, analysis of business revenue data, and document review. The findings will help planners and policymakers understand the political stakes and practical challenges involved in implementing complete streets projects, and how they can successfully be managed.]]></description>
      <pubDate>Thu, 09 Apr 2026 14:32:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2690983</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>
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