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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>
    <image>
      <title>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Reliability-Aware Accessibility Measurement and Planning for Rural Transportation Systems</title>
      <link>https://rip.trb.org/View/2703797</link>
      <description><![CDATA[Reliable access to essential destinations is a persistent challenge in rural transportation systems, where long travel distances, limited infrastructure, and exposure to environmental disruptions can significantly affect mobility. Transportation accessibility is widely used in planning to evaluate how well transportation networks connect people to services and opportunities, yet most accessibility measures assume deterministic travel conditions and do not account for travel-time variability, weather disruptions, or infrastructure reliability. As a result, existing accessibility metrics may overestimate the practical ability of rural residents to reach essential destinations and provide limited guidance for transportation planning under uncertain conditions.
This project develops a reliability-aware accessibility measurement and planning framework for rural transportation systems. The research will extend traditional accessibility measures by incorporating transportation network uncertainty through scenario-based modeling of travel-time variability and disruption conditions. Reliability-aware accessibility metrics will be benchmarked against conventional accessibility measures and embedded within an optimization-based planning model that helps identify transportation interventions that improve reliable access under resource constraints. The framework will be demonstrated through a rural transportation case study using publicly available data and implemented as a prototype decision-support workflow for transportation planners.]]></description>
      <pubDate>Sat, 16 May 2026 11:55:36 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703797</guid>
    </item>
    <item>
      <title>National Accessibility Evaluation Phase II</title>
      <link>https://rip.trb.org/View/2703751</link>
      <description><![CDATA[This project implements activities for the National Accessibility Evaluation (NAE) pooled-fund study, performing accessibility evaluations describing conditions in 2020, 2021, 2022, 2023, and 2024. The National Accessibility Evaluation creates national census block-level accessibility datasets that can be used by partners in local transportation system evaluation, performance management, planning, and research efforts. The project produced a series of annual reports describing accessibility to jobs by driving, biking, walking, and by transit in metropolitan areas across America.

Accessibility calculations rely on detailed travel-time calculations for both driving and transit, using commercially available, global positioning system (GPS)-based speed measurements and published transit schedules. Each NAE partner received digital access to the accessibility datasets covering their jurisdictions. These datasets quantify access to jobs, health care, schools, grocery stores, and other essential destinations. The annual Access Across America reports provide summaries of the detailed job accessibility datasets for the 50 most populous metropolitan areas across America. These reports were released to national and local media outlets and supported by publicity and communications efforts.]]></description>
      <pubDate>Fri, 15 May 2026 15:29:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703751</guid>
    </item>
    <item>
      <title>How Do People Receive Information About Public Transit?</title>
      <link>https://rip.trb.org/View/2696850</link>
      <description><![CDATA[Providing transit information helps passengers adapt when service is unreliable and has been shown to decrease wait times, reduce overall travel time, increase ridership, increase satisfaction with transit, and increase perceptions of personal security. However, to date, there is limited evidence for how riders prefer to access and use transit information. A variety of methods are available, including websites, apps, signage, and transit ambassadors or drivers, but which methods of information are most used by riders and how does it differ by the type of rider and type of trip? Riders need real time information to be accurate, but how does inaccurate information impact their trip? In addition, how do riders plan their travel pre-trip, such as understanding hours and frequency of service, finding the stop, and understanding payment mechanism? This research aims to explore how both transit riders and non-riders access public transit information for the purpose of planning and taking trips on transit to answer these questions. This work will improve understanding of customer perspective to aid agencies in providing better transit rider information in a cost-effective manner, thus improving the long-term viability of the transportation system by increasing demand for transit.]]></description>
      <pubDate>Tue, 28 Apr 2026 11:12:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696850</guid>
    </item>
    <item>
      <title>Development of a Real-Time Decision Support Framework for Resilient Bridge Infrastructure During Evolving Hazard Conditions
</title>
      <link>https://rip.trb.org/View/2696159</link>
      <description><![CDATA[Bridge infrastructure serves as a critical lifeline for transportation, emergency response, and economic continuity. In hazard-prone regions such as Florida, bridges face escalating risks from floods, hurricanes, and wildfires that can rapidly disrupt traffic flow and delay emergency operations. Existing bridge management systems primarily focus on long-term planning and condition assessment, offering limited capability for real-time decision-making during evolving hazard events. This project aims to develop a real-time decision support framework that enables dynamic management of bridge infrastructure under active hazard conditions. The proposed framework will integrate real-time hazard forecasts, sensor-based condition monitoring, and infrastructure performance data to guide rapid, data driven decisions. Using advanced analytics and scenario modeling, the system will support time-sensitive operational actions such as rerouting, temporary reinforcement, and emergency closures. A visual decision-support interface will convey hazard progression, bridge condition, and recommended response strategies to transportation agencies and emergency managers in an intuitive, spatially enabled format. Building upon prior work at Florida A&M University on the IntelliViz prioritization platform, this research extends the concept from long term resilience planning to operational support. A regional case study in Florida will demonstrate the practical implementation of the framework and its benefits for improving coordination, minimizing downtime, and enhancing public safety during flood and hurricane events. By integrating real-time data streams with predictive modeling and visualization tools, the project will bridge the gap between static risk assessment and dynamic hazard response, providing a scalable and implementable framework for strengthening transportation resilience and supporting informed, timely decisions during extreme events.]]></description>
      <pubDate>Mon, 27 Apr 2026 20:01:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696159</guid>
    </item>
    <item>
      <title>Road Network Restoration after Major Disruptions</title>
      <link>https://rip.trb.org/View/2447123</link>
      <description><![CDATA[This project develops practical optimization methods for selecting, sequencing, and scheduling restoration actions for disrupted road networks based on incomplete and gradually improving information. Road networks may be severely damaged by events such as hurricanes and earthquakes, and prompt restoration is often necessary for the resumption of emergency services, other essential services, and normal activities.

The proposed methods employ artificial intelligence heuristics such as genetic algorithms and particle swarm algorithms to optimize the schedules of restoration tasks. A hybrid optimization approach combines fast traffic assignment with microscopic simulation to refine solutions. The methods are designed to start with incomplete, uncertain information and adapt dynamically as additional data becomes available from weather forecasts, work crews, and the public. The project also develops methods for pre-planning purposes, including preparing effective restoration plans based on estimated probabilities of disruptions and their consequences.

The research team will collaborate with the Maryland State Highway Administration and other agencies to ensure the practical applicability of the methods. Technology transfer activities include journal papers, conference presentations, software with a user manual, a final technical report, and workshops for interested transportation organizations.]]></description>
      <pubDate>Wed, 11 Mar 2026 13:21:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447123</guid>
    </item>
    <item>
      <title>Enabling Mobility of Emergency Medical Service through Connected and Automated Vehicle Preemption</title>
      <link>https://rip.trb.org/View/2669655</link>
      <description><![CDATA[Emergency Medical Service (EMS) vehicles, typically ambulances, have time-critical transportation roles when responding to traffic incidents by bringing first medical responders and equipment from their bases to the incident scenes, and transferring injured persons from the scenes to medical facilities. Addressing the mobility of EMS vehicles supports but public health and safety goals, as well as those related to efficient mobility.     

The traditional way for EMS vehicles to reach their destinations faster is to use audible sirens to alert drivers of their presence. Upon hearing an EMS vehicle’s siren, drivers must yield the right of way to facilitate its passage. Previous research on traffic signal preemption for EMS vehicles has demonstrated its effectiveness in reducing delays at signalized intersections. With the advent of Connected and Automated Vehicle (CAV) technology, vehicles can now communicate directly with each other. EMS vehicles equipped as CAVs could leverage vehicle-to-vehicle (V2V) communication technology to transmit warning messages to the CAVs downstream along their routes, beyond the range of audible sirens. The CAVs that have received these messages can proactively move aside to create a clear lane for the EMS vehicle to pass. This “CAV preemption” concept has the potential to significantly improve EMS mobility, resulting in faster response times, earlier on-scene medical aid, and quicker patient transfer to hospitals. Furthermore, the proposed CAV preemption will accelerate incident clearance and the restoration of highway capacity.  

This research is based on an envisioned CAV preemption system in which an EMS vehicle broadcasts its impending arrival to downstream CAVs, while simultaneously sounding its siren and emitting high-intensity strobe light to request signal preemptions. All CAVs receiving this V2V message will automatically move to the right lane, while only a portion of the non-CAV drivers will comply and respond to the siren. The efficiency of this system depends the following factors: (1) The broadcast range of the warning messages to CAVs, (2) The market penetration rate of CAVs, (3) The move-aside compliance rate of non-CAV drivers, (4) The level of traffic congestion.  

This research will simulate and quantify the efficiency of the proposed CAV preemption system under varying operating conditions. An agent-based simulation model of the El Paso highway network will be used to assess the EMS vehicle’s travel time. Mobility efficiency is defined as the percentage reduction in the average travel time. The travel times of EMS vehicles from their bases (selected fire stations that house ambulances) to multiple incident sites (selected highway locations) will be simulated, extracted, and analyzed. The analyses will assess the impacts of broadcast range, CAV market penetration, non-CAV compliance rate, and traffic volume.   ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:34:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2669655</guid>
    </item>
    <item>
      <title>Drone Network Design for Emergency Response in Rural Utah</title>
      <link>https://rip.trb.org/View/2655749</link>
      <description><![CDATA[Rural areas of Utah face significant challenges in providing timely and comprehensive emergency response. Long distances, limited road infrastructure, mountainous and desert terrain, and weather-related disruptions can significantly delay ambulances and rescue teams. These factors often increase response times for medical, disaster, and search-and-rescue emergencies, directly impacting outcomes and endangering lives.

Traditional emergency services remain essential, but they are insufficient in covering all rural needs quickly. Unmanned Aircraft Systems (UAS), or drones, present a transformative opportunity to bypass geographic and infrastructure barriers. Drones can rapidly deliver critical supplies, e.g. medical kits, blood units, communication devices, food, water, or specialized equipment, within minutes rather than hours. However, to make such a system viable, Utah requires a data-driven framework to determine where drone bases should be located, what fleet capabilities are needed, and how to integrate these operations with regulatory and local constraints. This project addresses the need to design an optimized drone network for comprehensive emergency response in rural Utah.

The primary objective of this research project is to develop an optimized drone network design to significantly reduce emergency response times in rural Utah by identifying strategic drone base locations, fleet requirements, and deployment strategies. Secondary objectives of this research project are to evaluate the technological, regulatory, and operational feasibility of drone-based emergency response, ensuring alignment with community needs and positioning Utah Department of Transportation (UDOT) as a leader in innovative public safety solutions.]]></description>
      <pubDate>Mon, 19 Jan 2026 16:43:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2655749</guid>
    </item>
    <item>
      <title>Enhancing Rural Public Transportation Through Community Engagement and Technology</title>
      <link>https://rip.trb.org/View/2652178</link>
      <description><![CDATA[Rural public transportation in the United States faces persistent challenges due to low population densities, inadequate infrastructure, and limited mobility options. This project aims to address these issues by leveraging advanced technologies, including digital twins and mobile applications, to enhance transit planning, scheduling, and efficiency. The study will focus on rural Texas, exploring innovative transportation models that incorporate a mix of fixed-route transit, autonomous vehicles, transportation network companies, and on-demand services tailored to meet community needs. A key component of the project is the development of a digital twin, a virtual representation of the rural transportation network, to simulate and optimize transit operations. By integrating real-time data from mobile platforms, the digital twin will enable planners to test different service configurations, predict ridership patterns, and enhance accessibility, particularly for older adults and individuals with disabilities. This approach will facilitate cost-effective, demand-responsive transit solutions that enhance mobility and improve quality of life. Community engagement is central to the project, ensuring that transportation solutions align with the needs of residents. Public meetings and stakeholder discussions will guide decision-making, while performance metrics, including user adoption, service coverage, and cost efficiency, will assess the effectiveness of the implemented strategies. The outcomes of this study will provide a replicable framework for rural mobility solutions, demonstrating how digital tools and participatory planning can transform public transit systems in rural and low-density areas. ]]></description>
      <pubDate>Tue, 13 Jan 2026 15:14:48 GMT</pubDate>
      <guid>https://rip.trb.org/View/2652178</guid>
    </item>
    <item>
      <title>Understanding Risks and Opportunities for Ramp Metering Control in a Mixed-autonomy Future</title>
      <link>https://rip.trb.org/View/2651988</link>
      <description><![CDATA[Vehicle automation may change traffic flow dynamics. This will also impact the control of traffic flow via infrastructure-based systems such as ramp metering control. In this work the research team investigated the impact that different levels of automation and connectivity will have on ramp metering control, and proposed modifications to existing ramp metering algorithms to improve their performance under different automation scenarios. The team finds that low-level automation such as adaptive cruise control may decrease mainline throughput by up to 58% on average and increase travel time by 61%. However, full connectivity and automation may decrease travel time by up to 40%. Based on these potential impacts, modifications to the ramp metering algorithm settings were developed for each of the seven automation scenarios. These modifications are shown to improve operations in each scenario.]]></description>
      <pubDate>Thu, 08 Jan 2026 15:26:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2651988</guid>
    </item>
    <item>
      <title>OpenRoad Link: A Public-Private Data Exchange for Safer, Smarter Trucking </title>
      <link>https://rip.trb.org/View/2646948</link>
      <description><![CDATA[Work zones, lane closures, and traffic incidents significantly impact roadway safety and efficiency. When lanes are blocked due to construction, crashes, or other disruptions, roadways no longer function as designed—leading unexpected congestion, increased crash risk, and reduced operational reliability. Many work zones are established to perform critical maintenance on aging infrastructure—essential to improving durability and extending the service life of roadways—but they also introduce temporary risks and delays that must be better managed.  Effects of lane blockages are particularly severe for commercial motor vehicles (CMVs), which require more time and space to slow or reroute and are subject to strict hours-of-service regulations that make delays especially costly. 

This project proposes to develop and evaluate a data exchange framework—OpenRoad Link—to integrate and share real-time lane closure, work zone, and incident data from the Oklahoma Department of Transportation (ODOT), the Oklahoma City and Tulsa Traffic Operations Centers (TOCs), and other key transportation and traffic enforcement partners. To build this framework, the project will first identify and assess the roadway data already collected and shared by these agencies, as well as the types of information currently accessible to the CMV industry through private telematics platforms. Building on national standards such as the Work Zone Data Exchange and SAE J2735 (the standard message set for vehicle-to-everything communications), the project will extend the data scope to include lane-blocking crashes, maintenance activities, and other short-term or unplanned restrictions not currently emphasized in existing feeds. Through collaboration with ODOT, city TOCs, and trucking industry partners—including a pilot with a major trucking company such as ABF—the project will demonstrate the delivery of curated, high-value information directly to in-cab devices or fleet management systems.  

Key tasks will include identifying and cataloging roadway and incident data currently collected by the Oklahoma Department of Transportation (ODOT) and the Traffic Operations Centers (TOCs) of Oklahoma City and Tulsa, as well as evaluating what information is already being shared with the commercial vehicle industry through private telematics platforms. The project will establish partnerships with ODOT, city transportation and public safety agencies, and private industry stakeholders to design and implement a unified, standards-compliant data exchange framework. Following the design phase, the team will develop and deploy the OpenRoad Link data feed, ensuring compliance with existing national standards and verifying data accuracy and reliability. A pilot deployment will be conducted in collaboration with a trucking company using a selected in-cab device to deliver actionable, real-time information directly to CMV drivers.  

Anticipated outcomes include improved safety for CMV drivers, a reduction in secondary crashes, enhanced freight reliability, and a validated proof-of-concept for scalable public-private data exchange. By producing a replicable model for collaboration between state DOTs and private-sector technology providers, the project aims to accelerate national adoption of interoperable safety data systems and promote safer, more efficient freight transportation. ]]></description>
      <pubDate>Tue, 06 Jan 2026 08:59:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646948</guid>
    </item>
    <item>
      <title>Synthesizing Microtransit and Fixed Route Transit via Rider Hand Off to Improve Transit Efficiency</title>
      <link>https://rip.trb.org/View/2640190</link>
      <description><![CDATA[Microtransit programs can improve local mobility, but they often operate separately from fixed route bus networks. This separation can create gaps in connectivity and reduce the potential efficiency of both systems. This project will study how rider hand off strategies, where microtransit vehicles bring passengers directly to fixed route transit, can strengthen system performance. Using data from CTtransit, microtransit logs, and synthetic demand models, the research will simulate multimodal operations and evaluate how pickup schedules and transfer points influence wait times, travel times, and network utilization.

The project will develop an optimization framework to identify operating strategies that improve rider transfers and increase the efficiency of both modes. Scenario testing will measure the effects of integration on cost, ridership patterns, and service quality. The results will provide agencies with practical guidance on how to coordinate microtransit and fixed route services in ways that improve reliability and expand access to transit. These findings can support broader efforts to enhance mobility in Connecticut and inform similar initiatives in other regions.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:47:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640190</guid>
    </item>
    <item>
      <title>Transit Priority Expectations – Cost-Effectiveness and Service Provision Impacts



</title>
      <link>https://rip.trb.org/View/2636146</link>
      <description><![CDATA[There are multiple methods to improve transit priority, however, there is no standardized method to measure or predict these benefits across transit agencies and project types nationwide. This research aims to evaluate the impacts of transit priority infrastructure on cost-effectiveness and service provision. Specifically, it will examine how investments in transit priority measures—such as dedicated bus lanes, signal priority, and stop consolidation—affect transit operating costs, service reliability, and overall efficiency.

Caltrans currently holds an archive of General Transit Feed Specification (GTFS) and GTFS-Realtime derived transit speed and reliability data for most transit service across California, which the research team will be able to use in their analysis. To ensure the research results are applicable not just to one region or agency, researchers are also encouraged to pursue datasets from other agencies, including but not limited to the Chicago Transit Authority, Massachusetts Bay Transportation Authority, and Washington Metropolitan Area Transit Authority.

Through this research, transit agencies will be able to assess the potential benefits and trade-offs of implementing various transit priority strategies. State Departments of Transportation, Metropolitan Planning Organizations, and other local and regional stakeholders could then use an evidence-based approach to estimate the cost-effectiveness and service effects of potential transit priority measures based on existing data such as transit speed and reliability, land use, and road network characteristics. All of which can result in transit riders enjoying a faster, more frequent service through more efficient use of existing operating funds and increased investments in transit service are tied to transformative outcomes.
]]></description>
      <pubDate>Mon, 08 Dec 2025 19:58:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2636146</guid>
    </item>
    <item>
      <title>Enhancing Structural Safety and Promoting Equity in Infrastructure Maintenance through Human-Centered Bridge Inspection empowered by Artificial Intelligence and Augmented Reality
</title>
      <link>https://rip.trb.org/View/2627937</link>
      <description><![CDATA[Bridges are crucial civil infrastructure, but their deterioration over time poses significant safety risks. Traditional human visual inspections are limited in accuracy and efficiency, leading to challenges in maintaining the inventory of bridges in the United States, particularly in economically disadvantaged communities. Leveraging recent advancements in computer vision (CV), artificial intelligence (AI), and augmented reality (AR), the team proposes a novel human-centered approach to enhance the accuracy and efficiency of concrete bridge inspections and promote equity in infrastructure maintenance. By automating detection and documentation of damage in concrete bridges, and empowering human inspectors by overlaying real-time detection results onto bridges thereby enabling human-machine collaboration, the project aims to improve inspection effectiveness and efficiency, promote equity in infrastructure maintenance, and enhance public safety.
]]></description>
      <pubDate>Fri, 21 Nov 2025 14:16:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2627937</guid>
    </item>
    <item>
      <title>An AI-based Oversize Vehicle Warning System in Smart Work Zone
</title>
      <link>https://rip.trb.org/View/2627406</link>
      <description><![CDATA[Lane closures, when required during road repair and maintenance, can cause traffic congestion in adjacent open lanes. It is problematic when oversized vehicles are present, as they can create safety risks for workers and other drivers in work zones. The existing technologies in this regard are customized only for overheight vehicle detection and ignore the horizontal span of the vehicles. Therefore, those solutions cannot be extended directly to address the problem at hand. Additionally, the existing methods rely on expensive sensors such as LiDars and radars for automated vehicle detection. Exorbitant costs restrict the large-scale use of those devices. As a more economical solution, this study will leverage inexpensive Ref Green Blue-Depth (RGB-D) sensors for accurate learning-based vehicle size estimation. To address this issue, this project aims to develop an intelligent early warning system that uses low-cost 3D sensing cameras and artificial intelligence (AI)-based detection algorithms. The system will estimate the size of approaching vehicles and issue a real-time warning to any vehicle that is too large for the open lanes. This will help prevent potential accidents and encourage these vehicles to take alternate routes or slow down to ensure everyone's safety.
]]></description>
      <pubDate>Thu, 20 Nov 2025 16:26:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/2627406</guid>
    </item>
    <item>
      <title>Evaluation of Vehicle Telematics and Infrastructure-based Connected Vehicle Data for Real-Time Safety and Mobility Application
</title>
      <link>https://rip.trb.org/View/2625309</link>
      <description><![CDATA[The emergence of connected vehicle (CV) data has provided unprecedented opportunities for developing real-time, proactive applications to enhance safety and mobility. This project utilizes and compares telematics and infrastructure-based CV data to determine optimal applications for each and explore integration strategies for safety and mobility solutions. Specifically, telematics CV data provide the location, speed, and other key information on approximately 5-10% of vehicles on the road. In contrast, infrastructure-based CV data from the connected corridor in the City of Madison contain information about traffic signals, vehicles, and road geometry. By comparing and integrating these data sources, this project proposes physics models and neural network algorithms to detect real-time safety issues such as crashes. The detection results can be used to issue immediate warnings to drivers, traffic managers, and automated vehicle systems. To disseminate these warnings, the research team proposes utilizing roadside variable message signs and in-app notifications via platforms like HAAS, Google Maps, and Waze. The proposed applications can be piloted through field tests in the University of Wisconsin-Madison’s Level 3 CAV testbed and possibly later at Mcity.]]></description>
      <pubDate>Thu, 13 Nov 2025 15:31:52 GMT</pubDate>
      <guid>https://rip.trb.org/View/2625309</guid>
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