<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <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>
    </image>
    <item>
      <title>Drone Network Design for Comprehensive Emergency Response in Rural Utah</title>
      <link>https://rip.trb.org/View/2786771</link>
      <description><![CDATA[Develop an optimized drone network design for comprehensive emergency response in rural Utah to deliver much needed supplies during times of emergency.]]></description>
      <pubDate>Tue, 06 Oct 2026 17:13:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2786771</guid>
    </item>
    <item>
      <title>Safe Driving Across Domains</title>
      <link>https://rip.trb.org/View/2778232</link>
      <description><![CDATA[This project tackles the critical research question: How can pre-trained autonomous driving models be seamlessly and dynamically adapted across diverse operational design domains (ODDs) to achieve safe and robust deployment? ODDs define the specific conditions under which an autonomous driving system can operate safely—spanning factors like road types, traffic density, weather patterns, and geographic features. For instance, a model trained to navigate Pittsburgh’s urban intersections must also adapt to Pittsburgh’s rural routes with sparse signage and rolling hills, or even underdeveloped, unpaved, mixed-traffic roads. What is the right way for a robot to, in real time, smoothly switch between different planning trajectories to ensure safety and consistency amid changing driving conditions?

By focusing on adapting pre-trained models rather than retraining them from scratch, this project offers a scalable approach to ensure safe deployment. This is particularly critical in data-limited settings—collecting training data for every possible scenario can cost millions and take years. For example, equipping autonomous systems to operate in rural areas avoids costly new data collection while preserving high safety standards. A single deployment failure in an untested environment could cost lives and erode public trust, emphasizing the importance of rigorous adaptation. With an estimated 1.35 million road traffic deaths annually worldwide in drastically different driving conditions, this project aims to ensure that autonomous driving technologies can be deployed safely. In other words, this research algorithmically and proactively catalyzes translation of new technologies to data-limited areas, instead of passively relying on commercial technology spillover – the latter would take years or even decades. 

The research team leverages diverse datasets to ensure robust testing. These include driving data from TIER IV in Japan, and over 10 million miles of publicly available driving data from Waymo, collected across 25 U.S. cities and capturing diverse urban environments. In Pittsburgh, the team has access to truck-mounted camera data that covers mixed urban and rural routes, while stationary cameras funded through a PITA partnership with RIDC capture real-time trajectory data in high-traffic industrial areas. Simulation-based fine-tuning complements these datasets, addressing data gaps in under-documented scenarios, such as rural roads with limited signage. This rich dataset ecosystem provides the foundation for testing and validating model adaptations in diverse operational design domains.

The project employs open source, cutting-edge algorithms, including inverse reinforcement learning (RL), guided diffusion, blueprints, and fine-tuning. Inverse RL provides an overall framework to complement rule-based modules to directly and flexibly extract algorithm parameters (sometimes implicitly) from expert (human or simulated) driving. Guided diffusion allows for precise and smooth generation and adjustments of such parameters in constrained domains, such as adapting autonomous driving algorithms from Pittsburgh's dense urban streets to its hilly rural roads. Blueprinting ensures scalable and structured design of template models that are transferable across diverse contexts, while fine-tuning efficiently adapts pre-trained models to novel environments, particularly in low-data settings. Open source models already implement these algorithms. The team adapts these powerful emerging methods to address challenges in rural areas and infrastructure-limited regions.]]></description>
      <pubDate>Thu, 17 Sep 2026 16:37:29 GMT</pubDate>
      <guid>https://rip.trb.org/View/2778232</guid>
    </item>
    <item>
      <title>Local Tax on Dyed Fuels: Possible Revenue and Implementation Strategies</title>
      <link>https://rip.trb.org/View/2689755</link>
      <description><![CDATA[In rural areas throughout the U.S., in addition to standard gasoline and diesel fuel, “dyed fuel” is also available for purchase. This type of fuel, which is typically 28 to 98 cents less expensive per gallon, is not taxed because it is supposed to be used only for vehicles that do not use the local road systems. However, many of these vehicles, such as farm and construction equipment used in areas (e.g. liquid manure spreaders, grain carts, feed trucks, combine harvesters, excavators, etc.…) are much heavier than the average personal vehicle, and often use secondary roads for moving such equipment. These vehicles damage the many rural paved and unpaved roads. As such, those who maintain these roads systems do not have sufficient financial resources to repair these ongoing damages due to this lack of income from taxes. There is therefore an opportunity to consider methods of applying a local fee structure on the use of dyed fuel, to help to recuperate the cost of maintaining secondary road systems. This research will focus on assessing, first, the current state of this challenge, including a literature review and survey of the use of dyed fuels. Next, interviews of stakeholders will seek to identify the state of the challenges associated with dyed fuel use and to assess the support and/or resistance to establishing an additional mechanism to recuperate funds. It will then propose possible methods to address this challenge, supported by financial and policy analysis on the impact of each. 

The idea for this proposal originated directly from county engineers, specifically personnel in Iowa drove the development of the initial idea of this proposed effort, which was further strongly supported by Arizona DOT personnel. These individuals are responsible for maintaining local transportation infrastructure. These conversations made it clear that especially rural counties have significant infrastructure maintenance costs that in many cases are greater than the available funds. The proposed work is specifically designed to evaluate the scope of these challenges, and work with stakeholders to identify solutions. The Iowa Highway Research Board has already voted to provide a full one-to-one match in support of this proposed effort. They have expressed clear support and enthusiasm for the project, noting that counties across Iowa—and likely many other states—stand to benefit from a structured evaluation of needs and practical mechanisms that work toward implementation pathways. This underscores strong confidence in potential impacts.  ]]></description>
      <pubDate>Thu, 10 Sep 2026 09:18:44 GMT</pubDate>
      <guid>https://rip.trb.org/View/2689755</guid>
    </item>
    <item>
      <title>Rural access to healthcare: Building a longitudinal panel dataset to inform policy</title>
      <link>https://rip.trb.org/View/2775485</link>
      <description><![CDATA[Healthcare consolidation is a growing trend across the United States, particularly in rural areas, and may come with consequences for healthcare that is routine, recurrent, or time sensitive. Service reductions and closures may act as a barrier to care for rural patients, who already travel longer distances to reach health care services. At the same time, the expanding landscape of virtual healthcare services provides an opportunity for greater access to some types of care. This one-year project will develop a unique individual-level longitudinal panel dataset that can be used to evaluate the connections between spatial access and routine healthcare utilization in the largely rural state of Vermont. The dataset will be built from insurance enrollment and claims data from the Vermont Health Care Uniform Reporting and Evaluation System (VHCURES) that captures approximately 400,000 to 500,000 individuals annually from 2007 to 2027. Using insights from prior literature, the dataset will be built to capture spatial access to healthcare providers, healthcare utilization, and residential location and mobility. An exploratory analysis of the dataset will identify specific types of healthcare services and utilization that present opportunities for deeper evaluation of causal relationships. Based on gaps in prior literature and the exploratory analysis, this project will conduct a preliminary case study to demonstrate the value of the dataset. Expected outcomes include a literature review, a rigorously constructed and documented dataset, descriptive statistics, and a final report detailing dataset design, scope, limitations, and promising areas for future research. This project provides a foundation for future analyses to evaluate long-term causal relationships between spatial healthcare access and healthcare utilization to inform investments in health services in rural settings, including those in other regions/jurisdictions.]]></description>
      <pubDate>Tue, 08 Sep 2026 17:07:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2775485</guid>
    </item>
    <item>
      <title>Advancing Safe System Implementation in Rural and Small Urban Networks: Practical Screening Solutions</title>
      <link>https://rip.trb.org/View/2742138</link>
      <description><![CDATA[Roadway safety remains one of the most persistent transportation challenges across the Mid-Atlantic region, particularly in rural and small urban communities, which consistently experience disproportionately high rates of fatal and serious injury crashes. Federal, state, and local agencies have adopted the Safe System Approach (SSA), which emphasizes proactive, system-level strategies to prevent severe crashes, yet operational implementation gaps remain in rural environments characterized by low traffic volumes, dispersed crash patterns, and resource constraints. Safety screening and project prioritization in these contexts continue to rely heavily on historical crash concentrations, which may fail to capture latent risk in low-volume corridors or rare but severe crash types.

This project advances the operationalization of the Safe System Approach through the development and pilot application of a Safe System-aligned Risk Screening Tool for rural and small urban networks, supplementing traditional crash-based screening with structured systemic roadway risk factors and selected technology-enabled surrogate indicators, applied within a selected West Virginia network.]]></description>
      <pubDate>Sat, 01 Aug 2026 09:41:10 GMT</pubDate>
      <guid>https://rip.trb.org/View/2742138</guid>
    </item>
    <item>
      <title>Large Language Model-Driven Crash Risk Analysis System for Rural and Tribal Roadways</title>
      <link>https://rip.trb.org/View/2731923</link>
      <description><![CDATA[Rural and tribal roadways in the United States experience disproportionately high crash incidents due to a combination of infrastructure challenges, limited resources, and incomplete crash reporting. Traditional statistical, machine learning (ML), and deep learning (DL) models have struggled to address these issues because they rely heavily on structured data, perform poorly with incomplete or imbalanced datasets, and often fail to leverage the rich information contained in crash narratives. Large language models (LLMs) offer an alternative by reframing crash risk analysis as a text reasoning problem, enabling the extraction of contextual insights from narratives, the imputation of missing or inconsistent fields, and the integration of structured and unstructured data into unified predictive frameworks. This proposal aims to develop an LLM-based crash risk analysis system utilizing North Carolina’s statewide police-reported crash data as a foundation, with the goal of enhancing the accuracy, interpretability, and robustness of rural crash risk assessment. The project will proceed through four main tasks: crash data enhancement and model adaptation, predictive model development, model validation, and the design of an implementation plan for real-time crash risk warnings in connected vehicle (CV) environments. ]]></description>
      <pubDate>Fri, 17 Jul 2026 16:08:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2731923</guid>
    </item>
    <item>
      <title>Subsurface Analysis Planning Tool for Cost Reduction, Rapid Emergency Evaluation, And Data Support for Rural Service Areas</title>
      <link>https://rip.trb.org/View/2726188</link>
      <description><![CDATA[Transportation professionals often make important decisions about system dependability, project design, and emergency response with limited time and data. This is particularly true as it relates to the underlying soil, rock, and groundwater conditions that directly affect the design and repair of critical infrastructure. Typically, designers and engineers rely on drilling boreholes and performing in-situ tests to characterize subsurface conditions and associated problems for applications ranging from bridges to roadways to stream crossings to landslides. Unfortunately, these exploration techniques are expensive, time-consuming, inherently risky, and often accompanied by significant lead times. Further, the complex nature of this data can be very difficult to interpret and the uncertainty difficult to quantify.

This research project will leverage investments from CLiP, Oregon Department of Transportation (ODOT) SPR786, and SPR808 to improve GOSEP algorithms for predicting subsurface information for planning and emergency response, with a focused aim for extrapolation improvement in rural, data sparse regions. This project also aims to expand the GOSEP database and thereby its capabilities by adding more geotechnical data and parameter datasets as well as by improving the OCR borehole log scanner for archived handwritten borehole logs.]]></description>
      <pubDate>Wed, 08 Jul 2026 17:34:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2726188</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>
    </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>Tribal &amp; Rural Autonomous Vehicles for Efficiency, Livability and Safety (TRAVELS) </title>
      <link>https://rip.trb.org/View/2687129</link>
      <description><![CDATA[Tribal & Rural Autonomous Vehicles for Efficiency, Livability and Safety (TRAVELS) is the passenger transportation component of the U.S. Department of Transportation’s Rural Autonomous Vehicle (RAV) Research Program, led by the University of Wisconsin–Madison. The project addresses persistent mobility challenges in rural and Tribal communities, where residents often face limited transportation options, long-distance travel needs, restricted access to healthcare and essential services, and fewer after-hours or on-demand services. These challenges are further complicated by roadway, infrastructure, weather, and economic constraints that can limit the feasibility of conventional and emerging mobility solutions.


This research examines how automated vehicle technologies, supportive infrastructure, communications systems, and service design strategies can be integrated to improve safety, accessibility, and operational efficiency in rural settings. The project includes demonstration and deployment activities in Wisconsin, Georgia, and Oklahoma, focusing on use cases such as healthcare access, economic transportation, and tourist & event shuttle services. Through testing, evaluation, and before-and-after analyses, the project seeks to identify practical pathways to accelerate automated mobility deployment in rural and tribal areas.
]]></description>
      <pubDate>Fri, 03 Apr 2026 16:46:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/2687129</guid>
    </item>
    <item>
      <title>Advancing Rural Mobility through Innovative Charging Solutions</title>
      <link>https://rip.trb.org/View/2677557</link>
      <description><![CDATA[Electric vehicle (EV) ownership in the United States is growing. Since EVs have emerged as an additional mobility option available in the market, their proliferation requires charging infrastructure to support the growing number on EVs on the national highway system. While many EV owners predominantly charge their vehicles at home, EV fast chargers are needed to provide support for efficient long distance and interstate travel. The advent of Advanced Air Mobility (AAM) in rural areas has the potential to address long-standing challenges related to accessibility, connectivity, and service delivery using electric vertical take-off and landing [eVTOL] aircraft and drones. These mobility solutions also require charging infrastructure to enable their deployment.      

Development of new fast charging stations has been delayed by the need to connect the charging stations to the electric grid. Microgrids, which are small, local power grids that use locally sourced energy to supply electricity within that microgrid, provide an opportunity to provide needed energy without the need to connect to the electric grid. These solutions are preferable in areas where interconnection with the electric grid would be not feasible, not timely, or cost prohibitive, including rural areas. This project would investigate and evaluate microgrids and storage for EV charging solutions that allow for long-distance EV mobility, laying the groundwork for further research  and implementation to enable efficient mobility of electric vehicles across the US.  The project will also address non-traditional charging applications (beyond on-road vehicle charging) to address potential charging solutions for AAM operations in rural areas.     ]]></description>
      <pubDate>Wed, 04 Mar 2026 13:49:50 GMT</pubDate>
      <guid>https://rip.trb.org/View/2677557</guid>
    </item>
    <item>
      <title>Data-Driven Policy Guidance for CAV Deployment in Rural Southeastern Communities</title>
      <link>https://rip.trb.org/View/2666840</link>
      <description><![CDATA[This project aims to recalibrate the connected and autonomous vehicle (CAV) benefit analysis tool with regional specific inputs, which will guide policy deployment for safe and effective CAV deployment. With the promise of improving transportation safety, mobility, and other transportation system challenges, CAV technologies may face unique deployment challenges in rural communities, including limited infrastructure, lagging adoption, sparse resources and reduced technological readiness. To address these issues, the project will collect quantitative and qualitative data through surveys and stakeholder engagement targeting residents and stakeholders across eight southeastern states. This region-specific data collection is necessary because previous CAV benefit analysis tools were calibrated using data from the Puget Sound area, which does not reflect the characteristics of the Southeast. Using non-regional inputs would skew safety benefit estimates and reduce the tool’s effectiveness for policy and planning. Collecting data regionally allows for statistically representative input for scenario testing, model calibration, and policy analysis. Further, it facilitates data-driven policy decision making.   ]]></description>
      <pubDate>Mon, 23 Feb 2026 14:30:27 GMT</pubDate>
      <guid>https://rip.trb.org/View/2666840</guid>
    </item>
    <item>
      <title>Emergency Response and Access Mapping for Rural Navajo Communities </title>
      <link>https://rip.trb.org/View/2658056</link>
      <description><![CDATA[In the Navajo Nation Area, poorly maintained, unpaved, and seasonally hazardous road conditions in rural areas hinder timely response of emergency services. For example, in Crownpoint, heavy snowfall can make it difficult for ambulance services and firefighting vehicles to reach homes, as they must travel through unpaved or unmaintained roads to reach their destinations. Although current routing tools are able to locate the best route between two points, they do not contain pavement condition data or hazard data that would allow for the accurate determination of safe passage for emergency vehicles. Satellite images are also unable to show potholes, ruts, washouts, etc.; therefore, responders are forced to guess which is the best route based on their experience or try different routes until they find one that works. In many cases, this results in substantial delays, especially during severe weather when traditional navigation systems provide little guidance on actual road accessibility. A new platform is needed that has reliable and accessible data to help direct emergency responders to the safest route to the point of origin. Such a system would not only improve response time but also provide agencies with a standardized way to assess roadway risk during rapidly changing environmental conditions. This project will create a reliable, data driven, artificial intelligence (AI)-assisted Road Accessibility Index (RAI), and a geographic information services (GIS)-based routing dashboard utilizing Vialytics' smartphone-based road assessment capabilities, along with data on transportation, crashes, maintenance, and climate to provide real time accessibility ratings for each ten meter section of road within the Crownpoint area (150 miles total) and direct Emergency Medical Services, Fire and Law Enforcement departments towards the safest routes to travel to emergency locations. 
 ]]></description>
      <pubDate>Wed, 04 Feb 2026 19:20:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2658056</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>
  </channel>
</rss>