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    <title>Research in Progress (RIP)</title>
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    <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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Enhancing Heavy Vehicle Crash Prevention in North Dakota through Machine Learning and Weather Data Integration</title>
      <link>https://rip.trb.org/View/2683255</link>
      <description><![CDATA[Heavy vehicle crashes continue to be a persistent safety concern across the Midwest, with several states reporting disproportionately high rates of incidents involving large trucks. According to the National Safety Council, in 2023, North Dakota recorded 18% of its fatal crashes involving large trucks, placing it among the highest in the nation. Neighboring states, such as Nebraska (16%) and Iowa, also face elevated risks. Illinois reported 7,509 truck accidents in 2022, ranking among the top five states nationwide. In North Dakota, the risks are especially pronounced during the winter months. In 2023, 64% of heavy vehicle crashes occurred between October and March, with 81% of these crashes taking place in rural areas. These figures highlight how weather conditions and geography amplify the risk associated with large-truck travel in the region. Further, crashes in rural areas in challenging weather conditions poses immense issues for first responders and their ability to provide timely medical care to crash victims.   

Traditional safety strategies have struggled to account for the dynamic, real-time factors that contribute to crash risk. Static approaches often fall short when adverse weather, road conditions, and traffic volume interact in unpredictable ways. This gap highlights the urgent need for predictive, data-driven solutions.  

This proposal aims to investigate the application of machine learning (ML) models, combined with weather and crash data, to predict high-risk scenarios before accidents occur, to support planning for safety and emergency response needs. By leveraging predictive analytics, North Dakota could enhance resource allocation, deploy preventive interventions, and reduce the frequency and severity of heavy vehicle crashes. The high incidence of winter crashes and the limitations of conventional methods make North Dakota an ideal proving ground for an innovative, ML-driven approach to roadway safety.  

The study will utilize historical crash records for heavy vehicles in North Dakota, including crash type, severity, date, and time, combined with corresponding weather data such as temperature, precipitation, snowfall, and visibility. Feature engineering will create representations of temporal and weather conditions relevant to crash severity. Machine learning models, including Random Forest, XGBoost, and Neural Networks, will be trained to predict crash severity. To ensure interpretability, SHAP (SHapley Additive exPlanations) will be applied to quantify the contribution of each feature to individual predictions and overall model behavior. This analysis will reveal which weather or temporal factors most strongly influence severe crashes, both globally across the dataset and locally for specific incidents. High-risk periods and conditions identified by the model, along with explanations provided via SHAP, will be visualized both temporally and geographically, offering actionable insights to support targeted preventive measures and inform DOT decision-making.  ]]></description>
      <pubDate>Tue, 24 Mar 2026 14:09:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2683255</guid>
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    <item>
      <title>Quantitative Assessment of Anti-Icing Efficacy on Highway Surfaces Using Light Reflectance</title>
      <link>https://rip.trb.org/View/2620722</link>
      <description><![CDATA[The project aims to quantify road surface condition under adverse weather in North Dakota. First, the current winter maintenance and winter road condition monitoring practices across North Dakota will be reviewed. Historical crash data will be used to identify high-risk roadway segments encountering elevated crash frequencies and/or severities related to adverse road surface conditions. The project aims to quantify the safety performance to estimate and predict crash occurrences related to adverse weather conditions using statistical modeling techniques and network screening analysis. Once the importance of road surface condition on safety is quantify, type of surface condition (ice, snow, slush) on highways using diffuse reflectance spectroscopy (DRS) will be identified. A physical model for near-infrared reflectance of road surface will be developed to classify the surface condition in a noncontact manner. The models will be developed under controlled conditions and will be evaluated under field condition by determination of the optical properties of water, snow, and ice (and black ice). In the next objective, DRS-based discriminative to measure brine eutectic point and efficacy will be developed by generating samples with different portions of water, salt, and beet juice (primarily used in North Dakota) under different temperature. Through noncontact quantification of deicer efficacy, the project contributes to preservation of transportation infrastructure and safety. The research team will develop deterministic and data-driven models to correlate DRS features under different brine conditions with respect to eutectic points in a controlled environment, and benchmarking and compare the results of DRS brine models with observations. The final task of this objective is to develop a low-cost in-situ optical sensor framework for field deployment using diode lasers, super luminescent LEDs, hyperspectral camera, single-pixel and array photodiodes, and spectral filters. The payload SWaP (size, weight and power) analysis for potential Unmanned Aerial Systems (UAS) applications. The research team will establish an UAS operation training program for students to get UAS licensed, to shadow UAS-assisted inspections, to analyze UAS data.]]></description>
      <pubDate>Mon, 10 Nov 2025 09:43:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2620722</guid>
    </item>
    <item>
      <title>Development of In-Pavement LFBG Sensors for Vehicle WIM System Measurement and Monitoring in Rural Low-Volume Road Conditions Phase One: Theoretical Research</title>
      <link>https://rip.trb.org/View/2596477</link>
      <description><![CDATA[The research aims to address the growing challenge of accurately monitoring overweight truck loads on low-volume roads, which present unique issues for both infrastructure durability and road safety. Low-volume roads, defined as those carrying fewer than 2000 vehicle per day (and often fewer than 400 vehicles per day in rural areas), account for over 80% of the roads in North Dakota. Given the state’s reliance on agriculture and natural resources transport, overload trucks frequently travel these roads, which are not designed to withstand the repeated stress of excessively heavy loads. While special permits are issued for trucks carrying heavy loads under specific conditions, enforcing weight limits on numerous low-volume roads remains a significant challenge. This issue compromises the longevity of the road infrastructure and poses safety risks for all road users. Therefore, accurate monitoring and enforcement of weight limits on low-volume roads is crucial for maintaining infrastructure and enhancing road safety.]]></description>
      <pubDate>Mon, 08 Sep 2025 16:01:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/2596477</guid>
    </item>
    <item>
      <title>Exploring Post-Crash Care with EMS Response to Impaired Driving Crashes in North Dakota</title>
      <link>https://rip.trb.org/View/2553706</link>
      <description><![CDATA[Alcohol- and drug-impaired driving leads to severe crashes in North Dakota, yet police crash reports lack critical EMS response and patient care data. This study leverages NEMSIS data to assess EMS response times, treatment quality, and patient outcomes for impaired driving crashes. Using statistical analysis, time-series trends, and spatial mapping, the research identifies delays, care disparities, and high-risk locations. Findings will inform EMS resource allocation and improve post-crash care strategies, aligning with USDOT’s safety goals through advanced analytics that will transform foundational knowledge in this space.]]></description>
      <pubDate>Thu, 15 May 2025 15:13:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553706</guid>
    </item>
    <item>
      <title>Driving Futures: Empowering Tribal Communities through CDL Training Hub and Infrastructure Sustainability</title>
      <link>https://rip.trb.org/View/2447298</link>
      <description><![CDATA[This project outlines the benefits of establishing a testing hub for certifications such as Commercial Driver’s Licenses (CDL) and Heavy Equipment Operator (HEO) certifications. The creation of a centralized testing hub will increase convenience and efficiency for candidates by streamlining administrative processes, providing standardized evaluations, and ensuring quality assurance and integrity in assessments. This hub will serve as a focal point for educational resources and training opportunities for both CDL and HEO candidates.

By facilitating collaboration among tribal schools, workforce development organizations, and other stakeholders, the testing hub will contribute to economic growth while improving safety standards within the transportation and heavy equipment industries. The project aims to establish a testing hub at United Tribes Technical College (UTTC) to benefit students and professionals from tribal schools across North Dakota.

The project will impact various sectors, including transportation education, workforce development, and safety standards, ultimately leading to a more efficient and effective workforce. This testing hub will support both CDL and HEO certification processes, creating a centralized and standardized approach that benefits candidates, local industries, and the region as a whole.]]></description>
      <pubDate>Sun, 03 Nov 2024 17:45:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2447298</guid>
    </item>
    <item>
      <title>The Impact of Connected and Autonomous Vehicle Technologies on North Dakota's Highway Infrastructure</title>
      <link>https://rip.trb.org/View/2389227</link>
      <description><![CDATA[As the transportation landscape evolves, the integration of Connected and Autonomous Vehicle (CAV) technologies into highway infrastructure becomes increasingly critical. This proposal outlines research needs, objectives, methods, and expected outcomes regarding CAV implementation, particularly in rural areas like North Dakota. It emphasizes understanding the impact of CAVs on infrastructure, operations, safety, maintenance, and environment. Challenges, both technical and non-technical, are identified, underscoring the importance of proactive preparation.

The research objectives include synthesizing regulatory frameworks, assessing infrastructure needs, and evaluating CAV impacts through microsimulation modeling and safety assessments. Expected outcomes encompass valuable insights for policy development, infrastructure enhancement, and public acceptance strategies. Aligning with US Department of Transportation goals, this research aims to enhance safety, sustainability, and transportation efficiency.

The proposed work plan spans literature review, regulatory analysis, infrastructure assessment, microsimulation modeling, safety evaluation, expert review, and recommendation documentation. Through comprehensive analysis and stakeholder engagement, this research aims to provide actionable recommendations for navigating the CAV integration landscape, ensuring safe and efficient deployment within North Dakota's highway system.]]></description>
      <pubDate>Tue, 11 Jun 2024 12:22:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/2389227</guid>
    </item>
    <item>
      <title>North/West Passage Transportation Pooled Fund Study Phase 5</title>
      <link>https://rip.trb.org/View/2087623</link>
      <description><![CDATA[Interstates 90 and 94 between Wisconsin and Washington function as major corridors for commercial and recreational travel. Extreme winter weather conditions, prevalent in the northern states within this corridor, pose significant operational and travel-related challenges. Idaho, Minnesota, Montana, North Dakota, South Dakota, Washington, and Wyoming are predominantly rural and face similar transportation issues related to traffic management, traveler information, and commercial vehicle operations.
 
Recognizing the value of coordinated, cross-border collaboration for ITS deployment to address these issues, Minnesota initiated a meeting in 2002 with representatives from each of the states within the corridor. The group established itself as a Transportation Pooled Fund in 2003.  
 
North/West Passage members contribute annually to the pooled fund and are reimbursed for program travel.  A work plan is developed and approved annually by the Steering Committee.  Work Plan 1 and Work Plan 2 were funded through TFP-5(093).  That project number was closed out and Work Plan 3 - Work Plan 13 were funded through the FHWA number TPF-5(190). Work Plan 14 – Work Plan 17 are currently funded under the current TPF number: 5(376). To view each work plan and project results, visit the program website at: http://www.nwpassage.info/about/workplan.php.  
 
This new TPF number will fund Work Plan 18 – Work Plan 22.

OBJECTIVES: The vision of the North/West Passage (NWP) Corridor is to focus on developing effective methods for sharing, coordinating, and integrating traveler information, operational activities, and emerging technologies across state and provincial borders.  The vision provides a framework to guide the states’ future projects in the corridor. 
]]></description>
      <pubDate>Wed, 21 Dec 2022 16:58:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2087623</guid>
    </item>
    <item>
      <title>Ranking Public Transportation Services for Rural States in the Upper Midwest and Great Plains Region</title>
      <link>https://rip.trb.org/View/1986462</link>
      <description><![CDATA[The objective of this research is to compare the level of public transportation services provided in North Dakota to those of surrounding states. The study focuses on North Dakota, South Dakota, Montana, Wyoming, Nebraska (excluding the Omaha metro area), and Minnesota (excluding the Twin Cities metro area). The study is examining how well public transportation is serving the citizens in these mostly rural states. This involves an analysis of the level of service provided, measured in terms of geographic coverage, service availability, number of vehicles in use, and vehicle hours and miles of service; the amount of service consumed, measured in terms of ridership; the level of investment from different sources; and measures that evaluate the effectiveness and efficiency of service. The analysis also considers the varying levels of need throughout the region.

The scope of the project includes all public transportation in the states of North Dakota, South Dakota, Montana, and Wyoming, as well as all transit in Minnesota and Nebraska excluding the Twins Cities and Omaha metro areas. This includes all rural agencies receiving section 5311 funding and small urban systems receiving section 5307 funds.]]></description>
      <pubDate>Wed, 29 Jun 2022 08:45:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/1986462</guid>
    </item>
    <item>
      <title>Utilizing Public Transportation to End Food Insecurity in the Rural and Small Urban Area by Providing Better Access: A Case Study of Rural Counties in North Dakota</title>
      <link>https://rip.trb.org/View/1984636</link>
      <description><![CDATA[For many small urban rural counties, access to fresh healthy foods is a constant problem. When someone is without reliable access to a sufficient quantity and quality of affordable, nutritious food, they may be experiencing something called food insecurity. Households are defined as food insecure if they are unable to acquire adequate food at times during the year due to the lack of money or other resources. People particularly at risk of food insecurity may live in neighborhoods with limited public transportation options, an under-invested public transportation system, or no personal transportation options. Even though lack of money is just one factor that causes food insecurity, another factor that causes food insecurity and has a huge negative impact on the food insecurity problems is the lack of transportation accessibility. In this study, the SURCOM team will investigate the current public transportation accessibility and find out the impact of transportation accessibility on food insecurity among 53 counties in North Dakota. This study will find out the mobility gaps which affect food insecurity in the rural and small urban areas in North Dakota, and how a better designed public transportation can help to end food insecurity.]]></description>
      <pubDate>Thu, 23 Jun 2022 09:40:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/1984636</guid>
    </item>
    <item>
      <title>Representative Testing of Expansive Soil Treatment Technologies for Transportation Earthworks</title>
      <link>https://rip.trb.org/View/1487825</link>
      <description><![CDATA[The shrink-swell behavior of expansive soils reduces transportation infrastructure longevity in much of the Mountain Plains region (Colorado, North Dakota, South Dakota, Utah, and Wyoming). Roadways are particularly susceptible to the effects of expansive soils due the combination of low ground pressures and large surface areas. The pervasiveness of expansive soils in the continental U.S. is shown in Fig. 1. Current estimates for the annual cost of damage to transportation infrastructure from expansive soils are not readily available, but were estimated by U.S. Housing and Urban Development (HUD) to be approximately $1.1 billion in 1973 (Jones & Holtz 1973) and $4.3 billion in 1981 (Jones 1981; $12 billion in 2017 dollars adjusting for inflation). Given the prevalence of expansive soils in the Mountain Plains region, economical solutions to mitigate damage to transportation infrastructure is necessary to enhance transportation system longevity throughout the Mountain Plains region.
The current Mountain-Plains Consortium (MPC) project MPC-509 is aimed at evaluating expansive soil mitigation for transportation earthworks by polymer amendment. The goal of MPC-509 is to provide un-biased information on the effectiveness of commercially available polymer stabilizers. Testing to date has involved four commercially available polymer treatment technologies applied at different dosage rates to a highly expansive soil. Testing on the highly expansive soil has also been performed with varying dosages of Class-C fly ash, lime, and on untreated soil. Testing to date has included standard soil characterization, and tests of hydraulic conductivity (ASTM D5084), swelling potential (ASTM D4546), expansion index (ASTM D4829), and unconfined compressive strength (ASTM D5102). Results to date have shown that traditional stabilizers (lime and Class-C fly ash) are more effective at reducing swelling potential and expansion index than polymeric amendments. However, traditional stabilizers result in an order-of-magnitude increase in permeability, while polymer amendments result in reductions in permeability. Shrink-swell potential is mechanistically based on the addition of water, and the rate of ingress of water into soil is governed by the soil permeability. Thus, existing standard methods fail to provide a representative comparison of traditional and polymeric stabilizers (due to the ways these treatments modify the soils). An alternative method is needed to accurately compare traditional and polymeric stabilization of expansive soils, as well as other innovative materials proposed for use in treating expansive soils.]]></description>
      <pubDate>Mon, 06 Nov 2017 14:37:30 GMT</pubDate>
      <guid>https://rip.trb.org/View/1487825</guid>
    </item>
    <item>
      <title>Rural Intercity Bus Demand: Phase II</title>
      <link>https://rip.trb.org/View/1396127</link>
      <description><![CDATA[The objectives of the project are as follows: Incorporate previously estimated mode choice model into a North Dakota statewide travel demand model. Identify corridors in North Dakota with the greatest demand for intercity bus service. Conduct scenario analysis to estimate the effects of projected population and demographic changes, gas prices, and service changes. Describe attitudes to intercity travel in North Dakota, the impacts of attitudes on mode choice, and changes in attitudes in recent years.]]></description>
      <pubDate>Wed, 27 Jan 2016 09:06:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/1396127</guid>
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