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    <title>Research in Progress (RIP)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Evaluate the Safety Effects of Multiple Vehicle Synchronized Warning Lights in ODOT Work Zones
</title>
      <link>https://rip.trb.org/View/2701274</link>
      <description><![CDATA[In 2024, 56 Ohio Department of Transportation (ODOT) crews were struck while working on the highway system. As of March 2025, 43 ODOT crews have been struck. With safety being of the upmost importance to ODOT's Executive Leadership, protecting road crews and individuals working on ODOT jobsites remains a common theme when investigating new technologies and techniques to help reduce and minimize these accidents. Currently ODOT has a variety of light-emitting diode (LED) warning light systems in use on its fleet of maintenance vehicles. When these vehicles are concentrated in a work zone, there has been concern that these lights, while flashing independently, can lead to confusion among the motoring public as they enter the work zone. Added to this, ODOT operates work zones during all times of the day and in all weather conditions further exacerbates the situation.  This can result in unsafe driving practices and increased accidents. 

There is a growing opinion among transportation professionals that synchronizing warning lights and/or customizing patterns to evolve situationally could alleviate, if not resolve, these dangerous work zone crashes. ODOT is looking to evaluate the effectiveness of a system that synchronizes the warning systems of all vehicles present in a work zone.   A system that could increase driver awareness and reduce safety related incidents would be useful not only to ODOT but to local public agencies, emergency responders, and other state departments of transportation (DOTs).

OBJECTIVES: The goal of this research is to identify the effectiveness of using synchronized warning light systems versus non-synchronized warning light systems on work zone vehicles.
             ]]></description>
      <pubDate>Tue, 12 May 2026 10:43:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701274</guid>
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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>
    </item>
    <item>
      <title>Data-driven assessment of rigid pavement vulnerability in Texas coastal regions</title>
      <link>https://rip.trb.org/View/2663108</link>
      <description><![CDATA[This research aims to evaluate the vulnerability of rigid pavements in two major coastal districts of Texas (i.e., Beaumont and Houston) spanning about 900 miles using data-driven approaches. Particularly, the study will (1) identify the key factors contributing to rigid pavement distress under dynamic coastal weather conditions, and (2) develop data-driven strategies to enhance the durability and performance of these pavement networks. Multi-source datasets, such as weather, geotechnical, traffic, coastal proximity, and pavement conditions, will be collected and integrated to support this analysis. Weather data, including temperature and precipitation, will be obtained from national and global databases such as NOAA’s National Centers for Environmental Information (NCEI) and NASA Earthdata/GES DISC. Soil classification and geotechnical attributes will be sourced from the NRCS SSURGO (Soil Survey Geographic Database), while coastal proximity data will be derived from Google Earth. Traffic volumes and loading data will be gathered from TxDOT’s Statewide Traffic Analysis and Reporting System (STARS II). Pavement condition metrics, including distress quantity, distress score, condition score, and ride quality, will be extracted from the Texas Department of Transportation (TxDOT)’s Pavement Management Information System (PMIS) and supplemented with satellite imagery. By integrating these datasets, the project will perform statistical and spatial analyses to establish correlations between weather variables, geotechnical conditions, traffic patterns, and pavement performance indicators.]]></description>
      <pubDate>Thu, 29 Jan 2026 19:58:17 GMT</pubDate>
      <guid>https://rip.trb.org/View/2663108</guid>
    </item>
    <item>
      <title>Identifying and evaluating the most effective actions to prepare Puerto Rico’s primary ports and freight road transportation infrastructure for flooding disruptions using stochastic models</title>
      <link>https://rip.trb.org/View/2662990</link>
      <description><![CDATA[One of the seven issues listed in the freight assessment section of the 2050 Long Range Multimodal Transportation Plan (LRMTP, approved in 2023) encompasses the need for Puerto Rico’s ports and road freight transportation network (RFTN) to be less vulnerable to extreme weather events that affects the durability of the infrastructure and disrupts the movement of goods and services. Puerto Rico has an excellent geographic location for the transshipment of goods to other places in the Americas. Strategies to mitigate infrastructure damage to ports and roads resulting from overuse and to keep the system operating effectively will help Puerto Rico maintain its position as a global logistics hub. The development of an adaptable highway transport system is crucial, as railroads are not well-developed to undertake the freight transport needs, and the use of the marine-based freight M2 route connecting main and secondary ports is only emerging. 
The objective of this research project is to quantify and classify the impact of certain operational decisions made before and after flood-related weather events on four performance or optimization criteria: ports and RFTN infrastructure, traffic flows, safety, and flexibility to avoid delays and disruptions. The operational decisions to include are: increasing ports’ operating hours, locating regional hub-and-spoke points where freight coming from the ports is transferred from large trucks to smaller vehicles and routed to the distribution points, determining existing or to be developed alternative roads that reduce congestion at hotspots, and routing loads between ports. To accomplish the objective, TXST will develop a preliminary stochastic programming model to optimize a prototype of Puerto Rico’s RFTN, considering multiple flooding scenarios, forecasts of freight demand over 5 and 10 years, and the above-mentioned operational decisions and optimization criteria. A variant of the developed model, which represents the current operations of ports and roads without incorporating any of the proposed operational decisions, will be used for comparison purposes. The main freight distribution points and associated demands to input into the models will be identified in cooperation with the listed project partner faculty at UPRM.  Puerto Rico’s industry, government agencies, and consultants for these agencies will be sources to get the models’ input data, as well as information available online. If needed, the distribution points will be clustered.  In this preliminary model, the unavailable data will be identified and estimated. The model will demonstrate to the Puerto Rico Department of Transportation and Public Works, the Puerto Rico Highway and Transportation Authority, and other relevant agencies a process they can apply for making informed decisions to enhance the durability and resilience of port and RFTN infrastructure under uncertainty caused by flooding and the relevance of collecting any highly relevant and missing data.]]></description>
      <pubDate>Thu, 29 Jan 2026 16:19:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2662990</guid>
    </item>
    <item>
      <title>Impact of Pavement and Weather Conditions on Traffic Speed at Sharp Horizontal Curves  </title>
      <link>https://rip.trb.org/View/2646943</link>
      <description><![CDATA[Lane departure crashes on sharp horizontal curves are a major safety concern on both highways and freeways, accounting for a disproportionately high number of fatal and severe injury crashes. Research has shown that these crashes are often linked to speeds relative to curve geometry. While geometric design factors like curve radius and superelevation have been well studied, less attention has been given to how pavement and weather conditions influence traffic speed on these elements. Particularly, current safety models do not fully account for the effects of pavement surface conditions, such as friction, roughness, and texture, or adverse weather elements like precipitation, temperature drops, and reduced visibility. While the impact of factors like road curvature effects on traffic speed have been studied, current models often fail to integrate the complex interaction of pavement conditions and weather data in predicting traffic speeds. This results in inaccurate speed predictions, which can compromise safety and infrastructure planning. Without comprehensive, data-driven models, interventions such as speed limits, signage, or road maintenance are often poorly targeted, leading to higher risks of crashes, congestion, and inefficient resource allocation. The motivation for this project is to develop a predictive model that integrates pavement conditions, weather effects, and road geometry to estimate traffic speed at horizontal curves. This will provide safer roads by enabling better traffic management, targeted infrastructure improvements, and more efficient interventions. ]]></description>
      <pubDate>Mon, 05 Jan 2026 23:07:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646943</guid>
    </item>
    <item>
      <title>Study to Understand the Influence of Emergency Vehicle Color, Reflectance, Signing/Arrow Boards, and Lighting Configurations in Reducing Responder-Involved Crashes</title>
      <link>https://rip.trb.org/View/2642797</link>
      <description><![CDATA[The main objective of the research project is to develop a set of recommendations on emergency lighting, vehicle colors, markings, use of dynamic message boards, and placement of graphics to influence driver compliance with Florida’s “Move Over” law. The goal of this project is to understand the effect of different emergency lights color and flash patterns on human eyes and how to improve the conspicuity, visibility, and reflectivity of RRSP vehicles in varying light and weather conditions to improve Road Ranger Service Patrol (RRSP) safety.]]></description>
      <pubDate>Wed, 17 Dec 2025 15:58:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2642797</guid>
    </item>
    <item>
      <title>Connected Vehicle Data</title>
      <link>https://rip.trb.org/View/2640696</link>
      <description><![CDATA[The Compass IoT company is performing a pilot project with the Missouri Department of Transportation (MoDOT) to provide data, both historical and over a four-month period, for the research team to build a proof of concept for the member states in the Original Equipment Manufacturers (OEM) Pooled Fund. This will allow the research team to show the member states the benefits that can be realized with this information. The data will be focused on work zone information, near miss data, and winter weather events.]]></description>
      <pubDate>Tue, 16 Dec 2025 09:56:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2640696</guid>
    </item>
    <item>
      <title>Estimating Environmental Load Demands Considering Weather Extremes to Enhance Resiliency of Oklahoma Bridges</title>
      <link>https://rip.trb.org/View/2633314</link>
      <description><![CDATA[Bridges are critical components of transportation infrastructure facilitating uninterrupted flow of goods and services within communities. However, the growing frequency and intensity of natural hazards and extreme weather events are escalating the vulnerabilities of bridge infrastructure. In recent years, Oklahoma has faced an increasing frequency of extreme weather events, including tornadoes, rising temperatures, and flash floods. These threats pose significant challenges for bridge design and maintenance leading to safety and functionality concerns. Therefore, innovative strategies and solutions are needed to reduce the impact of changes in weather patterns and extreme events on bridge infrastructure. Enhancing resilience of bridge infrastructure requires the incorporation of weather factors into bridge design codes and standards. The proposed study plans to evaluate the effect of changes in weather patterns and extreme weather events on the environmental load demand related to temperature and wind speed for bridges in Oklahoma. The use of advanced climatic models to predict future changes in weather patterns and estimate environmental load demand will be explored. A risk analysis will be performed to assess the vulnerability of bridges to future predicted weather conditions. Recommendations for updating bridge design codes and standards to incorporate considerations of extreme weather events will be provided based on the findings of this study.]]></description>
      <pubDate>Tue, 02 Dec 2025 16:20:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/2633314</guid>
    </item>
    <item>
      <title>Assessing the Impacts of Evolving Rockfall Hazard Due to Hypothetical Variations in Weather Patterns on Colorado's Transportation Network</title>
      <link>https://rip.trb.org/View/2427590</link>
      <description><![CDATA[Geohazards present a substantial risk to Colorado’s transportation network. Hazard events regularly lead to maintenance and hazard management costs incurred directly by the Colorado Department of Transportation (CDOT), as well as indirect user costs due to mobility impacts. In extreme cases, geohazards can cause extended closures of key routes through the mountains (e.g. I-70) can occur where users experience lengthy detours and commerce can be greatly impacted in small mountain communities. CDOT’s system resilience is significantly impacted by rockfall events, and the potential for these events to become more common and/or severe in parts of Colorado as a function of evolving weather patterns represents a major source of uncertainty regarding the future performance of the state’s transportation network. To understand the influence of weather patterns on rockfall in the Colorado context requires dedicated consideration of the evolution of the state’s weather and rockfall patterns.
]]></description>
      <pubDate>Tue, 18 Nov 2025 08:02:53 GMT</pubDate>
      <guid>https://rip.trb.org/View/2427590</guid>
    </item>
    <item>
      <title>Harnessing Real-Time Weather Data for Improved Bridge Inspection after Flood Events</title>
      <link>https://rip.trb.org/View/2590605</link>
      <description><![CDATA[The research aim is to harness near real-time weather data to improve guidance for bridge inspections following rainfall events. Recent rainfall events in Indiana have washed out bridges, resulting in broken transportation networks and loss of life. The National Oceanic and Atmospheric Administration (NOAA) provides near real-time weather information that integrates data from radar, rain gauges, satellites, numerical predictions, and other observations (i.e., lightning, surface, upper air). This research will investigate the use of this publicly available data to trigger bridge inspections, with the aim of improving
public safety.
]]></description>
      <pubDate>Tue, 19 Aug 2025 15:02:26 GMT</pubDate>
      <guid>https://rip.trb.org/View/2590605</guid>
    </item>
    <item>
      <title>Creation of a Rapidly-Updating Road Weather Impacts Prediction System (RWIPS)</title>
      <link>https://rip.trb.org/View/2548657</link>
      <description><![CDATA[This proposal lays the framework for an end-to-end decision-support capability for the 
Missouri Department of Transportation (MODOT) that provides potentially impactful weather information coupled with a routing decision tool to aid in effective response to road weather hazards.  The project is a multi-stage one that will leverage expertise within NOAA and OU/CIWRO for real-time weather monitoring and forecasting and then expand upon capabilities developed and demonstrated by Missouri University Science and Technology.]]></description>
      <pubDate>Wed, 30 Apr 2025 09:20:16 GMT</pubDate>
      <guid>https://rip.trb.org/View/2548657</guid>
    </item>
    <item>
      <title>Investigating Driving Behavior in Rural Areas During Extreme Weather Conditions through Integrated Driving Simulators and Virtual Reality</title>
      <link>https://rip.trb.org/View/2509043</link>
      <description><![CDATA[This project addresses the impact of extreme weather on rural driving safety, focusing on the unique challenges posed by rural roads, such as narrow paths, limited visibility, and poor maintenance. Rural areas often experience severe weather effects, including snow and flooding, which influence driving behavior differently than urban settings. Existing field-based studies on adverse weather and driving behavior face limitations due to the unpredictable nature of weather and driver responses.  
To fill this research gap, the project proposes the development of the Immersive Reality Roadscapes Virtual Simulation (IRR-ViS), which will use integrated driving simulators and virtual reality to study driver behavior during extreme weather on rural roads. The research has three objectives:  
•	Examine how drivers adjust speed and change lanes in rural environments. 
•	Analyze the factors influencing speed, lane changes, and time-to-collision (TTC). 
•	Evaluate driver comprehension and responses under adverse weather conditions.  
This research is expected to contribute significantly to improving traffic emergency management and optimizing resource allocation during crises, ultimately enhancing rural road and traffic safety in the face of future extreme weather events.

]]></description>
      <pubDate>Wed, 12 Feb 2025 17:32:35 GMT</pubDate>
      <guid>https://rip.trb.org/View/2509043</guid>
    </item>
    <item>
      <title>Selection, Integration, and Effectiveness Study of Input Data for Nebraska Variable Speed Signs on I-80</title>
      <link>https://rip.trb.org/View/2507247</link>
      <description><![CDATA[Variable Speed Signs (VSS) are critical for enhancing road safety and traffic efficiency, particularly under adverse weather and traffic conditions. VSS dynamically adjusts speed limits based on real-time data inputs such as traffic flow, weather conditions, and visibility, providing drivers with contextually appropriate speed recommendations. The implementation of VSS systems has been shown to improve traffic safety, reduce crash rates, and enhance overall roadway efficiency, particularly on high-volume corridors such as Nebraska’s I-80.
Several studies highlight the efficacy of VSS systems [1 - 3]. Research conducted by state Departments of Transportation (DOTs), such as Minnesota and Wyoming, has demonstrated that VSS systems can reduce crashes by accounting for real-time environmental factors (e.g., wet or icy road conditions). Similarly, academic studies underscore the importance of integrating advanced sensing technologies and reliable algorithms for speed recommendation, which enhance the credibility and acceptance of VSS by drivers.
Despite their proven benefits, VSS implementations face challenges, including selecting optimal data inputs, integrating various sensor technologies, and evaluating system performance under extreme conditions. In Nebraska, where I-80 serves as a critical freight and passenger corridor, these challenges are amplified by frequent adverse weather conditions, including snow, ice, and fog. These conditions necessitate a robust approach to data collection, algorithm development, and system integration to ensure the VSS recommendations align with real-world conditions and driver behavior]]></description>
      <pubDate>Mon, 10 Feb 2025 14:01:19 GMT</pubDate>
      <guid>https://rip.trb.org/View/2507247</guid>
    </item>
    <item>
      <title>Assessment of Crash Reduction Traffic Control Measures at T-intersections on Rural Highways</title>
      <link>https://rip.trb.org/View/2507244</link>
      <description><![CDATA[T-intersections on rural highways in Nebraska represent a critical safety challenge due to their high crash frequencies and severity. These intersections are particularly challenging as they combine the risks associated with high-speed travel and the need for precise driver actions. When drivers approach T-intersections, they need to come to a complete stop before making a turning movement. This creates decision-making challenges given varying traffic control measures which could be exacerbated by weather conditions.
According to the Nebraska Department of Transportation (NDOT), roadway departure crashes, including those occurring at T-intersections, remain a critical emphasis area for enhancing traffic safety on rural highways. Between 2016 and 2020, NDOT recorded 3,133 lane and roadway departure crashes with fatalities or serious injuries comprising approximately 44% of all such incidents statewide. Of these, 70% occurred in rural areas, with 82% involving single-vehicle run-off-road events. As a result, T-intersections rank as the third-highest locations (following non-intersections and four-legged intersections) for both the total number of crashes and the severity of those crashes in Nebraska. Furthermore, reported crashes may not capture the full extent of the problem. NDOT receives numerous complaints from local landowners regarding hit-and-run crashes at the T-intersections where vehicles exit the roadway and damage property on the opposite side of the intersection. These crashes are often not filed in official crash reports.]]></description>
      <pubDate>Mon, 10 Feb 2025 11:29:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/2507244</guid>
    </item>
    <item>
      <title>Impact of Weather Variables on Emerging Micromoblity options (Bikeshare and E-Scooter) in the South-Central United States</title>
      <link>https://rip.trb.org/View/2480351</link>
      <description><![CDATA[As transportation infrastructure evolves, bikeshare programs, e-bikes, and e-scooters have become vital components. These systems require substantial infrastructure, including docking stations, pavements, sensors, and other elements. Cities invest in this infrastructure with the expectation of significant returns, such as reduced environmental impacts, decreased traffic congestion, and improved health outcomes for riders. Therefore, increasing the usage of these transportation modes is crucial for cities. However, weather extremes including hotter temperatures and more intense rainfall, may impact on ridership patterns. The aim of this pilot study is to examine how extreme temperatures and precipitation affect bikeshare system usage, including travel time and ridership, and then to use the relationships built to project how these extremes are expected to affect future ridership. The study will focus on cities within the Southern Plains Transportation Center (SPTC) region. The results will provide community and city planners with essential insights into the challenges and opportunities for enhancing or modifying infrastructure to support these emerging transportation modes in the face of extreme weather events. 
The objective is to develop relationships between bikeshare usage and both temperature and precipitation and apply those relationships to downscaled projections to project how bikeshare usage is expected to change with increasing temperatures and heavy precipitation events. Although micromobility systems are viewed as transformative for transportation infrastructure, it is possible that their usage will be limited by extreme temperatures and rainfall events Therefore, this study attempts to synthesize the results of this analysis and past literature to develop policy recommendations to optimize micromobility infrastructure in the SPTC region. For instance, policy options may include better shelters for micromobility infrastructure or increased tree canopy for bike lanes and sidewalks. 
The study will be carried out through the following detailed tasks. Task 1: Conduct a comprehensive review of existing literature on micromobility, temperature and precipitation, and urban transportation. Task 2: Identify gaps in the current research as related to micromobility usage and weather conditions. Task 3: Gather bikeshare and e-scooter usage data from service providers and municipal transportation departments in selected cities in SPTC region. Task 4: Collect historical weather data from Daymet Version 4 and downscaled weather projections from the South Central CASC. Task 5: Quality assure and integrate the collected datasets to ensure consistency and accuracy and conduct data aggregation for and re-gridding to a 5-km grid with daily data. Task 6: Identify and select appropriate statistical, AI, or machine learning methods for developing relationships during the historical period on the impact of temperature and precipitation on travel ridership, trip duration, travel type and time. Task 7: Develop the predictive model and apply it to projected data for midcentury and end-of-century. Task 8: Compare how micromobility transportation systems have been used during the historical period with that projected for mid-century and end-of-century. Task 9: Based on the study results, develop policy recommendations for optimizing micromobility programs for each study location. Task 10: Share research findings through academic publications, conferences, and workshops. Task 11: Engage with stakeholders, including city officials and transportation agencies, to discuss the implementation of recommendations.

]]></description>
      <pubDate>Wed, 01 Jan 2025 16:04:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2480351</guid>
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