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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>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>Ensemble Radar Nowcasts for Probabilistic Road Disruption Prediction</title>
      <link>https://rip.trb.org/View/2706035</link>
      <description><![CDATA[Heavy precipitation and flash flooding can rapidly degrade roadway operating conditions, causing speed reductions, lane closures, detours, and secondary crashes. Current traffic management systems largely confirm disruptions after they have already developed, limiting the ability of transportation operators to act proactively. Deterministic weather products also provide limited information about forecast uncertainty, which is critical for risk-based operational decision-making.
This project develops a probabilistic road disruption nowcasting system that integrates ensemble radar precipitation forecasts with traffic observations and roadway attributes to produce segment-level disruption probabilities at lead times of 30 to 180 minutes. Using a multi-member ensemble framework applied to real-time radar precipitation data, the system will generate exceedance probabilities and persistence metrics that quantify near-term hazard likelihood. These probabilistic precipitation indicators will be fused with traffic state variables and roadway characteristics to estimate the likelihood of operational disruption. The result is a calibrated, segment-level decision-support tool that provides actionable lead time and quantified uncertainty to support safer and more reliable corridor operations.

]]></description>
      <pubDate>Sat, 23 May 2026 18:00:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706035</guid>
    </item>
    <item>
      <title>Development of a Salt Spreader Controller Program Using Machine-Sensed Roadway Weather Parameters and Climate Data (Phase 2)</title>
      <link>https://rip.trb.org/View/2543223</link>
      <description><![CDATA[The Massachusetts Department of Transportation (MassDOT) has recently completed a research project on leveraging the instrumented mobile road weather information system (RWIS), computer vision, and a new salt application model. The research was aimed at developing four critical aspects of the intelligent salt application system, including hardware (i.e., data collection I/O and power supplies system), software (i.e., data logging, synchronization, and data fusion), algorithm (i.e., road surface classification (RSC) algorithm), and model (i.e., the salt rate prediction (SRP) model) so that an optimized salt application decision can be provided to the actuator to treat the road surfaces. Through this study, a complete hardware/software system with automated RSC and SRP algorithms has been developed, pilot-tested, and validated with promising performance. The performance of the developed system showed good results. Once implemented in a more extensive fleet of MassDOT’s material spreaders utilized during winter operations, it could save a significant amount of salt. The goal of this research is to leverage the prototype system from the previous study and to implement 1) a fully validated spreader controller system that is operated in a fleet of MassDOT’s snowplowing trucks and 2) an intelligent salt treatment program that will include weather forecasting information to better prepare for challenging situations, such as freezing rain, black ice, etc.]]></description>
      <pubDate>Wed, 23 Apr 2025 16:15:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2543223</guid>
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    <item>
      <title>Aurora Program (2025-2029)</title>
      <link>https://rip.trb.org/View/2437941</link>
      <description><![CDATA[The Aurora program is a partnership of highway agencies which beginning in 1996 collaborate on research, development, and the deployment of road weather information systems to improve the efficiency, safety, and reliability of surface transportation. Aurora’s initiatives are funded by member agencies to conduct research that serves the needs of its members who meet twice per year to set the agenda for road weather information system (RWIS) research, keep informed about progress on program initiatives, and discuss solutions for common in-the-field problems. Newly selected initiatives are led by “champion” member agencies, managed by committees of Aurora members, and funded out of the pooled fund. Aurora has a strong relationship with the American Association of State Highway and Transportation Officials (AASHTO) and its Snow and Ice Pooled Fund Cooperative Program (SICOP). In addition, Aurora coordinates with the American Meteorological Society (AMS), the National Severe Storm Laboratory, the ITS America, Clear Roads and the National Center for Atmospheric Research (NCAR). Furthermore, Aurora works closely with industry as well as university based research organizations. This forum facilitates the sharing of technological and institutional experiences gained from RWIS programs conceived and initiated by each participating entity.

OBJECTIVES: The cooperative and collaborative objective of the Aurora Program is to promote an efficient use of resources rather than a series of independent initiatives. The synergistic effect of this forum is an accelerated implementation of road weather programs as well as the integration and operationalized use of weather data in transportation management, safety, and in serving progressive levels of connected and automated transportation.]]></description>
      <pubDate>Tue, 08 Oct 2024 15:15:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2437941</guid>
    </item>
    <item>
      <title>An Intelligent Human-Centric Communication System for Adverse Weather and Road Conditions</title>
      <link>https://rip.trb.org/View/2342042</link>
      <description><![CDATA[Adverse weather conditions present significant risks to motorists, making safe navigation challenging. Artificial Intelligence (AI) advancements have facilitated the development of intelligent systems to address these concerns. This project introduces CARWIS (Conversational AI for Road Weather Information Systems), an AI-powered solution that provides real-time data on road conditions during severe weather. CARWIS gathers data from various sources, including weather forecasts and traffic cameras, and employs natural language processing to generate timely and accurate insights into road conditions. CARWIS can detect hazardous conditions, such as icy roads or low visibility due to fog or precipitation, and refine its predictions over time, enabling drivers to make informed decisions on their travel plans, potentially reducing accidents and enhancing safety. Additionally, CARWIS can assist transportation professionals in planning and responding to severe weather events by providing detailed information on road conditions. This enables officials to prioritize resources and make well-informed decisions regarding road closures and safety measures. This innovative solution harnesses the power of AI to improve road safety and reduce incidents during adverse weather conditions. As AI technology continues to progress, it is anticipated that more advanced systems will be developed to assist in navigating and managing severe weather events on the roadways.]]></description>
      <pubDate>Mon, 19 Feb 2024 18:34:01 GMT</pubDate>
      <guid>https://rip.trb.org/View/2342042</guid>
    </item>
    <item>
      <title>Road Weather Management Using Connected Vehicle Technology</title>
      <link>https://rip.trb.org/View/2342040</link>
      <description><![CDATA[Road weather systems are used by state and local agencies to mitigate and manage the disruptive impact of weather events on roadways. Some of the fundamental aspects of road weather systems are the collection of weather-related data from environmental sensor stations and probe vehicles, the processing/distribution of data, and the determination of how/when/where to deploy road maintenance resources and/or to issue general traveler advisories and/or issue location specific warnings to drivers.

As momentum behind connected vehicle technology continues to build, practitioners are showing interest in determining how connected vehicle technology can be leveraged to support traffic management activities, including road weather systems. Specifically, the ability to communicate with connected vehicles opens up new opportunities for collecting data from many vehicles, and targeted dissemination of information to drivers. Thus, it will be important to ascertain the types of data that can be communicated in connected vehicle messages, as well as other intrinsic aspects of connected vehicle communications to understand how connected vehicles can enhance existing and open up opportunities for new road weather strategies.

Research will be undertaken as part of this project to review connected vehicle data standards, and to engage Aurora members to determine which road weather strategies are of greatest interest to practitioners. The project team will apply knowledge gained from members, as well as their background in engineering systems, to develop a Concept of Operations, which will provide a description of how connected vehicle communications and data may be employed to enhance the capabilities of road weather systems.]]></description>
      <pubDate>Mon, 19 Feb 2024 18:12:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2342040</guid>
    </item>
    <item>
      <title>Fast Detection and Prediction of Slippery Roadway Conditions for Enhanced Safety</title>
      <link>https://rip.trb.org/View/2291287</link>
      <description><![CDATA[Black ice, a nearly invisible hazard, contributes to over 10% of weather-related crashes in the U.S., causing 200,000 annual accidents, 700 fatalities, and 65,000 injuries. Traditional methods for detecting black ice involve fixed sensors and signs, but new vehicle-based technology offers cost-effective real-time data. However, obtaining comprehensive road condition data during inclement weather remains expensive and risky. State agencies must collect pavement surface data for asset management, yet the relationships between surface characteristics, weather conditions, and ice formation are not adequately understood. Research is needed to predict slippery conditions using existing data. Prediction of slippery conditions can be potentially more critical than detecting slippery conditions due to changing weather patterns and weather extremes.
This project aims to develop predictive models for slippery road conditions by collecting data with Mobile Advanced Road Weather Information Sensors (MARWIS) sensors and Pave3D 8K on roadway segments before, during, and after inclement weather. The collected data will be used to create predictive models for different weather scenarios. The primary goal is to develop predictive models that can anticipate slippery road conditions under different weather scenarios. These prediction models can then be applied to identify potentially slippery areas across Oklahoma, using the annually collected PMS datasets by ODOT. The primary goal of this project is to enhance highway safety.
The aforementioned goals will be achieved through four tasks: Task 1: Data Collection: Use MARWIS technology to measure road conditions, including temperature, humidity, and road state. This data will be collected on selected testing sites based on weather forecasts and in collaboration with ODOT; Task 2: Surface Characteristics: Assess field friction values and collect pavement surface characteristics data using the Grip Tester and Pave3D 8K technology to understand their impact on road slipperiness; Task 3: Slippery Road Prediction Models: Leverage data from MARWIS and surface characteristics and create predictive models using statistical and machine learning methods for forecasting road conditions during rainy or icy days; Task 4: Implementation: Incorporate statewide surface characteristics data from ODOT into the predictive models, presenting results in a Geographic Information System (GIS) database for better situational awareness and road maintenance support.
]]></description>
      <pubDate>Wed, 15 Nov 2023 21:40:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2291287</guid>
    </item>
    <item>
      <title>Operate the Weather Data Environment (WxDE)</title>
      <link>https://rip.trb.org/View/2077920</link>
      <description><![CDATA[Continue to advance and operate the Weather Data Environment for State DOTs to house their ESS, RWIS and mobile RWIS data in one locations for research and data quality checks. Efforts will continue to tie in more datasets in preparation for NRED.]]></description>
      <pubDate>Tue, 06 Dec 2022 09:48:25 GMT</pubDate>
      <guid>https://rip.trb.org/View/2077920</guid>
    </item>
    <item>
      <title>Integrating Road Weather Technology Data in Highway Operations</title>
      <link>https://rip.trb.org/View/2071878</link>
      <description><![CDATA[A key element of traffic operations is the knowledge of current and predicted surface weather conditions on the road surface. Weather can quickly and dramatically impact safety and mobility on roads.  

Maryland's Office of Transportation Mobility and Operations has access to many radar products, how to consolidate and manage and best leverage all of these different weather measurements and untapped weather data sources more effectively is the objective of this project.]]></description>
      <pubDate>Thu, 01 Dec 2022 10:54:07 GMT</pubDate>
      <guid>https://rip.trb.org/View/2071878</guid>
    </item>
    <item>
      <title>Evaluating the Impacts of Real-Time Warnings and Variable Speed Limits on Safety and Travel Reliability during Weather Events







</title>
      <link>https://rip.trb.org/View/1854164</link>
      <description><![CDATA[Road Weather Management (RWM) has advanced significantly with new sources of road weather data and greater opportunities for more active management of roadways through direct communication with drivers. Pilot RWM applications and strategies have proven effective during weather events. Actively managing the system using variable speed limits (VSL) and real-time motorist warnings (RTW) based upon real-time weather and road condition data have proven to be effective strategies. However, additional research is needed to advance the practice and application of VSL and RTW nationally.

Because the effectiveness of these solutions depends on driver behavior, infrastructure owner operators (IOO) must understand the operational environment and the anticipated responses of drivers. For either VSL or RTW to be effective, the IOOs must know the current and anticipated environmental conditions, understand their impacts on mobility and safety, and formulate effective traffic management strategies to alert drivers. IOOs must understand how drivers receive VSL and RTW messages; interpret and integrate these messages with their own observations of roadway conditions; and react during different types of weather events. IOO’s must also understand the best ways to capture drivers’ attention; learn whether drivers respond differently to advisory or regulatory messaging; and understand the influence of other human factors and driver behaviors. Consistent and effective messaging would help IOOs achieve safer, more reliable transportation during adverse weather.

 OBJECTIVES: The objectives of this research are to (1) identify strategies and information needed to formulate effective messaging (including VSL and RTW) to elicit appropriate driver behavior and aid highway safety and mobility; (2) describe how to convey messaging with consideration of message locations, content, platform, and timing; and (3) identify the means to determine the effectiveness of the deployment of real-time messaging, including VSL and RTW, on safety and travel reliability.

 ]]></description>
      <pubDate>Tue, 25 May 2021 12:20:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/1854164</guid>
    </item>
    <item>
      <title>Automated Real-Time Weather Detection System using Artificial Intelligence</title>
      <link>https://rip.trb.org/View/1688736</link>
      <description><![CDATA[Adverse weather conditions, such as snow, rain, and fog, can directly impact roadway safety, by reducing the visibility and roadway surface friction, negatively affecting vehicles' and drivers' performance. In Wyoming, the number of snow-related crashes are particularly significant. Merely in winter 2018, there were 1,438 snow-related crashes, which resulted in fatalities, extended closures, and significant economic loss. Therefore, detection of real-time weather conditions and providing timely Traveler Information Messages to drivers are crucial for safe driving. The state-of-practice of broadcasting road weather information to travelers has been predominantly based on sporadic and expensive Road Weather Information Systems. With consideration of the limitations of the existing weather detection systems, and in view of the opportunity of the emerging video-image processing technologies, this research aims at developing an affordable weather detection system, which will use video images collected primarily by the WYDOT roadside fixed webcams and secondarily by examining the feasibility of extending the algorithms to snow plows trajectory-level cameras. The product of this research will assist WYDOT with providing road users with accurate and reliable road surface weather conditions, resulting in safer travel decisions and more conservative driving behaviors to mitigate the negative impacts of adverse weather on traffic safety.]]></description>
      <pubDate>Wed, 26 Feb 2020 16:21:32 GMT</pubDate>
      <guid>https://rip.trb.org/View/1688736</guid>
    </item>
    <item>
      <title>Aurora Program (FY20-FY24)</title>
      <link>https://rip.trb.org/View/1639799</link>
      <description><![CDATA[The cooperative and collaborative objective of the Aurora Program is to promote an efficient use of resources rather than a series of independent initiatives. The synergistic effect of this forum is an accelerated implementation of road weather programs as well as the integration and operationalized use of weather data in transportation management, safety, and in serving progressive levels of connected and automated transportation.]]></description>
      <pubDate>Sun, 21 Jul 2019 07:07:46 GMT</pubDate>
      <guid>https://rip.trb.org/View/1639799</guid>
    </item>
    <item>
      <title>Crash Modification Factors (CMFs) for Intelligent Transportation System (ITS) Applications</title>
      <link>https://rip.trb.org/View/1628621</link>
      <description><![CDATA[It is generally understood that Intelligent Transportation System (ITS) applications, such as variable/dynamic/changeable message signs, closed-circuit television (CCTV) cameras, traffic monitoring stations, ramp meters, and Road Weather Information Systems (RWIS), help manage traffic and improve incident response, thus enhancing roadway safety. However, actual data, specifically crash reduction data resulting from ITS applications, are very limited. Research is needed to develop crash modification factors (CMFs) for the various, typically deployed ITS applications.

OBJECTIVE: The objective of this research was to address a long-standing deficiency in safety related ITS data by developing (1) CMFs for commonly deployed ITS applications, independently and as a part of systems and (2) case studies of safety benefit/cost ratio calculations for such ITS applications. The CMFs shall be suitable for use by safety professionals in their analyses and shall meet at least a CMF Clearinghouse four-star quality rating.
 ]]></description>
      <pubDate>Thu, 06 Jun 2019 19:41:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1628621</guid>
    </item>
    <item>
      <title>Road Weather Management Capability Maturity Framework - Base</title>
      <link>https://rip.trb.org/View/1521565</link>
      <description><![CDATA[This project involves the deployment of the Road Weather Management Capability Maturity Framework.]]></description>
      <pubDate>Tue, 03 Jul 2018 09:49:54 GMT</pubDate>
      <guid>https://rip.trb.org/View/1521565</guid>
    </item>
    <item>
      <title>2017 RWM Performance Measures Update</title>
      <link>https://rip.trb.org/View/1521564</link>
      <description><![CDATA[New task to assess progress achieving the Road Weather Management Performance Measures.]]></description>
      <pubDate>Tue, 03 Jul 2018 09:44:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/1521564</guid>
    </item>
    <item>
      <title>National Academies Study on the Application of Social and Behavioral Science within the Weather Enterprise</title>
      <link>https://rip.trb.org/View/1521563</link>
      <description><![CDATA[Co-sponsor this study led by the National Academies under the Board on Atmospheric Sciences and Climate ties to the R&D Strategic Initiative on optimal messaging for non-recurrent events.]]></description>
      <pubDate>Tue, 03 Jul 2018 09:40:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/1521563</guid>
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