Institute for Transportation

About the research

Departments of Transportation (DOTs) play an important role in roadway maintenance before, during, and after hazardous weather events, working to ensure that transportation networks remain functional and safe. DOTs use tools such as mesoscale weather forecasts, combined with state and local knowledge of weather impacts, to anticipate when and where high-impact storms will degrade roadway safety. These tools enable DOTs to develop tactical approaches that inform the public and optimize the deployment of road maintenance vehicles. While these approaches help manage the immediate storm impacts, residual hazards such as flash freezing, blowing snow, localized flooding, and wind gusts may persist even after the main storm ends. These lingering hazards, often influenced by micro-weather conditions specific to regional and local terrain, can pose significant risks to drivers, particularly those unfamiliar with the area.

Mesoscale models used for storm planning and management can have difficulties capturing the localized turbulence and micro-weather effects that contribute to post-storm hazards. This research proposes to leverage large eddy simulation (LES) models, specifically the NCAR Weather Research and Forecasting (WRF) model coupled with the FastEddy® microscale model, to address this gap by simulating micro-weather phenomena at much higher grid spacings (1–10 m). LES models resolve the complex interactions between wind, temperature, and topography at very high resolution, providing transportation agencies with enhanced situational awareness to better understand the persistence of post-storm hazards.

This project’s objective is to develop a micro-weather risk assessment framework that can identify high-risk roadway segments during post-storm operations. By incorporating data from traffic incidents, roadside stations, geospatial sources, and FastEddy simulations, this framework will serve as a foundation for establishing ways to assess localized hazards like blowing snow, flash freeze, and small-scale flooding, with a goal of faster operational recovery and enhanced safety for road users

About the research

Road weather decision support systems provide a valuable tool for transportation agencies in planning for adverse weather conditions including personnel scheduling, resource allocations, equipment and material applications, traveler information, and post-storm after-action assessments. A variety of tools exist to accomplish these goals; however, costs associated with customization can become burdensome to some agencies. It is proposed that a consistent baseline tool would provide value to all agencies and offer customization on a select basis for agencies with more unique needs. The NCAR Pikalert® Road Weather Decision Support System is a big data system that provides segment-level integration of road conditions and weather observations alongside road weather forecasts, motorists hazard advisories, and treatment recommendations. The proposed objective is to deploy a minimally viable instance of the Pikalert road weather decision support system for two key interstate corridors (Interstates 70 and 75) that cross several of the Aurora Board member agency states for a 6-month demonstration period. The tasks to accomplish this objective include: (1) Configuration of the Pikalert System which includes acquisition of the necessary input road weather data and interstate shapefiles for configuration of the system, (2) Implementation of the Pikalert System, (3) Maintenance of the Pikalert System, (4) Integration of a simple road friction model into the Pikalert System, (5) Summary system capability and performance assessment from select case studies of opportunity, and (6) Project administration and reporting.

About the research

Friction is the ultimate metric for measuring the ability of a driver to control a vehicle on the road and inclement weather is the primary factor that influences roadway friction. Many state departments of transportation (DOTs) use roadway friction measurement devices as guidance for alerting the traveling public and for snow removal activities. However, these devices are not available universally along the highways and large gaps in friction information can result, especially where friction measuring devices are stationary.

An increasing number of mobile friction measurements are becoming available, from DOT fleet vehicles and more recently from private vehicles. These measurements provide additional road (friction) state given current weather, but more importantly provide an opportunity to create improved modeling of friction impacted by weather events. Improving highway friction forecasts using forecasted weather conditions would be beneficial to state DOTs for a variety of planning purposes, including Variable Message Signage, Variable Speed Limit adjustments, chain-up and chain-down timing and more. Improved friction modeling may also allow more accurate estimates of friction conditions along roadways where friction measurements are sparse.

The objective of this research project is to gather friction measurements from stationary (RWIS) and mobile (vehicle) sources as well as concurrent weather data and then develop a predictive roadway friction machine learning model. The project team expects to gather the appropriate data and develop a new machine learning model during the first year of this research, then test the new model on weather forecast data from the following winter season in the second year. A summary of the project’s efforts will include an examination of the friction model performance as well as a comparison of forecast quality between stationary and mobile friction locations.

About the research

For winter maintenance forensics, several methods to measure a winter maintenance key performance indicator (KPI) using CVFM data were explored. Two methods used CVFM data to calculate KPIs for winter storm mitigation on individual roads, one providing an illustrative overview of the impact of a winter season on a road and one providing a detailed analysis of how CVFM reflects the impact of a snowstorm on a road stretch. A third approach involved calculating a KPI based on CVFM data during periods of snowfall for larger road networks. The KPI provided a reliable measure of how snowfall affects road networks and how maintenance work mitigates these effects. The results can be used for post-season evaluation of the maintenance work to find areas of improvement.

About the research

Furthermore, to account for the relatively limited data volume, the researchers established a transfer learning framework, which greatly eliminated the need for training a large number of hyperparameters. The transfer learning algorithm achieved a precision of 88% using daytime optical images and an impressive precision of 94% when using nighttime thermal images, despite the constraints imposed by using a limited dataset.

About the research

Road weather systems collect, analyze, and share weather information to help mitigate the disruptive impact of weather events on the nation’s surface transportation system. Much research and development has been undertaken to advance road weather management practice.

Meanwhile, many state and local efforts have aimed to deploy connected vehicle (CV) equipment on the roadside and in vehicles. CV message exchanges present an opportunity to provide vehicle-produced, standardized road weather data to transportation infrastructure owners and operators while standardizing the dissemination of weather-related information to travelers. In particular, road weather systems have the potential to benefit from the data that vehicles may produce and to provide critical information to drivers of those vehicles.

It has been several years since any significant research, development, and deployment of weather-related connected vehicle applications have been undertaken. Many weather-related CV standards (such as SAE J2945/3) have been released recently but have not yet had much opportunity to be used in practice.

The goal of this project was to determine how the data in CV messages could be utilized to enhance existing use cases or potentially enable new use cases for road weather information. Section 2 of the report outlines the results of initial research, including an overview of CV technology and use cases for CV message data. Section 3 presents a Concept of Operations focusing on message exchanges that will enhance the quality of the weather data used in road weather and traffic management activities.

About the research

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 Conversational AI for Road Weather Information Systems (CARWIS), 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 about their travel plans, potentially reducing accidents and enhancing safety. Additionally, CARWIS can assist transportation professionals in planning for 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 roadways.

About the research

The lack of a national standard for winter weather road condition indices has led to inconsistencies in assessing road conditions and providing accurate information to drivers across the United States. This research aims to develop a national standard for winter weather road condition indices that is consistent, accurate, and reliable, enhancing driver safety and winter weather response effectiveness. The project involves conducting a comprehensive literature review, data analysis, and case study analysis, as well as engaging key stakeholders from public and private organizations. The anticipated outcomes include a national standard framework, implementation guide, training materials, monitoring and evaluation toolkit, best practices repository, communication templates, and an interactive map/dashboard for road conditions. The implementation of a national standard for winter weather road condition indices is expected to improve driver safety, reduce traffic crashes and congestion, and optimize winter weather response strategies by transportation agencies.

About the research

Variable speed limits (VSL) are useful in promoting highway safety. According to the Federal Highway Administration (FHWA), “the use of VSLs during inclement weather or other less than ideal conditions can improve safety by decreasing the risks associated with traveling at speeds that are higher than appropriate for the conditions.”

The project’s research objective was to investigate beneficial ways to recommend VSLs under a variety of adverse weather and road surface conditions. The report presents findings from three key research areas: (1) VSL data analysis and machine learning (ML) model development, (2) VSL physical model development, and (3) a review of VSL requirements and state department of transportation (DOT) rules of practice.

The research team gathered road weather information system (RWIS) and VSL event data covering the winter and early spring seasons from 2021 through 2023 across two study areas in Wyoming and Utah. Due to the timeframes and locations covered by the collected data, the study datasets included significant adverse and impactful weather events. The team used the collected data to develop an analysis methodology and algorithms for issuing VSLs that consider different weather conditions, terrain types, roadway geometries, and road surface conditions using ML and physical models. To understand how state DOTs currently issue VSLs and to guide research efforts, an extensive survey of VSL requirements and DOT rules of practice across multiple state DOTs was conducted, and the findings were summarized. Summary results from each of the three research areas are discussed in the report, followed by conclusions and recommendations for methods and techniques that state DOTs should consider when implementing VSLs or performing future VSL research. Detailed research findings can be found in the appendices.

About the research

Deciding when to remove spring load restrictions (SLRs) on roadways is complicated given the variable time window during and after thawing when excess moisture remains in the base and subgrade layers, causing the overall roadway structure to remain weak. The main objective of this project was to develop an economical and easy-to-use protocol for timing SLR removal.

To develop the model, the research team utilized falling weight deflectometer (FWD) data from three test cells at the Minnesota Department of Transportation’s (MnDOT’s) MnROAD research facility. FWD data from nine other sites were used to validate the model, with three sites in North Dakota, three in New Hampshire, two in New York, and one in Maine. Numerous statistical analyses were performed on the FWD data sets, and model/protocol development considered factors such as base layer and subgrade type, effects of moisture, and depth to the groundwater table.

The researchers created a decision tree to help agencies implement the SLR removal guidelines developed in this study. To use the decision tree effectively, it is necessary to know information about the roadway structure, base layer(s), and subgrade soils and the approximate depth to the groundwater table. Using this methodology may help transportation agencies lift their SLRs more quickly than they have in the past.