Project Details
TPF-5(435)
10/01/24
09/30/26
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))
Researchers
Bill Petzke
bpetzke@ucar.edu email >Software Engineer, NSF National Center for Atmospheric Research
Jim Cowie
cowie@ucar.edu email >Engineering Deputy Director, NSF National Center for Atmospheric Research
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.
Project Details
2023-01
01/03/23
08/06/24
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))
Researchers
Faruk Sehovic
About the research
In winter season 2023–2024, Aurora program member states had the opportunity to evaluate connected vehicle friction measurement (CVFM) data provided by NIRA Dynamics’s connected vehicle (CV) fleet. A detailed analysis was conducted in Colorado, Pennsylvania, and Utah to evaluate how the CVFM data can be utilized in three use cases: variable speed limits (VSLs), chain laws, and winter maintenance forensics.
For VSLs, a proof-of-concept friction signal was developed that proved capable of detecting slipperiness on the chosen corridors. A side-by-side comparison with historical speed restriction data demonstrated the value in utilizing a friction signal as an additional parameter for VSL decision-making.
The friction signal algorithm was applied to the chain law use case and showed reliable performance on I-70 in Colorado. During snowstorms over the winter of 2024, the friction signal was able to detect the presence of low friction. Using the friction signal alongside existing weather data could improve methods for issuing chain restrictions.
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.
Project Details
2020-02
06/01/20
02/29/24
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))
Researchers
Xuan Zhu
Xianfeng Yang
About the research
Safety is a principal concern for highway transportation, and slippery roads can pose high risks for vehicle crashes in snowy regions, which cover about 70% of road networks in the United States. Slippery road conditions can significantly increase the risk of vehicle crashes. Therefore, roadway agency staff find it critical to clear road surfaces in time to ensure traffic safety during ice and snow seasons. Moreover, the capability to estimate multi-lane roadway snow coverage is instrumental for snow plowing performance evaluation and resource planning for snowy regions during winter seasons.
The researchers developed and evaluated a sensing technology to evaluate multi-lane roadway snow coverage leveraging non-invasive dual-spectrum cameras, computer vision, and machine learning algorithms. The use of optical and infrared images for slippery roadway condition detection has the potential to operate in different illumination conditions.
The team deployed two dual-spectrum cameras, which can acquire both optical and infrared images of roadways. Computer vision algorithms were developed to perform image registration, segmentation, lane splitting, classification, and clustering.
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.
Project Details
TPF-5(435)
08/01/23
07/31/24
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))
Researchers
About the research
State agencies spend tens of millions of dollars on winter maintenance each year. For example, the State of Indiana spends upwards of $60M annually on salt, fuel, labor, equipment, and other maintenance costs, so it is imperative to make data-driven decisions. Advanced crowdsourced connected vehicle data have emerged in the past eight years that can leverage beyond weather data, vehicle dynamics data from engine output, and drivetrain and wheel sensors. Micro-slippage and roadway friction can be estimated at 75-ft segments of roadway aggregated at a 10-minute frequency without any additional instrumentation, from consumer vehicles off the production line. Traditionally, agencies have leveraged RWIS and other road weather sensors for tactical decision-making, but they are expensive to deploy and maintain and can only provide limited spatial coverage. Agency maintenance vehicles such as snowplows run on dedicated routes, and the timing of deployments, as well as the length and duration of routes, may not be representative of general traffic behavior. A crowdsourced solution provides more agile and broader network coverage because of the moving nature of vehicles.
This research includes a set of tasks to evaluate commercially available connected vehicle (CV) data to measure friction, wet-state, and ambient temperature over large road networks across multiple states. Static RWIS can be used for groundtruth if CV data is gathered in the proximity. If the new data source is found to be usable, a contingency plan on how these data can be integrated into the existing datasets, decision-making systems, and business processes will also be developed.
Project Details
2023-03
09/01/23
09/25/24
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))
Researchers
Frank Perry
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.
Project Details
TPF-5(435)
08/01/23
07/31/25
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))
Researchers
Ibrahim Demir
ibrahim-demir@uiowa.edu email >Associate Professor, IIHR-Hydroscience & Engineering, The University of Iowa
Yusuf Sermet
msermet@uiowa.edu email >Research Scientist, University of Iowa
About the research
Adverse weather conditions present significant risks to motorists, making safe navigation challenging. 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.
Project Details
23-858
11/01/23
04/30/25
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))
Researchers
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.
Project Details
2022-10
02/01/23
01/31/25
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))
Rutgers University, Western Transportation Institute - Montana State University
Researchers
About the research
Variable speed limits (VSLs) are useful in promoting highway safety. Along these lines, the Federal Highway Administration (FHWA) mentions, “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 goal of this proposal is to automatically recommend speeds for various weather conditions (rainfall, snow, ice, fog, etc.) at roadway segments that are good candidates for VSL. This means that the roadway segments should frequently experience adverse weather conditions (such as snow,
rain, fog, etc.), high traffic, or safety hazards. The crash rate at such road segments should generally be higher than average. The research team expects to gather road weather information system (RWIS), traffic, friction, incident, and potentially other data sets over one or more seasons that typically exhibit adverse weather. The team will then utilize the collected data and develop analysis methodology in establishing VSL algorithms that consider different terrain types, roadway geometries, and weather conditions (rainfall, snow, ice, fog, etc.). The team will explore the usage of machine learning (ML) algorithms and other approaches in establishing VSL. The speed limits will be set to satisfy the driver’s visibility and stopping sight distance requirements and also prevent lateral slippage at curved sections considering the loss of friction due to inclement weather conditions.
Project Details
2022-07
07/01/22
02/15/24
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))
Researchers
Heather Miller
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.
Project Details
2021-05
10/01/21
11/06/24
Federal Highway Administration Aurora Program Transportation Pooled Fund (TPF-5(435))
Researchers
Tae J. Kwon
About the research
Road weather information systems (RWIS), in both stationary and mobile forms, have become increasingly popular in recent decades for their ability to collect and disseminate road weather and surface data. In addition to meteorological measurements, highway agencies rely heavily on RWIS imagery data to guide winter road maintenance (WRM) operations. However, the analysis of imagery data is still performed manually by trained personnel. Moreover, the limited number of stationary RWIS stations and the infrequent deployment of mobile RWIS units result in significant spatial gaps along the highway network. In our previous project, we developed methodologies based on convolutional neural networks (CNNs) to automatically recognize road surface conditions (RSC) from dash camera imagery and employed regression kriging (RK) to estimate RSC in unmonitored areas using limited point measurements. These methods demonstrated feasibility and robustness in real-world case studies. Building on these efforts, this project aimed to further advance CNN development specifically for stationary RWIS imagery and assess its reliability using explainable artificial intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP) and class activation map (CAM)-based methods. Additionally, to automatically estimate snow coverage ratios from stationary RWIS imagery, two distinct deep learning-based computer vision techniques, pix-to-pix generative adversarial network and semantic segmentation, were employed. Furthermore, the RK method was revisited to better accommodate a wider range of weather events while considering their variability, and the potential monetary benefits of this approach were also explored. To address the limitations of RK in handling categorical variables, a novel geostatistical method, namely nested indicator kriging (NIK), was developed to interpolate RSC in unmonitored areas directly using CNN classification results. These methods were evaluated using data from two major highways, Interstate 35 and Interstate 80 in Iowa, US, spanning a five-year period and encompassing over 20,000 images. The results demonstrated high accuracy and reliability. Additionally, a web application was developed to integrate these methods, offering real-time monitoring, estimation, and historical data archiving. This project equips decision-makers with a powerful tool to implement WRM activities more swiftly, efficiently, and cost-effectively, ultimately promoting a safer, more mobile, and sustainable winter transportation system.