- Topic Submission
- Discussion & Evaluation
- Development
- Project
Asset Management
Data
Equipment
Highway Operations
Information Technology
Maintenance & Preservation
Environment
Is this related to or a continuation of a previous Iowa DOT research project?
Yes
Previous Project Number
TPF-5(290)
Previous Project Title
Aurora Program
Relationship to Previous Project
Next Phase
Does this idea include matching funds?
No
Anticipated Benefits
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.
Aurora members seek to advance road weather information systems (RWIS) to more fully integrate weather data as part of transportation systems operations in terms of readiness and decision making for efficient highway maintenance, operations, and real-time traveler information. Project areas of interest include information dissemination technologies, decision support systems, weather modeling and analysis, equipment evaluations, standards and architecture, road condition monitoring and performance measures as well as other pertinent areas as identified by member agencies. In addition, Aurora is committed to supporting a more connected and automated future for surface transportation.
Aurora members seek to advance road weather information systems (RWIS) to more fully integrate weather data as part of transportation systems operations in terms of readiness and decision making for efficient highway maintenance, operations, and real-time traveler information. Project areas of interest include information dissemination technologies, decision support systems, weather modeling and analysis, equipment evaluations, standards and architecture, road condition monitoring and performance measures as well as other pertinent areas as identified by member agencies. In addition, Aurora is committed to supporting a more connected and automated future for surface transportation.
Focus Area
Mobility / Safety / Sustainability / Technology
Research Program
SPR / 776: Pooled Fund Studies - IA Lead
Project Title
Aurora Program
Project Number
TPF-5(435)
Contracted Agency
Heather Miller & Associates / Iowa State University / Montana State University / University Corporation for Atmospheric Research / University of Iowa / University of Utah / Rutgers, The State University / NIRA Dynamics AB / WSP USA Inc.
Project Champion
Funding Program
State Planning and Research
Project Funding
$125,000
Project Funding Including External Sources
$2,450,000
Partner Agencies
Alaska DOT
Arizona DOT
California DOT
Colorado DOT
Illinois DOT
Kansas DOT
Maine DOT
Michigan DOT
Minnesota DOT
Missouri DOT
North Dakota DOT
Ohio DOT
Pennsylvania DOT
Utah DOT
Virginia DOT
Washington DOT
Wisconsin DOT
Arizona DOT
California DOT
Colorado DOT
Illinois DOT
Kansas DOT
Maine DOT
Michigan DOT
Minnesota DOT
Missouri DOT
North Dakota DOT
Ohio DOT
Pennsylvania DOT
Utah DOT
Virginia DOT
Washington DOT
Wisconsin DOT
Project Start Date
01/01/2020
Current Project End Date
12/31/2024
Research in Progress Brief
Project Abstract
Active Projects:
Automating Variable Speed Limits Using Weather, Traffic and Friction Data
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, orsafety 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.
Integration of Connected Vehicle and RWIS Technologies
Connected and automated vehicle (CAV) technology is progressing rapidly. Numerous research and deployment initiatives are underway as the transportation industry continues to examine how roadway assets such as traffic control signs, markings, signals, guardrail, computing systems, communications infrastructure and systems, and other permanent and temporary ancillary devices can be designed or enhanced to facilitate CAV operations. Road Weather Information Systems (RWIS) are relied on across the US to help predict and manage the impacts of weather on transportation safety and mobility. RWIS data are used heavily by road authorities as well as across the public and private spectrum of weather service providers. The integration of mobile observational data from connected vehicle (CV) and probe-based data are providing agencies with new options for vehicle centric operational and weather-related data. In some situations, CAVs may benefit from direct data exchanges with the RWIS, thus creating a huge potential for road weather information and other operations functions to be complemented by field devices, which can be collected from the communications of passing CAVs.
Optimal RWIS Sensor Density and Location - Phase IV
The primary objective of this project is to continue our previous research effort on developing highly transferrable and universally applicable methodologies, models, and tools for visualizing and inferring road surface conditions using data from RWIS and other road condition monitoring systems. More specifically, our research has the following five specific objectives: 1. Prepare and process a comprehensive training/testing dataset (e.g., RWIS and in-vehicle images) that covers a sufficiently large range of road, weather, and environmental conditions; 2. Test and improve the performance of alternative deep learning-based image classification models and determine the best model for real-time implementation; 3. Develop a system that can automate the process of updating the deep learning model as newly labelled data are received; 4. Generate LOS performance measures (e.g., BPRT, RSI) that can be used to quantitatively assess the efficiency of the existing maintenance program; and 5. Implement a web-based application for visualizing the spatial mapping and image recognition solutions and demonstrate the application with real world usage scenarios. This proposed continuation of the project will utilize datasets from the highways within the state of Iowa to take advantage of the substantial amount of archived stationary RWIS images, dash camera images (maintenance trucks), and GIS data that were downloaded and processed in previous projects. Ultimately, this project will provide winter maintenance personnel with newfound knowledge and analytical tools, of which they can employ to make better use of available resources, resulting in better maintenance and improvements to their highway infrastructure thereby promoting improved winter mobility and safety
Roadway Ice and Snow Detection using a Novel Infrared Thermography Technology
The proposed research aims to develop a convenient tool which is capable of conducting multilane roadway temperature mapping and pavement slippery condition evaluation in winter seasons. With the adoption of infrared and video cameras, the proposed technology will provide accurate and robust measures of road surface temperature and slippery conditions for winter weather severity index evaluation. The research outcomes include (i) an automated infrared-based data acquisition system; (ii) a dual-sensory road temperature and slippery condition evaluation system. Furthermore, the team will evaluate the feasibility of machine learning-empowered ice/snow detection algorithms. A series of field experimental tests will be conducted to obtain sufficient real-world data with the developed prototype for technology development and performance evaluation.
Standardized Framework for Winter Weather Road Condition Indices
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 project 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.
An Intelligent Human-Centric Communication System for Adverse Weather and Road Conditions
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.
Real User Friction for Winter Maintenance Operation and Evaluation
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, drivetrain and wheel sensors. Micro-slippage and roadway friction can be estimated at 75-foot segments of roadway aggregated at 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 provide only limited spatial coverage. Agency maintenance vehicles such as snowplows run on dedicated routes, and timing of deployments, 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 effort aims 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 be developed outside of the project scope.
Road Weather Management Using Connected Vehicle Technology
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 ITS and CV 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.
Automating Variable Speed Limits Using Weather, Traffic and Friction Data
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, orsafety 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.
Integration of Connected Vehicle and RWIS Technologies
Connected and automated vehicle (CAV) technology is progressing rapidly. Numerous research and deployment initiatives are underway as the transportation industry continues to examine how roadway assets such as traffic control signs, markings, signals, guardrail, computing systems, communications infrastructure and systems, and other permanent and temporary ancillary devices can be designed or enhanced to facilitate CAV operations. Road Weather Information Systems (RWIS) are relied on across the US to help predict and manage the impacts of weather on transportation safety and mobility. RWIS data are used heavily by road authorities as well as across the public and private spectrum of weather service providers. The integration of mobile observational data from connected vehicle (CV) and probe-based data are providing agencies with new options for vehicle centric operational and weather-related data. In some situations, CAVs may benefit from direct data exchanges with the RWIS, thus creating a huge potential for road weather information and other operations functions to be complemented by field devices, which can be collected from the communications of passing CAVs.
Optimal RWIS Sensor Density and Location - Phase IV
The primary objective of this project is to continue our previous research effort on developing highly transferrable and universally applicable methodologies, models, and tools for visualizing and inferring road surface conditions using data from RWIS and other road condition monitoring systems. More specifically, our research has the following five specific objectives: 1. Prepare and process a comprehensive training/testing dataset (e.g., RWIS and in-vehicle images) that covers a sufficiently large range of road, weather, and environmental conditions; 2. Test and improve the performance of alternative deep learning-based image classification models and determine the best model for real-time implementation; 3. Develop a system that can automate the process of updating the deep learning model as newly labelled data are received; 4. Generate LOS performance measures (e.g., BPRT, RSI) that can be used to quantitatively assess the efficiency of the existing maintenance program; and 5. Implement a web-based application for visualizing the spatial mapping and image recognition solutions and demonstrate the application with real world usage scenarios. This proposed continuation of the project will utilize datasets from the highways within the state of Iowa to take advantage of the substantial amount of archived stationary RWIS images, dash camera images (maintenance trucks), and GIS data that were downloaded and processed in previous projects. Ultimately, this project will provide winter maintenance personnel with newfound knowledge and analytical tools, of which they can employ to make better use of available resources, resulting in better maintenance and improvements to their highway infrastructure thereby promoting improved winter mobility and safety
Roadway Ice and Snow Detection using a Novel Infrared Thermography Technology
The proposed research aims to develop a convenient tool which is capable of conducting multilane roadway temperature mapping and pavement slippery condition evaluation in winter seasons. With the adoption of infrared and video cameras, the proposed technology will provide accurate and robust measures of road surface temperature and slippery conditions for winter weather severity index evaluation. The research outcomes include (i) an automated infrared-based data acquisition system; (ii) a dual-sensory road temperature and slippery condition evaluation system. Furthermore, the team will evaluate the feasibility of machine learning-empowered ice/snow detection algorithms. A series of field experimental tests will be conducted to obtain sufficient real-world data with the developed prototype for technology development and performance evaluation.
Standardized Framework for Winter Weather Road Condition Indices
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 project 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.
An Intelligent Human-Centric Communication System for Adverse Weather and Road Conditions
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.
Real User Friction for Winter Maintenance Operation and Evaluation
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, drivetrain and wheel sensors. Micro-slippage and roadway friction can be estimated at 75-foot segments of roadway aggregated at 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 provide only limited spatial coverage. Agency maintenance vehicles such as snowplows run on dedicated routes, and timing of deployments, 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 effort aims 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 be developed outside of the project scope.
Road Weather Management Using Connected Vehicle Technology
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 ITS and CV 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.
Project Deliverables
Media & Presentations
Final Report Abstract
Completed Projects:
Roadway Friction Modeling: Improving the Use of Friction Measurements in State DOTs
The objectives of this project were to determine the relationship between weather conditions and roadway friction measurements as observed in the laboratory, determine whether it is possible to standardize friction measurements coming from multiple friction sensors for identical weather conditions and roadway pavement types, determine whether the relationship between weather and roadway friction found in the laboratory is analogous to the relationship between weather and pavement friction found in practice on highways, and model roadway friction using weather conditions to predict it at sites where friction measurements may not be available. The objectives were accomplished through cold laboratory testing of stationary friction sensors, standardizing friction measurements from multiple stationary and mobile friction sensors, using meteorological measurements from Colorado and Minnesota to infer road friction conditions, and conducting a friction wheel measurement analysis using data from Sweden. Key findings from this effort include the following: • Machine learning models can be created using data from friction sensors in the cold laboratory that exhibit a good mean absolute error in predicting the laboratory friction response to meteorological conditions set in the laboratory, but the models have a higher mean absolute error when applied to data from the field. For this reason, the researchers do not recommend using the model developed in the laboratory with field data. • Collocated road weather information system (RWIS) and stationary friction sensor data can be used to develop state-specific friction models using machine learning techniques. These models can then be used to provide a synthetic friction estimate at RWIS sites that are not equipped with stationary friction sensors. The accuracy of the predictions can be determined at sites where friction sensors are available. The accuracy is improved when water thickness and/or snow thickness are available. • RWIS measurements including air temperature, surface temperature, dew point temperature, relative humidity, and road condition measurements including road state, water thickness, and snow thickness can be used to derive an accurate friction model that targets observed friction values. • Friction values from multiple sensor types are close in magnitude when friction is high, but when friction values drop, agreement among sensors is variable. To standardize the measurements from multiple friction sensors, friction values from multiple sensors can either be averaged or associated with a set of friction categories.
Automated Extraction of Weather Variables from Imagery
Department of transportation (DOT) maintenance supervisors utilize a variety of tools including maintenance decision support systems (MDSS) to gain a better understanding of current and future road surface conditions (RSC) during winter weather. MDSS automatically attempt to deduce current RSC based on road weather information system (RWIS) and other data to a greater or lesser degree of accuracy. Although current MDSS implementations present highway camera imagery, they typically do not incorporate automated camera image recognition in order to improve the MDSS assessment of winter RSC. Thus, there can be discrepancies between the road weather conditions in camera images and MDSS RSC assessments. For example, an MDSS assessment may determine that a highway is clear whereas associated camera images show snow or vice versa. Such discrepancies can lead to a loss of confidence, system criticism, and noncompliance with MDSS recommendations. From that point of view, the integration of automated RSC camera image recognition into MDSS implementations can have a number of benefits:
• Better RSC assessment performance
• Better road treatment recommendations owing to better RSC identification
• Improved MDSS use and compliance with system recommendations owing to user confidence
Recent research in RSC identification has applied convolutional neural networks (CNN) and related techniques to the winter RSC identification problem. The National Center for Atmospheric Research (NCAR) is interested in transitioning these automated RSC identification techniques from the research community to the DOT community. Since NCAR has significant experience in the research-to-operations arena, the NCAR team worked with Aurora Program members to develop a set of recommendations for transitioning the relevant technology to MDSS applications. Even though recent research efforts seem quite successful, the NCAR team was also interested in the potential to improve the initial CNN RSC identification by incorporating additional relevant data such as the following:
• RWIS precipitation/temperature data
• Vehicle speed/volume data
Evaluation of Spring Load Restriction Removal Protocols
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.
Roadway Friction Modeling: Improving the Use of Friction Measurements in State DOTs
The objectives of this project were to determine the relationship between weather conditions and roadway friction measurements as observed in the laboratory, determine whether it is possible to standardize friction measurements coming from multiple friction sensors for identical weather conditions and roadway pavement types, determine whether the relationship between weather and roadway friction found in the laboratory is analogous to the relationship between weather and pavement friction found in practice on highways, and model roadway friction using weather conditions to predict it at sites where friction measurements may not be available. The objectives were accomplished through cold laboratory testing of stationary friction sensors, standardizing friction measurements from multiple stationary and mobile friction sensors, using meteorological measurements from Colorado and Minnesota to infer road friction conditions, and conducting a friction wheel measurement analysis using data from Sweden. Key findings from this effort include the following: • Machine learning models can be created using data from friction sensors in the cold laboratory that exhibit a good mean absolute error in predicting the laboratory friction response to meteorological conditions set in the laboratory, but the models have a higher mean absolute error when applied to data from the field. For this reason, the researchers do not recommend using the model developed in the laboratory with field data. • Collocated road weather information system (RWIS) and stationary friction sensor data can be used to develop state-specific friction models using machine learning techniques. These models can then be used to provide a synthetic friction estimate at RWIS sites that are not equipped with stationary friction sensors. The accuracy of the predictions can be determined at sites where friction sensors are available. The accuracy is improved when water thickness and/or snow thickness are available. • RWIS measurements including air temperature, surface temperature, dew point temperature, relative humidity, and road condition measurements including road state, water thickness, and snow thickness can be used to derive an accurate friction model that targets observed friction values. • Friction values from multiple sensor types are close in magnitude when friction is high, but when friction values drop, agreement among sensors is variable. To standardize the measurements from multiple friction sensors, friction values from multiple sensors can either be averaged or associated with a set of friction categories.
Automated Extraction of Weather Variables from Imagery
Department of transportation (DOT) maintenance supervisors utilize a variety of tools including maintenance decision support systems (MDSS) to gain a better understanding of current and future road surface conditions (RSC) during winter weather. MDSS automatically attempt to deduce current RSC based on road weather information system (RWIS) and other data to a greater or lesser degree of accuracy. Although current MDSS implementations present highway camera imagery, they typically do not incorporate automated camera image recognition in order to improve the MDSS assessment of winter RSC. Thus, there can be discrepancies between the road weather conditions in camera images and MDSS RSC assessments. For example, an MDSS assessment may determine that a highway is clear whereas associated camera images show snow or vice versa. Such discrepancies can lead to a loss of confidence, system criticism, and noncompliance with MDSS recommendations. From that point of view, the integration of automated RSC camera image recognition into MDSS implementations can have a number of benefits:
• Better RSC assessment performance
• Better road treatment recommendations owing to better RSC identification
• Improved MDSS use and compliance with system recommendations owing to user confidence
Recent research in RSC identification has applied convolutional neural networks (CNN) and related techniques to the winter RSC identification problem. The National Center for Atmospheric Research (NCAR) is interested in transitioning these automated RSC identification techniques from the research community to the DOT community. Since NCAR has significant experience in the research-to-operations arena, the NCAR team worked with Aurora Program members to develop a set of recommendations for transitioning the relevant technology to MDSS applications. Even though recent research efforts seem quite successful, the NCAR team was also interested in the potential to improve the initial CNN RSC identification by incorporating additional relevant data such as the following:
• RWIS precipitation/temperature data
• Vehicle speed/volume data
Evaluation of Spring Load Restriction Removal Protocols
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.
Transportation Research Board - Transportation Research Information (TRID) Database Page
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Delivering targeted solutions for Iowa's transportation future.
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