Institute for Transportation

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.

About the research

The goal of this project is to develop a protocol to determine when spring load restrictions (SLRs) can ben removed. The protocol may consist of either a relatively simple (spreadsheet based) model or a set of tables/charts that transportation agency personnel can easily use to define time windows required for SLR removal. The project will consist of the following specific tasks:

1. Selection of Study Sites: The research team will query archives of historical data for potential data sets at sites with appropriate instrumentation and adequate frequency of falling weight deflectometer (FWD) testing during spring thaw and strength recovery periods.
2. Assemble data discovered during Task #1 and perform data reduction and manipulation.
3. Perform statistical analyses on the data assembled in Task 2. These analyses will help to determine what the key factor(s) are that define time windows required for stiffness recovery (after complete thawing is observed in temperature depth probes [TDPs]); develop an appropriate model and/or set of tables/charts for SLR removal based on these analyses.
4. Run the FrezTrax model for each test site and season; perform statistical analysis to determine whether this model can also reasonably predict SLR removal dates.
5. Use validation data reserved in Task 1 to confirm validity of the SLR removal model.
6. Prepare a final report summarizing work conducted for this project, conclusions, and recommendations.

About the research

Until recently, road weather information system (RWIS) stations have been overlooked and underutilized by many transportation agencies and maintenance personnel. It may stem from the fact that the data is point measurements with large spatial gaps in between, resulting in an incomplete view of road surface conditions (RSC) over the entire highway network. Also, one of the most important pieces about RSC, namely, snow coverage or bare pavement status, which is available from RWIS cameras or fleet cameras, is only accessible manually by maintenance personnel or road users. Therefore, there is a need to automate the recognition of snow coverage via images from RWIS or other data sources such as fleet dash cameras or traffic cameras, improve RSC inferences at unmeasured locations, and generate various RSC data and performance measures that can be used by maintenance personnel and road users.

The primary objective of this project is to continue the previous research efforts 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.

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.

About the research

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

About the research

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.

About the research

The research aims to develop a convenient tool that is capable of conducting multi-lane 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 1) an automated infrared-based data acquisition system and 2) 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.