Fast Detection and Prediction of Slippery Roadway Conditions for Enhanced Safety [supporting dataset]

Details

• Creators:
  Li, Joshua Qiang ; Munna, Masud Rana
• Corporate Creators:
  Oklahoma State University
• Corporate Contributors:
  The Southern Plains Transportation Center (SPTC) Region 6 University Transportation Center (UTC) ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Black Ice Data Files Detection And Identification Predictive Models Road Weather Information Systems Slipperiness Weather Conditions
• Resource Type:
  Dataset
• Right Statement:
  Public Domain
• Geographical Coverage:
  United States
• Corporate Publisher:
  The Southern Plains Transportation Center (SPTC) Region 6 University Transportation Center (UTC)
• Abstract:
  The research team collected four distinct datasets including Ice data, Rain data, Pavement Characteristics data, and Pavement friction data from State Highway (SH) 177, SH 51, SH 33, and a County Road. The datasets displayed diverse weather conditions (ice and rain), pavement surface characteristics​, and field-measured friction values. The Ice dataset captures time-​synchronized and location-​referenced measurements during icy weather events using Mobile Advanced Road Weather Information Sensors (MARWIS). The dataset includes: Temporal-​Spatial Variables: Date, Time, Latitude, Longitude, and Altitude. Vehicle Dynamics: Course and Speed. Environmental Conditions: Dew Point (°C), Surface Temperature (°C). Surface State Indicators: Road Condition, Friction, Ice Percentage (%), and Water Film Height (µm). These variables provide insight into the pavement’s condition and slipperiness during freezing conditions, essential for developing safety-focused predictive models. Rain Dataset Structured similarly to the ice dataset, the rain dataset was also collected by MARWIS observed during rainfall events. It includes: Temporal-​Spatial Data: Date, Time, Latitude, Longitude, Altitude. Environmental Parameters: Dew Point (°C), Surface Temperature (°C). Roadway Surface Indicators: Road Condition, Friction, Ice Percentage (%), and Water Film Height (µm). Vehicle Dynamics: Course and Speed. This dataset supports comparative analysis between wet and icy conditions and aids in understanding the effects of rainfall on surface friction. Pavement Surface Characteristics Dataset This data consists of georeferenced pavement condition parameters collected during rainy conditions. It captures the structural and textural characteristics of the pavement surfaces, including: Location: GPS Longitude and Latitude. Roughness and Texture Metrics: International Roughness Index (IRI), Rut Depth, Mean Profile Depth (MPD), Root Mean Square (RMS), Skewness, and Kurtosis. Geometric Features: Cross Slope. Surface Distress Indicators: Crack Density in Wheel Path (WP) and Non-Wheel Path (NWP). These features are essential for understanding how pavement texture and distress influence friction, especially under wet and icy conditions. Friction Dataset Friction data were collected using a high-speed Grip Tester across the locations and in rainy conditions. The dataset includes: Record ID and Speed. Geolocation: Latitude and Longitude. Grip Number: Represents the measured pavement friction. This dataset provides reference friction values to validate the friction predictions and surface condition classifications derived from the other datasets.
• Format:
  XLSX
• Funding:
  69A3552348306
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:b5e9fbf1d9f19380202db2028db36d9c2945916799f045edcfb192577d57e67d0f77a2103da6d5ac84de366a9d27f2cbf7da4389fa59512564f83554466a700b
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88403/dot_88403_DS1.pdf
• File Type:
  [PDF - 192.64 KB ]