Author
Stolle, Sinclair
Hyperlink Exit Door
Yes
Last Modified Date
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Historical data collected from 2016 to 2018, including road condition, road-weather information stations, and automated weather observing systems are poured into an analytical environment powered by SAS software. In this environment, these data are analyzed to produce actionable information.

User acceptance testing with Iowa DOT staff was conducted in October 2018 to compare the accuracy and utility of three modeling tools used to predict road weather conditions.

Research and testing participants included:
  • 9 Maintenance field staff from around the state
  • 5 Central Office project team members
  • 2 Staff from Iowa State/CTRE

Several models were tested before the Decision Tree model was selected as the preferred approach over Regression and Neural Networks models based on the number of correctly predicted road conditions. Of approximately 242,000 road condition observations, about 237,000 were correctly classified by the decision tree model, a 97.8 percent accuracy rate. This high-accuracy model can help field maintenance staff plan more efficiently for winter road condition changes. The relative simplicity of the model compared to the other approaches was also viewed as an asset.
Pages
26
Priority Research Area
Publication Sort Date
Publisher
Iowa DOT
Result Type
Source ID
1989
Title
Can A.I. Take Over Winter Road Condition Reporting? (Presentation)
UNID
3A28BE2F273CD24D852583DE006B0C32
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