A simple decision tree model was preferred over more complex regression and neural network models when predicting winter road weather conditions in Iowa.

The Iowa DOT is developing an approach to leverage the data already collected from assets and vehicles with a focus on the I-80 corridor to predict winter road conditions using artificial intelligence (AI).

Iowa; United States

Background (Show)

Lesson Learned

Data Governance is very important
  • Alongside the development of the algorithm, a user-interface was developed to communicate information produced by the algorithm. During testing, it was discovered that the data provided to SAS were incorrectly time-stamped, yielding some unexpected results in the visualizations that did not match real conditions at all. This experience illustrated the importance of carefully collecting, storing, and sharing data. Accurate data were then provided, which brought the results of both approaches in alignment.
  • Further exploration into historical data and mis-matches between the expected and actual outcomes are underway, as are efforts to improve the usability of the user interface.
Decision Tree model is best for modeling winter road condition
  • Three Model Building Methodologies were assessed: Decision Tree, Regression, and Neural Networks.
  • The decision tree is the best model based on the number of correctly predicted road conditions and that there are simpler and less variables without sacrificing accuracy.

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Can A.I. Take Over Winter Road Condition Reporting? (Presentation)

Author: Stolle, Sinclair

Published By: Iowa DOT

Source Date: 10/24/2018

URL: http://www.nationalruralitsconference.org/wp-content/uploads/2019/01/2018-Stolle-Can-AI-Report-Road-Conditions.pdf

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Lesson Analyst:

Paige Cassell


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Lesson ID: 2019-00884