Date of Award


Degree Type


Degree Name

Master of Science (MS)


Earth, Atmospheric, and Planetary Sciences

Committee Chair

Daniel R. Chavas

Committee Member 1

Michael E. Baldwin

Committee Member 2

Darcy M. Bullock


Winter weather causes major disruptions to road travel, increasing delays and accident rates. Significant investments are made towards maintaining the condition and safety of the road network. Weather forecasts provide the state of the art prognosis for winter weather phenomena, but are not designed to explicitly predict road surface conditions. This thesis implements supervised machine learning techniques, most successfully random forests, using weather forecast data to directly predict the Winter Driving Index of roadways as defined by the Indiana Department of Transportation (INDOT). Attempts are made to also predict the amount of traffic reductions due to winter weather using random forests and artificial neural networks. Different case studies are presented to demonstrate successes and pitfalls of the models, and possibilities for future development are discussed.