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Best Practices for Road Weather Management Version 2.0

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Title:

Regression Tree Models to Predict Winter Storm Costs

Abstract:

Historic weather forecasts and associated maintenance resources are used to create statistical models to estimate county-level resources to fight a forecasted snow or freezing rain event. County-level analysis allows for model refinement for slightly differing business practices and areas small enough to assume uniform weather effects. The statistical models are organized as regression trees that accommodate categorical variables for operational characteristics of winter maintenance, such as service level expectations, ranges of county size, and weekend and overtime events. The regression trees fit subsets of the data to form families of multiple linear models. By doing this, models can be refined for important categorical variables such as service level and county size. The models presented herein estimate labor, equipment, and material resources required to fight a storm in counties having 87 to 1,460 lane miles to be maintained. The models estimate resources, not cost.

Source(s):

85th Transportation Research Board (TRB) Annual Meeting, University of Wisconsin-Madison and Wisconsin DOT. For an electronic copy of this resource, please direct your request to WeatherFeedback@dot.gov.

Date: 2006

Author:

Adams, Juni, Sproul, Xu

Keywords:


Winter storm
Winter maintenance
Snow
Ice/Frost
Costs

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