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Abstract

While being aware of the air temperature during the winter months is very important, many overlook the importance of the road temperature. Knowing the temperature of the road helps transportation departments decide whether or not salt will need to be distributed onto the road, as well as what type of salt. This research was conducted in order to better a forecast model on surface temperature predictions that was shown to be inaccurate. Data was used from the 2013–2014 winter from three cities across Indiana. The data included variables such as air and surface temperatures, precipitation, wind speed, and other variables that could affect the road temperature. These variables were recorded hourly for approximately 5 months. The data was run through both Python and R Studio in order to better visualize and compare the predictions to the observed data. Variables were weighted in different ways to find the variables that contributed most to temperature discrepancies in the forecast. After many tests, the results showed that adding a decaying average over the last 14 days to the predicted temperature yielded the strongest correlation in comparison to the other options available. These results permit additional degrees to be added to our prediction model that will ultimately lead to more accurate predictions, allowing transportation departments to use the predictions to implement in their daily tasks.

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