Date of Award
12-17-2009
Document Type
Thesis
Abstract
"Road icing is a common problem in cold regions, such as Alaska, where it poses serious threat to drivers and result in the disruption of transportation facilities. Road surface temperature (RST) is considered as the most crucial factor for icing conditions. The objective of this research is to predict RST in Multiple Linear Regression (MLR) and three-layer back propagated Neural Network (NN) models using an optimum number of input variables (air temperature, dew point, relative humidity, wind gust, wind speed average, wind gust and wind directions). The data were analyzed using both randomized and chronological schemes. The results obtained from different models were compared to find the most suitable model for predicting RST. The performance of both MLR and NN models were very comparable. Therefore, in the interest of reducing modeling complexity the MLR models could be preferred instead of the complex neural network models for the aimed accuracy levels in RST prediction. It was also observed that models developed on the chronological data provided better prediction accuracy as compared to models developed on the randomized data indicating RST should probably be predicted from models that honor the time sequence"--Leaf iii
Recommended Citation
Panda, Rupali, "Modeling of road surface condition data for the prediction of road icing" (2009). Engineering . 467.
https://ualaska.researchcommons.org/uaf_grad_engineering/467
Handle
http://hdl.handle.net/11122/12826