Abstract Manager

A modelling system for predicting hourly salt application rate forecasting

Sepideh E. Tabrizi¹, Chris Passmore², J. Blake Stevenson², James Smith³,

Hani Farghaly¹, Bahram Gharabaghi^1*

1. School of Engineering, University of Guelph, Guelph, Ontario, Canada
2. Flodraulic Controls Ltd., Canada
3. Good Roads, Canada

• Correspondence: bgharaba@uoguelph.ca; Tel: 01 (519) 824-4120 (Ext: 58451)

ABSTRACT

The risk of road accidents depends on the winter storm severity, speed, and road surface conditions. Winter storm severity index indicates the relative effectiveness of winter metrological parameters such as air temperature, pavement temperature, and precipitation on roads. Snow and ice film layers occur on road surfaces below freezing, and reduce grip between tires and road surfaces. The main objective of this research is to develop a more accurate model for grip estimation using measurements of the thicknesses of water, snow, and ice film layers utilising real-time mobile laser measurement data collected by “smart” salt trucks and advanced machine learning methods. This method can further optimize salt application rates, especially important for environmental sustainability in salt-vulnerable areas and source water protection sites.

Keywords: Salt application rate, salt vulnerable areas, friction coefficient, temperature index, road surface conditions.