Abstract Manager

Real-Time Grip Monitoring and Predictive Modeling for Optimized Winter Road Maintenance

Sepideh E. Tabrizi¹, Marjo Hippi², James Sullivan³,

Hani Farghaly¹, Bahram Gharabaghi¹*

1. School of Engineering, University of Guelph, Guelph, Ontario, Canada
2. Finland Meteorological Institute, Helsinki, Finland
3. University of Vermont, Burlington, VT 05405, USA

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

Abstract:

Road authorities continuously strive to enhance safety and reduce vehicular crash risks during winter storm events, particularly on high-speed, high-traffic corridors. The effectiveness of winter road maintenance depends on factors such as pavement surface texture, pavement temperature, and meteorological conditions, which collectively influence snow and ice accumulation and significantly affect road grip. Advancements in road surface monitoring technologies are essential in enabling real-time, accurate measurement of snow and ice thickness and road grip levels. Real-time grip variability monitoring and forecasting allow road authorities to optimize salt application and prioritize fleet deployment on different salt routes, effectively reducing crash risks. Our research reveals an inverse correlation between road crash frequency and the 10th percentile of grip levels, underscoring the importance of timely and efficient snow and ice removal. A logarithmic relationship between ice/snow thickness and grip loss informed the development of a predictive model using Road Weather Information System (RWIS) data. Integrating real-time road weather forecasts enables optimized road salt application, improving road safety while preserving water quality in salt-vulnerable areas.