Modelling pavement temperature for winter maintenance operations

Abstract

Road and highway maintenance is vital for the safety of citizens and for enabling emergency and security services to perform their essential functions. Accumulation of snow and (or) ice on the pavement surface during the wintertime substantially increases the risk of road crashes and can have negative impact on the economy of the region. Recently, road maintenance engineers have used pavement surface temperature as a guide to the application of deicers. Stations for road weather information systems (RWIS) have been installed across Europe and North America to collect data that can be used to predict weather conditions such as air temperature. Modelling pavement surface temperature as a function of such weather conditions (air temperature, dew point, relative humidity, and wind speed) can provide an additional component that is essential for winter maintenance operations. This paper uses data collected by RWIS stations at the City of Ottawa to device a procedure that maximizes the use of a data batch containing complete, partially complete, and unusable data and to study the relationship between the pavement surface temperature and weather variables. Statistical models were developed, where stepwise regression was first applied to eliminate those variables whose estimated coefficients are not statistically significant. The remaining variables were further examined according to their contribution to the criterion of best fit and their physical relationships to each other to eliminate multicollinearities. The models were further corrected for the autocorrelation in their error structures. The final version of the developed models may then be used as a part of the decision-making process for winter maintenance operations.Key words: winter maintenance, pavement temperature, statistical modelling, RWIS.