Road surface temperature forecast is a key component of winter maintenance strategy in many developed countries. Numerical tools exist to help road managers to organize services and consequently to trigger de-icing operations. Forecasting strategies have been commonplace since the 1980s, and often based on numerical models. Traffic is one of the influencing parameters, specifically in urban areas. This work was undertaken to evaluate to which extent an accurate description of traffic might improve numerical model dedicated to road surface temperature forecasting. Two sets of experiments were run to detect and to quantify traffic effects on RST. First one consisted in driving above an infrared radiometer, a pyrgeometer and other atmospheric probes to measure the radiative contribution of a passing vehicle at various speeds. In the second set, an infrared camera was installed on a vehicle in an urban traffic flow. This camera was mounted on the roof and focused the pavement right behind the vehicle ahead, both circulating at the same speed. Infrared thermography indicated a fleeting contribution of traffic to RST. The temperature increase in circulated areas, with respect to uncirculated ones, does not last according to collected measurements. Measurements with atmospheric and radiometric probes provided elements to properly take into account traffic in a numerical model and to appreciate its contribution.

Thermal mapping uses IR thermometry to measure road pavement temperature at a high resolution to identify and to map sections of the road network prone to ice occurrence. However, measurements are time-consuming and ultimately only provide a snapshot of road conditions at the time of the survey. As such, there is a need for surveys to be restricted to a series of specific climatic conditions during winter. Typically, five to six surveys are used, but it is questionable whether the full range of atmospheric conditions is adequately covered. This work investigates the role of statistics in adding value to thermal mapping data. Principal components analysis is used to interpolate between individual thermal mapping surveys to build a thermal map (or even a road surface temperature forecast), for a wider range of climatic conditions than that permitted by traditional surveys. The results indicate that when this approach is used, fewer thermal mapping surveys are actually required. Furthermore, comparisons with numerical models indicate that this approach could yield a suitable verification method for the spatial component of road weather forecasts-a key issue currently in winter road maintenance.

A forecast road surface temperature (RST) helps winter services to optimize costs and to reduce the deicers environmental impacts. Data from road weather information systems (RWIS) and thermal mapping are considered inputs for forecasting physical numerical models. Statistical models include many meteorological parameters along routes and provide a spatial approach. It is based on typical combinations resulting from treatment and analysis of a database from measurements of road weather stations or thermal mapping, easy, reliable, and cost effective to monitor RST, and many meteorological parameters. A forecast dedicated to road networks should combine both spatial and time forecasts needs. This study contributed to building a reliable RST forecast based on principal component analysis (PCA) and partial least-square (PLS) regression. An urban stretch with various weather conditions and seasons was monitored over several months to generate an appropriate number of samples. The study first consisted of the identification of its optimum number to establish a reliable forecast. A second aspect is aimed at comparing RST forecasts from PLS model to measurements. Comparison indicated a forecast over an urban stretch with up to 94% of values within ±1°C and over 80% within ±3°C.

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