Abstract
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.
Highlights
On marginal nights in winter, a difficult decision is often faced by highway engineers of whether or not to treat the road network to prevent ice formation
The aim of this study is to use principal components analysis (PCA) to statistically analyse thermal mapping data to obtain a means of interpolation between surveys to provide a more comprehensive picture of road surface temperature (RST) variation for a broader range of atmospheric conditions than that traditionally covered by thermal mapping surveys
The first objective of this paper is to identify the global benefit of PCA and the correct number of thermal mapping runs required to produce an accurate daily temperature pattern along a given route based on PCA
Summary
On marginal nights in winter (i.e., where the temperature is close to freezing), a difficult decision is often faced by highway engineers of whether or not to treat the road network to prevent ice formation This decision is facilitated by consulting a road weather information system, consisting of a network of site-specific road weather outstations and associated daily forecasts. A range of forecast models exist and a significant body of literature has accrued and Hammond et al gave a thorough review [1] This approach is site-specific and with variations of over 10∘C not uncommon around a road network [2], a reliable means of forecast interpolation is required. This has traditionally been achieved via thermal mapping, but of all the components contained within road weather information systems (RWIS), it is this interpolation that has frequently been identified as the least satisfactory [3]
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