New Role of Thermal Mapping in Winter Maintenance with Principal Components Analysis

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.

Methodology to Forecast Road Surface Temperature with Principal Components Analysis and Partial Least-Square Regression: Application to an Urban Configuration

“…Results indicated that over 99% of the variance could be explained with the first principal component, indicating the data homogeneity. These results are consistent with a previous published study [12]. In the case of air , an average offset of −2.3 ∘ C was identified and this correction was then applied to air profiles calculated from PCA.…”

“…In the case of thermal mapping, each thermal fingerprint is considered to be a sample, and each variable as a data point in a multidimensional space. The variables are some of the physical parameters potentially included in a numerical model and affecting RST and as explained by Hammond et al [1,12]. By using the data from several thermal surveys, a data matrix is generated which can then be assimilated into clusters of points in this multidimensional space.…”

“…Marchetti et al [12] gave extensive details about the use of PCA to thermal mapping data and RST in particular. In this study, the same methodology was applied to air matrix.…”

“…They can also use numerical weather models outputs to provide a forecast for this specific spot, or more recently over the whole route using route-based forecasting techniques [8]. Marchetti et al [12] have indicated a way through PCA to obtain the whole RST profile using the RST at a single outstation, but which is not a forecast as it is usually understood. Furthermore, there is some controversy about the proper way to make RST measurement.…”

“…A numerical model will provide a temporal forecast on specific locations, and many computations will be necessary to build a forecast covering the full route or network. A new approach based on statistics was developed by Chapman et al [11,12] and provided RST over a full given route only through principal components analysis (PCA) but did not yet provide a RST forecast as it is usually understood, and it only relies on RST measurements which are not always available. To do so, partial least-square (PLS) regression could indeed contribute to such a goal.…”