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

Abstract

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.

Prediction Model for Asphalt Pavement Temperature in High-Temperature Season in Beijing

“…T air , N, U, Ff, and RRR represent the variables of air temperature, cloud cover, air humidity, wind speed, and precipitation, respectively; T 5 ,T 19 , and T 24 represent the temperature (°C) at the depth of 5 cm, 19 cm, and 24 cm from road surface, respectively. e observed data matrix of the independent variables is recorded as A � (a ij ) 248×5 and that of the dependent variables is recorded as B � (b ij ) 248×3 .…”

“…Later on, Diefenderfer et al [17] and Wang and Roesler [18] adopted the monthly mean temperature as a correction coefficient to extend the application scope of this model and thereby improved its prediction accuracy. Further, Marchetti et al [19] proposed a model based on principal component analysis (PCA) and partial least squares (PLS) regression that used air temperature to predict temperature of road surface. In the past decade, many similar studies have also emerged in China, such as the reports from Cao et al [20] and Kang et al [21] in 2007, Bai et al [22] in 2011, Xu et al [23] in 2013, Dong et al [24] in 2014, and Zeng et al [25] in 2016.…”

Identification of the Impacts of Climate Changes and Human Activities on Runoff in the Jinsha River Basin, China

“…where is the variance of ; , , and are parameters obtained by the least square method [26]. The calibration procedure of the method is listed as follows:…”

Appreciation of the delay in the benefits of the thermal energy released by PCM in civil engineering structures

“…Maintaining mobility on roads and the possibility for aircrafts to land and to take-off is a major issue in poor weather conditions as encountered in winter. Many efforts have been developed over the years to evaluate the economical costs of winter maintenance [1], its environmental impacts [2] or to forecast these adverse situations [3][4][5][6]. Still the amounts of de-icers are increasing, conducting to consider a modification of the infrastructure itself to make it compliant with the avoidance of ice occurrence and snow accumulation, as performed on the Serso bridge in Switzerland [7].…”