Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate

Abstract

The primary purpose of the analysis presented here is to assess the feasibility of effectively predicting the aggregate luminance coefficient. Current road lighting standards and recommendations are based on assessing the level and distribution of luminance on the road surface. The brightness of a road surface depends on the amount of light falling on it, as well as the reflective properties of the road surface, which in turn depend on its physical condition, type and mineralogical composition. The complexity of the factors on which the value of the luminance coefficient depends it makes that data mining techniques the most appropriate tools for evaluation luminance coefficient phenomenon. This article uses five types of techniques: C&RT, boosted trees, random forest, neural network, and support vector machines. After a preliminary analysis, it was determined that the most effective technique was the boosted tree method. The results of the analysis indicated that the actual value of the luminance coefficient has multiple modal values within a single aggregate stockpile, depending on the mineralogical composition and grain size, and cannot be determined by a single central measure. The present model allowed us to determine the value of the luminance coefficient Qd with a mean error of 4.3 mcd-m−2·lx−1. In addition, it was found that the best aggregate for pavement brightening that allows high visibility during the day Qd and at night RL is a limestone aggregate. In the group of those that have the ability to potentially brighten the pavement were quartzite and granite aggregates.

Modelling of the Luminance Coefficient in the Light Scattered by a Mineral Mixture Using Machine Learning Techniques

“…Despite advanced luminance ratio measurement methods, there are insufficient analyses of the extent of aggregate luminance ratio estimation in the world literature. The conclusions of the study, as reported in [15], indicate that the mineralogical composition of the aggregate is an important aspect in assessing the luminance level of the aggregate. The promising results have encouraged further work to evaluate the luminance coefficient of the mineral mix.…”

“…The detailed process of applying the boosted random tree (BT) algorithm required to predict the luminance ratio Qd is explained in detail in the article [15]. The results that fell within its scope provide the foundation for the search for further links that are the subject of this article.…”

“…Observing the results in Figure 7, it can be seen that the greatest dispersion of results was observed against the granite aggregate. With the results of the resulting BT model [15], a projection was made to illustrate the probability density of the predicted feature (Predicted Qd) by rock type. A graphical representation of the projection is shown in a ridge diagram developed in R (Figure 8).…”

“…The input file used to build the model was based on the data collected during the petrographic analysis and the data given in Table 1. The BT technique was chosen for the analysis in order to be able to refer to the results in the paper [15]. The optimal learning parameters were determined after taking the tuning process into account.…”

“…It was pointed out that this type of regression technique is only applicable to the object under study, and its generalisation requires consideration of more factors. A reason for the low applicability of regression models based on the least squares method may be the observations in the work of Mazurek et al [15]. It indicates that the probability distribution of the luminance ratio is not normal.…”