## 3 Results  
### Luminance Coefficient Qd of the Aggregate vs. Mineralogical Composition  
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. A summary of the test results, regarding the change in Qd in relation to aggregate type, is shown in Figure 7.  
The unpublished Figure 7 shown represents the quality of the model fit by aggregate type. Taking into account the level of fractions used in Table 1, the quality of the represen  
Figure 6: Grading curves of SMA and AC. tation of reality by the gradient-enhanced tree (BT) model was R\({}^{2}\) = 0.96 with relative error MAE = 4.3 mcd\(\cdot\)m\({}^{-2}\).lx\({}^{-1}\). 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). Considering the value of the luminance coefficient, classifying the road pavement as black, Qd! 52 mcd\(\cdot\)m\({}^{-2}\).lx\({}^{-1}\), it must be concluded that basalt aggregate and melaphyre aggregate do not significantly affect the brightening of the pavement. In the case of gabbro rock aggregate, it does not allow for pavement brightening in certain cases. A much more favourable result in terms of magmatic rocks can be obtained by using aggregate from amphibolite and granite rocks. According to WT-2/2014, for surface illumination, aggregate with a Qd? 70 mcd\(\cdot\)m\({}^{-2}\).lx\({}^{-1}\), should be used, i.e., in the examined set, all with a luminance coefficient close to the median value (Figure 8). In this group, the best results were obtained using quartzite sandstone (sedimentary) aggregate and limestone (sedimentary). In this  
Figure 8: Probability density of predicted luminance coefficient values Qd relative to aggregate type based on work [15]. Figure 7: Results of fitting a regression model of gradient-enhanced trees. group, very good results can be achieved using granite aggregate (igneous). However, it should be noted that all the rocks mentioned are formed differently and have different mineralogical structures. Therefore, it is to be expected that certain groups of minerals will be responsible for the luminosity factor Qd results obtained. However, before a mineralogical analysis is carried out, it is worth projecting the RL feature, i.e., the level of glare that the rocks generate from artificial light (car headlights). A graphical representation of the probability distribution of the measured RL feature is shown in Figure 9.  
Figure 9 indicates the average observed level of road surface reflectivity RL falling within the range of 10 \(\div\) 30 \(\mathrm{m}\mathrm{c}\mathrm{d}\mathrm{\cdot}\mathrm{m}^{-2}\mathrm{. }\mathrm{ }\mathrm{x}^{-1}\). Basalt aggregate, similar to the Qd, does not introduce any additional improvement in the reflectivity of the pavement. The amphibolite aggregate also has a reflectivity similar to the road surface, although the level of the Qd characteristic was significantly higher than the threshold value at which the pavement is considered 'black'. A significant contribution to reflectivity was observed in the results of limestone, granite and quartzite sandstone aggregate. It is notable that among the results of the reflectance coefficient in artificial light for quartzite sandstone aggregate, there was a certain group of results in quartzite sandstone aggregate with an RL value \(>\)70 \(\mathrm{m}\mathrm{c}\mathrm{d}\mathrm{\cdot}\mathrm{m}^{-2}\mathrm{. }\mathrm{ }\mathrm{x}^{-1}\).