## 3 Results
### Regression Model of Aggregate Luminance Coefficient Taking Petrography into Account
The learning flow and parameter validity, using the RMSE metric, are shown in Figure 12.  
Note the results of the importance indicators. The predictor validity graph (Figure 12b) suggests that the feature related to the reflective properties of the aggregate illuminated by car headlights had the greatest influence on the formulated set of rules for the learning process. In addition, the aggregate grain size had a very important influence on the discriminatory rules obtained. There is a logical rationale for this, as a higher proportion of fine aggregate results in an increase in its volumetric proportion in the composition of the mineral mixture and an increase in its specific surface area. With regard to the mineralogical composition, the percentage of minerals such as pyroxene, plagioclase, and olivine mainly determined the distribution of the aggregate luminance value (Qd). Of the mineral assemblage, calcite (limestone and quartzite) and potassium feldspars (granite) played an important role in shaping the high clarity of the aggregate. It can be added that these minerals are mainly colourless, white minerals with a glassy shine. The analysis carried out was complemented by a variability graph developed based on the predicted results of the Qd trait using the BT model. The diagram below is shown in Figure 13.  
Figure 10:0 BT model learning results: (**a**) optimal number of trees; (**b**) predictor validity graph. Observing the results in Figure 13, it should be noted that the presence of a pyroxene-type mineral strongly divides the whole community into dark- and light-coloured aggregates according to the classification of the report [52]. According to its results, an aggregate with a luminance ratio of Qd \(<\) 70 mcd\(\cdot\)m\({}^{-2}\cdot\)k\({}^{-1}\) is an aggregate that is considered dark. It should be noted that pyroxene is a major component of deep-sea magmatic rocks, e.g., basalt. A very high Qd value was obtained for rocks containing calcite, which is the main component of a sedimentary rock such as limestone. The same picture is given by rules characterised by the presence of plagioclase and quartz, i.e., granitic rock. A very interesting observation is the interaction of quartz and mica, the minerals found in some grains of quartz sandstone aggregate. The interaction of these two minerals allowed for achieving Qd \(>\) 100 mcd\(\cdot\)m\({}^{-2}\cdot\)k\({}^{-1}\), which means aggregates with high lightening values for the potential road surface. The presence of the mineral mica (lat. 'shine') has already been signalled as improving the reflectivity of the aggregate in artificial light. In order to compare the effectiveness of the modelling including petrographic analysis, the results of the model including mineralogical composition were compared with the results provided by the model included in the work [15]. The following three metrics were used for benchmarking:  
* Root mean square error (RMSE);
* R-squared (R\({}^{2}\));
* Mean absolute error (MAE). The results of the comparison are shown in Figure 10:1  
Observing the results in Table 4, it should be noted that the degree of improvement in the prediction of the Qd trait value compared to the results in the [15] paper increased slightly (MAE decrease of only 0.2 mcd\(\cdot\)m\({}^{-2}\cdot\)k\({}^{-1}\)). The consideration of the mineralogical composition was of greater importance in the cognitive context of the aggregate luminance phenomenon. In the regression approach analysed, expressing the petrographic characteristics using only the name of the rock from which the aggregate was produced can be considered sufficient from the point of view of the predictive quality of the model. It is likely that extending the research programme to include rock chemistry would have a far greater impact on the accuracy of aggregate luminance ratio prediction. Figure 10:2 Graph of the variability of the projected Qd value of the aggregate.