## 4 Conclusions  
It should be noted that aggregate resources are not inexhaustible. Therefore, their availability on the market will decrease over time. Currently, mines regulate the amount of extracted aggregate using the principle of sustainable development. Therefore, the amount of aggregate that is extracted depends on the forecasted demand in the long-term perspective. Moreover, it should also be taken into account that in the bituminous mixtures, there are also recycled aggregates, which will also require the assessment of properties and affect the pavement luminance value. Therefore, an effective numerical model will definitely facilitate their selection for the composition of mineral mixtures. The modelling process presented here proved that a very good representation of the luminance coefficient of the aggregate and the mineral mixture made with it can be obtained using machine learning techniques. Thus, the ability to predict the luminosity of a mineral mix will allow, at the design stage of a mineral and asphalt mix, the composition of the mix to be selected in such a way as to achieve the maximum level of luminosity while maintaining the required mechanical characteristics. The research and analysis resulted in the following conclusions:  
* The use of machine learning techniques is an excellent tool for modelling the luminance ratio of aggregates and mineral mixtures;
* The inclusion of a thorough petrographic analysis in the prediction of the aggregate luminance ratio resulted in some improvement in the prediction of the aggregate luminance ratio compared to the case in which its influence was expressed solely by the name of the aggregate;  
Figure 19: Probability distribution of the predicted diffuse luminance coefficient (Qdpredict) and the labelled luminance coefficient under artificial light (RL). * The mineral that was most decisive in the division between dark and light aggregates was pyroxene, which was characteristic of igneous rocks such as basalt and melaphyre;
* The mineral that is probably responsible for the increased reflectivity (nighttime visibility) was mica found in quartzite sandstone, among others;
* The best results in terms of surface brightening with Qd \(>\) 70 mcd\(\cdot\)m\({}^{-2}\).lx\({}^{-1}\) were obtained with quartzite sandstone (sedimentary) and granite (igneous), in which the quartz mineral predominated. In contrast, the sedimentary rock class was limestone, which contained mainly calcite;
* The amplified random tree (BT) technique used, supported by additional tuning and cross-check (test set) procedures, allowed the construction of a stable BT model characterised by an absolute error of MAE = 5.7 mcd\(\cdot\)m\({}^{-2}\).lx\({}^{-1}\) and RMSE = 4.4 mcd\(\cdot\)m\({}^{-2}\).lx\({}^{-1}\). The correlation value of the predicted values against the observed values was 87%. The present model effectively eliminated the singularities present, minimising outliers;
* The greatest influence on the construction of relationships in the amplified random tree model was the percentage and value of the luminance ratio of mainly the 2/5 and 2/8 fractions;
* An analysis of the individual impact of variables on the variability of the predicted BT model indicates that the use of light aggregate fractions with a grain size of 2/5 in quantities \(>\)40% or light aggregate 8/11 in quantities of 60% provides a rapid improvement in the degree of lightening of the mineral mixture;
* The presence of 5/8 fraction quartzite sandstone showed the best results in terms of brightening the mineral mix. In contrast, a sharp decrease in the luminance coefficient was recorded in mineral mixtures in which basalt aggregate occurring in the 5/8 fraction was used;
* It is important to note that the machine learning technique adopted was chosen as the most effective due to its quality in predicting the luminance coefficient. This does not mean that other techniques will not be effective.