# Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate
## 5 Conclusions  
Based on the research and analyses performed, the following conclusions were formulated:  
* Based on the preliminary analysis, the highest efficiency was achieved by the Boosted Tree (BT) model, which, despite the similar value of the coefficient of determination > 0.95, against the neural network technique had the lowest average absolute error < 4.3 mcd-m\({}^{-2}\).lx\({}^{-1}\).
* In the analysis presented here, it was observed that one aggregate can have several modal values of Qd, which depend on their mineralogical composition. Therefore, only DM techniques are able to correctly describe the variability resulting from the aforementioned random factors, taking into account the numerous sources of aggregates.
* The significant overestimation of Qd for aggregate with a grain size of 0/2, regardless of its petrographic origin.
* Obtained results are in line with WT-2/214, which suggests that coarse aggregate fractions with a grain size of >4 mm should be used to determine the luminance coefficient.
* The presence of certain minerals of a given aggregate causes the luminance coefficient that characterizes daytime and nighttime visibility to reach significant value variations. In this study, it was proven that the petrographic description has the greatest impact on Qd.
* The lack of a full correlation between Qd and RL suggests that nighttime visibility is related to the presence of certain \"inclusions\" of individual minerals that enhance the contrast of the pavement when illuminated by vehicle lighting.
* The best option for pavement brightening was obtained by limestone aggregate. The luminance coefficient determining daytime visibility (median) has a value of Qd = 85 mcd-m\({}^{-2}\).lx\({}^{-1}\), nighttime visibility RL = 49 mcd-m\({}^{-2}\).lx\({}^{-1}\), and a very good asphalt affinity. Other suitable aggregates for pavement brightening, but with lower RL values, were quartzite (Qd = 86 mcd-m\({}^{-2}\).lx\({}^{-1}\), RL = 48 mcd-m\({}^{-2}\).lx\({}^{-1}\)) and granite (Qd = 99 mcd-m\({}^{-2}\).lx\({}^{-1}\), RL = 43 mcd-m\({}^{-2}\).lx\({}^{-1}\)). Aggregates that reached the threshold value of 60 mcd-m\({}^{-2}\).lx\({}^{-1}\) were amphibolite and gabbro. The other aggregates in the data set did not allow for an acceptable level of pavement brightness enhancement.  
Conceptualization, G.M.; Methodology, G.M.; Validation, P.B.-P.; Formal analysis, G.M.; Investigation, P.B.-P.; Resources, P.B.-P.; Data curation, P.B.-P.; Writing--review and editing, G.M. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the program of the Minister of Science and Higher Education under the name: Regional Initiative of Excellence in 2019-2022, project number 025/RID/2018/19, financing amount PLN 12,000,000.  
Not applicable.  
This research was conducted as a part of the project \"The use of recycled materials\" under the RID (The Development of Road Engineering Innovations, number DZP/RID-I-06/1/NCBR/2016), project of the National Center for Research and Development-Poland, and the Polish General Directorate for National Roads and Motorways. The work is supported by the program of the Minister of Science and Higher Education under the name: Regional Initiative of Excellence in 2019-2022, project number 025/RID/2018/19, financing amount PLN 12,000,000.  
The authors declare no conflict of interest.