# Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate
## 1 Introduction
For the region of Poland, there is a document [12] that dictates the manner of lighting design, taking into account the luminance coefficient Qo. In addition, there are also additional recommendations for expressways according to which the value of the luminance coefficient for pavement Qd should be higher than 70 mcd m\({}^{-2}\) k\({}^{-1}\), while in tunnels, Qd \(>\) 90 mcd\(\cdot\)m\({}^{-2}\) k\({}^{-1}\) mcd\(\cdot\)m\({}^{-2}\) k\({}^{-1}\). In the 1990s, an alternative to Qo was introduced in the form of the luminance coefficient under diffuse illumination, Qd. The luminance coefficient used in the data analysis, Qd, is intended to characterize the reflectivity of road markings in daylight or under road lighting. Portable devices, available on the market, are used in large numbers to determine the brightness of horizontal road markings and can also be used to surface roads [13]. The alternative \"brightness\" parameter Qd, i.e., the luminance coefficient under diffuse illumination, has been adopted for road surface markings, but the Qo parameter is still used in the pavement classification system adopted for road lighting. It is important to be able to accurately reduce Qo values from r-tables to Qd, which is included to some extent in the CIE document [14]. Despite the presence of the Qd parameter, the parameter that is still widely accepted for assessing brightness is the mean (equivalent) luminance coefficient Qo, which determines the weighted mean of the solid angle of the luminance coefficients in the r-table. The average luminance coefficient, Qo, can be calculated from the r-table using the weighting coefficient procedure [15]. It was shown that the mean luminance coefficient Qo is strongly correlated with the mean luminance produced on the road surface [5]. Unfortunately, there are still no mathematical models for predicting the luminance coefficient of an aggregate/asphal mixture under diffused illumination Qd. The existence of such a model or method of determination would allow the photometric properties of aggregates to be taken into account in the design of the mineral-asphalt mixture. Data mining can be proposed as an effective method providing simple regression or classification rules. However, it requires a large learning dataset. In addition, it also requires full validation of the adopted model. Solutions to complex problems, for which no a priorimathematical model is known, in road engineering using data mining techniques are quite rare and only recently began to be applied. Data mining techniques are so far mainly used to correctly predict the mechanical or physical properties of road materials. The undeniable advantage of data mining (DM) methods is that they can take into account numerous variables, both qualitative and quantitative. In addition, they are quite resistant to the occurrence of abnormal values, which many times can carry some peculiar and interesting information about the phenomenon under study. In their paper, [PERSON] et al. [16] used a number of DM techniques to effectively predict the water resistance of mineral-asphal mixtures. Some authors have used DM to improve pavement rutting resistance [17]. In their work, [PERSON] and [PERSON] [18] used a random forest algorithm to assess pavement durability using information on emerging damage. The estimation of the stiffness modulus was successfully determined using a falling weight deflectometer (FWD), taking into account a number of factors related to the measurement, using artificial neural network (ANN) or support vector machines (SVMs) [19; 20]. Additionally, SVM methods were used to predict the ITS parameter [21]. DM techniques have also excelled in predicting IRI [22] or skid resistance [23]. Far fewer applications of DM techniques have been reported for predicting the luminance coefficient of asphalt pavements and of the aggregate itself. It should be noted that the evaluation of these parameters is complex. It depends on the aggregate system, its origin, the macro-texture of the pavement, etc.