###### Abstract
ML and AI algorithms, in fact, are built with an approach different from the one of the RTNMs: they take into input a series of independent parameters (not necessarily the same used by RTNMs) and an output (Leq), but the relationship between the independent input parameters and the output one is depicted and learned by statistical laws rather than physical ones. In such a way, many restrictions of usage fall, and a good prediction can also be performed. Several examples of such approaches can be found in the literature. In [19], a complete bibliographic review describing AI applications in the field of road traffic noise is provided, and in it, many techniques are described, including the ANN. In [20], an ANN is fed with the same parameters of conventional RTNMs: speed and types of vehicles are used to predict Leq values in an urban context, while in [21], a similar procedure is implemented but with a higher number of different parameters, and in [22], the asphalt type is also taken into consideration. ML techniques are presented in [23], also in comparison with ANNs: a Support Vector Machine has been used to retrieve noise levels. In [24], other algorithms have been added, like Decision Tree and Random Forest. AI application to the field can be found in [20; 25], where the number of flowing vehicles and the composition of vehicles in terms of heavy percentage and average speed are used as input to implement an artificial neural network (ANN) model to retrieve Leq in Indian roads. In [21], the authors used the same inputs but added a more detailed description of the vehicle category, and in [22], the pavement road type was also considered. The authors also successfully published contributions on the topic of ML and ANNs applied to road traffic noise modeling. As for ML, in [26], the authors provided a good prediction capability of multilinear regression methods on real data, with the peculiarity of being calibrated with a set of computed data (which have been further improved in [27; 28]). Regarding the usage of ANNs, in [29], the authors used 342 h of data collected at nineteen intersections of an Indian city to calibrate a very complete ANN. The here presented contribution, then, fits in a consolidated research field, and it proposes to bring a novelty regarding the calibration. The proposed model, in fact, is an ANN implemented to predict the stochastic component of LAeq, 16 h due to traffic noise,using as a unique input parameter the residuals of the application of a Time Series Analysis (TSA) model. A Deterministic Decomposition Time Series Analysis (DD-TSA) process, in fact, is adopted to highlight trends, seasonality and random components of the time series. An ANN is trained on the residuals (difference between real measured data and predictions of the DD-TSA model) of the calibration phase, to estimate the random component and to be used in cascade with the DD-TSA model. The aim is to improve the estimation of the random component and, consequently, to improve the prediction of the noise levels at future time steps, with a forecast horizon that depends on the ANN input vector. The DD-TSA model construction is based on the detection of a periodicity in the data and on the proper modeling of the seasonal coefficients. The Ljung-Box statistical test [30] was used to detect the presence of significant autocorrelation (and consequently of significant periodicity) in the data. Then, the seasonal lag was chosen, using autocorrelation function maximization. This technique highlighted a strong weekly periodicity in the time series studied that is coherent with the features of the noise source. The data are mostly related to urban noise in a medium-sized Italian city, collected at the edge of a main road. Though urban noise is not exclusively due to traffic, vehicles are surely the main contributor, and it is well known that the traffic phenomenon is strongly influenced by the day of the week.