# Calibration and Validation of a Measurements-Independent Model for Road Traffic Noise Assessment
## 2 Materials and Methods  
An algorithm for the generation of a 2000-occurrences data frame was implemented. Each row of the data frame represents a fictive traffic flow situation in which different vehicles are moving at a given average speed, and the impact of the noise can be evaluated in variable timespans--hours, fractions of an hour, or multiples of an hour--at variable distances. Vehicles flowing can belong to \"light\", \"medium\", or \"heavy\" vehicle categories. According to French regulations [24], for instance, \"light\" vehicles are common passenger cars with a gross weight of less than 4500 kg, \"medium\" vehicles have a gross weight below 3500 kg but a length equal to or larger than 3 m, and \"heavy\" vehicles exceed 3500 kg of gross weight. The first step was the generation of a computed dataset, and it consists of the random generation of 2000 fictitious traffic conditions identified by 6 independent variables and the subsequent calculation of noise equivalent levels. The chosen six independent variables are as follows: volume of traffic (\(Q\)), expressed as the number of moving vehicles per hour; average velocity of each vehicle type (light vehicles, medium vehicles, and heavy vehicles, respectively, \(V_{L}\), \(V_{M}\), and \(V_{H}\)); percentage, \(P\), of heavy vehicles over the total flow; and the distance between the center of the highway and the base of the acoustic masts (receiver), \(d\). The choice of working with average speeds and with a single value for the traffic flow for both directions was necessary to reduce the complexity of the model and optimize the computational effort. However, this choice may represent a limitation to the model, since some hours of the day, in particular circumstances, are characterized by a large variation in single vehicle speed and by a strong asymmetry in the flow's directions. The range of the values of the independent variables are as follows: \(P\) values range from a minimum of 0 to a maximum of 20%, with steps of 0.1%; \(V_{L}\) values range from 30 to a maximum of 130 km/h, with steps of 5 km/h; \(V_{M}\) values span from 30 to a maximum of 100 km/h, with steps of 5 km/h; and \(V_{H}\) values span from 30 to a maximum of 80 km/h, with steps of 5 km/h. Variable \(d\) values range from 10 to 100 m, with steps of 1 m. The independent variables were chosen with two different criteria: a ladder function and a random function. The execution of the functions is sequential, as indicated by the pseudocode available in the Supplementary Materials, where the ranges of each variable value are also reported in detail. The first independent parameter chosen is \(Q\), and it is built with the ladder approach, which is a simple function generating integer values from a minimum of 10 to a maximum of 2000 veh/h, with intervals of 10 veh/h. For each of these flow generation step, the other variables are added, finalizing the construction of the dataset. Specifically, for every \(Q\) value sampling step, the algorithm implements \(20\times\) loops to associate all the other independent variables, which were chosen using the second criterion, i.e., randomly. For each \(Q\), then, 20 casual combinations of the other independent values (\(P\), \(V_{L}\), \(V_{M}\), \(V_{H}\), \(d\)) are generated,completing each final occurrence, now described by the whole set of independent variables and useful for the \(L_{eq,h}\) calculation. The random choice of the values of the independent variables is performed in a specific order: after \(Q\), \(P\) values are generated by randomly picking a value from the described interval. Once a \(P\) value is obtained, the number of medium and heavy vehicles is calculated from \(P\) by rounding the results to obtain an integer number and dividing it by two (so that the number of heavy and medium vehicles is the same for each occurrence). Finally, the number of light vehicles is obtained by subtracting medium and heavy vehicles by the whole of \(Q\).