# Calibration and Validation of a Measurements-Independent Model for Road Traffic Noise Assessment
## 4 Conclusions  
In this paper, a new model for road traffic noise level assessment was presented. The idea was to build a model with a multilinear regression technique calibrated with computed data rather than measured ones. The implemented formula for computing the \(L_{eq}\) values for the model calibration is simple and versatile since it easily permits to choose a different the Noise Emission Model for the computation of source power levels and provides the opportunity to compute equivalent noise levels over different timespans. The modularity of the choice of the NEM makes the model compatible with any national model and with the EU's harmonized CNOSSOS framework, also allowing for the inclusion of different propulsion systems, such as electric and hybrid engines, which are more and more present in national fleets. Such a paradigm permitted the creation of a calibration dataset that is as general as possible to cover a multiplicity of traffic flow situations. The random generation of the independent variable values used for the calibration of the model most likely improved this characteristic of the model even more. It is worth noting that the multilinear regression technique allows for the inclusion of different variables according to the situation. All these features make the model usable in a very large number of situations. A statistical analysis of the residuals of the model in the calibration phase and the errors when validating it with a real dataset allowed us to investigate the model performance as a function of the measured noise levels, total traffic flow, and percentage of heavy vehicles. The validation was performed also removed unconventional conditions related to traffic and/or the measured levels, thus removing the outliers identified during the analysis. The error metrics confirmed a slight improvement in the absolute errors, although the model also provided average errors lower than 2 dBA, including nonstandard situations. This result is promising since the indicators usually adopted by national regulations are defined by a large time range average, which can be day, evening, or night timespans, as well as full days (\(L_{den}\)). Thus, a model that provides good performance on average is expected to furnish reliable predictions for these time ranges. The limitations of the model can be highlighted firstly in the use of average speed for the light, medium, and heavy vehicle categories. To achieve a fully dynamic and microscopic model, in fact, the speed of each vehicle is needed. In addition, the presented model assumes that the traffic flows are equally distributed in both direction lanes, resulting in possible underestimation and overestimation during special hours of the day, in which there is a privileged direction of vehicles (for instance, ingoing or outgoing from downtown, respectively, at the beginning and end of working days). To address such limitations, the future steps of this research will be in the direction of the generation of a fully microscopic model, which will take into account the single velocity of each vehicle and its position with respect to the receiver. Finally, as discussed in the outlier analyses, there are possible phenomena that can lead to overestimation or underestimation in the low- and high-value regions of the considered variables. A detailed study of unconventional situations is beyond