# Calibration and Validation of a Measurements-Independent Model for Road Traffic Noise Assessment
## 1 Introduction  
Noise, especially when coming from road traffic, is one of the most pervasive pollutants. Especially in urban areas, a large percentage of the population is estimated to be exposed to noise levels largely exceeding the limits fixed by laws [1]. Since urbanization has expanded, people are constantly in contact with a large number of vehicles, and continuous exposure to the generated noise levels leads to physical and psychological detriments, such as high blood pressure and sleep deprivation, together with mental disorders [2; 3; 4]. Much evidence for this correlation can be found in the literature; for example, some works show that intrusive sounds can severely affect sleep [5] and mental health [6]. Traffic noise in urban areas is not only related to the direct exposure of pedestrians but also accounts for people in their houses since many buildings can be reached by high noise levels [4]. For this reason, the estimation of the amount of noise in urban areas is very important for human health assessments [7]. Aside from the measurement of such noise levels to ease inhabitants' conditions, many different models for the estimation of noise in a certain area can be found, all with the common goal of best describing the noise level, starting with different parameters such as the number and type of passing vehicles, the distance of the receiver from the noise source (the vehicle itself), the presence of roundabouts and/ or intersections [8], and speed, as well as also climate conditions [9], the location or absence of acoustic barriers, and so on [10; 11]. Such a comprehensive traffic noise model could provide a proper prediction of noise levels to assist authorities and planners in setting up and applying noise mitigation measures, for example, for the design of new roads or for the renewal of existing ones [7]. Models canbe generated at many different levels of complexity and efficiency, and a proper balance between these two aspects is necessary to implement a practical and effective one. For instance, computational fluid-dynamic approaches are often used in the automotive and airplane industries to design noise reduction systems [12]. Generally speaking, the main types of models are statistic, analytic, microscopic, macroscopic, semi-dynamic, and dynamic [13]. Focusing on time dependence, the models are usually built to predict noise indices over a certain period of time dependent on selected independent variables [14]. Variables chosen can vary in type and number, but the most used are certainly traffic volume, the percentage of heavy vehicles, and the distance between the source and receiver. Additional parameters considered can be the road slope, the texture of the asphalt, the average speed of the flow, and the weather conditions. These kinds of models are flexible and easy to use, including for non-expert users. They are basically functions in which selected \"independent\" parameters are related to the noise level through proper coefficients. Their efficiency strongly depends on the calibration step, that is, the process that establishes and details the correlation between each parameter and the noise level. When this process is run through regression using in-field-measured data, the model belongs to the statistical category. The importance of this process is evident when trying to apply such a model to a road traffic framework very different from the one used for calibration. In such cases, in fact, the efficiency of the model output can easily be lowered. For this reason, many national laws establish which model can be used in the country. Some of the models adopted in national regulations are listed in Ref. [15]: the CoRTN model in the United Kingdom [16], the RLS90 model in Germany [17], the NMPB model in France [18], the AS) model in Japan [19], and the SONROAD model in Switzerland [20].