In order to manage noise pollution and reduce its environmental impact and health outcomes, several regulations have been issued in the last few decades, defining acoustic indicators and their thresholds. However, the acoustic environment can be considered a resource, focusing on people’s subjective perception of sounds in accordance with the soundscape approach. The integration of the tools, already applied by the legislation, and the soundscape technique produces a more thorough and comprehensive evaluation of the environmental noise that is necessary for its management. Starting from the best practice of the soundscape in urban planning, this paper presents an application of this approach at the Fisciano campus of the University of Salerno (Italy). The overarching goal is the comparison between the physical parameters, obtained by measuring the sound pressure level, and the psychoacoustic ones, derived by questionnaires given to a group of local experts during a soundwalk. The results will show, for example, some areas characterized by high sound pressure levels and a good perception of the soundscape. As a consequence, the application would seem to have discrepancies between the results of the two methods, but a deeper analysis can reveal further information to the traditional measurements that allow a more accurate knowledge of the acoustic environment.

Purpose of the Review: This paper is devoted to the review of the most popular literature Road Traffic Noise Models (RTNMs) frameworks, from the oldest ones to the recent machine learning techniques. A dedicated section is reserved to the review of Noise Emission Models (NEMs), with specific focus on approaches that allow the assessment of single vehicles’ emissions. Finally, some propagation models are also briefly presented, along with the assessment of the impact on the population of road traffic noise, in terms of time-averaged indicators and exposure descriptors. Recent Findings: In recent years, many efforts have been devoted to developing methods and models to assess the impact of environmental noise. Considering the primary role of road traffic as a noise source, estimating its impact is fundamental when evaluating the acoustic environment of a specific urban area. The scope of RTNMs is to provide an assessment of the noise emitted by the source in terms of traffic flows, propagate it at any desired point, including possible corrective factors, assess the impact at the receiver, and use this information to provide maps and other useful outputs. Summary: This review summarizes the so-far developed approaches for road traffic noise evaluation and furthermore underscores the ongoing necessity for research to develop more precise tools useful for managing road traffic noise’s adverse effects on urban environments and public well-being. Challenges and limitations of such models are discussed in the conclusions, highlighting the need for providing high quality input data and avoiding site-dependent approaches.

Road traffic noise is a severe environmental hazard, to which a growing number of dwellers are exposed in urban areas. The possibility to accurately assess traffic noise levels in a given area is thus, nowadays, quite important and, on many occasions, compelled by law. Such a procedure can be performed by measurements or by applying predictive Road Traffic Noise Models (RTNMs). Although the first approach is generally preferred, on-field measurement cannot always be easily conducted. RTNMs, on the contrary, use input information (amount of passing vehicles, category, speed, among others), usually collected by sensors, to provide an estimation of noise levels in a specific area. Several RTNMs have been implemented by different national institutions, adapting them to the local traffic conditions. However, the employment of RTNMs proves challenging due to both the lack of input data and the inherent complexity of the models (often composed of a Noise Emission Model–NEM and a sound propagation model). Therefore, this work aims to propose a methodology that allows an easy application of RTNMs, despite the availability of measured data for calibration. Four different NEMs were coupled with a sound propagation model, allowing the computation of equivalent continuous sound pressure levels on a dataset (composed of traffic flows, speeds, and source–receiver distance) randomly generated. Then, a Multilinear Regressive technique was applied to obtain manageable formulas for the models’ application. The goodness of the procedure was evaluated on a set of long-term traffic and noise data collected in a French site through several sensors, such as sound level meters, car counters, and speed detectors. Results show that the estimations provided by formulas coming from the Multilinear Regressions are quite close to field measurements (MAE between 1.60 and 2.64 dB(A)), confirming that the resulting models could be employed to forecast noise levels by integrating them into a network of traffic sensors.

The assessment of road traffic noise is very important for the health of people living in urban areas. Noise is usually assessed by field measurements, and predictive models play an important role when experimental data are not available. Nevertheless, when they are based on regression techniques, predictive models suffer from the drawback of strong dependence on the calibration data. In this paper, the authors present a regressive model calibrated on computed noise levels without the need for field measurements. The independence from field measurements makes the model flexible and adjustable for any road traffic condition possible. A multilinear regression technique is applied to establish the correlation between the computed equivalent noise levels and several independent variables, including, among others, traffic flow and distance. The model is then validated on a large field measurement database to check its efficiency in terms of prediction accuracy. The validation is performed both via error distribution analysis and using different error metrics. The results are encouraging, showing that the model provides good results in terms of the average error (less than 2 dBA) and is not susceptible to the presence of outliers in the input data that correspond to unconventional conditions of the traffic flow.

Noise coming from road traffic represents a major contributor to the high levels of noise to which people are continuously exposed—especially in urban areas—throughout all of Europe. Since it represents a very detrimental pollutant, the assessment of such noise is an important procedure. Noise levels can be measured or simulated, and, in this second case, for the building of a valid model, a proper collection of input data cannot be left out of consideration. In this paper, the authors present the development of a methodology for the collection of the main inputs for a road traffic noise model, i.e., vehicle number, category, and speed, from a video recording of traffic on an Italian highway. Starting from a counting and recognition tool already available in the literature, a self-written Python routine based on image inference has been developed for the instantaneous detection of the position and speed of vehicles, together with the categorization of vehicles (light or heavy). The obtained data are coupled with the CNOSSOS-EU model to estimate the noise power level of a single vehicle and, ultimately, the noise impact of traffic on the selected road. The results indicate good performance from the proposed model, with a mean error of −1.0 dBA and a mean absolute error (MAE) of 3.6 dBA.

A university campus is a complex environment, as it comprises many areas, each with distinctive acoustic characteristics. There are zones where a quiet environment is crucial for work and study, parking lots and internal road networks, as well as vibrant spaces where students and personnel stay during leisure time. Thus, university campuses clearly offer a remarkable opportunity for the evaluation of soundscape analysis tools and the exploration of new methodologies for assessing and mapping soundscapes. In this work, the Fisciano campus of the University of Salerno (Italy) has been depicted as a case study. Specifically, the outcomes of a soundwalk conducted following ISO/TS 12913 standards will be presented, together with data gathered through crowd-sourcing during NoiseCapture parties. The data from NoiseCapture parties have been used to test and validate an innovative methodology based on an inverse distance weighting interpolation to produce soundscapes’ attributes maps, using the mean pleasantness as an example and exploring the relative influence of the maximum distance used to characterize an homogeneous sound environment to build the maps themselves. The results will offer valuable insights into the proposed methodology.