Many countries around the world have chosen lockdown and restrictions on people’s mobility as the main strategies to combat the COVID-19 pandemic. These actions have significantly affected environmental noise and modified urban soundscapes, opening up an unprecedented opportunity for research in the field. In order to enable these investigations to be carried out in a more harmonized and consistent manner, this paper makes a proposal for a set of indicators that will enable to address the challenge from a number of different approaches. It proposes a minimum set of basic energetic indicators, and the taxonomy that will allow their communication and reporting. In addition, an extended set of descriptors is outlined which better enables the application of more novel approaches to the evaluation of the effect of this new soundscape on people’s subjective perception.

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.

As part of the Agence Nationale de Recherche Caractérisation des ENvironnements SonorEs urbains (Characterization of urban sound environments) project, a questionnaire was sent in January 2019 to households in a 1 km2 study area in the city of Lorient, France, to which about 318 responded. The main objective of this questionnaire was to collect information about the inhabitants' perception of the sound environments in their neighborhoods, streets, and dwellings. In the same study area, starting mid-2019, about 70 sensors were continuously positioned, and 15 of them were selected for testing sound source recognition models. The French lockdown due to the COVID-19 crisis occurred during the project, and the opportunity was taken to send a second questionnaire during April 2020. About 31 of the first 318 first survey respondents answered this second questionnaire. This unique longitudinal dataset, both physical and perceptual, allows the undertaking of an analysis from different perspectives of such a period. The analysis reveals the importance of integrating source recognition tools, soundscape observation protocol, in addition to physical level analysis, to accurately describe the changes in the sound environment.