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Noise barriers are the most widespread solution to mitigate noise produced by the continuous growth of vehicular traffic, thus reducing the large number of people exposed to it and avoiding unpleasant effects on health. However, conventional noise barriers present the well-known issues related to the diffraction at the edges which reduces the net insertion loss, to the reflection of sound energy in the opposite direction, and to the complaints of citizens due to the reduction of field of view, natural light, and air flow. In order to avoid these shortcomings and maximize noise abatement, recent research has moved toward the development of sonic crystals as noise barriers. A previous review found in the literature was focused on the theoretical aspects of the propagation of sound through crystals. The present work on the other hand reviews the latest studies concerning the practical application of sonic crystal as noise barriers, especially for road traffic noise mitigation. The paper explores and compares the latest developments reported in the scientific literature, focused on integrating Bragg’s law properties with other mitigation effects such as hollow scatterers, wooden or recycled materials, or porous coating. These solutions could increase the insertion loss and frequency band gap, while inserting the noise mitigation action in a green and circular economy. The pros and cons of sonic crystal barriers will also be discussed, with the aim of finding the best solution that is actually viable, as well as stimulating future research on the aspects requiring improvement.

Noise maps and action plans represent the main tools in the fight against citizens’ exposure to noise, especially that produced by road traffic. The present and the future in smart traffic control is represented by Intelligent Transportation Systems (ITS), which however have not yet been sufficiently studied as possible noise-mitigation tools. However, ITS dedicated to traffic control rely on models and input data that are like those required for road traffic noise mapping. The present work developed an instrumentation based on low-cost cameras and a vehicle recognition and counting methodology using modern machine learning techniques, compliant with the requirements of the CNOSSOS-EU noise assessment model. The instrumentation and methodology could be integrated with existing ITS for traffic control in order to design an integrated method, which could also provide updated data over time for noise maps and action plans. The test was carried out as a follow up of the L.I.S.T. Port project, where an ITS was installed for road traffic management in the Italian port city of Piombino. The acoustic efficacy of the installation is evaluated by looking at the difference in the acoustic impact on the population before and after the ITS installation by means of the distribution of noise exposure, the evaluation of Gden and Gnight, and the calculation of the number of highly annoyed and sleep-disturbed citizens. Finally, it is shown how the ITS system represents a valid solution to be integrated with targeted and more specific sound mitigation, such as the laying of low-emission asphalts.

This paper proposes a system based on Neural Networks (NN), designed for providing an efficient, non-invasive and automated method for monitoring the health status of road pavements by using features derived from Tyre Cavity Noise (TCN) analysis. Indeed, visual inspection remains to date the most common choice for evaluating the condition of road pavements; however, this method is both labor intensive and time consuming. The system presented in this work uses a microphone placed inside the vehicle tyre that measures TCN while travelling normally, and an embedded data acquisition system based on a Raspberry Pi which feeds the NN tools to recognize and classify road deterioration. We also present a preliminary analysis of features based on temporal and spectral characteristics of TCN signals generated by tyre/road interaction and acquired on three different kind of road distresses. The results show good classification capability and, moreover, the sound pressure measured inside the tyre was correlated accelerometric data measured on-board.