Traffic Flow Detection Using Camera Images and Machine Learning Methods in ITS for Noise Map and Action Plan Optimization.

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.

A procedure for deriving wind turbine noise limits by taking into account annoyance.

With the increasing installation of wind farms, the attention of citizens towards wind turbine noise (WTN) has grown. Differently from some national legislations, the scientific community has promptly responded, increasing the studies and the social surveys in order to better understand the cause of disturbance and the indicators that relate to it. At first, the paper underlines the importance of low WTN levels for indirect health effects such as sleep disturbance and annoyance. The importance to consider noise annoyance in legislation is also discussed, as WTN is more disturbing than other most common noise sources. Then, conversion curves for equally highly annoyed are introduced considering the annoyance perceived by population in relation with the type of source. Finally, a specific limit value of 43 dB(A) for WTN is derived and suggested, comparable with British and Danish standards.