Urban advanced noise indicator mapping relying on street categorization and measurements

In order to more accurately estimate health outcomes related to environmental noise exposure such as sleep disturbance and noise annoyance, indicators beyond long-term equivalent sound pressure levels might be needed (such as statistical levels, number of events, psycho-acoustical indices etc). In urban noise mapping, predicting these more advanced noise indicators is especially challenging. In the current work, an open source noise mapping code (NoiseModelling) is combined with micro-traffic simulations. However, in most cities, traffic data availability is poor, especially in low traffic streets. To overcome this issue, the noise mapping procedure developed here assumes no access at all to traffic information and fully relies on Open Street Map street categorization. These street categorizations were then assigned sets of plausible traffic compositions, counts and speeds; various scenarios were explicitly simulated. In a next step, these traffic scenarios were weighted to best fit a set of 29 noise indicators on 23 measurement stations deployed in the city of Barcelona, during various periods of the day. It was shown that this procedure leads to adequate assessments of a wide range of noise indicators in a specific city.