# Optimized Sensors Network and Dynamical Maps for Monitoring Traffic Noise in a Large Urban Zone
## 2 State of the Art
### The Spatial and Temporal Structure of Road Traffic Noise in Large Urban Zones  
Monitoring road traffic noise has become a widespread technique to assess the impact of noise on public health in large cities, drawing a great deal of attention to researchers in this field. For example, the temporal and spatial variability of road traffic noise in the city of Toronto (Canada) has been studied in quite a lot of detail [77]. Real-time measurements of road traffic noise at about 600 locations across the city have been collected over a period of six months. It was observed that noise variability was predominantly spatial in nature, rather than temporal, accounting for 60% of the total observed variations in traffic noise. Traffic volume, length of road stretch, and industrial area were identified as the three most important factors explaining such spatial variability of noise. This suggests that there is a well defined spatial structure of noise associated with different types of roads.  
An efficient noise control approach has been discussed, leading to a classification of given locations, in particular in sensitive areas, according to the different prevailing traffic conditions. An expert system, based on machine learning type of algorithms, is developed aimed at classifying urban locations based on their traffic composition. The procedure was tested on a full database from the city of Granada (Spain) [78], including urban locations with road traffic as the dominant noise source, suggesting useful tools for addressing problems related to traffic noise, and also how to mitigate them.  
It has been also shown that categorization is a powerful method for describing urban sound environments within a district of the city of Marseille (France) [79]. The method is based on a statistical clustering analysis selecting relevant noise indicators for a better classification of sound environments. The clustering analysis shows that a limited number of indicators is sufficient to discriminate between sound environments. Such theoretical studies, relying on noise monitoring data, have been used to construct urban noise maps as an attempt to estimate the actual population exposure to environmental noise, and, eventually, being able to identify the most appropriate mitigation actions. Furthermore, automatic sound recognition (ASR) techniques have been reviewed [80], showing that, similarly to speech recognition systems, the robustness of ASR largely depends on the choice of feature(s) and classifier(s). The review provides an overview of its past and recent applications such as sound event recognition, audio surveillance, and environmental sound recognition.  
A model-based interpolation method to calculate dynamic noise maps, for medium-density noise monitoring networks, has been proposed [81]. The model is able to track varying sound powers over time, and can also account for temporal variations in the sound propagation conditions. Both equivalent levels and percentile noise levels are considered, and the basic assumption is that there is a reasonably good model for predicting sound indicators in the area under study. The model is, however, not very accurate for instantaneous level prediction, as inaccuracies may occur in the emission of the source but also in the calculated propagation path. The interpolation tunes the source and propagation properties on the basis of measurements, and in that way one may improve the predictions at locations where no measurements are available.