Cluster categorization of urban roads to optimize their noise monitoring

Abstract

Road traffic in urban areas is recognized to be associated with urban mobility and public health, and it is often the main source of noise pollution. Lately, noise maps have been considered a powerful tool to estimate the population exposure to environmental noise, but they need to be validated by measured noise data. The project Dynamic Acoustic Mapping (DYNAMAP), co-funded in the framework of the LIFE 2013 program, is aimed to develop a statistically based method to optimize the choice and the number of monitoring sites and to automate the noise mapping update using the data retrieved from a low-cost monitoring network. Indeed, the first objective should improve the spatial sampling based on the legislative road classification, as this classification is mainly based on the geometrical characteristics of the road, rather than its noise emission. The present paper describes the statistical approach of the methodology under development and the results of its preliminary application to a limited sample of roads in the city of Milan. The resulting categorization of roads, based on clustering the 24-h hourly LAeqh, looks promising to optimize the spatial sampling of noise monitoring toward a description of the noise pollution due to complex urban road networks more efficient than that based on the legislative road classification.

Classification of Urban Road Traffic Noise based on Sound Energy and Eventfulness Indicators

“…Road categorization is often applied to stratified samplings of urban road traffic noise based on some road attributes. This sampling, aimed to get data variability within each stratum less than that between the strata, can be fruitfully applied not only to optimize the resources and time involved in noise monitoring [37], but also to be integrated in the noise mapping process [39]. Unfortunately, among the possible road attributes to be selected for categorization, the Italian road functional classification appears to not be appropriate because, as clearly shown in Table 2, the partitions determined by k-means cluster analysis include different road types.…”

“…It can be seen that, for 12 out of 26 bins (46.2%), there is an overlap between the two 263 distributions. In order to figure out a threshold value to discriminate the cluster membership, the 264 procedure to determine an analytical representation of the distribution functions in each cluster, as 265 described in[37,38], was applied. The probability distributions for the two clusters P(x) can be 266 obtained from the analytical fit of the cumulative distribution I(x) according to the following267 relationship: 268 ( ) = ln (10) • ( )· ( ) (8) where f(x) is a 3rd order polynomial, f ' (x) is the derivative of f(x), and I(x) is the cumulative 269 distributions of x.…”

Dynamic Noise Mapping in the Suburban Area of Rome (Italy)

“…Given the different environmental features of the two pilot areas, a diverse approach was used to size and set up the DYNAMAP system. In the pilot area of Milan a statistical approach was employed [23][24][25], while in the pilot area of Rome a deterministic method was applied. In the following paragraphs, a detailed description of the design and configuration of the system installed in the pilot area of Rome is reported, starting from the selection of the test sites, up to the preparation and update of the basic noise maps.…”

Design of a Mobile Low-Cost Sensor Network Using Urban Buses for Real-Time Ubiquitous Noise Monitoring

“…In [ 102 ], a statistically-based method to choose the optimal number and location of static monitoring sites to improve the noise mapping is presented, being focused to the development of noise maps with data obtained from a low-cost network. They present some preliminary results of the application of the method to the streets of Milan, improving the spatial sampling considering legislative road divisions, which are notonly based on geometry, but on noise emission.…”