Monitoring and Prediction of Traffic Noise in Large Urban Areas

Abstract

Featured Application: The method discussed in this paper has applications in the context of predicting traffic noise in large urban environments. The system designed by the authors provides an accurate description of traffic noise by relying on measurements of road noise from few monitoring stations appropriately distributed over the zone of interest. A prescription is given of how to choose the location of the noise stations.Abstract: Dynamap, a co-financed project by the European Commission through the Life+ 2013 program, aims at developing a dynamic approach for noise mapping that is capable of updating environmental noise levels through a direct link with a limited number of noise monitoring terminals. Dynamap is based on the idea of finding a suitable set of roads that display similar traffic noise behavior (temporal noise profile over an entire day) so that one can group them together into a single noise map. Each map thus represents a group of road stretches whose traffic noise will be updated periodically, typically every five minutes during daily hours and every hour during night. The information regarding traffic noise will be taken continuously from a small number of monitoring stations (typically 24) appropriately distributed over the urban zone of interest. To achieve this goal, we have performed a detailed analysis of traffic noise data, recorded every second from 93 monitoring stations randomly distributed over the entire urban area of the City of Milan. Our results are presented for a restricted area, the urban Zone 9 of Milan. We have separated the entire set of (about 2000) stretches into six groups, each one represented by a noise map, and gave a prescription for the locations of the future 24 monitoring stations. From our analysis, it is estimated that the mean overall error for each group of stretches (noise map), averaged over the 24 h, is about 2 dB.

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.…”

Time-Series Prediction of Environmental Noise for Urban IoT Based on Long Short-Term Memory Recurrent Neural Network

“…IoT must overcome some challenges to extract new insights from data [49]. In previous studies [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], environmental noise prediction mainly focused on the spatial propagation of noise, and there were few studies focused on the variation of noise in short-term. The noise data set used in this study is in seconds, which is more random.…”

Noise Annoyance in Urban Life: The Citizen as a Key Point of the Directives

“…The events captured in these four recording locations, which have been named hb115, hb124, hb127, and hb133, offer enough samples from each type and the widest variety of loudness measurements. See more details about the distribution of the sensors in District 9 of the city of Milan in [17]. Once the event database has been defined and before analyzing the samples in detail, the files have been shortened in duration to make them suitable for the perceptive tests.…”