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

Noise energetic indicators, like Lden, show good correlations with long term annoyance, but should be supplemented by other parameters describing the sound fluctuations, which are very common in urban areas and negatively impact noise annoyance. Thus, in this paper, the hourly values of continuous e...

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Bibliographic Details
Published in:Applied sciences 2020-04, Vol.10 (7), p.2451
Main Authors: Brambilla, Giovanni, Benocci, Roberto, Confalonieri, Chiara, Roman, Hector Eduardo, Zambon, Giovanni
Format: Article
Language:English
Subjects:
Acoustics
Classification
Cluster analysis
Clustering
Data processing
Daytime
Indicators
Noise
noise events
Noise levels
Noise monitoring
road classification
Roads
Roads & highways
Sound
Time series
Traffic flow
Transportation noise
Urban areas
urban road traffic noise
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Summary:Noise energetic indicators, like Lden, show good correlations with long term annoyance, but should be supplemented by other parameters describing the sound fluctuations, which are very common in urban areas and negatively impact noise annoyance. Thus, in this paper, the hourly values of continuous equivalent level LAeqh and the intermittency ratio (IR) were both considered to describe the urban road traffic noise, monitored in 90 sites in the city of Milan and covering different types of road, from motorways to local roads. The noise data have been processed by clustering methods to detect similarities and to figure out a criterion to classify the urban sites taking into account both equivalent noise levels and road traffic noise events. Two clusters were obtained and, considering the cluster membership of each site, the decimal logarithm of the day-time (06:00–22:00) traffic flow was used to associate each new road with the clusters. In particular, roads with average day-time hourly traffic flow ≥1900 vehicles/hour were associated with the cluster with high traffic flow. The described methodology could be fruitfully applied on road traffic noise data in other cities.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10072451