Classification of Urban Road Traffic Noise based on Sound Energy and Eventfulness Indicators
Open Access
- 3 April 2020
- journal article
- research article
- Published by MDPI AG in Applied Sciences
- Vol. 10 (7) , 2451
Abstract
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.Keywords
This publication has 29 references indexed in Scilit:
- A Street Categorization Method to Study Urban Noise: The Valladolid (Spain) StudyEnvironmental Engineering Science, 2011
- A model for the perception of environmental sound based on notice-eventsThe Journal of the Acoustical Society of America, 2009
- Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approachBioinformatics, 2007
- Computational cluster validation in post-genomic data analysisBioinformatics, 2005
- Comparisons and validation of statistical clustering techniques for microarray gene expression dataBioinformatics, 2003
- Model-Based Clustering, Discriminant Analysis, and Density EstimationJournal of the American Statistical Association, 2002
- Phylogenetic Reconstruction Using an Unsupervised Growing Neural Network That Adopts the Topology of a Phylogenetic TreeJournal of Molecular Evolution, 1997
- Silhouettes: A graphical aid to the interpretation and validation of cluster analysisJournal of Computational and Applied Mathematics, 1987
- Algorithm AS 136: A K-Means Clustering AlgorithmJournal of the Royal Statistical Society Series C: Applied Statistics, 1979
- A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated ClustersJournal of Cybernetics, 1973