Predicting Hourly Traflc Noise from Traflc Flow Rate Model: Underlying Concepts for the DYNAMAP Project

Abstract The DYNAMAP project aims at obtaining a dynamic noise map of a large residential area such as the City of Milan (Italy), by recording traffic noise from a limited number of noise sensors. To this end,we perform a statistical analysis of road stretches and group them into different clusters showing a similar measured hourly traffic noise behavior. In the sameway,we group simulated hourly traffic flow rates and compare their compositions with those of the traffic noise groups. The best agreement with the traffic noise was found by using the so-called normal traffic flow rate, yielding overlaps between 68 and 97%. Finally, we derive a simple analytical model to predict the hourly traffic noise from the simulated normal traffic flow, in very good agreement with the measured values.

[1]  W. Babisch Transportation noise and cardiovascular risk: updated review and synthesis of epidemiological studies indicate that the evidence has increased. , 2006, Noise & health.

[2]  Salvatore Ingrassia,et al.  An R Package for Cluster-Weighted Models , 2017 .

[3]  James Johnson,et al.  Temporal and spatial variability of traffic-related noise in the City of Toronto, Canada. , 2014, The Science of the total environment.

[4]  Raffaella Bellomini,et al.  Correlation between traffic flows and noise reduction in HUSH project strategic actions , 2011 .

[5]  Pierre Aumond,et al.  Dynamic noise mapping based on fixed and mobile sound measurements , 2015 .

[6]  J. H. Ward Hierarchical Grouping to Optimize an Objective Function , 1963 .

[7]  Xin Jin,et al.  K-Means Clustering , 2010, Encyclopedia of Machine Learning.

[8]  Ian H. Flindell,et al.  Variability in road traffic noise levels , 2005 .

[9]  Bert De Coensel,et al.  Dynamic noise mapping: a map-based interpolation between noise measurements with high temporal resolution , 2016 .

[10]  Vasyl Pihur,et al.  Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach , 2007, Bioinform..

[11]  Maurice K. Wong,et al.  Algorithm AS136: A k-means clustering algorithm. , 1979 .

[12]  Kin-che Lam,et al.  Urban noise surveys , 1987 .

[13]  Arnaud Can,et al.  Describing and classifying urban sound environments with a relevant set of physical indicators. , 2015, The Journal of the Acoustical Society of America.

[14]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[15]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[16]  Diego P. Ruiz,et al.  Automated classification of urban locations for environmental noise impact assessment on the basis of road-traffic content , 2016, Expert Syst. Appl..

[17]  Guy N. Brock,et al.  clValid , an R package for cluster validation , 2008 .