Skip to content
BY-NC-ND 3.0 license Open Access Published by De Gruyter Open Access May 4, 2016

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

  • M. Smiraglia , R. Benocci , G. Zambon and H.E. Roman
From the journal Noise Mapping


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] Directive 2002/49/EC of the European Parliament and of the Council of 25 June 2002 relating to the assessment andmanagement of environmental noise. Oflcial Journal of the European Communities, L 189/12, 2002. Search in Google Scholar

[2] W. Wei, T. V. Van Renterghem, B. De Coensel, D. Botteldooren, Dynamic noise mapping: A map-based interpolation between noise measurements with high temporal resolution, Applied Acoustics 101, 127–140 (2016). DOI: 10.1016/ j.apacoust.2015.08.005 Search in Google Scholar

[3] B. De Coensel, K. Sun, W. Wei, T. Van Renterghem, Dynamic Noise Mapping based on Fixed and Mobile Sound Measurements, EuroNoise, May 31 – June 3, Maastricht (2015). Search in Google Scholar

[4] A. L. Brown, K. C. Lam, Urban noise surveys. Appl. Acoust. 20, 23–39 (1987). Search in Google Scholar

[5] J. Alberola, I. H. Flindell, A. J. Bullmore, Variability in road traflc noise levels. Appl. Acoust. 66, 1180–1195 (2005). Search in Google Scholar

[6] F. Zuo, Y. Li, S. Johnson, J. Johnson, S. Varughese, R. Copes, F. Liu, H. Jiang Wu, R. Hou, H. Chen, Temporal and spatial variability of traflc-related noise in the City of Toronto, Canada. Science of the Total Environment 472, 1100–1107 (2014). 10.1016/j.scitotenv.2013.11.138Search in Google Scholar PubMed

[7] W. Babisch, Transportation noise and cardiovascular risk: updated review and synthesis of epidemiological studies indicate that the evidence has increased. Noise Health 8, 1–29 (2006). 10.4103/1463-1741.32464Search in Google Scholar PubMed

[8] A. J. Torija, D. P. Ruiz, Automated classification of urban locations for environmental noise impact assessment on the basis of road-traflc content, Expert Systems with Applications, 53, 1– 13 (2016). Search in Google Scholar

[9] A. Can, B. Gauvreau, Describing and classifying urban sound environments with a relevant set of physical indicators, Journal of the Acoustical Society of America 137, 208–218 (2015). Search in Google Scholar

[10] G. Baldinelli, R. Bellomini, F. Borchi, M. Carfagni, S. Curcuruto, S. Luzzi, R. Silvaggio, M. Stortini, Correlation between traflc flows and noise reduction in HUSH project strategic actions, in Proceedings of Forum Acusticum 2011, Aalborg, Denmark (2011). Search in Google Scholar

[11] Decree Ministry of Environment 16 March 1998, Tecniche di rilevamento e di misurazione dell’inquinamento acustico (Measurement techniques of noise pollution), Gazzetta Uflciale serie generale (Oflcial Journal of Italian Republic) n. 76, 1/4/1998. Search in Google Scholar

[12] Search in Google Scholar

[13] J. H.Ward, Hierarchical Grouping to Optimize an Objective. Journal of the American Statistical Association, 58, 236–244 (1963). 10.1080/01621459.1963.10500845Search in Google Scholar

[14] J. A. Hartigan, M. A. Wong, A K-means clustering, Applied Statistics, 28, 100–108 (1979). Search in Google Scholar

[15] L. Kaufman, P. J. Rousseeuw, Finding Groups in Data. Wiley Series in Probability and Mathematical Statistics, Hoboken (NJ) (1990). 10.1002/9780470316801Search in Google Scholar

[16] Search in Google Scholar

[17] G. Brock, P. Pihur, S. Datta, S. Datta, clValid: An R Package for Cluster. Journal of Statistical Software, 25, 1–22 (2008). 10.18637/jss.v025.i04Search in Google Scholar

[18] V. Pihur, S. Datta, S. Datta, Weighted Rank Aggregation of Cluster Validation Measures: a Monte Carlo Cross-entropy Approach. Bioinformatics 23, 1607–1615 (2007). 10.1093/bioinformatics/btm158Search in Google Scholar PubMed

[19] A. F. Zuur, E. N. Leno, G. M. Smith, Statistics for Biology and Health – Analysing Ecological Data, Springer, New York (2007). ISBN 978-0-387-45967-7 (Print), 978-0-387-45972-1 (Online). Search in Google Scholar

[20] G. B. Cannelli, K. Glueck, S. Santoboni, A Mathematical Model for Evaluation and Prediction of Mean Energy level of Traflc Noise in Italian Cities, Acustica 53, 31 (1983). Search in Google Scholar

Received: 2015-10-1
Accepted: 2016-4-19
Published Online: 2016-5-4

©2016 M. Smiraglia et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Downloaded on 3.6.2023 from
Scroll to top button