Citation

Torija AJ, Ruiz DP. Sci. Total Environ. 2012; 435-436: 270-279.

Copyright

(Copyright © 2012, Elsevier Publishing)

DOI

10.1016/j.scitotenv.2012.07.014

PMID

22858535

Abstract

Road traffic has a heavy impact on the urban sound environment, constituting the main source of noise and widely dominating its spectral composition. In this context, our research investigates the use of recorded sound spectra as input data for the development of real-time short-term road traffic flow estimation models. For this, a series of models based on the use of Multilayer Perceptron Neural Networks, multiple linear regression, and the Fisher linear discriminant were implemented to estimate road traffic flow as well as to classify it according to the composition of heavy vehicles and motorcycles/mopeds. In view of the results, the use of the 50-400Hz and 1-2.5kHz frequency ranges as input variables in multilayer perceptron-based models successfully estimated urban road traffic flow with an average percentage of explained variance equal to 86%, while the classification of the urban road traffic flow gave an average success rate of 96.1%.

Language: en